CN103596223B - The cellular network energy consumption of mobile equipment optimization method that dispatching sequence is controlled - Google Patents

Abstract

The energy consumption optimization method of cellular network mobile devices with controllable scheduling sequence discretizes the time into time slots. On the premise of ensuring the transmission performance requirements of mobile applications, the mobile application traffic is processed according to the pre-calculated optimal scheduling transmission time slots. Scheduling and sending data, when the mobile device has multiple applications that need to send a certain flow of data, divide the traffic of the mobile application into multiple task units; based on the given task unit scheduling order, arrival time slot, deadline and user performance requirements Boundary, in the case of known transmission energy consumption function, tail energy consumption function and performance overhead function, according to the dynamic programming model of energy consumption optimization, using iterative algorithm to solve the transmission scheduling time slot of each task unit, according to the transmission scheduling time slot pair The traffic unit performs transmission scheduling; the present invention comprehensively optimizes transmission energy consumption and tail energy consumption under the premise of ensuring the performance requirements of users for mobile applications, has the characteristics of controllable transmission scheduling sequence of task units, and can be implemented on mobile terminal equipment.

Description

Energy consumption optimization method for cellular network mobile devices with controllable scheduling sequence

technical field

The invention belongs to the technical field of energy consumption optimization of mobile terminal equipment, and relates to a method for optimizing energy consumption of cellular network mobile equipment with controllable scheduling sequence.

Background technique

In recent years, with the rapid development of mobile devices and cellular communication technologies, the processing capability of mobile devices has been significantly improved, and the transmission bandwidth of cellular networks has also been continuously increased, resulting in rapid growth of cellular mobile applications. However, since a large number of mobile applications (especially some energy-intensive high-bandwidth applications, such as VoD and VoIP) consume a lot of energy, the problems of energy consumption and battery life of mobile devices have become increasingly prominent. Therefore, in a cellular mobile network, how to reduce the energy consumption of mobile devices, prolong battery life, and improve user experience is a topic worthy of in-depth research and very meaningful.

In a 3G cellular mobile network, a large part of the energy consumption of mobile devices comes from tail energy consumption and transmission energy consumption. Transmission energy consumption refers to the energy consumption of actually transmitting data, which is related to the size and signal strength of the transmitted data packet. Tail energy consumption refers to the energy consumed by a period of inactivity during the transition from a high energy consumption state to a low energy consumption state after data transmission is completed, which is caused by the radio resource control strategy RRC designed in the cellular mobile network , a large amount of tail energy consumption will cause a significant decline in the energy efficiency of the system. In order to reduce these two parts of energy consumption, a lot of research work has been carried out. In terms of transmission energy consumption optimization, the impact of signal strength on transmission energy is mainly discussed, mostly by optimizing data unit scheduling, so that data can be transmitted under better channel conditions, thereby reducing transmission energy consumption. In terms of tail energy optimization, the research focuses on timer optimization, delayed transmission, and fast sleep strategies, and reduces tail energy consumption by adjusting various optimization algorithms such as RRC configuration, data queuing and batch scheduling, and active sleep based on traffic prediction.

However, analyzing the existing research work, it can be found that there are still the following problems: First, only one of the energy consumption issues is usually considered. Since both transmission energy consumption and tail energy consumption are considered at the same time, it is difficult to minimize the sum of these two energy consumptions and try to find the optimal optimization scheme, so the existing literature work is usually devoted to optimizing and reducing one of the energy consumption . The second is to ignore the user experience. Attempts to reduce tail energy consumption and transmission energy consumption generally lead to packet transmission delays. Existing optimization schemes usually only aim at reducing energy consumption, and seldom consider user requirements for transmission performance. Third, it is relatively difficult to implement the proposed scheme. Existing low-level system support schemes (such as optimized RRC timers) usually need to modify some configurations controlled by mobile network operators, while high-level transmission scheduling mechanisms usually need to deal with transport layers (such as TCP congestion control and flow control) and application layers (such as some application interfaces) protocol and configuration, which makes it relatively difficult to implement in the actual system.

