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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.

Recently, massive Multiple-Input Multiple-Output (MIMO) has become one of the key technologies to handle the emerging demands of future 5 G wireless networks due to the capability of improving both spectral efficiency (SE) and energy efficiency (EE). Although, several practical issues such as pilot contamination, optimal spectrum utilization, computational complexity and pilot overhead still need to get more attentions. To address the above challenges, this paper views the massive MIMO system as a Multiple Random Access (MRA) problem and introduces a unified interference management framework based on compressive sampling to enhance its performance. Due to the sporadic characteristics of such a network and considering channel reciprocity, a novel uplink transmission scheme is developed which only permits active users to send pilot sequences. Then, an efficient Joint Sparse Recovery problem solver is adopted that enables Base Station (BS) to simultaneously perform user identification, channel estimation and data decoding in a one-shot paradigm. Consequently, two closed-form expressions are obtained for the maximum allowable sparsity level of uplink transmission and minimum channel gain of the proposed approach. Furthermore, sufficient constraint in order to achieve the maximum channel capacity and corresponding maximum throughput are determined as a function of system parameters. Numerical simulations illustrate the effectiveness of suggested approach in terms of spectral efficiency, pilot overhead and implementation costs, even for crowded scenarios where the sparsity constraint is not satisfied adequately.

Recently, lattice Boltzmann method (LBM) has drawn attention as an alternative and promising numerical technique for simulating fluid flows. The stability of LBM is a challenging problem in the simulation of compressible flows with different types of embedded discontinuities. This study, proposes a complementary scheme for simulating inviscid flows by a compressible lattice Boltzmann model in order to improve the instability using a shock-detecting procedure. The advantages and disadvantages of using a numerical hybrid filter on the primitive or conservative variables, in addition to, macroscopic or mesoscopic variables are investigated. The study demonstrates that the robustness of the utilized LB model is improved for inviscid compressible flows by implementation of the complementary scheme on mesoscopic variables. The validity of the procedure to capture shocks and resolve contact discontinuity and rarefaction waves in well-known benchmark problems is investigated. The numerical results show that the scheme is capable of generating more robust solutions in the simulation of compressible flows and prevents the formation of oscillations. Good agreements are obtained for all test cases.

The stability of the lattice Boltzmann model (LBM) is a challenging problem in the simulation of compressible flows with different types of embedded discontinuities. This study, proposes a complementary scheme for simulation of inviscid compressible flows by the lattice Boltzmann models using the numerical filters to improve the stability. The advantages and disadvantages of the implementation of numerical filters on the primitive and conservative variables, in addition to, mesoscopic and macroscopic variables are investigated. Moreover, a shock-detecting sensor, which activates a second-order linear filter near the discontinuities and a higher-order linear filter in smooth regions, is described and assessed. This study demonstrates that the proposed complementary scheme is practical. Also the accuracy and robustness of the utilized LB models are improved for inviscid compressible flows by implementation of the numerical filters on primitive variables. The validity of the procedure to capture shocks and to resolve contact discontinuity and rarefaction waves in well-known benchmarks is investigated and good agreements are obtained for all test cases.

Attempts to simulate compressible flows with moderate Mach number to relatively high ones using Lattice Boltzmann Method (LBM) have been made by numerous researchers in the recent decade. The stability of the LBM is a challenging problem in the simulation of compressible flows with different types of embedded discontinuities. The present study proposes an approach for simulation of inviscid flows by a compressible LB model in order to enhance the robustness using a combination of Essentially NonOscillatory (ENO) scheme and Shock-Detecting Sensor (SDS) procedure. A sensor is introduced with adjustable parameters which is active near the discontinuities and affects less on smooth regions. The validity of the improved model to capture shocks and to resolve contact discontinuity and rarefaction waves in the well-known benchmarks such as, Riemann problem, and shock reflection is investigated. In addition, the problem of supersonic flow in a channel with ramp is simulated using a skewed rectangular grid generated by an algebraic grid generation method. The numerical results are compared with analytical ones and those obtained by solving the original model. The numerical results show that the presented scheme is capable of generating more robust solutions in the simulation of compressible flows and is almost free of oscillations for high Mach numbers. Good agreements are obtained for all problems.