Mahmoud Ashour

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.

On spectrum sharing between energy harvesting cognitive radio users and primary users

This paper investigates the maximum throughput for a rechargeable secondary user (SU) sharing the spectrum with a primary user (PU) plugged to a reliable power supply. The SU maintains a finite energy queue and harvests energy from natural resources and primary radio frequency (RF) transmissions. We propose a power allocation policy at the PU and analyze its effect on the throughput of both the PU and SU Furthermore, we study the impact of the bursty arrivals at the PU on the energy harvested by the SU from RF transmissions. Moreover, we investigate the impact of the rate of energy harvesting from natural resources on the SU throughput. We assume fading channels and compute exact closed-form expressions for the energy harvested by the SU under fading. Results reveal that the proposed power allocation policy along with the implemented RF energy harvesting at the SU enhance the throughput of both primary and secondary links.

Energy-Aware Cooperative Wireless Networks With Multiple Cognitive Users

In this paper, we study and analyze cooperative cognitive radio networks with arbitrary number of secondary users (SUs). Each SU is considered a prospective relay for the primary user (PU) besides having its own data transmission demand. We consider a multi-packet transmission framework that allows multiple SUs to transmit simultaneously because of dirty-paper coding. We propose power allocation and scheduling policies that optimize the throughput for both PU and SU with minimum energy expenditure. The performance of the system is evaluated in terms of throughput and delay under different opportunistic relay selection policies. Toward this objective, we present a mathematical framework for deriving stability conditions for all queues in the system. Consequently, the throughput of both primary and secondary links is quantified. Furthermore, a moment generating function approach is employed to derive a closed-form expression for the average delay encountered by the PU packets. Results reveal that we achieve better performance in terms of throughput and delay at lower energy cost as compared with equal power allocation schemes proposed earlier in the literature. Extensive simulations are conducted to validate our theoretical findings.

Optimal spectrum access for a rechargeable cognitive radio user based on energy buffer state

This paper investigates the maximum throughput for a rechargeable secondary user (SU) sharing the spectrum with a primary user (PU) plugged to a reliable power supply. The SU maintains a finite energy queue and harvests energy from natural resources, e.g., solar, wind and acoustic noise. We propose a probabilistic access strategy by the SU based on the number of packets at its energy queue. In particular, when the energy queue is in a certain state, the SU probabilistically uses a total number of energy packets that is at most equal to the number of packets at its energy queue. The probability of using certain amount of energy in a given state is optimizable. We investigate the effect of the energy arrival rate, the amount of energy per energy packet, and the capacity of the energy queue on the SU throughput under fading channels.

Power-optimal feedback-based random spectrum access for an energy harvesting cognitive user

In this paper, we study and analyze cognitive radio networks in which secondary users (SUs) are equipped with energy harvesting (EH) capability. We design a random spectrum sensing and access protocol for the SU that exploits the primary links feedback and requires less average sensing time. Unlike previous works proposed earlier in literature, we do not assume perfect feedback. Instead, we take into account the more practical possibilities of overhearing unreliable feedback signals and accommodate spectrum sensing errors. Moreover, we assume an interference-based channel model where the receivers are equipped with multi-packet reception (MPR) capability. Furthermore, we perform power allocation at the SU with the objective of maximizing the secondary throughput under constraints that maintain certain quality-of-service (QoS) measures for the primary user (PU).

On the power efficiency for cognitive radio networks with multiple relays

In this paper, we study and analyze cooperative cognitive radio networks with multiple secondary users (SUs). Each SU is considered a prospective relay for the primary user (PU) besides having its own data demand. The proposed scheme leverages the spectral efficiency of the system via allowing two SUs to transmit simultaneously thanks to dirty-paper coding. We propose a power allocation policy that minimizes the average transmitted power at each SU. Moreover, we are concerned with enhancing the throughput of both primary and secondary links. Towards this objective, we investigate multiple opportunistic relay selection policies. We develop a mathematical framework for deriving stability conditions for the queues involved in the system using outage probabilities. Results reveal that we achieve better performance in terms of average power and throughput as compared to uniform power allocation schemes proposed earlier in literature. Extensive numerical simulations are conducted to validate our theoretical findings.

Non-concave network utility maximization: A distributed optimization approach

This paper proposes an algorithm for optimal decentralized traffic engineering in communication networks. We aim at distributing the traffic among the available routes such that the network utility is maximized. In some practical applications, modeling network utility using non-concave functions is of particular interest, e.g., video streaming. Therefore, we tackle the problem of optimizing a generalized class of non-concave utility functions. The approach used to solve the resulting non-convex network utility maximization (NUM) problem relies on designing a sequence of convex relaxations whose solutions converge to that of the original problem. A distributed algorithm is proposed for the solution of the convex relaxation. Each user independently controls its traffic in a way that drives the overall network traffic allocation to an optimal operating point subject to network capacity constraints. All computations required by the algorithm are performed independently and locally at each user using local information and minimal communication overhead. The only non-local information needed is binary feedback from congested links. The robustness of the algorithm is demonstrated, where the traffic is shown to be automatically rerouted in case of a link failure or having new users joining the network. Numerical simulation results are presented to validate our findings.

Non-concave network utility maximization in connectionless networks: A fully distributed traffic allocation algorithm

This paper considers the optimization-based traffic allocation problem among multiple end points in connectionless networks. The network utility function is modeled as a non-concave function, since it is the best description of the quality of service perceived by users with inelastic applications, such as video and audio streaming. However, the resulting non-convex optimization problem, is challenging and requires new analysis and solution techniques. To overcome these challenges, we first propose a hierarchy of problems whose optimal value converges to the optimal value of the non-convex optimization problem as the number of moments tends to infinity. From this hierarchy of problems, we obtain a convex relaxation of the original non-convex optimization problem by considering truncated moment sequences. For solving the convex relaxation, we propose a fully distributed iterative algorithm, which enables each node to adjust its date allocation/rate adaption among any given set of next hops solely based on information from the neighboring nodes. Moreover, the proposed traffic allocation algorithm converges to the optimal value of the convex relaxation at a O(1/K) rate, where K is the iteration counter, with a bounded optimality. At the end of this paper, we perform numerical simulations to demonstrate the soundness of the developed algorithm.