Power Control for Multi-Cell Networks With Non-Orthogonal Multiple Access

191

115

This work considers the problem of dynamic power allocation in the downlink of multi-cell networks, where each cell utilizes non-orthogonal multiple access (NOMA)-based resource allocation. Also, coordinated multi-point (CoMP) transmission is utilized among multiple cells to serve users experiencing severe inter-cell interference (ICI).More specifically, we consider a two-tier heterogeneous network (HetNet) consisting of a high-power macro cell underlaid with multiple low-power small cells each of which uses the same resource block. Under this CoMP-NOMA framework, CoMP transmission is applied to a user experiencing high channel gain with multiple base stations (BSs)/cells, while NOMA is utilized to schedule CoMP and non-CoMP users over the same transmission resources, i.e., time, spectrum and space. Different CoMP-NOMA models are discussed, but focus is primarily on the joint transmission CoMP-NOMA (JT-CoMP-NOMA) model. For the JT-CoMP-NOMA model, an optimal joint power allocation problem is formulated and the solution is derived for each CoMP-set consisting of multiple cooperating BSs (i.e., CoMP BSs). To overcome the substantial computational complexity of the joint power optimization approach, we propose a distributed power optimization problem at each cooperating BS whose optimal solution is independent of the solution of other coordinating BSs. The validity of the distributed solution for the joint power optimization problem is provided and numerical performance evaluation is carried out for the proposed CoMP-NOMA models including JT-CoMP-NOMA and coordinated scheduling CoMP-NOMA (CS-CoMP-NOMA). The obtained results reveal significant gains in spectral and energy efficiency in comparison with conventional CoMPorthogonal multiple access (CoMP-OMA) systems. Index TermsNon-orthogonal multiple access (NOMA), coordinated multi-point (CoMP) transmission, multi-cell downlink transission, heterogeneous networks (HetNets), dynamic power allocation, spectral efficiency, energy efficiency.

Section: A Preliminariesmentioning

confidence: 99%

Downlink Power Allocation for CoMP-NOMA in Multi-Cell Networks

Ali

Hossain

Al-Dweik

et al. 2018

191

115

“…Finally, to obtain the solution of optimization problem (21), we need to iteratively solve (39 k } are updated according to the solution obtained at previous iteration, and problem (39) is resolved until the results converge or the iteration index reaches its maximum value. Additionally, since (39) without rankone constraint is a convex optimization problem, iteratively updating all variables will increase or at least maintain the value of the OF in (39) [41], [42]. Given the limited transmit power, the value of the OF should be monotonically non-decreasing sequence with an upper bound, which converges to a stationary solution that is at least locally optimal.…”

Section: B Problem Solutionmentioning

confidence: 99%

Secure Millimeter Wave Cloud Radio Access Networks Relying on Microwave Multicast Fronthaul

Hao

Sun

Zhang

et al. 2020

In this paper, we investigate the downlink secure beamforming (BF) design problem of cloud radio access networks (C-RANs) relying on multicast fronthaul, where millimeter-wave and microwave carriers are used for the access links and fronthaul links, respectively. The base stations (BSs) jointly serve users through cooperating hybrid analog/digital BF. We first develop an analog BF for cooperating BSs. On this basis, we formulate a secrecy rate maximization (SRM) problem subject both to a realistic limited fronthaul capacity and to the total BS transmit power constraint. Due to the intractability of the non-convex problem formulated, advanced convex approximated techniques, constrained concave convex procedures and semidefinite programming (SDP) relaxation are applied to transform it into a convex one. Subsequently, an iterative algorithm of jointly optimizing multicast BF, cooperative digital BF and the artificial noise (AN) covariance is proposed. Next, we construct the solution of the original problem by exploiting both the primal and the dual optimal solution of the SDP-relaxed problem. Furthermore, a per-BS transmit power constraint is considered, necessitating the reformulation of the SRM problem, which can be solved by an efficient iterative algorithm. We then eliminate the idealized simplifying assumption of having perfect channel state information (CSI) for the eavesdropper links and invoke realistic imperfect CSI. Furthermore, a worst-case SRM problem is investigated. Finally, by combining the so-called S-Procedure and convex approximated techniques, we design an efficient iterative algorithm to solve it. Simulation results are presented to evaluate the secrecy rate and demonstrate the effectiveness of the proposed algorithms.

“…Recently, non-orthogonal multiple access (NOMA) has been recognized as a potential technology for the next generation wireless mobile communication networks to tackle the explosive growth of data traffic [18]- [28]. Due to superposition coding at the transmitter and successive interference cancelation (SIC) at the receiver, NOMA can achieve higher spectral efficiency than conventional orthogonal multiple access (OMA), such as TDMA and FDMA [29].…”

Section: Introductionmentioning

confidence: 99%

Efficient Resource Allocation for Mobile-Edge Computing Networks With NOMA: Completion Time and Energy Minimization

Yang

Pan

Hou

et al. 2019

Self Cite

This paper investigates an uplink non-orthogonal multiple access (NOMA)-based mobile-edge computing (MEC) network. Our objective is to minimize a linear combination of the completion time of all users' tasks and the total energy consumption of all users including transmission energy and local computation energy subject to computation latency, uploading data rate, time sharing and edge cloud capacity constraints. This work can significantly improve the energy efficiency and end-to-end delay of the applications in future wireless networks. For the general minimization problem, it is first transformed into an equivalent form. Then, an iterative algorithm is accordingly proposed, where closedform solution is obtained in each step. For the special case with only minimizing the completion time, we propose a bisection-based algorithm to obtain the optimal solution. Also for the special case with infinite cloud capacity, we show that the original minimization problem can be transformed into an equivalent convex one. Numerical results show the superiority of the proposed algorithms compared with conventional algorithms in terms of completion time and energy consumption.