Amr A. El-Sherif

Joint Design of Spectrum Sensing and Channel Access in Cognitive Radio Networks

Spectrum sensing is an essential functionality of cognitive radio networks. However, the effect of errors in the spectrum sensing process on the performance of the multiple access layer of both primary and secondary networks has not gained much attention. This paper aims at bridging the gap between the study of spectrum sensing and the multiple access of cognitive radio networks. To achieve this goal we pose and answer the question how the spectrum sensing errors affects the performance of cognitive radio networks from a multiple access protocol design point of view. The negative effects of the spectrum sensing errors on the throughput of both primary and secondary networks are characterized through queuing theory analysis of both networks. To alleviate these negative effects a novel joint design of the spectrum sensing and channel access mechanisms is proposed. This design is based on the observation that, in a binary hypothesis testing problem, the value of the test statistics could be used as a confidence measure for the test outcome. Therefore, this value will be used to define different channel access probabilities for secondary users. Results reveal a significant performance improvement in the maximum stable throughput of both primary and secondary networks by virtue of the proposed technique.

Joint Routing and Resource Allocation for Delay Minimization in Cognitive Radio Based Mesh Networks

This paper studies the joint design of routing and resource allocation algorithms in cognitive radio based wireless mesh networks. The mesh nodes utilize cognitive overlay mode to share the spectrum with primary users. Prior to each transmission, mesh nodes sense the wireless medium to identify available spectrum resources. Depending on the primary user activities and traffic characteristics, the available spectrum resources will vary between mesh transmission attempts, posing a challenge that the routing and resource allocation algorithms have to deal with to guarantee timely delivery of the network traffic. To capture the channel availability dynamics, the system is analyzed from a queuing theory perspective, and the joint routing and resource allocation problem is formulated as a non-linear integer programming problem. The objective is to minimize the aggregate end-to-end delay of all the network flows. A distributed solution scheme is developed based on the Lagrangian dual problem. Numerical results demonstrate the convergence of the distributed solution procedure to the optimal solution, as well as the performance gains compared to other design methods. It is shown that the joint design scheme can accommodate double the traffic load, or achieve half the delay compared to the disjoint methods.

Cognitive Radio Networks With Probabilistic Relaying: Stable Throughput and Delay Tradeoffs

This paper studies fundamental throughput and delay tradeoffs in cognitive radio systems with cooperative secondary users. We focus on randomized cooperative policies, whereby the secondary user (SU) serves either its own queue or the primary users (PU) relayed packets queue with certain service probability. The proposed policy opens room for trading the PU delay for enhanced SU delay, and vice versa, depending on the application QoS requirements. Towards this objective, the systems stable throughput region is characterized. Furthermore, the moment generating function approach is employed and generalized for our system to derive closed-form expressions for the average packet delay for both users. The accuracy of these expressions is validated through simulations. Analytical and simulation results reveal that the service probability can steer the system into prioritizing PUs traffic at the expense of SUs QoS, or vice versa, independently from the admission probability. Alternatively, the ability of the admission probability to control the throughput and delay at the PU or the SU depends on the selected value for the service probability as well as the channel conditions. Finally, it is shown how the service and admission probabilities could be used to achieve the desired QoS level to both PU and SU.

Cooperative access in cognitive radio networks: stable throughput and delay tradeoffs

In this paper, we study and analyze fundamental throughput-delay tradeoffs in cooperative multiple access for cognitive radio systems. We focus on the class of randomized cooperative policies, whereby the secondary user (SU) serves either the queue of its own data or the queue of the primary user (PU) relayed data with certain service probabilities. The proposed policy opens room for trading the PU delay for enhanced SU delay. Towards this objective, stability conditions for the queues involved in the system are derived. Furthermore, a moment generating function approach is employed to derive closed-form expressions for the average delay encountered by the packets of both users. Results reveal that cooperation expands the stable throughput region of the system and significantly reduces the delay at both users. Moreover, we quantify the gain obtained in terms of the SU delay under the proposed policy, over conventional relaying that gives strict priority to the relay queue.

A feedback-based access scheme for cognitive radio systems

In this paper, we consider the design of access schemes for secondary users in cognitive radio systems based on the primary user feedback information. We consider a secondary user employing a random access scheme with an access probability that depends on the primary user feedback state. We show that the proposed scheme can enhance the system performance in terms of the secondary throughput and/or primary user delay while guaranteeing a certain quality of service (QoS) for the primary user; this is due to the fact that the proposed scheme avoids sure collisions between the primary and secondary users. The proposed scheme can be deployed with any other random access based scheme and it always results in a performance gain using the extra piece of information coming from the primary user feedback.

