Channel Capacity

Cognitive radio has the ability to sense the environment and adapt its behavior to optimize communication features, such as quality of service in the presence of interference and noise. In physical layer to achieve this goal, different phases of sensing, channel estimation, and configuration selection are necessary. The sensing part measures the interference level, recognize the spectrum holes, and send this information to channel estimator. In the next step, channel state information (CSI) is used for data detection and also sent to the transmitter through a limited feedback. CSI feedback consists of achievable rate, SNR value, modulation or coding schemes (MCS). Feedback link improves the system performance in the cost of complexity and delay. In this paper, we present and compare different feedback schemes for cognitive radio and study the channel capacity when an imperfect feedback link is corrupted by noise and delay.

When classical information is sent through a quantum channel of nonorthogonal states, there is a possibility that transmittable classical information exceeds a channel capacity in a single use of the initial channel by extending it into multi-product channel. In this paper, it is shown that this remarkable feature of a quantum channel, so-called superadditivity, appears even in as low as the third extended coding of the simplest binary input channel. A physical implementation of this channel is indicated based on cavity QED techniques.

The recent advances in miniaturization and the creation of low-power circuits, combined with small-sized batteries have made the development of wireless sensor networks a working reality. Lately, the production of cheap complementary metal-oxide semiconductor cameras and microphones, which are able to capture rich multimedia content, gave birth to what is called Wireless Multimedia Sensor Networks (WMSNs). WMSNs will boost the capabilities of current wireless sensor networks, and will fuel several novel applications, like multimedia surveillance sensor networks. WMSNs introduce several new research challenges, mainly related to mechanisms to deliver application-level Quality-of-Service (e.g., latency minimization). To address this goal in an environment with extreme resource constraints, with variable channel capacity and with requirements for multimedia in-network processing, the caching of multimedia data, exploiting the cooperation among sensor nodes is vital. This article presents a cooperative caching solution particularly suitable for WMSNs. The proposed caching solution exploits sensor nodes which reside in “positions” of the network that allow them to forward packets or communicate decisions within short latency. These so-called “mediator” nodes are selected dynamically, so as to avoid the creation of hot-spots in the communication and the depletion of their energy. The mediators are not more powerful than the rest of the nodes, but they have some special role in implementing the cooperation among the sensors. The proposed cooperative caching protocol includes components for locating cached data as well as for implementing data purging out of the sensor caches. The proposed solution is evaluated extensively in an advanced simulation environment, and it is compared to the state-of-the-art cooperative caching algorithm for mobile ad hoc networks. The results confirm that the proposed caching mechanism prevails over its competitor.

In many packet-based communication systems such as TCP/IP-based systems, packets are communicated over a noisy physical layer (a channel), and if a packet cannot be decoded correctly, then the transport layer retransmits it. Of course, retransmissions consume significant resources and their use should be limited. However, decreasing the likelihood of retransmission requires to encode the packets with strong channel codes in the physical layer, which also requires additional channel resources. In this paper, we study the cross-layer tradeoff between coding and packet retransmissions, and optimize over the total channel resource consumption. We show that as the packet length k increases, the redundancy r beyond the k/C channel uses implied by Shannon's channel capacity C is Theta(radickln(k)) extra channel uses. Moreover, as k increases we must use stronger channel codes. We then apply these results to universal coding over a piecewise memoryless channel with transitions between unknown i.i.d. statistics. Our constructive universal algorithm has redundancy r=O(k2/3radicln(k)) using packets of polynomially increasing lengths while accounting for possible packet drops caused by transitions in the statistics.

Capacity planning is a powerful tool for managing quality of service on the Web. This tutorial presents a capacity planning methodology for Web-based environments, where the main steps are: understanding the environment, characterizing the workload, modeling the workload, validating and calibrating the models, forecasting the workload, predicting the performance, analyzing the cost-performance plans, and suggesting actions. The main steps are based on two models: a workload model and a performance model. The first model ...

This paper compares MIMO vs SISO and mention difference between SISO and MIMO techniques. These are techniques based on number of antennas used at the transmitter and the receiver. SISO has been in use since the invention of wireless system.MIMO concept has been recently added to the wireless system. There are different MIMO algorithms which have been developed for two main reasons to increase coverage and to increase the data rates. SISO means Single Input Single Output while MIMO means Multiple Input Multiple Output.