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Joint sparse recovery is a recently proposed signal processing approach which has become conventional due to its capability of solving a wide range of optimization problems. However, despite the appropriate performance of existing joint sparse recovery algorithms, all of them suffer from the same drawback: They collect pre-defined number of compressed measurements, which may be too small to precisely reconstruct the row support, or may be unnecessarily large which leads to the wastage of sampling resources. To address this issue and unlike the previous studies, this paper proposes a novel compressed sensing-based method to adaptively adjust the optimal sampling rate of joint sparse recovery problem. A data-driven approach based on Monte Carlo simulation is introduced to obtain the minimum number of samples which is required for precise row support estimation using several well-known joint sparse recovery techniques. Then, a sequential joint sparse recovery framework is developed where the first step predicts the optimal number of measurements and the second step reconstructs signal vectors applying the determined sampling rate. Numerical simulations investigate the effectiveness of suggested method to reduce both the required number of measurements and average algorithm runtime, without losing the recovery performance.

This paper introduces a sparsity controlled multiple random access scheme for efficient user scheduling in Device-to-Device (D2D) underlaid massive MIMO systems. In order to both avoid collision and enhance the Energy Efficiency (EE) of two-tier Heterogeneous Cellular Networks (HCNs), a unified Compressed Sensing (CS) based interference management strategy is proposed which guarantees concurrent cellular and D2D multi-user transmissions without collision. Specifically, supposing the natural sparsity in practical fifth generation (5G) scenarios and employing the sparse signal processing techniques, an analytical random access based model is adopted where provides several user scheduling and channel gain constraints to permit user identification, channel estimation and data decoding simultaneously. Furthermore, by developing a tractable tradeoff between the total power consumption and overall throughput of D2D tier, the transmission power is optimized such that the EE of D2D tier is maximized. Numerical simulations demonstrate the effectiveness of suggested approach to improve the collision avoidance capability and EE of D2D underlaid massive MIMO systems, even for crowded scenarios where the sparsity constraint does not meet sufficiently.

This paper presents a novel adaptive wideband compressed spectrum sensing scheme for cognitive radio (CR) networks. Compared to the traditional CSSbased CR scenarios, the proposed approach reconstructs neither the received signal nor its spectrum during the compressed sensing procedure. On the contrary, a precise estimation of wide spectrum support is recovered with a fewer number of compressed measurements. Then, the spectrum occupancy is determined directly from the reconstructed support vector. To carry out this process, a data-driven methodology is utilized to obtain the minimum number of necessary samples required for support reconstruction, and a closed-form expression is obtained that optimally estimates the number of desired samples as a function of the sparsity level and number of channels. Following this phase, an adjustable sequential framework is developed where the first step predicts the optimal number of compressed measurements and the second step recovers the sparse support and makes sensing decision. Theoretical analysis and numerical simulations demonstrate the improvement achieved with the proposed algorithm to significantly reduce both sampling costs and average sensing time without any deterioration in detection performance. Furthermore, the remainder of the sensing time can be employed by secondary users for data transmission, thus leading to the enhancement of the total throughput of the CR network.