CN104023399B - For the down channel allocation method based on demand of single carrier base stations system - Google Patents
For the down channel allocation method based on demand of single carrier base stations system Download PDFInfo
- Publication number
- CN104023399B CN104023399B CN201410222180.8A CN201410222180A CN104023399B CN 104023399 B CN104023399 B CN 104023399B CN 201410222180 A CN201410222180 A CN 201410222180A CN 104023399 B CN104023399 B CN 104023399B
- Authority
- CN
- China
- Prior art keywords
- mrow
- mtd
- msub
- mtr
- mover
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 29
- 238000013459 approach Methods 0.000 claims abstract description 6
- 239000013598 vector Substances 0.000 claims description 24
- 239000011159 matrix material Substances 0.000 claims description 20
- 230000005540 biological transmission Effects 0.000 claims description 12
- 238000005516 engineering process Methods 0.000 claims description 8
- 230000003068 static effect Effects 0.000 claims description 7
- 230000003044 adaptive effect Effects 0.000 claims description 4
- 238000004364 calculation method Methods 0.000 claims description 3
- 238000009826 distribution Methods 0.000 abstract description 7
- 238000005562 fading Methods 0.000 description 16
- 238000004422 calculation algorithm Methods 0.000 description 9
- 238000010586 diagram Methods 0.000 description 6
- 238000004891 communication Methods 0.000 description 2
- 238000013468 resource allocation Methods 0.000 description 2
- 238000004088 simulation Methods 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000001066 destructive effect Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000007726 management method Methods 0.000 description 1
- 238000012163 sequencing technique Methods 0.000 description 1
- 230000008054 signal transmission Effects 0.000 description 1
- 239000002699 waste material Substances 0.000 description 1
Landscapes
- Mobile Radio Communication Systems (AREA)
Abstract
The invention discloses a kind of down channel allocation method based on demand for single carrier base stations system, methods described includes prior information acquisition, Service control, User Priority sequence and channel assignment strategy and formulated.The inventive method can make base station realize that downlink throughput capacity approaches downlink capacity, both the business demand of more users had been supported, the different business for multi-user provides different service quality guarantees again, quantifiable distribution according to need is realized to a certain extent, the quality of service requirement of the different business of different user is preferably supported while base station down handling capacity is maximized, there is higher value in theory and actual application.
Description
Technical Field
The invention relates to a method for allocating downlink channels, in particular to a method for allocating downlink channels as required by a single carrier base station system. Belongs to the technical field of wireless digital communication.
Background
The link adaptive transmission technique adaptively changes parameters such as a modulation scheme, a coding rate, and power allocation according to channel variation, and maximally utilizes radio link resources when transmission error performance is allowable. At present, the civil 3G, 4G and WIMAX base station systems establish detailed standards for the technology, and the wireless resource management technology matched with the technology is vital to improving the resource utilization rate of the base station system and meeting different service quality requirements of various services. The base station can allocate resources to users with better channel conditions by using the random time-varying characteristic of a wireless channel according to the obtained user channel state information, thereby improving the actual throughput of the base station system to the maximum extent through multi-user diversity gain and improving the overall communication performance of the base station system.
Traditional radio resource scheduling algorithms, such as proportional fairness algorithms in various forms, starting from a compromise between allocation efficiency and fairness, can only achieve convergence of different user rates, but cannot efficiently support different service rate requirements of multiple users, namely, allocation according to needs.
Disclosure of Invention
The present invention aims to solve the defects of the prior art, and provides a downlink channel on-demand allocation method for a single carrier base station system, which can meet the requirement of unnecessary service rates of different users while maximizing downlink throughput as much as possible.
The purpose of the invention can be achieved by adopting the following technical scheme:
a downlink channel on-demand distribution method for a single carrier base station system comprises the following steps:
1) the base station system adopts the adaptive modulation coding technology on each basic scheduling block with time T, applies different modulation coding schemes according to the channel state information of the users, and is arranged in a certain period of time, and the base station needs to support the downlink service requirements of K users which are uniformly distributed in a cell and are in a static or low-speed moving state;
2) obtaining prior information: the channel allocation strategy is established by taking M scheduling blocks as basic units, and the base station obtains channel state statistical matrixes of K users in the previous M basic transmission block time at an initial point of establishing the channel allocation strategy through the prior record; wherein M is more than or equal to 1000;
3) calculating the average symbol rate of K users based on the prior information, sorting K elements in the vector from big to small, and taking the nextData of individual user, obtainingA channel state statistical matrix of each user according toObtaining data in channel state statistical matrix of each userThe probability that the maximum value of the channel state of each user is n is obtained, so that the theoretical value of the channel capacity of the base station is obtained; wherein n is more than or equal to 1;
4) and (3) service control: if the sum of the service demand rates of the K downlink users is greater than 0.85 of the theoretical value of the channel capacity, service control measures are started, the demand rates of some low-level services are gradually reduced according to the service priority specified in advance, even part of services are abandoned until the sum of the demand rates of all services is reduced to 0.85 of the theoretical value of the channel capacity; if the sum of the service required rates of the K downlink users is less than 0.7 of the theoretical value of the channel capacity, service control measures are started, and the required rates of the services with unlimited rates are increased until the sum of the service required rates is increased to about 0.8 of the theoretical value of the channel capacity;
5) and sequencing the priorities of the K users, and making a channel allocation strategy according to the priorities of the K users.
Preferably, the channel state statistical matrix of the K users in the first M basic transport block times in step 2) is as follows:
wherein, Prk,nThe ratio of the sum of the times of the channel state n of the kth user in the first M basic transmission block time to M is represented; k is more than or equal to 1 and less than or equal to K, and N is more than or equal to 1 and less than or equal to N.
Preferably, the step 3) is as follows:
3.1) calculating the average symbol rate of K users as follows:
wherein r is1,r2,...,rN-1,rNSymbol rates for N modulation coding schemes;
3.2) sorting the sizes of K elements in the vector from big to small, and taking the backData of individual user, obtainingThe channel state statistical matrix of each user is as follows:
3.3) obtaining data based on equation (3)The probability that the maximum value of the channel state of each user is n is as follows:
the theoretical value of the channel capacity of the base station is as follows:
wherein, X ═ T/B, which is expressed as the number of modulation symbols per basic scheduling block; and B is the bandwidth of the single carrier base station system.