Soft Sensing-Based Multiple Access for Cognitive Radio Networks

We consider the effects of spectrum sensing errors on the performance of cognitive radio networks from a queueing theory point of view. In order to alleviate the negative effects of those errors, a novel design of spectrum access mechanism is proposed. This design is based on the observation that, in a binary hypothesis testing problem, the value of the test statistic can be used as a confidence measure for the test outcome. This value is hence used to specify a channel access probability for the secondary network. The access probabilities as a function of the sensing metric are obtained via solving an optimization problem designed to maximize the secondary service rate given a constraint on primary queue stability. The problem is shown to be convex and, hence, the global optimum can be obtained efficiently. Numerical results reveal a significant performance improvement in the maximum stable throughput of both primary and secondary networks over the conventional technique of making a hard binary decision and then transmitting with a certain probability if the primary is sensed to be inactive.

Interference-aware energy-efficient cross-layer design for healthcare monitoring applications

Body Area Sensor Networks (BASNs) leverage wireless communication technologies to provide healthcare stakeholders with innovative tools and solutions that can revolutionize healthcare provisioning; BASNs thus promotes new ways to acquire, process, transport, and secure the raw and processed medical data to provide the scalability needed to cope with the increasing number of elderly and chronic disease patients requiring constant monitoring. However, the design and operation of BASNs is challenging, mainly due to the limited power source and small form factor of the sensor nodes. The main goal of this paper is to minimize the total energy consumption to prolong the lifetime of the wireless BASNs for healthcare applications. An Energy-Delay-Distortion cross-layer framework is proposed in order to ensure transmission quality for medical signals under limited power and computational resources. The proposed cross-layer framework spans the Application-MAC-Physical layers. The optimal encoding and transmission energy are computed to minimize the total energy consumption in a delay constrained wireless BASN. The proposed framework considers three scheduling techniques: TDMA, TDMA-Simultaneous Transmission and dynamic frequency allocation scheduling. The TDMA-ST scheme schedules the weakly interfering links to transmit simultaneously, and schedules the strongly interfering links to transmit at different time slots. The dynamic frequency allocation scheme allocates the time-frequency slots optimally based on the applications requirements. Simulation results show that these proposed scheduling techniques offer significant energy savings, compared to the algorithms that ignore cross-layer optimization.

C5. Performance analysis of massive MIMO multiuser transmit beamforming techniques over generalized spatial channel model

In this paper, the performance analysis of massive multiple input multiple output (MIMO) multiuser transmit beamforming techniques is studied using a generalized spatial channel model. In particular, heuristic beamforming techniques (such as maximum ratio transmission beamforming technique, zero forcing beamforming technique and regularized zero forcing beamforming technique) as well as optimal beamforming technique are considered. A generalized spatial channel model is used for more practical results in mobile communication system. The spectral efficiency is used in the analysis to describe the performance. Special attention is given to the effect of massive MIMO technique on the performance of different considered beamforming techniques. The numerical results show that with a relatively small number of antennas there is a need for optimal beamforming to achieve higher spectral efficiency. It is also shown that the performance of heuristic beamforming techniques approaches the performance of optimal beamforming techniques when the number of transmitting antennas is highly increased.

On the performance of massive multiuser MIMO with different transmit beamforming techniques and antenna selection

In this paper, the performance of massive multiuser multiple-input-multiple-output (MIMO) transmit beamforming techniques and antenna selection is analyzed. Transmit antenna selection is used to reduce the number of radio frequency units, system complexity and system cost, making massive MIMO more applicable. Special attention is given to study the effect of the number of available antennae and radio frequency units on the performance of the system. Three types of transmit beamforming techniques are considered, namely, maximum ratio transmission beamforming, zero forcing beamforming, and minimum mean square error beamforming. The numerical results of the paper clearly show that transmit antenna selection affects the performance of massive MIMO systems for all considered types of transmit beamforming. In particular, it is shown that lower bit error rate can be achieved with small number of selected antennae from a large group of actual transmit antennae. This is of course in addition to the great advantage from the system complexity perspectives.

Decentralized Throughput Maximization in Cognitive Radio Wireless Mesh Networks

Scheduling and spectrum allocation are tasks affecting the performance of cognitive radio wireless networks, where heterogeneity in channel availability limits the performance and poses a great challenge on protocol design. In this paper, we present a distributed algorithm for scheduling and spectrum allocation with the objective of maximizing the networks throughout subject to a delay constraint. During each time slot, the scheduling and spectrum allocation problems involve selecting a subset of links to be activated, and based on spectrum sensing outcomes, allocate the available resources to these links. This problem is addressed as an aggregate utility maximization problem. Since the throughput of any data flow is limited by the throughput of the weakest link along its end-to-end path, the utility of each flow is chosen as a function of this weakest links throughput. The throughput and delay performance of the network are characterized using a queueing theoretic analysis, and throughput is maximized via the application of Lagrangian duality theory. The dual decomposition framework decouples the problem into a set of subproblems that can be solved locally, hence, it allows us to develop a scalable distributed algorithm. Numerical results demonstrate the fast convergence rates of the proposed algorithm, as well as significant performance gains compared to conventional design methods.