General Power Allocation Research Articles

Performance Analysis of Cooperative NOMA for Different Power Allocation Strategies

Cooperative None Orthogonal Multiple Access (C-NOMA) is a promising approach for 5G and beyond communication networks. Users will benefit from the full bandwidth of the channel without time constraints. NOMA’s features are incomplete without efficient power allocation that ensures power distribution among users fairly. Integrating power management (allocation) with Cooperative-NOMA (C-NOMA) may improve the system metrics. In addition, a physical layer security (PLS) is added to make the process of sending and receiving safe, and the system works in an integrated manner, preventing any interrupting or eavesdropping inside or outside the network. This paper demonstrates the user and channel performance of C-NOMA with Amplify-and-forward (AF) and Decode-and-forward (DF) approaches. The analysis is performed by varying the power allocation techniques to derive the best system configurations. The simulation results confirmed the analytic findings and showed that the proposed system outperforms orthogonal multiple access (OMA), conventional NOMA, and conventional cooperative NOMA, enhancing the performance metrics in terms of throughput, sum rate, and outage probability. The bit-error-rate (BER) of the far user can be identical to that of the near user if power allocation is properly set. All techniques excelled except for the fixed power allocation, which had the same BER. The Channel capacity and outage probability were also considered. A slight variation in the channel capacity in all the experiments for different numbers of users was found. The generalized power allocation for AF and DF models had the optimum channel capacity close to 14 bps/Hz. Moreover, far users always had a higher outage probability than near users and channels, and generalized power allocation was the highest outage probability technique when the transmit power was close to (4 dB).

Massive MIMO With Group SIC Receivers and Low-Resolution ADCs Over Rician Fading Channels

This paper investigates the performance of uplink massive multiple-input multiple-output (MIMO) systems with low-resolution analog-to-digital converters (ADCs) operating over Rician fading channels. We study zero-forcing (ZF) group successive interference cancellation (GSIC) receivers, which cover the ZF and ZF-SIC receivers as two special cases. The spectrum efficiency (SE) is analyzed for general power allocation under both perfect and imperfect channel state information (CSI). We further derive the optimal power allocation for a given quality-of-service (QoS) for the GSIC receivers. Based on random matrix theory, the corresponding asymptotic approximations of the signal-to-interference-plus-noise-ratio (SINR) for the system and the power allocation are derived for both perfect and imperfect CSI. The derived approximations are verified and shown highly accurate, providing fast characterization of the performance and near-optimal resource allocation for different system parameters. The numerical and approximation results both indicate that power allocation improves the overall energy efficiency (EE), while the GSIC receiver can yield a high EE with a very small number of groups. Besides, the EE of the system can be improved with intermediate-resolution ADCs and ordered SIC (OSIC).

Max-Min fairness-based resource allocation in massive MIMO systems

This work deals with power and spectrum allocation approaches for massive MIMO (M-MIMO) systems. An analysis is made to verify the efficiency of the solutions provided by schemes used for the spectral and energy efficiency (SE-EE) trade-off problem in massive antenna-based wireless communication systems. We first introduce the Geometry Based Stochastic Model (GBSM) channel model (One-ring model), describing the behavior of a uniform linear array of antennas (ULA) arrangement, revealing how the channel parameters affect the channel capacity. We also show that under this model, the SE still increases without boundary when the massive number of base-station (BS) antennas $M$ increases, provided that pilot contamination is substantially mitigated or eliminated but when the number of users equipment (UEs) $K$ increases with a fixed number of antennas in the BS, there is a increasing limitation from the combiners in mitigating the inter-user interference, making decoding difficult. The downlink (DL) M-MIMO scenario is analyzed, by introducing the generalized power allocation problem and derived the max-min fairness scheme from it. We propose a procedure to solve the max-min problem and

Non-Convex Generalized Nash Games for Energy Efficient Power Allocation and Beamforming in mmWave Networks

