In this paper, downlink resource allocation for a sparse code multiple access (SCMA) based heterogeneous cellular networks (HCN) is investigated. The main aim of this paper is to obtain joint power allocation and codebook assignment to achieve the least total system transmit power. In this regard, the proposed objective function is formulated as total transmit power subject to users minimum rate requirement, total available power in each base station (BS), and SCMA constraint. To solve the proposed problem an iterative algorithm considering SUCCESSIVE CONVEX APPROXIMATION ((SCA)) is applied. Also, convergence of the iterative method is investigated. In the simulation results the effectiveness of proposed resource allocation method are shown.

This paper investigates beamforming design for Simultaneous transmission and reflection reconfigurable intelligent surface (Star RIS) assisted secure wireless communication with three operating protocols (energy splitting (ES), mode switching (MS), and time switching (TS)) with the assumption of the availability of imperfect CSI of two eavesdroppers at BS. We maximize the sum rate of legitimate users by designing both active and passive beamforming at BS and RIS, respectively, under the constraint of the limited maximum tolerable rate of eavesdroppers and a limited transmission power budget. The non-CONVEX problem is solved by alternating optimization (AO) for active and passive beamforming optimization, and also SUCCESSIVE CONVEX APPROXIMATION ((SCA)), and the penalty concave-CONVEX procedure (PCCP). According to the simulation results, Star-RIS outperforms conventional RIS, ES mode outperforms MS and TS, and the performance is higher with lower channel estimation error.

In this paper, a new method for calculating the numerical APPROXIMATION of the nonlinear Urysohn integral equations is proposed based on Haar wavelets. Also, the convergence analysis and numerical stability of these method are discussed. Conducting numerical experiments confirm the theoretical results of the applied method and endorse the accuracy of the method.

This paper considers the power-efficient resource allocation problem in a cloud radio access network (CRAN). The C-RAN architecture consists of a set of base-band units (BBUs) which are connected to a set of radio remote heads (RRHs) equipped with massive multiple input multiple output (MIMO), via fronthaul links with limited capacity. We formulate the power-efficient optimization problem in C-RANs as a joint resource allocation problem in order to jointly allocate the RRH and transmit power to each user, and fronthaul link and BBU assign to active RRHs while satisfying the minimum required rate of each user. To solve this non-CONVEX optimization problem we suggest iterative algorithm with two-step based on the complementary geometric programming (CGP) and the SUCCESSIVE CONVEX APPROXIMATION ((SCA)). The simulation results indicate that our proposed scheme can significantly reduce the total transmission power by switching off the under-utilized RRHs.

A reliable and effective hybrid beamforming design for dual functioning multi-input multi-output (MIMO) radar is a challenging research problem because of the concerns related to limited user capacity, interference, and lack of performance trade-off. Due to the shortage of available spectrum, radar frequency spectrum sharing has become vital in emerging 5G communication systems. This will reduce spectrum congestion, therefore receiving significant attention. The existing hybrid beamforming methods reduce the radio frequency (RF)  chains but improving user capacity is still a major concern. Future dual radar-communication designs are having challenges in enhancing the user capacity with minimum RF chains, interference mitigation, and hardware cost reduction. This work proposes a novel approach to a hybrid beamforming mechanism for dual-functioning MIMO radar. This mechanism uses the dimension-reduced baseband piecewise SUCCESSIVE APPROXIMATION integrated with a digital precoder. At the analog precoder, the piecewise SUCCESSIVE iterative APPROXIMATION approach is applied to perform the analog beamforming. The novel hybrid beamforming with lens antenna array integration improves the user capacity and reduces power requirement, interference, and expenses. The simulation results showed improved performances compared to existing state-of-the-art methods in terms of bit error rate, spectral efficiency, energy efficiency, and response time.

This paper presents a modification of SUCCESSIVE APPROXIMATION method by using projection operator to solve nonlinear Volterra-Hammerstein integral equations of the second kind. In this paper, it is proved that under some conditions the sequence of iterated solutions converges to the exact solution. Applicability of this modification has been shown with some numerical examples. Comparisons with some other methods are also addressed which highlight superiority of the method.

In this paper, the energy efficient resource allocation in NOMA-based heterogeneous cellular network (HCN) systems is investigated. Since energy consumption and its limitations is one of the important challenges in fifth generation of cellular networks (5G), we propose a resource allocation problem which allocates power and subcarriers to multiple users to achieve the best system energy efficiency (EE). To solve the corresponding problem an iterative algorithm is used. In each iteration power and subcarrier are assigned separately. In each iteration, to solve the power allocation problem dual method considering SUCCESSIVE CONVEX APPROXIMATION ((SCA)) approach is used, and to solve the subcarrier allocation problem, mixed integer non-linear programing (MINLP) of optimization toolboxes is applied. The effectiveness of this resource allocation method is illustrated in simulation results section.

Mobile cloud computing (MCC) is a new technology that has been developed to overcome the restrictions of smart mobile devices (e. g. battery, processing power, storage capacity, etc. ) to send a part of the program (with complex computing) to the cloud server (CS). In this paper, we study a multi-cell with multi-input and multi-output (MIMO) system in which the cell-interior users request service for their processing from a common CS. Also, the problem of the optimum offloading is considered as an optimization problem with optimization parameters including communication resources (such as bandwidth, transmit power and backhaul link capacity) and computational resources (such as the capacity of cloud server) in the downlink network. The main goal is to minimize the total energy consumption by mobile users (MUs) for processing with the delay limitation for each use. This issue leads to a non-CONVEX problem and to solve the problem, we use SUCCESSIVE CONVEX APPROXIMATION ((SCA)) method. We finally show that the joint optimization of these parameters leads to reducing the energy consumption of the network with simulation examples.