Joint Local Quantization and Linear Cooperation in Spectrum Sensing for Cognitive Radio Networks

Abstract

In designing cognitive radio networks (CRNs), protecting the license holders from harmful interference while maintaining acceptable quality-of-service (QoS) levels for the secondary users is a challenge effectively mitigated by cooperative spectrum sensing schemes. In this paper, cooperative spectrum sensing in CRNs is studied as a three-phase process composed of local sensing, reporting, and decision/data fusion. Then, a significant tradeoff in designing the reporting phase, i.e., the effect of the number of bits used in local sensing quantization on the overall sensing performance is identified and formulated. In addition, a novel approach is proposed to jointly optimize the linear soft-combining scheme at the fusion phase with the number of quantization bits used by each sensing node at the reporting phase. The proposed optimization is represented using the conventional false alarm and missed detection probabilities, in the form of a mixed-integer nonlinear programming (MINLP) problem. The solution is developed as a branch-and-bound procedure based on convex hull relaxation, and a low-complexity suboptimal approach is also provided. Finally, the performance improvement associated with the proposed joint optimization scheme, which is due to better exploitation of spatial/user diversities in CRNs, is demonstrated by a set of illustrative simulation results.

Extension of deflection coefficient for linear fusion of quantized reports in cooperative sensing

“…We first extend our previous analysis in [6] and [7] to the case in which the CR nodes use analogue-to-digital converters (ADC) with non-integer bit resolutions. Then, we propose a new version of the deflection coefficient which captures the effects of the quantization process at the CR nodes jointly with the linear fusion at the FC.…”

“…Even if the optimal solution exists, the threshold calculations are not trivial and complex optimization schemes are needed to solve them. These difficulties are commonly avoided by assuming a linear fusion scheme [2], [4]- [7] which is the base for our considered architecture.…”

“…Maximizing the so-called deflection coefficient (DC) [8], [9] or its modified version, modified deflection coefficient (MDC) [2], [4], [6], [7] is commonly used in the literature as a fast suboptimal approach to design effective fusion schemes in distributed detection scenarios. Using this parameter, i.e., the variance-normalized distance between the centers of two conditional distributions of the global test summary, is effective in the sense that it provides very close results to the ones obtained by the optimal LRT method at low computational cost.…”

“…where w H (n) denotes the Hamming weight of the binary representation of n. In order to derive the pmf of r i , we use the fact that the assumed reporting channel contamination does not depend on the reported bit sequence s i , nor the behavior of the PU, i.e., [7] P ri|Hj (n) =…”

A Low-Latency Zone-Based Cooperative Spectrum Sensing

“…To address the sensing accuracy issue, cooperative spectrum sensing techniques are introduced, see, e.g., [6], [7], [8]. Spectrum availability decision is made by combining the collected sensing information by SUs based on a rule, e.g., AND, OR or K-out-of-N [3].…”