Beyond Idle Channels: Unlocking Idle Space with Signal Alignment in Massive MIMO Cognitive Radio Networks
Weidong Zhu, Xueqian Li, Longwei Wang, Zheng Zhang

TL;DR
This paper proposes a novel framework combining massive MIMO and signal alignment to utilize idle spatial dimensions in cognitive radio networks, significantly improving spectrum efficiency and interference management.
Contribution
It introduces a new paradigm leveraging idle spatial space with signal alignment in massive MIMO CRNs, enabling interference-free secondary transmissions.
Findings
Substantial gains in spectrum efficiency and throughput.
Effective interference mitigation with primary users.
Validation through theoretical analysis and extensive simulations.
Abstract
Cognitive radio networks (CRNs) have traditionally focused on utilizing idle channels to enhance spectrum efficiency. However, as wireless networks grow denser, channel-centric strategies face increasing limitations. This paper introduces a paradigm shift by exploring the underutilized potential of idle spatial dimensions, termed idle space, in co-channel transmissions. By integrating massive multiple-input multiple-output (MIMO) systems with signal alignment techniques, we enable secondary users to transmit without causing interference to primary users by aligning their signals within the null spaces of primary receivers. We propose a comprehensive framework that synergizes spatial spectrum sensing, signal alignment, and resource allocation, specifically designed for secondary users in CRNs. Theoretical analyses and extensive simulations validate the framework, demonstrating…
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Taxonomy
TopicsAdvanced MIMO Systems Optimization · Cognitive Radio Networks and Spectrum Sensing · Cooperative Communication and Network Coding
