Distributed Cognitive Multiple Access Networks: Power Control, Scheduling and Multiuser Diversity

TL;DR

This paper analyzes optimal distributed power control and scheduling in cognitive multiple access networks, demonstrating that such networks can achieve multiuser diversity gains through local decisions without centralized coordination.

Contribution

It establishes the joint optimality of water-filling and threshold-based policies and characterizes the throughput scaling law in distributed cognitive networks.

Findings

Optimal policies are water-filling and threshold-based.

Throughput scales as (1/e n_h) log log N.

Distributed networks can harness multiuser diversity without centralized control.

Abstract

This paper studies optimal distributed power allocation and scheduling policies (DPASPs) for distributed total power and interference limited (DTPIL) cognitive multiple access networks in which secondary users (SU) independently perform power allocation and scheduling tasks using their local knowledge of secondary transmitter secondary base-station (STSB) and secondary transmitter primary base-station (STPB) channel gains. In such networks, transmission powers of SUs are limited by an average total transmission power constraint and by a constraint on the average interference power that SUs cause to the primary base-station. We first establish the joint optimality of water-filling power allocation and threshold-based scheduling policies for DTPIL networks. We then show that the secondary network throughput under the optimal DPASP scales according to $\frac{1}{\e{}n_h}\log\logp{N}$, where $n_h$ is a parameter obtained from the distribution of STSB channel power gains and $N$ is the total number of SUs. From a practical point of view, our results signify the fact that distributed cognitive multiple access networks are capable of harvesting multiuser diversity gains without employing centralized schedulers and feedback links as well as without disrupting primary's quality-of-service (QoS)