Contents of the invention

In order to overcome the above-mentioned shortcomings of the prior art, the purpose of the present invention is to provide a method for optimizing energy consumption of cellular network mobile devices with controllable scheduling sequence, which not only considers transmission energy consumption and tail energy consumption, but also considers user experience requirements, and can directly Implemented on mobile terminal equipment.

In order to achieve the above object, the present invention takes the following technical solutions:

A method for optimizing energy consumption of mobile devices in a cellular network with controllable scheduling sequence, the mobile devices are implemented in the following steps:

Step (1): Decompose the traffic of multiple mobile applications to be transmitted into n task units to form a task unit set U;

Step (2): Input the scheduling order ω of each task unit u∈U, the arrival time slot t _a (u), the given deadline t _d (u) and the user performance requirement limit

Step (3): Calculate the scheduling time slot t _s (u _i ) corresponding to u _i ∈ U(1≤i≤n) and the minimum energy consumption value g(n) after the scheduled transmission of all n task units is completed according to the following steps ;

Step (3.1): For all u∈U, calculate the upper limit value of the end slot of each task unit transmission $t_{\mathrm{dd}}\left(u\right)=t_a\left(u\right)+{\mathrm{\φ}}_u^{-1}\left(\widetilde{\mathrm{\Φ}}\left(u\right)\right);$

Step (3.2): sort u∈U according to the task unit scheduling order ω, and the sorted task set is U={u ₁ ,u ₂ ,...,u _n };

Step (3.3): Construct a set of discrete time slots ∏(t _s (u ₁ ));

Step (3.4): For all k∈∏(t _s (u ₁ )), calculate the energy consumption f( ₁ ,k)=E _trans (k,k+t _r (u ₁ ))+E _tail (T), where t _r (u ₁ ) represents the transmission delay slot of u ₁ , and T represents a complete tail time;

Step (3.5): Calculate the minimum energy consumption value after the transmission of the first task unit in U is completed $g\left(1\right)=\underset{k\∈\mathrm{\Π}\left(t_{\mathrm{the\; s}}\left(u_1\right)\right)}{\min }\left\{f(1,k)\right\};$

Step (3.6): Let parameter i=2;

Step (3.7): Construct a set of discrete time slots ∏(t _s (u _i ));

Step (3.8): For all k∈∏(t _s ( _{u i} )), calculate the minimum energy consumption value f(i ,k), where t _r (u _i ) represents the transmission delay slot of u _i :

$f(i,k)=\underset{j\∈\mathrm{\Π}\left(t_{\mathrm{the\; s}}\left(u_{i-1}\right)\right)\mathrm{and\; j}+t_r\left(u_{i-1}\right)\≤k}{\min }\{{\mathrm{E.}}_{\mathrm{trans}}(k,k+t_r\left(u_i\right))+{\mathrm{E.}}_{\mathrm{tail}}(k-(j+t_r\left(u_{i-1}\right)))+f(i-1,j)\},$ And record the j value corresponding to the minimum value of f(i,k), and assign the j value to Last(i,k);

Step (3.9): Calculate the minimum energy consumption value after the transmission of the first i task units in U $g\left(i\right)=\underset{k\∈\∏\left(t_{\mathrm{the\; s}}\left(u_i\right)\right)}{\min }\left\{f(i,k)\right\};$

Step (3.10): let parameter i=i+1;

Step (3.11): If the parameter i≤n, go to step (3.7), otherwise go to step (3.12);

Step (3.12): Calculate $t_{\mathrm{the\; s}}\left(u_{\mathrm{no}}\right)=\arg \underset{j\∈\mathrm{\Π}\left(t_{\mathrm{the\; s}}\left(u_{\mathrm{no}}\right)\right)}{\min }\left\{f(i,j)\right\};$

Step (3.13): let the parameter i=n-1;

Step (3.14): Calculate t _s (u _i )=Last(i,t _s (u _i+1 ));

Step (3.15): let parameter i=i-1;

Step (3.16): If parameter i>0, go to step (3.14), otherwise go to step (3.17);

Step (3.17): Output the transmission scheduling time t _s (u _i ) of each task unit and the corresponding minimum energy consumption value g(n) after the transmission of all task units is completed;

Step (4): Scheduling and sending the transmission task units u _i in sequence according to t _s (u _i ).