Network management is a fundamental ingredient for efficient operation of wireless networks. With increasing bandwidth, number of antennas and number of users, the amount of information required for network management increases significantly. Therefore, distributed network management is a key to efficient operation of future networks. This paper focuses on the problem of distributed joint beamforming control and power allocation in ad-hoc mmWave networks. Over the shared spectrum, a number of multi-input-multi-output links attempt to minimize their supply power by simultaneously finding the locally optimal power allocation and beamformers in a self-organized manner. Our design considers a family of non-convex quality-of-service constraint and utility functions characterized by monotonicity in the strategies of the various users. We propose a two-stage, decentralized optimization scheme, where the adaptation of power levels and beamformer coefficients are iteratively performed by each link. We first prove that given a set of receive beamformers, the power allocation stage converges to an optimal generalized Nash equilibrium of the generalized power allocation game. Then we prove that iterative minimum-mean-square-error adaptation of the receive beamformer results in an overall converging scheme. Several transmit beamforming schemes requiring different levels of information exchange are also compared in the proposed allocation framework. Our simulation results show that allowing each link to optimize its transmit filters using the direct channel results in a near optimum performance with very low computational complexity, even though the problem is highly non-convex.

Generalized Power Allocation (GPA) Scheme for Non-Orthogonal Multiple Access (NOMA) Based Wireless Communication System

Generalized Power Allocation (GPA) Scheme for Non-Orthogonal Multiple Access (NOMA) Based Wireless Communication System

Joint Sensing Duration Adaptation, User Matching, and Power Allocation for Cognitive OFDM-NOMA Systems

In this paper, the non-orthogonal multiple access (NOMA) technology is integrated into cognitive orthogonal frequency-division multiplexing (OFDM) systems, called cognitive OFDM-NOMA, to boost the system capacity. First, a capacity maximization problem is considered in half-duplex cognitive OFDM-NOMA systems with two accessible users on each subcarrier. Due to the intractability of the considered problem, we decompose it into three subproblems, i.e., the optimization of, respectively, sensing duration, user scheduling, and power allocation. By investigating and exploiting the characteristics of each subproblem, the optimal sensing duration adaptation, a matching-theory-based user scheduling, and the optimal power allocation are proposed correspondingly. An alternate iteration framework is further proposed to jointly optimize these three subproblems, with its convergence proved. Moreover, based on the non-cooperative game theory, a generalized power allocation algorithm is proposed and then used in the framework to accommodate half-duplex cognitive OFDM-NOMA systems with multiple users on each subcarrier. Finally, the proposed framework is extended to solve the capacity maximization problem in full-duplex cognitive OFDM-NOMA systems. Simulation results validate the superior performance of the proposed algorithms. For example, for the case of two accessible users, the proposed framework approaches the optimal solution with less than 1% capacity loss and 120 times lower complexity compared with exhaustive search.

Precoded Chebyshev-NLMS-Based Pre-Distorter for Nonlinear LED Compensation in NOMA-VLC

Visible light communication (VLC) is one of the main technologies driving the future 5G communication systems due to its ability to support high data rates with low power consumption, thereby facilitating high speed green communications. To further increase the capacity of VLC systems, a technique called non-orthogonal multiple access (NOMA) has been suggested to cater to increasing demand for bandwidth, whereby users’ signals are superimposed prior to transmission and detected at each user equipment using successive interference cancellation. Some recent results on NOMA exist which greatly enhance the achievable capacity as compared with the orthogonal multiple access techniques. However, one of the performance-limiting factors affecting VLC systems is the nonlinear characteristics of a light emitting diode (LED). This paper considers the nonlinear LED characteristics in the design of pre-distorter for cognitive radio inspired NOMA in VLC, and proposes singular value decomposition-based Chebyshev precoding to improve performance of nonlinear multiple-input multiple-output NOMA-VLC. A novel and generalized power allocation strategy is also derived in this paper, which is valid even in scenarios when users experience similar channels. Additionally, in this paper, analytical upper bounds for the bit error rate of the proposed detector are derived for square $M$ -quadrature amplitude modulation.

