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

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

Channel-reuse for accumulative repetition message-forwarding on wireless ad-hoc networks

Maximizing the rate of transmission over a network with fixed amount of power gives the most economic way to communicate in a network. This paper investigates accumulative repetition forwarding strategy in a wireless ad-hoc network using cooperative forwarding approaches that allow adaptive power allocation among nodes. We analyze the feasibility and performance gain of channel reuse over this network, where multiple transmit-and-receive pairs coexist simultaneously using the same channel. Under this framework, we present the jointly optimal power allocation and channel reuse solution. This solution can be computed in polynomial time and brings significant improvement in network performance. © 2008 IEEE