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Tamer ElBatt

Publications and source records attributed to Tamer ElBatt.

32 records · Page 2Linked to original sources

An Information-theoretic Model for Knowledge Sharing in Opportunistic Social Networks

In this paper we establish fundamental limits on the performance of knowledge sharing in opportunistic social net- works. In particular, we introduce a novel information-theoretic model to characterize the performance limits of knowledge sharing policies. Towards this objective, we first introduce the notions of knowledge gain and its upper bound, knowledge gain limit, per user. Second, we characterize these quantities for a number of network topologies and sharing policies. This work constitutes a first step towards defining and characterizing the performance limits and trade-offs associated with knowledge sharing in opportunistic social networks. Finally, we present nu- merical results characterizing the cumulative knowledge gain over time and its upper bound, using publicly available smartphone data. The results confirm the key role of the proposed model to motivate future research in this ripe area of research as well as new knowledge sharing policies.

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Maximum Throughput Opportunistic Network Coding in Two-Way Relay Networks

In this paper, we study Two-way relaying networks well-known for its throughput merits. In particular, we study the fundamental throughput delay trade-off in two-way relaying networks using opportunistic network coding. We characterize the optimal opportunistic network coding policy that maximizes the aggregate network throughput subject to an average packet delay constraint. Towards this objective, first, we consider a pair of nodes communicating through a common relay and develop a two dimensional Markov chain model capturing the buffers' length states at the two nodes. Second, we formulate an optimization problem for the delay constrained optimal throughput. Exploiting the structure of the problem, it can be cast as a linear programming problem. Third, we compare the optimal policy to two baseline schemes and show its merits with respect to throughput, average delay and power consumption. First, the optimal policy significantly outperforms the first baseline with respect to throughput, delay and power consumption. Moreover, it outperforms the second baseline with respect to the average delay and power consumption for asymmetrical traffic arrival rates.

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On the Stable Throughput of Cooperative Cognitive Radio Networks with Finite Relaying Buffer

In this paper, we study the problem of cooperative communications in cognitive radio systems where the secondary user has limited relaying room for the overheard primary packets. More specifically, we characterize the stable throughput region of a cognitive radio network with a finite relaying buffer at the secondary user. Towards this objective, we formulate a constrained optimization problem for maximizing the secondary user throughput while guaranteeing the stability of the primary user queue. We consider a general cooperation policy where the packet admission and queue selection probabilities, at the secondary user, are both dependent on the state (length) of the finite relaying buffer. Despite the sheer complexity of the optimization problem, attributed to its non-convexity, we transform it to a linear program. Our numerical results reveal a number of valuable insights, e.g., it is always mutually beneficial to cooperate in delivering the primary packets in terms of expanding the stable throughput region. In addition, the stable throughput region of the system, compared to the case of infinite relaying queue capacity, marginally shrinks for limited relaying queue capacity.

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Interference-Based Optimal Power-Efficient Access Scheme for Cognitive Radio Networks

In this paper, we propose a new optimization-based access strategy of multipacket reception (MPR) channel for multiple secondary users (SUs) accessing the primary user (PU) spectrum opportunistically. We devise an analytical model that realizes the multipacket access strategy of SUs that maximizes the throughput of individual backlogged SUs subject to queue stability of the PU. All the network receiving nodes have MPR capability. We aim at maximizing the throughput of the individual SUs such that the PU's queue is maintained stable. Moreover, we are interested in providing an energy-efficient cognitive scheme. Therefore, we include energy constraints on the PU and SU average transmitted energy to the optimization problem. Each SU accesses the medium with certain probability that depends on the PU's activity, i.e., active or inactive. The numerical results show the advantage in terms of SU throughput of the proposed scheme over the conventional access scheme, where the SUs access the channel randomly with fixed power when the PU is sensed to be idle.