Preferably, the step 5) is as follows:
5.1) after the service control of the step 4) is set, if the services of K users are not abandoned, the services need to be givenThe rate is adjusted toMatrix calculation based on equation (1):
wherein, thetak,nThe probability that the channel state of the kth user is N and the channel states of other users are smaller than the value is shown, K is more than or equal to 1 and less than or equal to K, and N is more than or equal to 1 and less than or equal to N; then, calculating:
wherein λ iskThe ratio of the estimated dispatching symbol rate and the service requirement rate is obtained when the kth user can occupy the channel when the own channel state value is larger than that of other users; based on lambdakThe value of (2) carries out priority ranking on the users, the smaller the value is, the higher the user priority is, the position of the matrix of the formula (1) is adjusted according to the ranking result, and the adjusted matrix is as follows:
5.2) based on formula (8), let
Wherein K is more than or equal to 1 and less than or equal to K, r is more than or equal to 1 and less than or equal to N, and N is more than or equal to 1 and less than or equal to N;
the following values were calculated:
wherein,represents the convolution operation of these vectors, [ x ]]n-1The n-1 th element representing a vector; and (3) recalculating:
the channel states of the users after the priority sorting are s in turn arranged on a certain scheduling block of the scheduling area1,s2,...,sK-1,sKAnd s ismax＝Max(s1,s2,...,sK-1,sK) Then, a channel allocation strategy is made according to the result of equation (9).
Preferably, the step 5.2) of formulating the channel allocation strategy is as follows:
a. if the vector is ═ 21,2,...,K]Is greater than or equal to 1, the channel state is smaxAnd the user with the highest priority obtains the use right of the scheduling block;
b. if the vector is ═ 21,2,...,K]Is less than 1, searching the first channel state as s according to the priority from high to lowmaxIf the user corresponds to an element value in the vectorkIf the number of the scheduling blocks is not more than 1, obtaining the use right of the scheduling block; if the user corresponds to the element value in the vectorkGreater than 1, an interval of [0,1 ] is generated]Uniformly distributed random number ω ifThe user obtains the usage right of the scheduling block; if it is notThe scheduling opportunity is used by the user whose L elements are smaller than 1 in a competition, and the competition policy is as follows:
wherein s isylIndicates the channel state of the user, and IylA priority ranking representing a user; l is more than or equal to 1, L is more than or equal to 1 and less than or equal to L, and y is more than or equal to 1 and less than or equal to yl≤K。
Preferably, the method further comprises:
if a real-time service with lower delay index requirement exists in the service, allocating a small number of mutually staggered special time slots for each real-time service by taking the maximum allowable delay of the service as a period; when the data delay of the real-time service approaches the maximum delay, the time slot is directly occupied on the special time slot, and if the time slot is not used, all users compete for use according to the step 5).
Compared with the prior art, the invention has the following beneficial effects:
1. the method of the invention provides a set of downlink channel demand allocation technology based on user channel state statistical information for multiple users of a base station system in a static or low-speed moving state aiming at a single carrier base station system supporting a link self-adaptive transmission technology, so that the base station can realize that the downlink throughput approaches the downlink capacity, thereby not only supporting the service requirements of more users, but also providing different service quality guarantees for different services of the multiple users and realizing quantifiable demand allocation to a certain extent.
2. The method of the invention utilizes the static or low-speed mobile user channel state statistical characteristics to keep the characteristic of long-time stability (the characteristic is obtained by a great amount of research), realizes quantifiable wireless channel resource allocation with the help of classical probability, can better support the service quality requirements of different services of different users while maximizing the downlink throughput of the base station, and has higher value in theory and practical application.
Drawings
Fig. 1 is a schematic diagram of a basic scheduling block according to embodiment 1 of the present invention.
Fig. 2 is a schematic view of a service situation of a downlink of a base station according to embodiment 1 of the present invention.
Fig. 3 is a schematic diagram illustrating a downlink scheduling policy of a base station according to embodiment 1 of the present invention.
Fig. 4 shows time slot situations of real-time service and non-real-time service in embodiment 1 of the present invention.
Fig. 5 is a waveform diagram of the time slot average downlink throughput of the base station in each scheduling window in embodiment 1 of the present invention.
Fig. 6 is a waveform diagram of the time slot average downlink scheduling rate of 1 st to 4 th users in each scheduling window in the whole scheduling process of embodiment 1 of the present invention.
Fig. 7 is a waveform diagram of the time slot average downlink scheduling rate of 5 th to 8 th users in each scheduling window in the whole scheduling process according to embodiment 1 of the present invention.
Detailed Description
Example 1:
the method for allocating downlink channels as required in this embodiment specifically includes the following steps:
1) let the bandwidth of the single carrier base station system be BHz and the signal transmission power be PmaxChannel state information (CSI, N2) of the user can be obtained in real timek) (ii) a The base station system transmits a basic scheduling block (shown in fig. 1, the number of only one user is transmitted) with a time TAccording to the adaptive modulation and coding technique adopted on the modulation symbols containing X ═ T/B, different modulation and coding schemes (MCS, total N schemes, rate r in turn) are applied according to the channel state information CSI of the user1,r2,...,rN-1,rNBits) to obtain maximum transmission efficiency;
if the base station needs to support downlink service requirements of K users uniformly distributed in a cell and in a stationary or low-speed mobile state within a certain period of time, as shown in fig. 2, the service requirement rate is sequentially R _ request ═ R1,R2,...,RK]；
2) Prior information acquisition
The channel allocation strategy is formulated by taking M scheduling blocks as basic units, as shown in fig. 3, and through the prior record, the base station obtains the channel state statistical matrix of K users in the preceding M basic transmission block time at the starting point of formulating the channel allocation strategy, as follows:
wherein, Prk,nThe ratio of the sum of the times of the channel state n of the kth user in the first M basic transmission block time to M is represented; m is more than or equal to 1000, K is more than or equal to 1 and less than or equal to K, and N is more than or equal to 1 and less than or equal to N;
3) calculating the average speed of K users, sorting K elements in the vector from big to small, and taking the nextData of individual user, obtainingA channel state statistical matrix of each user according toObtaining data in channel state statistical matrix of each userThe maximum value of the channel state of each user is the probability of n, so as to obtain the theoretical value of the channel capacity of the base station, which is specifically as follows:
3.1) calculate the average rate of K users as follows:
wherein r is1,r2,...,rN-1,rNSymbol rates for N modulation coding schemes;
