Joint User Scheduling and Power Allocation for Energy Efficient Millimeter Wave NOMA Systems With Random Beamforming

International audience

Multi-carrier based non-orthogonal multiple access (NOMA) is an effective method to meet the ever-increasing demands of both user throughput and energy efficiency by multiplexing multiple users on the same carrier. Since interference from users with a poorer channel gain can be canceled at a user with a strong channel gain by successive interference cancellation, NOMA can enhance the system performance. To improve the downlink system performance, it is crucial to appropriately determine users scheduled on each carrier and power allocation at the base station. However, the existing works are generally either heuristic or local optimal due to the mixed optimization problem. In this paper, we focus on the global optimal solutions to maximize user throughput and energy efficiency in NOMA, respectively. In particular, we first formulate the mixed integer optimization problem which are intractable to be solved. Fortunately, by the provided analytical results, the optimization models can be largely simplified. Then, we propose the architectures of joint user scheduling and power allocation in NOMA, as well as the corresponding optimal algorithms. Simulation results demonstrate that our proposed algorithms indeed outperform existing works in terms of the user throughput and energy efficiency, respectively.

Large-scale multiple-input multiple-output (MIMO) system with tens to hundreds of antennas is getting increasing attention for its very high spectral efficiency and energy efficiency. However, for frequency division duplexing (FDD) system, the cost of CSIT feedback becomes a bottleneck with the BS antennas increasing. To reduce the cost of the downlink training and the CSIT uplink feedback, joint spatial division and multiplexing (JSDM) scheme is proposed by Ansuman Adhikary, et al., which divides users into different groups according to their channel covariance eigenvectors and adopts the two-stage precoding. This paper proposes a joint user selection and power allocation (JUSPA) algorithm for JSDM scheme in massive MIMO downlink systems, which schedules users and allocates power iteratively with the MAX user scheduling and the Lagrange power optimization method. For user scheduling, it is noticed that the intra-group interference is so large that would disturb the user selection. Then the signal-to-interference noise ratio (SINR) of each beam is computed, neglecting the intra-group interference. The simulation results show that the proposed JUSPA algorithm performs better than the average power allocation method in the original JSDM scheme.

Small cell, mmWave, and massive Multi-Input Multi-Output (MIMO) technologies in 5G cellular networks becomes inevitable trend caused by killer applications such as holographic video which is traffic-intensive. In this paper, we study a sequential activation beam selection, user scheduling, and power allocation problem in a mmWave network with massive MIMO utilizing a physical layer preceding technique. We aim to maximize the time-averaged utility of users with a time-averaged transmit power constraint on top of the EdgeSON architecture, which takes advantage of both centralized and distributed characteristics. We decompose the original long-term problem into two-time scales in which solving the problem of choosing beam pattern is run at a slower time scale than solving user scheduling and power allocation. Then, to solve user scheduling and power allocation, we leverage the Lyapunov drift-plus-penalty framework which transforms an original long-term average problem into per-slot modified sub-problems. Since provided per-slot problem to find a set of user scheduling and power allocation is known as NP-hard, we propose a low-complex and practical interference management algorithm, namely CRIM, by introducing two critical users with the highest interference channel gain in intra-BS and inter-BS respectively. Finally, via extensive simulations, we verify and compare the performance of the proposed algorithm and comparing algorithms in a real mmWave network environment.

User scheduling and power allocation for downlink multi-cell multi-carrier NOMA systems

In this paper, we present a joint game-theoretic approach to perform inter-cell interference coordination in uplink multi-cell orthogonal frequency division multiple access networks. The coordinated user scheduling and power allocation are considered simultaneously. We prove the existence of the joint-strategy Nash equilibrium (NE) in which both the user scheduling and power allocation strategy reach NE. Then, we design a distributed joint-strategy iterative algorithm to perform interference-aware resource allocation where only partial information exchange is involved. Simulation results demonstrate the effectiveness of the proposed algorithm.

