Abstract
Multi-antenna wireless communication improves spectral efficiency by reusing frequencies at different locations in space using beamforming and spatial multiplexing. In the past, research has extensively focused on dynamically reusing unused frequency bands to optimize spectrum usage, but methods that identify unused resources in space appear to be unexplored. In this paper, we propose a sample-efficient whitespace detection pipeline for multi-antenna radio-frequency (RF) transceivers that detects unused resources in both frequency and space. Our spatio-spectral whitespace detection pipeline relies on multi-antenna nonuniform wavelet sampling, which identifies unused frequencies in space at sub-Nyquist sampling rates. We demonstrate the efficacy of our approach via system simulations and show that reliable spatio-spectral whitespace detection is possible with 16× lower sampling rates than methods relying on Nyquist sampling.
Highlights
The trend towards global digitalization requires ubiquitous wireless connectivity and most wireless services rely on mobile devices with limited battery capacity
We show that properly-designed nonuniform wavelet sampling (NUWS)-based sensing matrices yield low overall block mutual coherence, which results in better whitespace detection performance than applying the methods from [30] to multi-antenna systems
[sH1, . . . , sHBK ]H ; and, thanks to sparsity, J of these blocks have large magnitudes, i.e., the vector s is modeled as a J-block-sparse signal [35]. This block sparsity is key in the whitespace detection pipeline we develop in the remainder of the paper
Summary
The trend towards global digitalization requires ubiquitous wireless connectivity and most wireless services rely on mobile devices with limited battery capacity. Driven by the Internet of Things (IoT), the number of connected devices is predicted to grow to 13.1 B by 2023 [1]. Such excessively large numbers of wireless devices combined with the ever-growing need for higher data-rates will inevitably cause congestion in the radio-frequency (RF) spectrum and lead to significant challenges in making efficient use of the spectrum. Because of the limitations in RF spectrum allocation and limited battery capacity of IoT devices, it is critical to deploy energy-efficient sensing methods that identify unused RF channels with the goal of opportunistically reusing the available resources among devices at both the infrastructure base station (BS) and the user equipment (UE) sides. The impact of these technologies is evident, only little attention has been given to identifying unused resources in space
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