PROJECT TITLE :
On the Feedback Reduction of Multiuser Relay Networks Using Compressive Sensing
This paper presents a comprehensive performance analysis of full-duplex multiuser relay networks using opportunistic scheduling with noisy and compressive feedback. Specifically, two feedback techniques based on compressive sensing (CS) theory are introduced and their effect on the system performance is analyzed. The problem of joint user identity and signal-to-noise ratio (SNR) estimation at the base-station is casted as a block sparse signal recovery drawback in CS. Using existing CS block recovery algorithms, the identity of the robust users is obtained and their corresponding SNRs are estimated using the best linear unbiased estimator (BLUE). To minimize the result of feedback noise on the estimated SNRs, a backoff strategy that optimally backsoff on the noisy estimated SNRs is introduced, and the error covariance matrix of the noise when CS recovery is derived. Finally, closed-type expressions for the tip-to-end SNRs of the system are derived. Numerical results show that the proposed techniques drastically scale back the feedback air-time and achieve a rate close to that obtained by scheduling techniques that need dedicated error-free feedback from all network users. Key findings of this paper counsel that the choice of [*fr1]-duplex or full-duplex SNR feedback relies on the channel coherence interval, and on low coherence intervals, full-duplex feedback is superior to the interference-free 0.5-duplex feedback.
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