LoRa has been recognized as one of the most promising low-power wide-area (LPWA) techniques. Since LoRa devices are usually powered by batteries, energy efficiency (EE) is an essential consideration. In this paper, we investigate the energy efficient resource allocation in LoRa networks to maximize the system EE (SEE) and the minimal EE (MEE) of LoRa users, respectively. Specifically, our objective is to maximize the corresponding EE by jointly exploiting user scheduling, spreading factor (SF) assignment, and transmit power allocations. To solve them efficiently, we first propose a suboptimal algorithm, including the low-complexity user scheduling scheme based on matching theory and the heuristic SF assignment approach for LoRa users scheduled on the same channel. Then, to deal with the power allocation, an optimal algorithm is proposed to maximize the SEE. To maximize the MEE of LoRa users assigned to the same channel, an iterative power allocation algorithm based on the generalized fractional programming and sequential convex programming is proposed. Numerical results show that the proposed user scheduling algorithm achieves near-optimal EE performance, and the proposed power allocation algorithms outperform the benchmarks.

Join trajectory optimization and communication design for UAV-enabled OFDM networks

Opportunistic beamforming (OBF) is a potential technique in the fifth generation (5G) and beyond 5G (B5G) that can boost the performance of communication systems and encourage high user quality of service (QoS) through multi-user selection gain. However, the achievable rate tends to be saturated with the increased number of users, when the number of users is large. To further improve the achievable rate, we proposed a multi-antenna opportunistic beamforming-based relay (MOBR) system, which can achieve both multi-user and multi-relay selection gains. Then, an optimization problem is formulated to maximize the achievable rate. Nevertheless, the optimization problem is a non-deterministic polynomial (NP)-hard problem, and it is difficult to obtain an optimal solution. In order to solve the proposed optimization problem, we divide it into two suboptimal issues and apply a joint iterative algorithm to consider both the suboptimal issues. Our simulation results indicate that the proposed system achieved a higher achievable rate than the conventional OBF systems and outperformed other beamforming schemes with low feedback information.

This paper investigates the resource management problem in multi-carrier rate-splitting multiple access (MC-RSMA) systems with imperfect channel state information (CSI) and successive interference cancellation (SIC) for ultra-reliable and low-latency communications (URLLC) applications. Effective throughput (ET) is employed as the key performance metric in this study to explore the trade-off between the decoding error probability and achievable rate. A mixed-integer non-convex problem is formulated to jointly optimize power allocation, rate control, and user grouping. To tackle this challenge, the achievable ET is approximated using a lower bound, and a decomposition approach is developed to decouple the optimization variables. Specifically, for a given user grouping scheme, an iteration-based concave-convex programming (CCCP) method and an iteration-free lower-bound approximation (LBA) method are proposed for power allocation and rate control. Furthermore, a greedy search-based scheme and a minimum correlation user grouping (MCUG) scheme are designed for the user grouping problem. Simulation results verify the effectiveness of the CCCP and LBA methods in power allocation and rate control and the MCUG method in user grouping when compared to the greedy and random methods. Besides, the superiority of RSMA for URLLC services is demonstrated when compared to spatial division multiple access.

This paper develops a unified framework to maximize the network sum-rate in a multi-user, multi-BS downlink terahertz (THz) network by optimizing user associations, number and bandwidth of sub-bands in a THz transmission window (TW), bandwidth of leading and trailing edge-bands in a TW, sub-band assignment, and power allocations. The proposed framework incorporates multi-connectivity and captures the impact of molecular absorption coefficient variations in a TW, beam-squint, molecular absorption noise, and link blockages. To make the problem tractable, we first propose a convex approximation of the molecular absorption coefficient using curve fitting in a TW, determine the feasible bandwidths of the leading and trailing edge-bands, and then derive closed-form optimal solution for the number of sub-bands considering beam-squint constraints. We then decompose joint user associations, sub-band assignment, and power allocation problem into two sub-problems: 1) joint user association and sub-band assignment, and 2) power allocation. To solve the former problem, we analytically prove the unimodularity of the constraint matrix which enables us to relax the integer constraint without loss of optimality. To solve power allocation sub-problem, a fractional programming (FP)-based centralized solution as well as an alternating direction method of multipliers (ADMM)-based light-weight distributed solution is proposed. The overall problem is then solved using alternating optimization until convergence. Complexity analysis of the algorithms and numerical convergence are presented. Numerical findings validate the effectiveness of the proposed algorithms and extract useful insights about the interplay of the density of base stations (BSs), Average order of multi-connectivity (AOM), molecular absorption, hardware impairment, imperfect CSI, and link blockages.