Power optimization for multiple QoS, delay, and BER classes relying on finite-delay information theory

Future communication systems will include diffierent types of messages requiring diffierent transmission rates, packet lengths, and service qualities. We address the power-optimization issues of communication systems conveying multiple message types based on finite-delay information theory. Given both the normalized transmission rate and the packet length of a system, the actual residual decoding error rate is a function of the transmission power. We propose a generalized power allocation framework for multiple message types. Two diffierent optimization cost functions are adopted: the number of service-quality violations encountered and the sum log ratio of the residual decoding error rate. We provide the optimal analytical solution for the former cost function and a heuristic solution based on a genetic algorithm for the latter one. Finally, the performance of the proposed solutions are evaluated numerically.

A General Power Allocation Scheme to Guarantee Quality of Service in Downlink and Uplink NOMA Systems

In this paper, a novel dynamic power allocation scheme is proposed to downlink and uplink non-orthogonal multiple access (NOMA) scenarios with two users for more flexibly meeting various quality of service requirements. The exact expressions for the outage probability and the average rate achieved by the proposed scheme, as well as their high signal-to-noise ratio approximations, are established. Compared with the existing works, such as NOMA with fixed power allocation and cognitive radio inspired NOMA, the proposed scheme can: 1) strictly guarantee a performance gain over conventional orthogonal multiple access; and 2) offer more flexibility to realize different tradeoffs between the user fairness and system throughput. Monte Carlo simulation results are provided to demonstrate the accuracy of the developed analytical results and the performance gain of the proposed power allocation scheme.

A Framework for Location-Aware Strategies in Cognitive Radio Systems

Sensing and power strategy optimization are important research topics in cognitive radio systems that hold the promise of advancing green communication. This letter gives a brief overview of the existing power allocation design in the literature and unifies them into a general power allocation framework. Based on the closed-form solution derived for this general problem, the impact of network topology on the system performance is highlighted, which motivates us to propose a novel location-aware strategy that intelligently utilizes frequency and space opportunities and minimizes the overall power consumption while maintaining the quality of service of the primary system. This work shows that in addition to exploring spectrum holes in time and frequency domains, spatial opportunities can be utilized to further enhance energy efficiency for CR systems.

Comprehensive Capacity Ensured Distributed Binary Power Allocation in Dense Cognitive Networks

Considering the capacity gain of the secondary system and the capacity loss of the primary system caused by the newly accessing user, a distributed binary power allocation (admittance criterion) is proposed in dense cognitive networks including plentiful secondary nodes. The case that the secondary nodes can detect both transmitter and receiver of the primary system is analyzed first; considering that it is hard for the secondary system to detect the primary receiver without assuming the cooperation with primary system, the case that the secondary nodes only have the information about the primary transmitter is analyzed thereafter. In particular, we propose a simple estimation strategy to transform the problem of detecting primary receiver to the problem of detecting primary transmitter for the latter case. And a general power allocation scheme which is suitable for both of two cases is proposed afterward. By simulations, the restriction on the distance between secondary transmitter and primary receiver, and the restriction on the distance between secondary and primary transmitters are respectively achieved for the two cases. These two restrictions reflect that the capacities of both primary system and secondary system can be ensured to the predefined extents if these two restrictions on the distances are respectively satisfied in two cases.

Optimal Power Allocation for Spectrum Sharing in Frequency-Selective Unlicensed Bands

Power allocation is an important issue for spectrum sharing of unlicensed bands, in which multiple unlicensed systems may coexist and operate. Recently some works have been reported on spectrum sharing in frequency-flat unlicensed bands. However, there has not been much work on power allocation for spectrum sharing in frequency-selective unlicensed bands. In this letter, we propose an optimal power allocation strategy on frequency-selective interference channels (IC). This strategy, also referred as general power allocation (GPA), allows the transmission power density to vary within one subcarrier. By showing the duality of frequency-selective and parallel frequency- flat channels, we can therefore compute the achievable rate region of GPA. Our method can be regarded as an extension of previous results for frequency-flat scenarios. It will also be shown that over frequency-selective Gaussian IC, the proposed GPA can achieve larger rate region than conventional power allocation strategies, where the transmission power density on each subcarrier is set equal.