Decentralized Dynamic Hop Selection and Power Control in Cognitive Multi-hop Relay Systems

TL;DR

This paper proposes a decentralized, low-complexity algorithm for dynamic hop selection and power control in cognitive multi-hop relay systems, improving throughput while sharing spectrum with primary users.

Contribution

It introduces an asymptotically optimal, decentralized solution for joint hop selection and power control under causal CSI knowledge in cognitive relay networks.

Findings

Significant throughput gains from dynamic hop selection and power control.

Algorithm complexity is polynomial in the number of secondary users.

Effective spectrum sharing with primary users achieved.

Abstract

In this paper, we consider a cognitive multi-hop relay secondary user (SU) system sharing the spectrum with some primary users (PU). The transmit power as well as the hop selection of the cognitive relays can be dynamically adapted according to the local (and causal) knowledge of the instantaneous channel state information (CSI) in the multi-hop SU system. We shall determine a low complexity, decentralized algorithm to maximize the average end-to-end throughput of the SU system with dynamic spatial reuse. The problem is challenging due to the decentralized requirement as well as the causality constraint on the knowledge of CSI. Furthermore, the problem belongs to the class of stochastic Network Utility Maximization (NUM) problems which is quite challenging. We exploit the time-scale difference between the PU activity and the CSI fluctuations and decompose the problem into a master problem and subproblems. We derive an asymptotically optimal low complexity solution using divide-and-conquer and illustrate that significant performance gain can be obtained through dynamic hop selection and power control. The worst case complexity and memory requirement of the proposed algorithm is O(M^2) and O(M^3) respectively, where $M$ is the number of SUs.