Successive Convex Approximation For Low complExity Research Articles

Energy efficient collaborative computation for double-RIS assisted mobile edge networks

Computation offloading in mobile edge computing (MEC) systems emerges as a novel paradigm of supporting various resource-intensive applications. However, the potential capabilities of MEC cannot be fully unleashed when the communication links are blocked by obstacles. This paper investigates a double-reconfigurable-intelligent-surfaces (RISs) assisted MEC system. To efficiently utilize the limited frequency resource, the users can partially offload their computational tasks to the MEC server deployed at base station (BS) by adopting non-orthogonal multiple access (NOMA) protocol. We aim to minimize the energy consumption of users with limited resource by jointly optimizing the transmit power of users, the offloading fraction of users and the phase-shifts of RISs. Since the problem is non-convex with highly coupled variables, the block coordinate descent (BCD) method is leveraged to alternatively optimize the decomposed four subproblems. Specifically, we invoke successive convex approximation for low complexity (SCALE) and Dinkelbach technique to tackle the fractional programming of power optimization. Then the offloading fraction is obtained by closed-form solution. Further, we leverage semidefinite relaxation (SDR) and bisection method to address the phase-shifts design of double RISs. Finally, numerical results illustrate that the proposed double-RIS assisted NOMA scheme is capable of efficiently reducing the energy consumption and achieves significant performance gain over the benchmark schemes.

An Energy Efficient Scheme for WPCN-NOMA Based Device-to-Device Communication

The capability of Non-Orthogonal Multiple Access (NOMA) for minimizing interference at the receiver using successive interference cancellation (SIC) makes it as a promising solution for 5G mobile communication. Device-to-device (D2D) communication is a part of the 5G network which provides frequency reuse in cellular networks and provides a better quality of service (QoS) to co-channel users. In this paper, we investigate the resource allocation and time scheduling algorithms for a NOMA based D2D communication system. In addition, to overcome the problem of energy at D2D transmitter and receivers, wireless powered communication network (WPCN) is used. Considering these issues, the objective of the proposed scheme is to maintain energy efficient communication among D2D transmitters and receivers without compromising energy efficiency (EE) of cellular users. To solve the fractional form of an optimization problem, we employ the Dinkelbach method. A many-to-many matching game approach is used for channel allocation and successive convex approximation for low complexity (SCALE) is used to control the power of the D2D transmitter. The simulation results demonstrate that by maximizing the EE and optimizing the time and transmission power of D2D groups, the energy efficiency of the network can be significantly improved in comparison to the other competitive schemes in the existing literature.

Cross Layer NOMA Interference Mitigation for Femtocell Users in 5G Environment

In this paper, we propose a joint channel allocation and power control algorithm by using cognitive radio non-orthogonal multiple access (CR-NOMA) for femtocell users (FUs). The aim is to maximize the sum rate of the FUs for guaranteed quality of service (QoS). With an aim to have guaranteed QoS for FUs, we use CR-NOMA at the femto base station (FBS). Then, an algorithm for pairing among strong and weak users is proposed by using the channel gain difference. Using pairing, the NOMA interference between them reduces which results in better channel utilization. Moreover, we differentiate the even/odd number of FUs in a femtocell to provide the QoS for weak users also. For this purpose, OMA is used to get a predefined data rate using a greedy channel allocation algorithm. The power of each FBS is controlled by using the successive convex approximation for low complexity (SCALE) protocol with Karush-Kuhn-Tucker (KKT) conditions. Numerical results demonstrate that the proposed scheme enhances the sum rate and provides guaranteed QoS for CR-NOMA based femtocell users in comparison to the existing conventional OMA based-femtocell techniques.