3.2) sorting the sizes of K elements in the vector from big to small, and taking the backEstimating the theoretical value and symbol of the downlink capacity of the base station by the data of each userRepresents lower rounding; is provided withThe channel state statistical matrix of each user is as follows:
3.3) if a channel is always allocated to a user having the largest channel state value at the time of scheduling, the actual throughput is equal to the channel capacity, and thus based on the data of equation (3), the data is obtainedThe probability that the maximum value of the channel state of each user is n is as follows:
the theoretical value of the channel capacity of the base station is as follows:
the unit of the theoretical value C of the channel capacity is bit/second;
4) traffic control
If the sum of the service requirement rates of the K downlink users is greater than 0.85 of the theoretical value of the channel capacity, that is to say
This means that the service demand rate is too high, and the base station is likely to be unable to meet the service quality requirements of these services at the same time, so the service control measures will be started, the demand rate of some low-grade services will be gradually reduced according to the service priority specified in advance, even part of services will be abandoned, until the sum of the demand rates of all services is reduced to 0.85 of the theoretical value of the channel capacity;
if the sum of the service requirement rates of the K downlink users is less than 0.7 of the theoretical value of the channel capacity, that is to say
This indicates that the traffic demand rate is too low, which may cause waste of downlink resources. Therefore, service control measures are started, the required speed of the speed non-limited services (such as FTP downloading services) is increased to a certain extent, until the sum of the required speed of the services is increased to about 0.8 of the theoretical value of the channel capacity;
5) the priorities of the K users are sequenced, and a channel allocation strategy is formulated according to the priorities of the K users, which specifically comprises the following steps:
5.1) without loss of generality, after the service control of the step 4) is set, if the services of K users are not abandoned, the service requirement rate is adjusted to beMatrix calculation based on equation (1):
wherein, thetak,nThe probability that the channel state of the kth user is N and the channel states of other users are smaller than the value is shown, K is more than or equal to 1 and less than or equal to K, and N is more than or equal to 1 and less than or equal to N; then, calculating:
wherein λ iskThe ratio of the estimated dispatching symbol rate and the service requirement rate is lambda, which is obtained when the kth user can occupy the channel when the state value of the channel is larger than that of other userskThe larger the value of (a) is, the more easily the service requirement of the kth user can be satisfied. Based on the value, the users are subjected to priority ranking, the smaller the value is, the higher the user priority is, and the position of the matrix of the formula (1) is adjusted according to the ranking result, wherein the adjusted matrix is as follows:
5.2) based on formula (8), let
Wherein K is more than or equal to 1 and less than or equal to K, r is more than or equal to 1 and less than or equal to N, and N is more than or equal to 1 and less than or equal to N;
the following values were calculated:
wherein,represents the convolution operation of these vectors, [ x ]]n-1The n-1 th element representing a vector; and (3) recalculating:
the channel states of the users after the priority sorting are s in turn arranged on a certain scheduling block of the scheduling area1,s2,...,sK-1,sKAnd s ismax＝Max(s1,s2,...,sK-1,sK) Then, according to the result of equation (9), the following channel allocation strategy is made:
a. if the vector is ═ 21,2,...,K]Is greater than or equal to 1, the channel state is smaxAnd the user with the highest priority obtains the use right of the scheduling block;
b. if the vector is ═ 21,2,...,K]Is less than 1, searching the first channel state as s according to the priority from high to lowmaxIf the user corresponds to an element value in the vectorkIf the number of the scheduling blocks is not more than 1, obtaining the use right of the scheduling block; if the user corresponds to the element value in the vectorkGreater than 1, an interval of [0,1 ] is generated]UniformityDistributed random number ω ifThe user obtains the usage right of the scheduling block; if it is notThe scheduling opportunity is used by the user whose L elements are smaller than 1 in a competition, and the competition policy is as follows:
wherein s isylIndicates the channel state of the user, and IylA priority ranking representing a user; l is more than or equal to 1, L is more than or equal to 1 and less than or equal to L, and y is more than or equal to 1 and less than or equal to yl≤K。
6) The above algorithm can only provide sufficient satisfaction of service required rate in the probabilistic sense, and can only ensure that the actual downlink rate of each service reaches its required rate within a long period of time, in order to better support the low delay requirement of real-time services (such as voice), if there is a real-time service with lower delay index requirement in the service, then a small number of mutually staggered dedicated time slots are allocated to each real-time service with the maximum allowable delay of the service as a period, as shown in fig. 4; when the data delay of the real-time service approaches the maximum delay, the time slot can be directly occupied on the special time slot regardless of the channel state, and if the time slot is not used, all users compete for use according to the algorithm.
The main characteristic of a wireless channel is the change of the channel strength with time and frequency, and the change can be roughly divided into two types: large scale fading and small scale fading. Large-scale fading is caused by the signal path loss varying with distance and by the shadowing of various large obstacles, and small-scale fading is mainly caused by constructive and destructive interference of multiple signal paths. While both the transmitter and receiver remain fixed, large-scale fading generally does not change over time, while small-scale fading may change dramatically over time.
In the method of the present invention, since all users of the base station are assumed to be in a static or low-speed moving state, and the distance change from the base station is relatively slow, it can be considered that within 2 consecutive short time periods (on the order of several seconds), the time-varying property of the channel state of the user signal is mainly caused by the small-scale fading factor, but not the user mobility. Although the small-scale fading has strong time-varying property, a great deal of research shows that the small-scale fading value satisfies a certain probability distribution under most of the scenes.
Based on the above analysis, the method of the present invention considers that the channel state statistics of the user in any M basic scheduling block time is very close to the channel state statistics in the following M basic scheduling block time. If the channel state of a user at a certain basic scheduling block time is a discrete random variable satisfying a probability distribution, K mutually independent user channel state random variables form a K-dimensional discrete probability space omega, and N are included in totalKA basic event.
Define the event Domain { Al,k,nL is more than or equal to 1 and less than or equal to N, K is more than or equal to 1 and less than or equal to K, and N is more than or equal to 1 and less than or equal to K, wherein the event domain Al,k,nIt means that on a basic scheduling block, the maximum value of the channel state of K users is l, the channel state values of the 1 st to K-1 st users are all less than l, the channel state value of the K +1 st user is l, and the total of the K +1 st to K user channel state values is n-1.