Compared with the prior art, the present invention has the following advantages: (1) The transmission energy consumption and the tail energy consumption of the mobile device are optimized as a whole, so that the comprehensive energy consumption of the mobile device is minimized; (2) The energy consumption optimization of mobile devices is based on the premise of meeting the user's mobile application performance requirements, ensuring that users have a better application transmission experience; (3) The transmission scheduling order of task units is determined by the user, so that the transmission scheduling order of task units is controllable, and at the same time It can meet the transmission sequence requirements of some specific mobile applications and avoid unnecessary reordering of task units by the receiving terminal; (4) The energy consumption optimization method only optimizes the transmission scheduling time of mobile application traffic, and does not involve any mobile operators The modification of the control parameters and the network protocol of the mobile system can be completely realized on the mobile terminal equipment.

Description of drawings

Fig. 1 is the general flowchart of the present invention.

Fig. 2 is a specific flow chart of the present invention.

detailed description

The specific implementation manner of the present invention will be described in detail below with reference to the drawings and embodiments.

As shown in FIG. 1 , the energy consumption optimization method for cellular network mobile devices with controllable scheduling order in the present invention is mainly to solve the optimal task transmission scheduling time according to the energy consumption optimization model considering user performance requirements. The design idea is to discretize time into time slots, and schedule the transmission of flow data units according to time slots. Specifically, according to the established energy consumption optimization model, the optimal transmission scheduling time slot for each data unit is calculated in advance, and The mobile application traffic data is scheduled and sent based on the time slot, so as to minimize energy consumption under the condition of meeting user performance requirements. The invention establishes a comprehensive optimization model of transmission energy consumption and tail energy consumption considering user performance requirements, and introduces task unit scheduling order to convert the optimization model into a dynamic programming problem. The specific content is as follows:

Decompose the traffic from the mobile application into n transmission task units. For a transmission task unit u, let its arrival time slot be t _a (u), the time slot to be dispatched is t _s (u), the transmission delay overhead is t _r (u) time slots, and it is completely transmitted The end time slot of t _e (u)=t _s (u)+t _r (u), the transmission deadline time slot is t _d (u).

The energy consumption optimization model considering user performance requirements is as follows, where U={u _i |1≤i≤n} is the set of transmission task units, and Γ is the given time:

$\left\{\begin{array}{c}\mathrm{<span\; class="notranslate">\; min</span>}<span\; class="notranslate">\; \{</span>{\stackrel{<span\; class="notranslate">\; ~</span>}{\mathrm{<span\; class="notranslate">\; E.</span>}}}_{\mathrm{<span\; class="notranslate">\; d</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; u</span>}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; the\; s</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; u</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; \&Gamma;</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; +</span>{\stackrel{<span\; class="notranslate">\; ~</span>}{\mathrm{<span\; class="notranslate">\; E.</span>}}}_{\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; u</span>}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; the\; s</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; u</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; \&Gamma;</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \}</span>\\ {\mathrm{<span\; class="notranslate">\; \&phi;</span>}}_{\mathrm{<span\; class="notranslate">\; u</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; e</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; u</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; a</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; u</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&le;</span>\stackrel{<span\; class="notranslate">\; ~</span>}{\mathrm{<span\; class="notranslate">\; \&Phi;</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; u</span>}<span\; class="notranslate">\; )</span>\\ \underset{\mathrm{<span\; class="notranslate">\; u</span>}<span\; class="notranslate">\; \&Element;</span><span\; class="notranslate">\; \{</span>\mathrm{<span\; class="notranslate">\; u</span>}<span\; class="notranslate">\; |</span>{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; the\; s</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; u</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; \}</span>}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}{\mathrm{<span\; class="notranslate">\; v</span>}}_{\mathrm{<span\; class="notranslate">\; u</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&le;</span>\mathrm{<span\; class="notranslate">\; c</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span>\end{array}\right.$

in, Respectively represent the total transmission energy consumption and the total tail energy consumption of the transmission task unit set U after scheduled transmission in time slot t _s (U) within time Γ. φ _u (.) represents the mobile application performance overhead function, Indicates the performance threshold expected by the user. c(t) represents the channel capacity of a time slot t, and v(t) represents the transmission rate of a transmission task u in time slot t.