Power saving transmission in interference networks

This study proposes a power control scheme to obtain minimum aggregate power in a system composing of interfering links (N-link) while a target sum-rate is met. The authors develop an analytical framework for two interfering links which results in the optimal transmission policy. For N > 2, they propose a suboptimal solution based on successive convex approximation for low-complexity (SCALE) algorithm. They modify the SCALE algorithm to converge to a point satisfying Karush–Kuhn–Tucker conditions when high signal-to-noise-and-interference approximation of rate function is not valid. They also provide a suboptimal solution based on the geometric programming. They perform numerical evaluations in a dense interference environment and compare the performance of the modified SCALE with that of the SCALE algorithm. They also compare the performance of proposed power control with that of sum-rate maximisation of interfering links, in which the individual power constraint on each user is considered. The simulation results indicate that the modified SCALE performs better than the SCALE algorithm. The results also demonstrate that the proposed power control can save power comparing to the sum-rate maximisation in most considered cases.

Weighted Bandwidth–Power Product Optimization in Downlink Femtocell Networks

In order to develop a completely distributed resource allocation scheme to improve data rates and mitigate mutual interference across orthogonal frequency-division multiple access (OFDMA)-based femtocell networks, a space–bandwidth product (SBP) has been proposed as an effective measurement of network coverage. Intuitively, a femtocell minimizing its SBP tends to utilize subchannels experiencing less interference and will thus minimize its interference on other femtocells, which represents good self-organizing performance and enables a completely distributed resource allocation algorithm. However, for an OFDMA-based femtocell, most existing studies have applied the same weights on the bandwidth and the power without distinguishing the different significance on them. In this letter, we first formulate a weighted bandwidth–power product minimization problem for an OFDMA-based femtocell, applying different emphases on the consumed bandwidth and power while satisfying all users' data rate requirements. Then, in this direction, we further reformulate and iteratively solve this nonconvex problem via the Successive Convex Approximation for Low-complExity (SCALE) approach. Simulation results show that our proposed algorithm converges to stable solutions in a finite number of iterations.

Convergence of Price-Based Resource Allocation Algorithms in Multicellular Multicarrier Systems

Inter-cell interference mitigation is a key challenge in the heterogeneous wireless networks which are expected to use an aggressive frequency reuse factor and a high-density access point deployment to improve coverage and spectral efficiency. In this paper, the problem of resources allocation in multicell Orthogonal Frequency Division Multiple Access wireless system is considered with universal frequency reuse and target of Weighted Sum-Rate Maximization. We address multi cell modified iterative water filling as an iterative power allocation algorithm. Also, a new extension of fixed point implementation of Successive Convex Approximation for Low complExity (SCALE) algorithm to multicellular system [referred to as Multi Cell Fixed point SCALE (MCF-SCALE)] is presented and it has been shown both of them resulted to the same convergence point. It is also demonstrated that using Lagrangian multiplier instead of noise variance in Standard Yates framework (as has been used in some previous papers) is not a suitable method for proving convergence and all the previous results based on this pattern need to be revised. Finally, a new framework is presented for proving the convergence of MCF-SCALE algorithm based on Jacobi iterative algorithm. Moreover, some previous convergence criteria are shown to be interpreted as a special case of this condition.

On the SCALE Algorithm for Multiuser Multicarrier Power Spectrum Management

This paper studies the successive convex approximation for low complexity (SCALE) algorithm, which was proposed to address the weighted sum rate (WSR) maximized dynamic power spectrum management (DSM) problem for multiuser multicarrier systems. To this end, we first revisit the algorithm, and then present geometric interpretation and properties of the algorithm. A geometric programming (GP) implementation approach is proposed and compared with the low-complexity approach proposed previously. In particular, an analytical method is proposed to set up the default lower-bound constraints added by a GP solver. Finally, numerical experiments are used to illustrate the analysis and compare the two implementation approaches.

Dynamic Spectrum Management (DSM) Algorithms for Multi-User xDSL

Dynamic Spectrum Management (DSM) is an effective method for reducing the effect of crosstalk in Digital Subscriber Line (DSL) systems. This paper discusses various DSM algorithms, including Optimal Spectrum Balancing (OSB), Iterative Spectrum Balancing (ISB), Autonomous Spectrum Balancing (ASB), Iterative Water-Filling (IWF), Selective Iterative Water-filling (SIW), Successive Convex Approximation for Low complExity (SCALE), the Difference of Convex functions Algorithm (DCA), and Distributed Spectrum Balancing (DSB). They are compared in terms of performance (achievable data rate) and computational complexity.