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Effective Capacity of Cognitive Radio Links: Accessing Primary Feedback Erroneously

We study the performance of a cognitive system modeled by one secondary and one primary link and operating under statistical quality of service (QoS) delay constraints. We analyze the effective capacity (EC) to quantify the secondary user (SU) performance under delay constraints. The SU intends to maximize the benefit of the feedback messages on the primary link to reduce SU interference for primary user (PU) and makes opportunistic use of the channel to transmit his packets. We assume that SU has erroneous access to feedback information of PU. We propose a three power level scheme and study the tradeoff between degradation in EC of SU and reliability of PU defined as the success rate of the transmitted packets. Our analysis shows that increase in error in feedback access causes more interference to PU and packet success rate decreases correspondingly.

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

In this paper, we study and analyze fundamental throughput and 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. Moreover, admission control is introduced at the relay queue, whereby a PU's packet is admitted to the relay queue with an admission probability. The proposed policy introduces a fundamental tradeoff between the delays of the PU and SU. Consequently, it opens room for trading the PU delay for enhanced SU delay and vice versa. Thus, the system could be tuned according to the demands of the intended application. 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. The effect of varying the service and admission probabilities on the system's throughput and delay is thoroughly investigated. Results show that cooperation expands the stable throughput region. Moreover, numerical simulation results assert the extreme accuracy of the analytically derived delay expressions. In addition, we provide a criterion for the SU based on which it decides whether cooperation is beneficial to the PU or not. Furthermore, we show the impact of controlling the flow of data at the relay queue using the admission probability.

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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.

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Improved Spectrum Mobility using Virtual Reservation in Collaborative Cognitive Radio Networks

Cognitive radio technology would enable a set of secondary users (SU) to opportunistically use the spectrum licensed to a primary user (PU). On the appearance of this PU on a specific frequency band, any SU occupying this band should free it for PUs. Typically, SUs may collaborate to reduce the impact of cognitive users on the primary network and to improve the performance of the SUs. In this paper, we propose and analyze the performance of virtual reservation in collaborative cognitive networks. Virtual reservation is a novel link maintenance strategy that aims to maximize the throughput of the cognitive network through full spectrum utilization. Our performance evaluation shows significant improvements not only in the SUs blocking and forced termination probabilities but also in the throughput of cognitive users.

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Coverage Probability Analysis for Wireless Networks Using Repulsive Point Processes

The recent witnessed evolution of cellular networks from a carefully planned deployment to more irregular, heterogeneous deployments of Macro, Pico and Femto-BSs motivates new analysis and design approaches. In this paper, we analyze the coverage probability in cellular networks assuming repulsive point processes for the base station deployment. In particular, we characterize, analytically using stochastic geometry, the downlink probability of coverage under a Matern hardcore point process to ensure minimum distance between the randomly located base stations. Assuming a mobile user connects to the nearest base station and Rayleigh fading, we derive two lower bounds expressions on the downlink probability of coverage that is within 4% from the simulated scenario. To validate our model, we compare the probability of coverage of the Matern hardcore topology against an actual base station deployment obtained from a public database. The comparison shows that the actual base station deployment can be fitted by setting the appropriate Matern point process density.

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Effective Capacity of Delay Constrained Cognitive Radio Links Exploiting Primary Feedback

In this paper, we analyze the performance of a secondary link in a cognitive radio (CR) system operating under statistical quality of service (QoS) delay constraints. In particular, we quantify analytically the performance improvement for the secondary user (SU) when applying a feedback based sensing scheme under the "SINR Interference" model. We leverage the concept of effective capacity (EC) introduced earlier in the literature to quantify the wireless link performance under delay constraints, in an attempt to opportunistically support real-time applications. Towards this objective, we study a two-link network, a single secondary link and a primary network abstracted to a single primary link, with and without primary feedback exploitation. We analytically prove that exploiting primary feedback at the secondary transmitter improves the EC of the secondary user and decreases the secondary user average transmitted power. Finally, we present numerical results that support our analytical results.