In the model, the total transmission energy consumption It can be estimated by:

${\stackrel{<span\; class="notranslate">\; ~</span>}{\mathrm{<span\; class="notranslate">\; E.</span>}}}_{\mathrm{<span\; class="notranslate">\; d</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; u</span>}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; the\; s</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; u</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; \&Gamma;</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; u</span>}<span\; class="notranslate">\; \&Element;</span>\mathrm{<span\; class="notranslate">\; u</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}{\mathrm{<span\; class="notranslate">\; E.</span>}}_{\mathrm{<span\; class="notranslate">\; trans</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; the\; s</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; u</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; e</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; u</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span>$

Here E _trans (t ₁ ,t ₂ ) represents the energy consumption of data transmission from time slot t ₁ to t ₂ , which can be adopted For calculation, signal(τ) represents the strength of the signal at time slot τ, and P _sig (signal) represents the power of the generated signal.

total tail energy consumption It can be estimated by the following methods:

${\stackrel{<span\; class="notranslate">\; ~</span>}{\mathrm{<span\; class="notranslate">\; E.</span>}}}_{\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; u</span>}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; the\; s</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; u</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; \&Gamma;</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; \&le;</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; \&le;</span>\mathrm{<span\; class="notranslate">\; no</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}{\mathrm{<span\; class="notranslate">\; E.</span>}}_{\mathrm{<span\; class="notranslate">\; tail</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; \&Delta;</span>}{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; E.</span>}}_{\mathrm{<span\; class="notranslate">\; tail</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; T</span>}<span\; class="notranslate">\; )</span>$

Here E _tail (t) represents the tail energy consumption in the time slot from the end of the transmission of the previous task unit to the time slot before the next task unit is scheduled pD and pF represent the power consumption corresponding to DCH state and FACH state respectively, δ _D and δ _F represent the transition time of states DCH→FACH and FACH→IDLE respectively; T=δ _D + δ _F represents a complete tail time.

The application performance overhead function φ _u (t) can be calculated by the following method:

Here D(u)=t _e (u)-t _a (u) represents the task delay (including buffer delay and transmission delay), S(u) represents the data size of task u, f ₁ and f ₂ represent an application pair delay The sensitivity of the weight w _u represents the user's preference for the task u generated by the mobile application.

Solving the above energy consumption optimization model is an NP-hard problem. In order to solve it quickly, the transmission task unit scheduling order ω is introduced, and the NP-hard problem solved by the optimization model is transformed into a dynamic programming problem. The specific description is as follows:

Sort the elements in U according to the ω value corresponding to each task unit. The sorted task set is U={u ₁ ,u ₂ ,...,u _n }, then the objective function of the optimization model is transformed into:

$\mathrm{<span\; class="notranslate">\; E.</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; u</span>}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; the\; s</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; u</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; \&Gamma;</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; E.</span>}}_{\mathrm{<span\; class="notranslate">\; trans</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; the\; s</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; u</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; e</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; u</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span><span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; E.</span>}}_{\mathrm{<span\; class="notranslate">\; tail</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; T</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; +</span>\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 2</span>}}{\overset{\mathrm{<span\; class="notranslate">\; no</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}<span\; class="notranslate">\; \{</span>{\mathrm{<span\; class="notranslate">\; E.</span>}}_{\mathrm{<span\; class="notranslate">\; trans</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; the\; s</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; u</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; e</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; u</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span><span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; E.</span>}}_{\mathrm{<span\; class="notranslate">\; tail</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; the\; s</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; u</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; e</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; u</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span><span\; class="notranslate">\; \}</span>$

