Compressive Sensing Based Adaptive Active User Detection and Channel Estimation: Massive Access Meets Massive MIMO (1906.09867v4)

Abstract: This paper considers massive access in massive multiple-input multiple-output (MIMO) systems and proposes an adaptive active user detection and channel estimation scheme based on compressive sensing. By exploiting the sporadic traffic of massive connected user equipments and the virtual angular domain sparsity of massive MIMO channels, the proposed scheme can support massive access with dramatically reduced access latency. Specifically, we design non-orthogonal pseudo-random pilots for uplink broadband massive access, and formulate the active user detection and channel estimation problems as a generalized multiple measurement vector compressive sensing problem. Furthermore, by leveraging the structured sparsity of the uplink channel matrix, we propose an efficient generalized multiple measurement vector approximate message passing (GMMV-AMP) algorithm to realize simultaneous active user detection and channel estimation based on a spatial domain or an angular domain channel model. To jointly exploit the channel sparsity presented in both the spatial and the angular domains for enhanced performance, a Turbo-GMMV-AMP algorithm is developed for detecting the active users and estimating their channels in an alternating manner. Finally, an adaptive access scheme is proposed, which adapts the access latency to guarantee reliable massive access for practical systems with unknown channel sparsity level. Additionally, the state evolution of the proposed GMMV-AMP algorithm is derived to predict its performance. Simulation results demonstrate the superiority of the proposed active user detection and channel estimation schemes compared to several baseline schemes.

• The paper introduces a compressive sensing framework that leverages sporadic user traffic and structured sparsity to perform active user detection and channel estimation efficiently.
• It details the design of pseudo-random pilots and the development of a GMMV-AMP algorithm, including a Turbo-GMMV-AMP iteration for enhanced spatial and angular domain processing.
• Simulation results show significantly lower access latency and improved detection accuracy over baseline schemes, supporting robust IoT communications in massive MIMO systems.

Compressive Sensing Based Adaptive Active User Detection and Channel Estimation in Massive MIMO

Overview

The paper addresses the challenge of massive access in massive MIMO systems, where a high number of user equipments (UEs) need simultaneous access. The proposed method, centered around compressive sensing (CS), seeks to improve the efficiency of active user detection (AUD) and channel estimation (CE) under these conditions.

Proposed Scheme and Key Results

The authors introduce a compressive sensing-based framework that leverages the sporadic nature of user traffic and the structured sparsity inherent in massive MIMO channels. The primary contributions include:

1. Design of Pseudo-Random Pilots: These pilots are crafted for the uplink broadband communications, enabling non-orthogonal signal transmission while maintaining a structured sparsity in the virtual angular domain.
2. GMMV-AMP Algorithm: A generalized multiple measurement vector-approximate message passing (GMMV-AMP) algorithm is formulated. This innovative algorithm operates over both spatial and angular domains to efficiently conduct simultaneous AUD and CE, exploiting the structured sparsity of channel matrices.
3. Turbo-GMMV-AMP: Building on the GMMV-AMP, the Turbo-GMMV-AMP algorithm iteratively alternates between spatial and angular domain processing, enhancing performance by utilizing channel sparsity more effectively.
4. Adaptive Scheme: An adaptive scheme is proposed that dynamically adjusts the access latency to match the changing conditions without pre-known channel sparsity levels.

Simulation results indicate that the proposed methods substantially outperform existing baseline schemes in AUD and CE with lower access latency and better reliability.

Implications and Future Directions

The research presents a viable pathway for enhancing the capacity and efficiency of future wireless networks, especially in supporting Internet-of-Things (IoT) applications. By reducing the latency and improving detection accuracy without deploying exhaustive resources, this method aids in maintaining robust communication channels in environments with sporadic UE activity.

Future research could explore the integration of this method with learning-based approaches to refine sparsity pattern predictions based on historical access patterns, further reducing latency and improving network resilience. Additionally, extending this adaptive access mechanism to multi-cell environments under cooperative massive MIMO frameworks could enhance spatial reuse and system scalability.

In conclusion, the paper provides significant insights and methodologies for advancing massive access techniques in massive MIMO systems, laying a cornerstone for future explorations in adaptive and efficient wireless communication paradigms.