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A Cooperative Q-learning Approach for Real-time Power Allocation in Femtocell Networks

In this paper, we address the problem of distributed interference management of cognitive femtocells that share the same frequency range with macrocells (primary user) using distributed multi-agent Q-learning. We formulate and solve three problems representing three different Q-learning algorithms: namely, centralized, distributed and partially distributed power control using Q-learning (CPC-Q, DPC-Q and PDPC-Q). CPCQ, although not of practical interest, characterizes the global optimum. Each of DPC-Q and PDPC-Q works in two different learning paradigms: Independent (IL) and Cooperative (CL). The former is considered the simplest form for applying Qlearning in multi-agent scenarios, where all the femtocells learn independently. The latter is the proposed scheme in which femtocells share partial information during the learning process in order to strike a balance between practical relevance and performance. In terms of performance, the simulation results showed that the CL paradigm outperforms the IL paradigm and achieves an aggregate femtocells capacity that is very close to the optimal one. For the practical relevance issue, we evaluate the robustness and scalability of DPC-Q, in real time, by deploying new femtocells in the system during the learning process, where we showed that DPC-Q in the CL paradigm is scalable to large number of femtocells and more robust to the network dynamics compared to the IL paradigm

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A Soft Sensing-Based Cognitive Access Scheme Exploiting Primary Feedback

In this paper, we examine a cognitive spectrum access scheme in which secondary users exploit the primary feedback information. We consider an overlay secondary network employing a random access scheme in which secondary users access the channel by certain access probabilities that are function of the spectrum sensing metric. In setting our problem, we assume that secondary users can eavesdrop on the primary link's feedback. We study the cognitive radio network from a queuing theory point of view. Access probabilities are determined by solving a secondary throughput maximization problem subject to a constraint on the primary queues' stability. First, we formulate our problem which is found to be non-convex. Yet, we solve it efficiently by exploiting the structure of the secondary throughput equation. Our scheme yields improved results in, both, the secondary user throughput and the primary user packet delay. In addition, it comes very close to the optimal genie-aided scheme in which secondary users act upon the presumed perfect knowledge of the primary user's activity.

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Distributed Cooperative Q-learning for Power Allocation in Cognitive Femtocell Networks

In this paper, we propose a distributed reinforcement learning (RL) technique called distributed power control using Q-learning (DPC-Q) to manage the interference caused by the femtocells on macro-users in the downlink. The DPC-Q leverages Q-Learning to identify the sub-optimal pattern of power allocation, which strives to maximize femtocell capacity, while guaranteeing macrocell capacity level in an underlay cognitive setting. We propose two different approaches for the DPC-Q algorithm: namely, independent, and cooperative. In the former, femtocells learn independently from each other while in the latter, femtocells share some information during learning in order to enhance their performance. Simulation results show that the independent approach is capable of mitigating the interference generated by the femtocells on macro-users. Moreover, the results show that cooperation enhances the performance of the femtocells in terms of speed of convergence, fairness and aggregate femtocell capacity.

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On the Role of Vehicular Mobility in Cooperative Content Caching

In this paper, we analyze the performance of cooperative content caching in vehicular ad hoc networks (VANETs). In particular, we characterize, using analysis and simulations, the behavior of the probability of outage (i.e. not finding a requested data chunk at a neighbor) under freeway vehicular mobility. First, we introduce a formal definition for the probability of outage in the context of cooperative content caching. Second, we characterize, analytically, the outage probability under vehicular and random mobility scenarios. Next, we verify the analytical results using simulations and compare the performance under a number of plausible mobility scenarios. This provides key insights into the problem and the involved trade-offs and enable us to assess the potential opportunity offered by the, somewhat structured, vehicular mobility that can be exploited by cooperative content caching schemes. The presented numerical results exhibit complete agreement between the analytical and simulation studies. Finally, we observe that vehicular mobility creates opportunities for enhanced outage performance under practically relevant scenarios.

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