Decentralized Fair Scheduling in Two-Hop Relay-Assisted Cognitive OFDMA Systems

TL;DR

This paper proposes a decentralized, relay-assisted cognitive OFDMA system that enhances spectrum efficiency, fairness, and robustness through an asymptotically optimal joint control scheme, outperforming existing methods.

Contribution

It introduces a novel cluster-based relay architecture with an asymptotically optimal, decentralized scheduling solution for cognitive OFDMA systems, improving throughput and fairness.

Findings

Significant throughput gains over existing designs.

Enhanced user fairness in relay-assisted cognitive networks.

Robust performance under imperfect channel information.

Abstract

In this paper, we consider a two-hop relay-assisted cognitive downlink OFDMA system (named as secondary system) dynamically accessing a spectrum licensed to a primary network, thereby improving the efficiency of spectrum usage. A cluster-based relay-assisted architecture is proposed for the secondary system, where relay stations are employed for minimizing the interference to the users in the primary network and achieving fairness for cell-edge users. Based on this architecture, an asymptotically optimal solution is derived for jointly controlling data rates, transmission power, and subchannel allocation to optimize the average weighted sum goodput where the proportional fair scheduling (PFS) is included as a special case. This solution supports decentralized implementation, requires small communication overhead, and is robust against imperfect channel state information at the transmitter (CSIT) and sensing measurement. The proposed solution achieves significant throughput gains and better user-fairness compared with the existing designs. Finally, we derived a simple and asymptotically optimal scheduling solution as well as the associated closed-form performance under the proportional fair scheduling for a large number of users. The system throughput is shown to be $\mathcal{O}\left(N(1-q_p)(1-q_p^N)\ln\ln K_c\right)$, where $K_c$ is the number of users in one cluster, $N$ is the number of subchannels and $q_p$ is the active probability of primary users.