Abstract
Existing distributed opportunistic spectrum management schemes do not consider the inability of today's cognitive transceivers to measure interference at the primary receivers. Consequently, optimizing the constrained cognitive radio network performance based only on the local interference measurements at the cognitive senders does not lead to truly optimal performance due to the existence of hidden (or exposed) primary senders. In this paper, we present a probabilistic framework for opportunistic spectrum management in cognitive ad hoc networks that optimizes the constrained cognitive user goodput while taking the unavoidable inaccuracy of spectrum sensing into account. The proposed framework (i) randomly explores individual spectrum bands as local interference measurements lead to inaccurate spectrum access decisions and (ii) adopts a non-greedy probabilistic spectrum access policy that prevents a single cognitive transmission from monopolizing an available spectral opportunity. In contrast to existing techniques, our approach allows multiple cognitive flows to fairly share the available opportunities without explicit inter-flow coordination. We analytically formulate the cognitive user performance optimization problem as a mixed-integer non-linear programming to derive the optimal parameter values. We use packet-level simulations to show that our approach achieves up to 138% higher goodput with significantly better fairness characteristics compared to greedy approaches.
Highlights
The proliferation of the wireless communication industry has led to spectrum scarcity as the majority of spectrum has already been licensed
In this paper, we have presented a framework for opportunistic spectrum management
We have adopted a probabilistic and non-greedy approach to counter the limitations of cognitive radio networks such as the inability to base the spectrum management decisions on the interference at primary receivers and the increased complexity of accurate high-speed wideband sensors
Summary
The proliferation of the wireless communication industry has led to spectrum scarcity as the majority of spectrum has already been licensed. Our contributions Our objective is to realize a practical spectrum management scheme for cognitive radio networks that (i) counters the unavoidable inaccuracies in spectrum measurements and their consequent negative impact on the CRN and PRNs performance and (ii) allows secondary users to fairly share the spectral opportunities without explicit inter-flow coordination. We use packet-level simulations to demonstrate that RAP-MAC probabilistic spectrum management achieves up to 138% higher goodput compared to greedy spectrum management depending on the CRN traffic demand This superior performance is attributed to the RAP-MAC probabilistic sensing and transmission policies, which explores more spectral opportunities and leads to fewer transmission failures compared to deterministic and hypothetically optimal spectrum management.
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