SIC-free Multicast Scheduling for Multi-antenna Coded Caching

TL;DR

This paper introduces a SIC-free multicast scheduling scheme for multi-antenna coded caching that simplifies decoding and beamforming, leading to improved symmetric data rates in wireless networks.

Contribution

It proposes a novel bit-level multicast scheduling method with sparse coefficient matrices and optimized user ordering, reducing complexity and enhancing performance.

Findings

Sparse strategy minimizes parallel multicast streams.

Significant symmetric rate gains demonstrated.

Decouples data terms for simplified decoding.

Abstract

Multi-antenna coded caching (CC) with multicast beamforming typically relies on a complex successive interference cancellation (SIC) structure to decode a superposition of multiple streams received by each user. Signal-level CC schemes require the regeneration and cancellation of interfering signals at the physical layer of each receiver, which complicates practical implementations. To address this, we propose a bit-level multicast scheduling scheme enabling linear, SIC-free decoding of parallel streams by repeatedly transmitting data terms with linearly independent coefficients. Two reference strategies and a novel sparse strategy are considered for constructing the coefficient matrix. The reference cases include the random strategy, which lacks control over matrix construction, and the equal-distant strategy, which balances users' interference and data terms equally. In contrast, the sparse strategy minimizes the number of multicast streams transmitted in parallel during each interval. This approach simplifies both the decoding process and the beamforming design by decoupling the desired data terms for each user and reducing the number of SINR constraints, respectively. To further enhance the symmetric rate, a successive projection algorithm is applied to exploit channel properties and optimize user ordering. With the coefficient matrix and optimized user ordering in place, multicast beamformers are devised to aggregate desired data from relevant multicast streams. Numerical simulations validate the effectiveness of the sparse strategy and user scheduling, demonstrating significant gains in symmetric rate.