Low Complexity Antenna Selection for Low Target Rate Users in Dense Cloud Radio Access Networks

TL;DR

This paper introduces a low complexity antenna selection algorithm for dense cloud radio access networks, optimizing performance for low target rate users with minimal computational load, suitable for high-density RRH deployments.

Contribution

The paper presents a novel two-phase antenna selection algorithm that maintains constant complexity regardless of RRH density, with performance analysis based on stochastic geometry.

Findings

Optimal selection threshold derived for maximum SIR coverage

Algorithm maintains constant complexity in dense RRH environments

Performance characterized in asymptotic high-density regimes

Abstract

We propose a low complexity antenna selection algorithm for low target rate users in cloud radio access networks. The algorithm consists of two phases: In the first phase, each remote radio head (RRH) determines whether to be included in a candidate set by using a predefined selection threshold. In the second phase, RRHs are randomly selected within the candidate set made in the first phase. To analyze the performance of the proposed algorithm, we model RRHs and users locations by a homogeneous Poisson point process, whereby the signal-to-interference ratio (SIR) complementary cumulative distribution function is derived. By approximating the derived expression, an approximate optimum selection threshold that maximizes the SIR coverage probability is obtained. Using the obtained threshold, we characterize the performance of the algorithm in an asymptotic regime where the RRH density goes to infinity. The obtained threshold is then modified depending on various algorithm options. A distinguishable feature of the proposed algorithm is that the algorithm complexity keeps constant independent to the RRH density, so that a user is able to connect to a network without heavy computation at baseband units.