Use f(i,k) to represent the minimum energy consumption of the first i units in U when u _i is scheduled for transmission at time slot k, and use g(i) to represent the minimum energy consumption of the first _i units in U under all scheduling possibilities. The minimum energy consumption value for the unit. If the set ∏(t _s (u))={t _s (u)|t _s (u)≥t _a (u)andt _s (u)≤t _dd (u)-t _r (u)} is introduced, where is the user's performance expectation limit value for each task unit u, then the corresponding dynamic programming formula can be constructed:

$\mathrm{<span\; class="notranslate">\; f</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; \&Element;</span>\mathrm{<span\; class="notranslate">\; \&Pi;</span>}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; the\; s</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; u</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span>\mathrm{<span\; class="notranslate">\; and\; j</span>}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; r</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; u</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&le;</span>\mathrm{<span\; class="notranslate">\; k</span>}}{\mathrm{<span\; class="notranslate">\; min</span>}}<span\; class="notranslate">\; \{</span>{\mathrm{<span\; class="notranslate">\; E.</span>}}_{\mathrm{<span\; class="notranslate">\; trans</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; r</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; u</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span><span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; E.</span>}}_{\mathrm{<span\; class="notranslate">\; tail</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; r</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; u</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span><span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; f</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \}</span>$

$\mathrm{<span\; class="notranslate">\; g</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; \&Element;</span>\mathrm{<span\; class="notranslate">\; \&Pi;</span>}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; the\; s</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; u</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span>}{\mathrm{<span\; class="notranslate">\; min</span>}}<span\; class="notranslate">\; \{</span>\mathrm{<span\; class="notranslate">\; f</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; k</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \}</span>$

The boundary conditions are f(1,k)=E _trans (k,k+t _r (u ₁ ))+E _tail (T).

The above dynamic programming problem can be solved by an iterative algorithm, so as to obtain the optimal target value g(n) and the optimal task unit scheduling time slot result t _s (U).

As shown in Figure 2, specifically, when the transmission energy consumption function E _trans (t ₁ ,t ₂ ), the tail energy consumption function E _tail (t) and the performance overhead function φ _u (t) are known, the implementation process Proceed as follows:

Step (1): Decompose the traffic of multiple mobile applications to be transmitted into n task units to form a task unit set U;

Step (2): Input the scheduling order ω of each task unit u∈U, the arrival time slot t _a (u), the given deadline t _d (u) and the user performance requirement limit

Step (3): Calculate the scheduling time slot t _s (u _i ) corresponding to u _i ∈ U(1≤i≤n) and the minimum energy consumption value g(n) after the scheduled transmission of all n task units is completed according to the following steps ;

Step (3.1): For all u∈U, calculate the upper limit value of the end slot of each task unit transmission $t_{\mathrm{dd}}\left(u\right)=t_a\left(u\right)+{\mathrm{\&phi;}}_u^{-1}\left(\widetilde{\mathrm{\&Phi;}}\left(u\right)\right);$

Step (3.2): sort u∈U according to the task unit scheduling order ω, and the sorted task set is U={u ₁ ,u ₂ ,...,u _n };

Step (3.3): Construct a set of discrete time slots ∏(t _s (u ₁ ));

Step (3.4): For all k∈∏(t _s (u ₁ )), calculate the energy consumption f( ₁ ,k)=E _trans (k,k+t _r (u ₁ ))+E _tail (T), where t _r (u ₁ ) represents the transmission delay slot of u ₁ , and T represents a complete tail time;

Step (3.5): Calculate the minimum energy consumption value after the transmission of the first task unit in U is completed $g\left(1\right)=\underset{k\&Element;\mathrm{\&Pi;}\left(t_{\mathrm{the\; s}}\left(u_1\right)\right)}{\min }\left\{f(1,k)\right\};$

Step (3.6): Let parameter i=2;

Step (3.7): Construct a set of discrete time slots ∏(t _s (u _i ));

