Robust Symbol Level Precoding for Overlay Cognitive Radio Networks

TL;DR

This paper develops robust symbol-level precoding methods for overlay cognitive radio networks, optimizing power and ensuring quality of service under perfect and imperfect channel information.

Contribution

It introduces novel robust SLP schemes for overlay CR networks that handle channel uncertainties using convex optimization techniques.

Findings

Robust SLP schemes reduce transmission power while satisfying QoS constraints.

Proposed methods outperform non-robust approaches under channel uncertainties.

Simulations demonstrate effective cooperation between primary and secondary networks.

Abstract

This paper focuses on designing robust symbol-level precoding (SLP) in an overlay cognitive radio (CR) network, where the primary and secondary networks transmit signals concurrently. When the primary base station (PBS) shares data and perfect channel state information (CSI) with the cognitive base station (CBS), we derive an SLP approach that minimizes the CR transmission power and satisfies symbol-wise Safety Margin (SM) constraints of both primary users (PUs) and cognitive users (CUs). The resulting optimization has a quadratic objective and linear inequality (LI) constraints, which can be solved by standard convex methods. For the case of imperfect CSI from the PBS, we propose robust SLP schemes. First, with a norm-bounded CSI error model to approximate the uncertain channels, we adopt a max-min philosophy to conservatively achieve robust SLP constraints. Second, we use the additive quantization noise model (AQNM) to describe the quantized PBS CSI and employ a stochastic constraint to formulate the problem. Both robust approaches also result in a quadratic objective with LI constraints. Simulation results show that, rather than simply trying to eliminate the network's cross-interference, the proposed robust SLP schemes enable the primary and secondary networks to aid each other in meeting their quality of service constraints.