The following conclusions can be drawn according to the above definitions:
(empty Collection), (l)1,k1,n1)≠(l2,k2,n2)
I.e., event field { Al,k,nL is more than or equal to 1 and less than or equal to N, K is more than or equal to 1 and less than or equal to K, and N is more than or equal to 1 and less than or equal to K, and completely cutting the K-dimensional discrete probability space omega of the user without overlapping.
Is provided with
ak,r＝Pk,r；1≤k≤K,1≤r≤N
Event field A according to the correlation definitionl,k,nHas an occurrence probability of
If the event field A is defined in advance on a certain scheduling blockl,k,nWhen present, the kth user gets the usage rights of the scheduling block. Then the k-th user obtains the theoretical value of the downlink rate of the k-th user in a longer period of time
If the downlink rate theoretical value gamma of the kth userkAnd its traffic demand rateThe ratio of (a) to (b) is greater than 1, which indicates that the user obtains too many scheduling opportunities, so a random number is set to give up the surplus scheduling opportunities to the users with too few scheduling opportunities; if the value is less than 1, this indicates that the user obtains too few scheduling opportunities and cannot meet the service requirement rate, so that besides the scheduling opportunities of the user, the user also needs to compete for the scheduling blocks that the user with too many scheduling opportunities gives way.
In order to verify the distribution effect of the algorithm of the embodiment 1, the related simulation results are given below; setting a single carrier base station system to simultaneously support the downlink service requirements of 8 users uniformly distributed in a cell; the base station system can obtain the maximum transmission rate by adopting the scheme in the table 1 according to the ideal feedback of the user on each scheduling time slot; after the bandwidth and the symbol time length are normalized, the downlink data amount of the base station adopting the 7 modulation coding schemes in unit time is 1/4, 1/2, 1, 3/2, 2, 3 and 4 in sequence.
Table 1 data transmission scheme
The influence of large-scale fading and small-scale fading is comprehensively considered, and in the whole scheduling process, users are in low-speed and static states, namely the change rate of large-scale fading factors is slow, and the channel conditions on different scheduling time slots are kept independent.
The downlink transmitting power of the base station is 48dB, and the large-scale fading coefficients of 8 users are [ -34.5, -33.5, -35, -32.5, -33, -31, -30.5, -30 in sequence during initialization]dB, the scheduling window contains M1000 scheduling time slots, and the change value of the large-scale fading coefficient of each user in two adjacent scheduling windows is from the interval [ -0.2,0.2 [ ]]Random decimation in dB, and 100 scheduling windows, 10 in total5And the small-scale fading coefficients in the scheduling process of each scheduling time slot are all Rayleigh fading distributions with the average value of 1.
The starting point of some traditional channel allocation algorithms, such as various types of proportional fairness algorithms, is mainly to achieve convergence of allocation rates of users, but cannot achieve satisfaction of various different required rates. The channel allocation technology provided by the method has the main advantage that the method can meet the requirement of unnecessary service rates of different users while maximizing the downlink throughput as much as possible. Therefore, assuming that there are 3 different non-real-time services among 8 users, the required rates are normalized to [0.1925,0.385,0.1925,0.385,0.385,0.77,0.77,0.77] in turn, and the sum of the required rates is 3.85.
FIG. 5 shows the average downlink throughput per time slot of the base station in each scheduling window during the whole scheduling process; and fig. 6 and fig. 7 respectively show the average downlink actual rate values obtained by the 1 st to 4 th users and the 5 th to 8 th users in each time slot in each scheduling window in turn in the whole scheduling process, wherein the horizontal line in the diagram is the required rate of the downlink traffic of the user. As can be seen from fig. 5, the downlink throughput of the base station is between 3.96 and 3.99, which substantially approaches the downlink capacity value of the base station; fig. 6 and fig. 7 show that, except that the downlink scheduling rate obtained by the 6 th user is slightly higher than the service requirement rate, the downlink scheduling rates obtained by the other 7 users are basically equivalent to the service requirement rate.
In summary, the simulation result shows that the algorithm provided by the present invention can maximize the throughput of the base station system, and at the same time, each user can obtain a scheduling rate equivalent to the service requirement rate thereof, so that the wireless resources of the base station system are fully utilized and reasonably allocated and used.
The scheduling process of the whole set of algorithm of the invention can be seen, the characteristic of keeping long-time stability by using the static or low-speed mobile user channel state statistical characteristics is utilized, quantifiable wireless channel resource allocation is realized with the help of classical probability, the service quality requirements of different services of different users can be better supported while the downlink throughput of the base station is maximized, and the invention has higher value in theory and practical application.
The above description is only for the preferred embodiments of the present invention, but the protection scope of the present invention is not limited thereto, and any person skilled in the art can substitute or change the technical solution and the inventive concept of the present invention within the scope of the present invention.