Step (3.8): For all k∈∏(t _s ( _{u i} )), calculate the minimum energy consumption value f(i ,k), where t _r (u _i ) represents the transmission delay slot of u _i :

$f(i,k)=\underset{j\&Element;\mathrm{\&Pi;}\left(t_{\mathrm{the\; s}}\left(u_{i-1}\right)\right)\mathrm{and\; j}+t_r\left(u_{i-1}\right)\&le;k}{\min }\{{\mathrm{E.}}_{\mathrm{trans}}(k,k+t_r\left(u_i\right))+{\mathrm{E.}}_{\mathrm{tail}}(k-(j+t_r\left(u_{i-1}\right)))+f(i-1,j)\},$ And record the j value corresponding to the minimum value of f(i,k), and assign the j value to Last(i,k);

Step (3.9): Calculate the minimum energy consumption value after the transmission of the first i task units in U $g\left(i\right)=\underset{k\&Element;\mathrm{\&Pi;}\left(t_{\mathrm{the\; s}}\left(u_i\right)\right)}{\min }\left\{f(i,k)\right\};$

Step (3.10): let parameter i=i+1;

Step (3.11): If the parameter i≤n, go to step (3.7), otherwise go to step (3.12);

Step (3.12): Calculate $t_{\mathrm{the\; s}}\left(u_{\mathrm{no}}\right)=\arg \underset{j\&Element;\mathrm{\&Pi;}\left(t_{\mathrm{the\; s}}\left(u_{\mathrm{no}}\right)\right)}{\min }\left\{f(i,j)\right\};$

Step (3.13): let the parameter i=n-1;

Step (3.14): Calculate t _s (u _i )=Last(i,t _s (u _i+1 ));

Step (3.15): let parameter i=i-1;

Step (3.16): If parameter i>0, go to step (3.14), otherwise go to step (3.17);

Step (3.17): Output the transmission scheduling time t _s (u _i ) of each task unit and the corresponding minimum energy consumption value g(n) after the transmission of all task units is completed;

Step (4): Scheduling and sending the transmission task units u _i in sequence according to t _s (u _i ).

In the above steps (3.3) and (3.7), the sets ∏(t _s (u ₁ )), ∏(t _s (u _i )) are constructed as follows:

∏(t _s (u))={t _s (u)|t _s (u)≥t _a (u)andt _s (u)≤t _dd (u)-t _r (u)}

Among them, t _r (u) can use all t _s (u) to pass For calculation, S(u) is the data size of the task unit u, and R _sig (signal(τ)) is the average rate of data transmission corresponding to the signal strength within a given time slot.

Claims (2)

