Interference Aware Reliable Cooperative Cognitive Networks for Real-Time Applications

Hyder, Chowdhury Sayeed; Islam, Aminul; Xiao, Li; Torng, Eric

doi:10.1109/tccn.2016.2566641

Cited by 5 publications

(5 citation statements)

References 27 publications

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“…Cooperative cognitive radio networks (CCRNs) have recently attracted considerable attention [24], [25], [28]- [35]. The notion of cooperation in cognitive radio networks is that the SU helps the PU in successfully transmitting its data packets to the destination so as to decrease the number of time slots dedicated to retransmit the PU's lost packets over its direct link.…”

Section: Arxiv:170709681v4 [Csit] 18 Jun 2018mentioning

confidence: 99%

“…along with (35), the steady state distribution of Q sp can be completely characterized. In the next subsection, we formulate the stable throughput region optimization problem and discuss its solution.…”

Section: A Stability Conditionsmentioning

confidence: 99%

“…Therefore, both the PU and SU benefit from the cooperation. The most relevant literature can be categorized into two sets: i) CCRNs with energy harvesting capability [24], [25], and ii) CCRNs without energy harvesting capability [28]- [35]. Incorporating a relay queue at the SS to the system setup in [22], [24] formulated the maximum weighted sum of the service rates at the SS queues problem as a Markov Decision Process (MDP), and [25] characterized the stable throughput region for Poisson energy harvesting processes using the Dominant System approach [36].…”

Section: Introductionmentioning

confidence: 99%

“…The authors in [34] characterized the throughput region when the relay buffer at the SU has finite length, and in [33] generalized the model studied in [32] to the scenario of having multiple cognitive nodes, and characterized the optimal transmission probability of SU that maximizes the individual achievable throughputs. In [35], the authors incorporated the overhead of forming the cooperation and the potential interference caused by SUs' transmissions. The system is modeled as a Markov decision process (MDP), and the impact of cooperation overhead and secondary interference are quantified in the actions and their rewards.…”

Section: Introductionmentioning

confidence: 99%

“…The system is modeled as a Markov decision process (MDP), and the impact of cooperation overhead and secondary interference are quantified in the actions and their rewards. Note that in [32]- [35], it was implicitly assumed that the SU is equipped with unlimited energy supply, i.e., the SU does not suffer from any energy limitations whenever it has the opportunity to access the channel. It is worth noting that a similar system setup to that in [32] has recently studied in the context of cooperative relay networks [37], [38].…”

Section: Introductionmentioning

confidence: 99%

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Stable Throughput of Cooperative Cognitive Networks With Energy Harvesting: Finite Relay Buffer and Finite Battery Capacity

Abd-Elmagid

ElBatt

Seddik

et al. 2018

IEEE Trans. Cogn. Commun. Netw.

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This paper studies a generic model for cooperative cognitive radio networks where the secondary user is equipped with a finite relay queue as well as a finite battery queue. Our prime objective is to characterize the stable throughput region. Nevertheless, the complete characterization of the stable throughput region for such system is notoriously difficult, since the computation of the steady state distribution of the twodimensional Markov Chain (MC) model for both finite queues is prohibitively complex. We first propose an algorithm to characterize the stable throughput region numerically, and show its sheer computational complexity for large queue lengths. To lend tractability and explore the nature of design parameters optimization at the cognitive node, we next focus on two simpler systems, namely, finite battery queue with infinite relay queue and finite relay queue with infinite battery queue (referred henceforth as dominant system 1 and 2, respectively). For each proposed dominant system, we investigate the maximum service rate of the cognitive node subject to stability conditions. Despite the complexity of the formulated optimization problems, due to their non-convexity, we exploit the problems' structure to transform them into linear programs. Thus, we are able to solve them efficiently using standard linear programming solvers. Our numerical results demonstrate that, in practical systems, finite battery and relay queues achieve the same level of benefits of a system with infinite queue sizes, when their sizes are sufficiently large. They also reveal that the achievable stable throughput region significantly expands when the arrival rate of the energy harvesting process increases.

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