Determination of spectrum utilization profiles for 30 MHz–3 GHz frequency band

Efficient Spectrum Availability Information Recovery for Wideband DSA Networks: A Weighted Compressive Sampling Approach

“…This suggests that wideband spectrum is block-like heterogeneous, in that band occupancy patterns are not the same across the different band blocks. Therefore, sparsity levels may vary significantly from one block to another, a trend that has also been confirmed by recent measurement studies [10,16].…”

“…For this, we assume that the blocks have sufficient different average sparsity levels (otherwise, blocks with similar sparsity levels are merged into one block with a sparsity level corresponding to their average). This is supported by practical observations where typically each block of bands is assigned to a particular application, and the average occupancy could be quite different from one block to another [16,31,32]. These averages are often available via measurement studies, and can easily be estimated, or provided by spectrum operators [31].…”

“…The proposed scheme relies on the average occupancy of blocks at a per-block granularity to be able to improve the recovery accuracy of sensed signal. From a practical viewpoint, one approach of acquiring the average occupancy (sparsity level) of each block is by monitoring the occupancy of each band within the block and averaging them over time, as already been proposed in [16,31]. Other machine learning based prediction approaches can also be used to provide good estimates of the average occupancy.…”

Extracting and Exploiting Inherent Sparsity for Efficient IoT Support in 5G: Challenges and Potential Solutions

“…Motivated by the sparsity nature of spectrum occupancy and in an effort to address the overhead caused by these high sampling rates, researchers have recently been focusing on exploiting compressed sampling theory to develop wideband spectrum sensing approaches that can recover information at sub-Nyquist sampling rates [7]. In Section III-A, we present a novel wideband spectrum sensing technique that extracts key sparsity properties inherent to the wideband spectrum occupancy heterogeneity nature [8] and exploits them through compressive sensing theory to improve the efficiency of spectrum sensing. An illustration of the wideband spectrum occupancy is shown in Fig.…”

“…More specifically, the key limitations of such existing approaches lie in their high sampling rates and hardware capabilities needed to be able to recover sensing information for wideband spectrum access. However, since (wideband) spectrum is heavily under-utilized [8] in that the number of occupied bands is significantly less than the total number of bands (i.e., the vector representing spectrum occupancy information is sparse), compressive sensing theory is an ideal candidate for fully recovering spectrum occupancy information while using sampling rates lower than sub-Nyquist rates [10]. In other words, the recovery of the (sparse) spectrum occupancy vector can be done with a fewer number of sensing measurements.…”