1. A method for optimizing energy consumption of cellular network mobile devices with controllable scheduling sequence, characterized in that the mobile devices are implemented in the following steps: Step (1): Decompose the traffic of multiple mobile applications to be transmitted into n task units to form a task unit set U; Step (2): Input the scheduling order ω of each task unit u∈U, the arrival time slot t _a (u), the given deadline t _d (u) and the user performance requirement limit Step (3): Calculate the scheduling time slot t _s (u _i ) corresponding to u _i ∈ U(1≤i≤n) and the minimum energy consumption value g(n) after the scheduled transmission of all n task units is completed according to the following steps ; Step (3.1): For all u ∈ U, calculate the upper limit value of the transmission end time slot of each task unit The inverse function of the mobile application performance overhead function φ _u (t) given by the user, φ _u (t) is defined as follows: Among them, D(u) represents the task delay including buffer delay and transmission delay, S(u) represents the data size of task u, f ₁ and f ₂ represent the sensitivity of a mobile application to delay; weight w _u represents the user's The liking degree of the task u generated by the mobile application; Step (3.2): sort u∈U according to the task unit scheduling order ω, and the sorted task set is U={u ₁ ,u ₂ ,...,u _n }; Step (3.3): Construct a set of discrete time slots ∏(t _s (u ₁ )); Step (3.4): For all k∈∏( _{t s} (u ₁ )), calculate the energy consumption value f(1,k)=E _trans (k,k+t _r (u ₁ ))+E _tail (T), where t _r (u ₁ ) represents the transmission delay slot of u ₁ , and T represents a complete tail time; Step (3.5): Calculate the minimum energy consumption value after the first task unit transmission in U is completed $g\left(1\right)=\underset{k\&Element;\mathrm{\&Pi;}\left(t_{\mathrm{the\; s}}\right(u_1\left)\right)}{mi\mathrm{no}}\left\{f(1,k)\right\};$ Step (3.6): make parameter i=2; Step (3.7): Construct a set of discrete time slots ∏(t _s (u _i )); Step (3.8): For all k∈∏(t _s ( _{u i} )), calculate the minimum energy consumption value f(i ,k), where t _r (u _i ) represents the transmission delay slot of u _i : And record the j value corresponding to the minimum value of f(i,k), and assign the j value to Last(i,k); Step (3.9): Calculate the minimum energy consumption value after the transmission of the first i task units in U is completed $g\left(i\right)=\underset{k\&Element;\mathrm{\&Pi;}\left(t_{\mathrm{the\; s}}\right(u_i\left)\right)}{mi\mathrm{no}}\left\{f(i,k)\right\};$ Step (3.10): make parameter i=i+1; Step (3.11): If the parameter i≤n, then go to step (3.7), otherwise go to step (3.12); Step (3.12): Calculate Step (3.13): make parameter i=n-1; Step (3.14): Calculate t _s (u _i )=Last(i,t _s (u _i+1 )); Step (3.15): make parameter i=i-1; Step (3.16): If parameter i>0, then go to step (3.14), otherwise go to step (3.17); Step (3.17): Output the transmission scheduling time t _s (u _i ) of each task unit and the corresponding minimum energy consumption value g(n) after the transmission of all task units is completed; In the step (3.4) and step (3.8), the transmission energy consumption E _trans (t ₁ , t ₂ ) of the task unit from the time slot t ₁ to t ₂ is calculated as follows: ${\mathrm{<span\; class="notranslate">\; E.</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}\mathrm{<span\; class="notranslate">\; r</span>}\mathrm{<span\; class="notranslate">\; a</span>}\mathrm{<span\; class="notranslate">\; no</span>}\mathrm{<span\; class="notranslate">\; the\; s</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\underset{{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; \&le;</span>\mathrm{<span\; class="notranslate">\; \&tau;</span>}<span\; class="notranslate">\; \&le;</span>{\mathrm{<span\; class="notranslate">\; t</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}}{<span\; class="notranslate">\; \&Sigma;</span>}{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; the\; s</span>}\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; g</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; the\; s</span>}\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; g</span>}\mathrm{<span\; class="notranslate">\; no</span>}\mathrm{<span\; class="notranslate">\; a</span>}\mathrm{<span\; class="notranslate">\; l</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; \&tau;</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span>$ Wherein, P _sig (signal(τ)) is the power of a given signal generated at time slot τ; In said step (3.4) and step (3.8), the tail energy consumption E _tail (t) in the time period before the last task unit transmission ends to the next task unit is scheduled is calculated as follows: Among them, pD and pF are the power consumption corresponding to the given DCH state and FACH state respectively, and δ _D and δ _F are the conversion time of the given state DCH→FACH and FACH→IDLE respectively; Step (4): Scheduling and sending the transmission task units u _i in sequence according to t _s (u _i ). 2. The method for optimizing energy consumption of cellular network mobile devices with controllable scheduling sequence according to claim 1, characterized in that, in the step (3.3) and step (3.7), the set ∏(t _s (u ₁ )) , ∏(t _s (u _i )) is constructed as follows: ∏(t _s (u))＝{t _s (u)|t _s (u)≥t _a (u)andt _s (u)≤t _dd (u)-t _r (u)} Among them, t _r (u) represents the transmission delay time slot of the task unit, and its value can be passed by using all t _s (u) For calculation, S(u) is the data size of the task unit u, and R _sig (signal(τ)) is the average rate of data transmission corresponding to the signal strength within a given time slot.