Claims (2)
1. A downlink channel demand allocation method for a single carrier base station system is characterized by comprising the following steps:
1) the base station system adopts the adaptive modulation coding technology on each basic scheduling block with time T, applies different modulation coding schemes according to the channel state information of the users, and is arranged in a certain period of time, and the base station needs to support the downlink service requirements of K users which are uniformly distributed in a cell and are in a static or low-speed moving state;
2) obtaining prior information: the channel allocation strategy is formulated by taking M scheduling blocks as basic units, and through the record in advance, the base station obtains channel state statistical matrixes of K users in the previous M basic transmission block time at the starting point of formulating the channel allocation strategy, as follows:
<mrow> <mi>Pr</mi> <mo>=</mo> <mfenced open = "[" close = "]"> <mtable> <mtr> <mtd> <msub> <mi>Pr</mi> <mrow> <mn>1</mn> <mo>,</mo> <mn>1</mn> </mrow> </msub> </mtd> <mtd> <msub> <mi>Pr</mi> <mrow> <mn>1</mn> <mo>,</mo> <mn>2</mn> </mrow> </msub> </mtd> <mtd> <mn>...</mn> </mtd> <mtd> <msub> <mi>Pr</mi> <mrow> <mn>1</mn> <mo>,</mo> <mi>N</mi> </mrow> </msub> </mtd> </mtr> <mtr> <mtd> <msub> <mi>Pr</mi> <mrow> <mn>2</mn> <mo>,</mo> <mn>1</mn> </mrow> </msub> </mtd> <mtd> <msub> <mi>Pr</mi> <mrow> <mn>2</mn> <mo>,</mo> <mn>2</mn> </mrow> </msub> </mtd> <mtd> <mn>...</mn> </mtd> <mtd> <msub> <mi>Pr</mi> <mrow> <mn>2</mn> <mo>,</mo> <mi>N</mi> </mrow> </msub> </mtd> </mtr> <mtr> <mtd> <mo>.</mo> </mtd> <mtd> <mo>.</mo> </mtd> <mtd> <mo>.</mo> </mtd> <mtd> <mo>.</mo> </mtd> </mtr> <mtr> <mtd> <mo>.</mo> </mtd> <mtd> <mo>.</mo> </mtd> <mtd> <mo>.</mo> </mtd> <mtd> <mo>.</mo> </mtd> </mtr> <mtr> <mtd> <mo>.</mo> </mtd> <mtd> <mo>.</mo> </mtd> <mtd> <mo>.</mo> </mtd> <mtd> <mo>.</mo> </mtd> </mtr> <mtr> <mtd> <msub> <mi>Pr</mi> <mrow> <mi>K</mi> <mo>,</mo> <mn>1</mn> </mrow> </msub> </mtd> <mtd> <msub> <mi>Pr</mi> <mrow> <mi>K</mi> <mo>,</mo> <mn>2</mn> </mrow> </msub> </mtd> <mtd> <mn>...</mn> </mtd> <mtd> <msub> <mi>Pr</mi> <mrow> <mi>K</mi> <mo>,</mo> <mi>N</mi> </mrow> </msub> </mtd> </mtr> </mtable> </mfenced> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>1</mn> <mo>)</mo> </mrow> </mrow>
wherein, Prk,nThe ratio of the sum of the times of the channel state n of the kth user in the first M basic transmission block time to M is represented; k is more than or equal to 1 and less than or equal to K, N is more than or equal to 1 and less than or equal to N, and M is more than or equal to 1000;
3) calculating the average symbol rate of K users based on the prior information, sorting K elements in the vector from big to small, and taking the nextData of individual user, obtainingA channel state statistical matrix of each user according toObtaining data in channel state statistical matrix of each userThe maximum value of the channel state of each user is the probability of n, so as to obtain the theoretical value of the channel capacity of the base station, which is specifically as follows:
3.1) calculating the average symbol rate of K users as follows:
<mrow> <msub> <mi>R</mi> <mrow> <mi>a</mi> <mi>v</mi> <mi>e</mi> </mrow> </msub> <mo>=</mo> <mfenced open = "[" close = "]"> <mtable> <mtr> <mtd> <msub> <mi>Pr</mi> <mrow> <mn>1</mn> <mo>,</mo> <mn>1</mn> </mrow> </msub> </mtd> <mtd> <msub> <mi>Pr</mi> <mrow> <mn>1</mn> <mo>,</mo> <mn>2</mn> </mrow> </msub> </mtd> <mtd> <mn>...</mn> </mtd> <mtd> <msub> <mi>Pr</mi> <mrow> <mn>1</mn> <mo>,</mo> <mi>N</mi> </mrow> </msub> </mtd> </mtr> <mtr> <mtd> <msub> <mi>Pr</mi> <mrow> <mn>2</mn> <mo>,</mo> <mn>1</mn> </mrow> </msub> </mtd> <mtd> <msub> <mi>Pr</mi> <mrow> <mn>2</mn> <mo>,</mo> <mn>2</mn> </mrow> </msub> </mtd> <mtd> <mn>...</mn> </mtd> <mtd> <msub> <mi>Pr</mi> <mrow> <mn>2</mn> <mo>,</mo> <mi>N</mi> </mrow> </msub> </mtd> </mtr> <mtr> <mtd> <mo>.</mo> </mtd> <mtd> <mo>.</mo> </mtd> <mtd> <mo>.</mo> </mtd> <mtd> <mo>.</mo> </mtd> </mtr> <mtr> <mtd> <mo>.</mo> </mtd> <mtd> <mo>.</mo> </mtd> <mtd> <mo>.</mo> </mtd> <mtd> <mo>.</mo> </mtd> </mtr> <mtr> <mtd> <mo>.</mo> </mtd> <mtd> <mo>.</mo> </mtd> <mtd> <mo>.</mo> </mtd> <mtd> <mo>.</mo> </mtd> </mtr> <mtr> <mtd> <msub> <mi>Pr</mi> <mrow> <mi>K</mi> <mo>,</mo> <mn>1</mn> </mrow> </msub> </mtd> <mtd> <msub> <mi>Pr</mi> <mrow> <mi>K</mi> <mo>,</mo> <mn>2</mn> </mrow> </msub> </mtd> <mtd> <mn>...</mn> </mtd> <mtd> <msub> <mi>Pr</mi> <mrow> <mi>K</mi> <mo>,</mo> <mi>N</mi> </mrow> </msub> </mtd> </mtr> </mtable> </mfenced> <mfenced open = "[" close = "]"> <mtable> <mtr> <mtd> <msub> <mi>r</mi> <mn>1</mn> </msub> </mtd> </mtr> <mtr> <mtd> <msub> <mi>r</mi> <mn>2</mn> </msub> </mtd> </mtr> <mtr> <mtd> <mo>.</mo> </mtd> </mtr> <mtr> <mtd> <mo>.</mo> </mtd> </mtr> <mtr> <mtd> <mo>.</mo> </mtd> </mtr> <mtr> <mtd> <msub> <mi>r</mi> <mi>N</mi> </msub> </mtd> </mtr> </mtable> </mfenced> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>2</mn> <mo>)</mo> </mrow> </mrow>
wherein r is1,r2,...,rN-1,rNSymbol rates for N modulation coding schemes;
3.2) sorting the sizes of K elements in the vector from big to small, and taking the backData of individual user, obtainingThe channel state statistical matrix of each user is as follows:
<mrow> <mover> <mi>Pr</mi> <mo>&OverBar;</mo> </mover> <mo>=</mo> <mfenced open = "[" close = "]"> <mtable> <mtr> <mtd> <msub> <mover> <mi>Pr</mi> <mo>&OverBar;</mo> </mover> <mrow> <mn>1</mn> <mo>,</mo> <mn>1</mn> </mrow> </msub> </mtd> <mtd> <msub> <mover> <mi>Pr</mi> <mo>&OverBar;</mo> </mover> <mrow> <mn>1</mn> <mo>,</mo> <mn>2</mn> </mrow> </msub> </mtd> <mtd> <mn>...</mn> </mtd> <mtd> <msub> <mover> <mi>Pr</mi> <mo>&OverBar;</mo> </mover> <mrow> <mn>1</mn> <mo>,</mo> <mi>N</mi> </mrow> </msub> </mtd> </mtr> <mtr> <mtd> <msub> <mover> <mi>Pr</mi> <mo>&OverBar;</mo> </mover> <mrow> <mn>2</mn> <mo>,</mo> <mn>1</mn> </mrow> </msub> </mtd> <mtd> <msub> <mover> <mi>Pr</mi> <mo>&OverBar;</mo> </mover> <mrow> <mn>2</mn> <mo>,</mo> <mn>2</mn> </mrow> </msub> </mtd> <mtd> <mn>...</mn> </mtd> <mtd> <msub> <mover> <mi>Pr</mi> <mo>&OverBar;</mo> </mover> <mrow> <mn>2</mn> <mo>,</mo> <mi>N</mi> </mrow> </msub> </mtd> </mtr> <mtr> <mtd> <mo>.</mo> </mtd> <mtd> <mo>.</mo> </mtd> <mtd> <mo>.</mo> </mtd> <mtd> <mo>.</mo> </mtd> </mtr> <mtr> <mtd> <mo>.</mo> </mtd> <mtd> <mo>.</mo> </mtd> <mtd> <mo>.</mo> </mtd> <mtd> <mo>.</mo> </mtd> </mtr> <mtr> <mtd> <mo>.</mo> </mtd> <mtd> <mo>.</mo> </mtd> <mtd> <mo>.</mo> </mtd> <mtd> <mo>.</mo> </mtd> </mtr> <mtr> <mtd> <msub> <mover> <mi>Pr</mi> <mo>&OverBar;</mo> </mover> <mrow> <mover> <mi>K</mi> <mo>~</mo> </mover> <mo>,</mo> <mn>1</mn> </mrow> </msub> </mtd> <mtd> <msub> <mover> <mi>Pr</mi> <mo>&OverBar;</mo> </mover> <mrow> <mover> <mi>K</mi> <mo>~</mo> </mover> <mo>,</mo> <mn>2</mn> </mrow> </msub> </mtd> <mtd> <mn>...</mn> </mtd> <mtd> <msub> <mover> <mi>Pr</mi> <mo>&OverBar;</mo> </mover> <mrow> <mover> <mi>K</mi> <mo>~</mo> </mover> <mo>,</mo> <mi>N</mi> </mrow> </msub> </mtd> </mtr> </mtable> </mfenced> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>3</mn> <mo>)</mo> </mrow> </mrow>
3.3) obtaining data based on equation (3)The probability that the maximum value of the channel state of each user is n is as follows:
<mrow> <msub> <mi>pr</mi> <mi>n</mi> </msub> <mo>=</mo> <mfenced open = "{" close = ""> <mtable> <mtr> <mtd> <mrow> <munderover> <mo>&Pi;</mo> <mrow> <mi>k</mi> <mo>=</mo> <mn>1</mn> </mrow> <mover> <mi>K</mi> <mo>~</mo> </mover> </munderover> <msub> <mover> <mi>Pr</mi> <mo>&OverBar;</mo> </mover> <mrow> <mi>k</mi> <mo>,</mo> <mn>1</mn> </mrow> </msub> <mo>,</mo> <mi>n</mi> <mo>=</mo> <mn>1</mn> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <munderover> <mo>&Pi;</mo> <mrow> <mi>k</mi> <mo>=</mo> <mn>1</mn> </mrow> <mover> <mi>K</mi> <mo>~</mo> </mover> </munderover> <mrow> <mo>(</mo> <munderover> <mo>&Sigma;</mo> <mrow> <mi>r</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>n</mi> </munderover> <msub> <mover> <mi>Pr</mi> <mo>&OverBar;</mo> </mover> <mrow> <mi>k</mi> <mo>,</mo> <mi>r</mi> </mrow> </msub> <mo>)</mo> </mrow> <mo>-</mo> <munderover> <mo>&Pi;</mo> <mrow> <mi>k</mi> <mo>=</mo> <mn>1</mn> </mrow> <mover> <mi>K</mi> <mo>~</mo> </mover> </munderover> <mrow> <mo>(</mo> <munderover> <mo>&Sigma;</mo> <mrow> <mi>r</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>n</mi> </munderover> <msub> <mover> <mi>Pr</mi> <mo>&OverBar;</mo> </mover> <mrow> <mi>k</mi> <mo>,</mo> <mi>r</mi> </mrow> </msub> <mo>)</mo> </mrow> <mo>,</mo> <mi>n</mi> <mo>&GreaterEqual;</mo> <mn>2</mn> </mrow> </mtd> </mtr> </mtable> </mfenced> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>4</mn> <mo>)</mo> </mrow> </mrow>
the theoretical value of the channel capacity of the base station is as follows:
<mrow> <mi>C</mi> <mo>=</mo> <munderover> <mo>&Sigma;</mo> <mrow> <mi>n</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>N</mi> </munderover> <msub> <mi>pr</mi> <mi>n</mi> </msub> <mo>&times;</mo> <msub> <mi>r</mi> <mi>n</mi> </msub> <mo>&times;</mo> <mi>X</mi> <mo>&divide;</mo> <mi>T</mi> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>5</mn> <mo>)</mo> </mrow> </mrow>
wherein, X ═ T/B, which is expressed as the number of modulation symbols per basic scheduling block; b is the bandwidth of a single carrier base station system;
4) and (3) service control: if the sum of the service demand rates of the K downlink users is greater than 0.85 of the theoretical value of the channel capacity, service control measures are started, the demand rates of some low-level services are gradually reduced according to the service priority specified in advance, even part of services are abandoned until the sum of the demand rates of all services is reduced to 0.85 of the theoretical value of the channel capacity; if the sum of the service required rates of the K downlink users is less than 0.7 of the theoretical value of the channel capacity, service control measures are started, and the required rates of the services with unlimited rates are increased until the sum of the service required rates is increased to about 0.8 of the theoretical value of the channel capacity;
5) the priorities of the K users are sequenced, and a channel allocation strategy is formulated according to the priorities of the K users, which specifically comprises the following steps:
5.1) after the service control of the step 4) is set, if the services of K users are not abandoned, the service requirement rate is adjusted to beMatrix calculation based on equation (1):
<mrow> <msub> <mi>&theta;</mi> <mrow> <mi>k</mi> <mo>,</mo> <mi>n</mi> </mrow> </msub> <mo>=</mo> <mfrac> <msub> <mi>Pr</mi> <mrow> <mi>k</mi> <mo>,</mo> <mi>n</mi> </mrow> </msub> <mrow> <munderover> <mo>&Sigma;</mo> <mrow> <mi>r</mi> <mo>=</mo> <mn>1</mn> </mrow> <mrow> <mi>n</mi> <mo>-</mo> <mn>1</mn> </mrow> </munderover> <msub> <mi>Pr</mi> <mrow> <mi>k</mi> <mo>,</mo> <mi>r</mi> </mrow> </msub> </mrow> </mfrac> <munderover> <mo>&Pi;</mo> <mrow> <mi>l</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>K</mi> </munderover> <mrow> <mo>(</mo> <munderover> <mo>&Sigma;</mo> <mrow> <mi>r</mi> <mo>=</mo> <mn>1</mn> </mrow> <mrow> <mi>n</mi> <mo>-</mo> <mn>1</mn> </mrow> </munderover> <msub> <mi>Pr</mi> <mrow> <mi>l</mi> <mo>,</mo> <mi>r</mi> </mrow> </msub> <mo>)</mo> </mrow> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>6</mn> <mo>)</mo> </mrow> </mrow>