Large number of antennas and radio frequency (RF) chains at the base stations (BSs) lead to high energy consumption in massive MIMO systems. Thus, how to improve the energy efficiency (EE) with a computationally efficient approach is a significant challenge in the design of massive MIMO systems. With this motivation, a learning-based stochastic gradient descent algorithm is proposed in this article to obtain the optimal joint uplink and downlink EE with joint antenna selection and user scheduling in single-cell massive MIMO systems. Using Jensen's inequality and the characteristics of wireless channels, a lower bound on the system throughput is obtained. Subsequently, incorporating the power consumption model, the corresponding lower bound on the EE of the system is identified. Finally, learning-based stochastic gradient descent method is used to solve the joint antenna selection and user scheduling problem, which is a combinatorial optimization problem. Rare event simulation is embedded in the learning-based stochastic gradient descent method to generate samples with very small probabilities. In the analysis, both perfect and imperfect channel side information (CSI) at the BS are considered. Minimum mean-square error (MMSE) channel estimation is employed in the study of the imperfect CSI case. In addition, the effect of a constraint on the number of available RF chains in massive MIMO system is investigated considering both perfect and imperfect CSI at the BS.

In non-orthogonal multiple access (NOMA) systems, the outage is regarded to happen when a user cannot correctly decode the messages for users with higher decoding order and hence cannot perform the successive interference cancelation (SIC). However, in this case, the user may still correctly decode its message by treating the uncanceled signal as interference and avoid the outage. By considering this behavior, the outage probability should be redefined. In this paper, we investigate user scheduling and power allocation for a downlink NOMA system with imperfect SIC by using the alternative outage probability as a constraint. In order to tackle the complicated non-convex resource allocation problem, we propose a two-phase algorithm, in which the user scheduling is first optimized through a matching theory based algorithm, and then power allocation is performed with the aid of the branch and bound technique and the concave–convex procedure method. Simulation results show that the performance of the proposed low-complexity algorithm is near-optimal and the algorithm based on the alternative outage probability outperforms that based on the traditional one when the residual interference from imperfect SIC significantly affects the decoding.

This paper proposes applying random (opportunistic) beamforming to base station (BS) cooperative multiuser multiple-input multiple-output (MIMO) transmission. This proposal comprises two parts. First, we propose a block-diagonalized random unitary beamforming matrix. The proposed beamforming matrix achieves better throughput distribution compared to the purely random unitary beamforming matrix when the average path loss based on distance-dependent loss and shadowing loss is largely different among transmitter antennas, which is true in BS cooperative MIMO. Second, we propose an online update algorithm for a random beamforming matrix to improve the throughput compared to the purely random and channel-independent beamforming matrix generation, especially when the number of users is low. Different from conventional approaches, the proposed online update algorithm does not increase the overhead of the reference signal transmission and control delay. Simulation results show the effectiveness of the proposed method using a block-diagonalized random unitary beamforming matrix with online updates in a BS cooperative multiuser MIMO scenario.

Full-duplex (FD) radio with wireless backhauling, is a key technology for dense small cell deployments in fifth generation (5G) cellular networks. In this paper, the problem of joint user scheduling, operation mode selection and power allocation in a two-tier cellular network with FD-capable small basestations is considered. The objective is to maximize the network sum rate under quality-of-service requirements and backhaul capacity constraints. In order to solve the problem, a distributed algorithm is developed which divides the original problem into two subproblems, viz., (i) user scheduling and mode selection; and (ii) power allocation. For the first subproblem, a low-complexity search method is proposed for opportunistically finding the best user scheduling and operation modes. In the second subproblem, an iterative successive convex approximation method is proposed to transform the nonconvex power allocation problem into a sequence of convex subproblems. The proposed solution provides a hybrid strategy which opportunistically selects FD or half-duplex (HD) operation modes for access and backhaul links. It is observed that the proposed algorithm converges in few iterations and mitigates the interferences by properly adjusting the transmission powers. Numerical results demonstrate the superiority of the proposed scheme over other possible scenarios. In particular, the proposed algorithm can achieve up to 44% gain in overall sum rate compared to a network that solely uses HD.