wherein, thetak,nThe probability that the channel state of the kth user is N and the channel states of other users are smaller than the value is shown, K is more than or equal to 1 and less than or equal to K, and N is more than or equal to 1 and less than or equal to N; then, calculating:
<mrow> <msub> <mi>&lambda;</mi> <mi>k</mi> </msub> <mo>=</mo> <mfrac> <mrow> <munderover> <mo>&Sigma;</mo> <mrow> <mi>n</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>N</mi> </munderover> <msub> <mi>&theta;</mi> <mrow> <mi>k</mi> <mo>,</mo> <mi>n</mi> </mrow> </msub> <mo>&times;</mo> <msub> <mi>r</mi> <mi>n</mi> </msub> </mrow> <msub> <mover> <mi>R</mi> <mo>^</mo> </mover> <mi>k</mi> </msub> </mfrac> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>7</mn> <mo>)</mo> </mrow> </mrow>
wherein λ iskThe estimated dispatch obtained when the kth user can occupy the channel under the condition that the state value of the own channel is larger than that of other usersThe ratio of the symbol rate to its traffic demand rate; based on lambdakThe value of (2) carries out priority ranking on the users, the smaller the value is, the higher the user priority is, the position of the matrix of the formula (1) is adjusted according to the ranking result, and the adjusted matrix is as follows:
5.2) based on formula (8), let
Wherein K is more than or equal to 1 and less than or equal to K, r is more than or equal to 1 and less than or equal to N, and N is more than or equal to 1 and less than or equal to N;
the following values were calculated:
<mrow> <msub> <mi>Z</mi> <mrow> <mi>l</mi> <mo>,</mo> <mi>k</mi> <mo>,</mo> <mi>n</mi> </mrow> </msub> <mo>=</mo> <msub> <mi>b</mi> <mrow> <mn>1</mn> <mo>,</mo> <mi>l</mi> </mrow> </msub> <mo>&times;</mo> <msub> <mi>b</mi> <mrow> <mn>2</mn> <mo>,</mo> <mi>l</mi> </mrow> </msub> <mo>...</mo> <mo>&times;</mo> <msub> <mi>b</mi> <mrow> <mi>k</mi> <mo>-</mo> <mn>1</mn> <mo>,</mo> <mi>l</mi> </mrow> </msub> <mo>&times;</mo> <msub> <mi>a</mi> <mrow> <mi>k</mi> <mo>,</mo> <mi>l</mi> </mrow> </msub> <mo>&times;</mo> <msub> <mrow> <mo>&lsqb;</mo> <mrow> <mo>(</mo> <msub> <mi>a</mi> <mrow> <mi>k</mi> <mo>+</mo> <mn>1</mn> <mo>,</mo> <mi>l</mi> </mrow> </msub> <mo>,</mo> <msub> <mi>b</mi> <mrow> <mi>k</mi> <mo>+</mo> <mn>1</mn> <mo>,</mo> <mi>l</mi> </mrow> </msub> <mo>)</mo> </mrow> <mo>&CircleTimes;</mo> <mrow> <mo>(</mo> <msub> <mi>a</mi> <mrow> <mi>k</mi> <mo>+</mo> <mn>2</mn> <mo>,</mo> <mi>l</mi> </mrow> </msub> <mo>,</mo> <msub> <mi>b</mi> <mrow> <mi>k</mi> <mo>+</mo> <mn>2</mn> <mo>,</mo> <mi>l</mi> </mrow> </msub> <mo>)</mo> </mrow> <mo>...</mo> <mo>&CircleTimes;</mo> <mrow> <mo>(</mo> <msub> <mi>a</mi> <mrow> <mi>K</mi> <mo>,</mo> <mi>l</mi> </mrow> </msub> <mo>,</mo> <msub> <mi>b</mi> <mrow> <mi>K</mi> <mo>,</mo> <mi>l</mi> </mrow> </msub> <mo>)</mo> </mrow> <mo>&rsqb;</mo> </mrow> <mrow> <mi>n</mi> <mo>-</mo> <mn>1</mn> </mrow> </msub> </mrow>
wherein,represents the convolution operation of these vectors, [ x ]]n-1The n-1 th element representing a vector; and (3) recalculating:
<mrow> <msub> <mi>&delta;</mi> <mi>k</mi> </msub> <mo>=</mo> <mfrac> <mi>T</mi> <mrow> <msub> <mover> <mi>R</mi> <mo>^</mo> </mover> <mi>k</mi> </msub> <mi>B</mi> </mrow> </mfrac> <mo>&times;</mo> <munderover> <mo>&Sigma;</mo> <mrow> <mi>l</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>N</mi> </munderover> <munderover> <mo>&Sigma;</mo> <mrow> <mi>n</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>K</mi> </munderover> <msub> <mi>Z</mi> <mrow> <mi>l</mi> <mo>,</mo> <mi>k</mi> <mo>,</mo> <mi>n</mi> </mrow> </msub> <mo>&times;</mo> <msub> <mi>r</mi> <mi>l</mi> </msub> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>9</mn> <mo>)</mo> </mrow> </mrow>
the channel states of the users after the priority sorting are s in turn arranged on a certain scheduling block of the scheduling area1,s2,...,sK-1,sKAnd s ismax＝Max(s1,s2,...,sK-1,sK) Then, according to the result of equation (9), a channel allocation strategy is made, as follows:
a. if the vector is ═ 21,2,...,K]Is greater than or equal to 1, the channel state is smaxAnd the user with the highest priority obtains the use right of the scheduling block;
b. if the vector is ═ 21,2,...,K]Is less than 1, searching the first channel state as s according to the priority from high to lowmaxIf the user corresponds to an element value in the vectorkIf the number of the scheduling blocks is not more than 1, obtaining the use right of the scheduling block; if the user corresponds to the element value in the vectorkGreater than 1, an interval of [0,1 ] is generated]Uniformly distributed random number ω ifThe user obtains the usage right of the scheduling block; if it is notThe scheduling opportunity is used by the user whose L elements are smaller than 1 in a competition, and the competition policy is as follows:
<mrow> <mi>&chi;</mi> <mo>=</mo> <mi>m</mi> <mi>a</mi> <mi>x</mi> <mrow> <mo>(</mo> <msub> <mi>s</mi> <msub> <mi>y</mi> <mi>l</mi> </msub> </msub> <mo>+</mo> <mfrac> <mn>1</mn> <msub> <mi>I</mi> <msub> <mi>y</mi> <mi>l</mi> </msub> </msub> </mfrac> <mo>)</mo> </mrow> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>10</mn> <mo>)</mo> </mrow> </mrow>
wherein,indicates the channel state of the user, anda priority ranking representing a user; l is more than or equal to 1,
2. the method of claim 1 for on-demand allocation of downlink channels for a single carrier base station system, the method further comprising:
if a real-time service with lower delay index requirement exists in the service, allocating a small number of mutually staggered special time slots for each real-time service by taking the maximum allowable delay of the service as a period; when the data delay of the real-time service approaches the maximum delay, the time slot is directly occupied on the special time slot, and if the time slot is not used, all users compete for use according to the step 5).
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201410222180.8A CN104023399B (en) | 2014-05-23 | 2014-05-23 | For the down channel allocation method based on demand of single carrier base stations system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201410222180.8A CN104023399B (en) | 2014-05-23 | 2014-05-23 | For the down channel allocation method based on demand of single carrier base stations system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN104023399A CN104023399A (en) | 2014-09-03 |
| CN104023399B true CN104023399B (en) | 2017-12-15 |
Family
ID=51439913
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201410222180.8A Active CN104023399B (en) | 2014-05-23 | 2014-05-23 | For the down channel allocation method based on demand of single carrier base stations system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN104023399B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107124769B (en) * | 2017-04-24 | 2019-09-17 | 京信通信系统（中国）有限公司 | A kind of resource allocation method and base station |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101103574A (en) * | 2005-01-11 | 2008-01-09 | 松下电器产业株式会社 | Transmission power control method, transmission power control device, and wireless transmission device |
| CN101977440A (en) * | 2007-01-09 | 2011-02-16 | 株式会社Ntt都科摩 | User device, base station apparatus, and method |
| CN102577485A (en) * | 2009-09-28 | 2012-07-11 | 夏普株式会社 | Wireless communication system, mobile station apparatus, base station apparatus, communication control method and control program |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2002078021A (en) * | 2000-08-31 | 2002-03-15 | Nec Corp | Information distribution system and information distribution method |
-
2014
- 2014-05-23 CN CN201410222180.8A patent/CN104023399B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101103574A (en) * | 2005-01-11 | 2008-01-09 | 松下电器产业株式会社 | Transmission power control method, transmission power control device, and wireless transmission device |
| CN101977440A (en) * | 2007-01-09 | 2011-02-16 | 株式会社Ntt都科摩 | User device, base station apparatus, and method |
| CN102577485A (en) * | 2009-09-28 | 2012-07-11 | 夏普株式会社 | Wireless communication system, mobile station apparatus, base station apparatus, communication control method and control program |
Also Published As
| Publication number | Publication date |
|---|---|
| CN104023399A (en) | 2014-09-03 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109905918B (en) | NOMA cellular Internet of vehicles dynamic resource scheduling method based on energy efficiency | |
| Lim et al. | Proportional fair scheduling of uplink single-carrier FDMA systems | |
| CN114051222B (en) | Wireless resource allocation and communication optimization method based on federal learning in Internet of vehicles environment | |
| EP2681886B1 (en) | Lte scheduling | |
| CN105075165A (en) | CSI feedback method, UE scheduling method, UE and base station | |
| CN104507169B (en) | Reduce the three dimensional resource dynamic allocation method and device of system uplink propagation delay time | |
| JP2007074718A (en) | Scheduler for controlling transmitter / receiver, transmitter / receiver for mobile communication network, and method and computer program for operating them | |
| CN103826312A (en) | Improved LTE (Long Term Evolution) scheduling method based on exponential rule | |
| CN113079577A (en) | Resource allocation method based on coexistence scene of EMBB and URLLC | |
| CN105472754A (en) | Resource allocation method and device | |
| CN102300326B (en) | Scheduling method of multi-user multi-input multi-output (MIMO) communication system and base station | |
| CN103369690B (en) | The distribution method of a kind of Radio Resource and device | |
| CN102958172B (en) | Based on the resource allocation algorithm of proportional fairness in grouping MIMO-MC-CDMA system | |
| CN102056305B (en) | User grouping and resource allocation method for wireless relay network | |
| CN104023400B (en) | For the down channel allocation method based on demand of OFDM base station systems | |
| CN107734697A (en) | Low-complexity multi-user group technology based on NOMA systems | |
| CN102316596B (en) | Control station and method for scheduling resource block | |
| CN104684051A (en) | Method for allocating resources under non-full load of LTE (Long Term Evolution) and LTE-Advanced system | |
| CN104023399B (en) | For the down channel allocation method based on demand of single carrier base stations system | |
| CN106793111B (en) | Joint virtual MIMO resource allocation methods are matched based on dynamic multiple cell user | |
| CN105072686B (en) | A kind of wireless resource allocation methods based on OFDMA junction network | |
| CN103002583A (en) | Wireless communication device and wireless communication system | |
| CN105451350A (en) | Combined unicast and multicast mechanism-based resource allocation method | |
| CN106060945A (en) | LTE resource scheduling algorithm based on RB feedback | |
| CN105611640A (en) | Fairness adjustable CoMP downlink user scheduling method |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |