Downlink Power Allocation Research Articles

Coalitional game‐based user pairing and power allocation in downlink non‐orthogonal multiple access networks

AbstractThis article studies the user pairing and power allocation in a downlink non‐orthogonal multiple access (NOMA) network in which users form different pairs accessing orthogonal channels and NOMA is utilized in each channel. Existing studies mainly focused on throughput optimization, while throughput cannot completely reflect users' quality of experience. Therefore, we aim to maximize the network's sum of mean opinion scores rather than throughput to meet different traffic demands of users. The mixed integer nonlinear programming problem is decomposed into two subproblems. The first user pairing problem is modeled as a coalition formation game, and a Nash‐stable suboptimal pairing scheme is obtained via proposed coalition formation algorithm. The second power allocation problem is modeled as a knapsack problem, and we obtain the optimal power allocation scheme through dynamic programming with bisection method. These two subproblems are solved alternately until converging to the final resource allocation strategy. Simulation results show that the proposed strategy outperforms existing NOMA resource allocation techniques and achieves near‐optimal performance.

Multi‐agent deep reinforcement learning‐based energy efficient power allocation in downlink MIMO‐NOMA systems

NOMA and MIMO are considered to be the promising technologies to meet huge access demands and high data rate requirements of 5G wireless networks. In this paper, the power allocation problem in a downlink MIMO-NOMA system to maximize the energy efficiency while ensuring the quality-of-service of all users is investigated. Two deep reinforcement learning-based frameworks are proposed to solve this non-convex and dynamic optimization problem, referred to as the multi-agent DDPG/TD3-based power allocation framework. In particular, with current channel conditions as input, every single agent of two multi-agent frameworks dynamically outputs the optimum power allocation policy for all users in every cluster by DDPG/TD3 algorithm, and the additional actor network is also added to the conventional multi-agent model in order to adjust power volumes allocated to clusters to improve overall performance of the system. Finally, both frameworks adjust the entire power allocation policy by updating the weights of neural networks according to the feedback of the system. Simulation results show that the proposed multi-agent deep reinforcement learning based power allocation frameworks can significantly improve the energy efficiency of the MIMO-NOMA system under various transmit power limitations and minimum data rates compared with other approaches, including the performance comparison over MIMO-OMA.

Fairness‐aware power allocation in downlink MIMO‐NOMA systems

Fairness‐aware power allocation in downlink MIMO‐NOMA systems

Downlink Power Allocation Optimization in Pattern Division Multiple Access

Non-orthogonal multiple access (NOMA), in particular pattern division multiple access (PDMA), has been recently considered as the critical multiple access technologies deployed for 5G applications. However, optimizing power allocation (PA) at the base station (BS) to increase the sum throughput of the downlink (DL) PDMA system still remains a challenge. Although this problem has been explored to some extent by assuming that the BS can obtain perfect channel state information (CSI), this assumption is unfeasible in harsh environments. Therefore, to effectively improve the sum throughput of communications in the real world, we propose using PDMA with optimized PA. With perfect CSI, iterative power allocation (IPA) that can iteratively update the Lagrange multipliers is proposed. Using the Karush-Kuhn-Tucher (KKT) conditions, a PA solution with closed-form expressions is derived from increasing the sum throughput. With imperfect CSI, the outage probability is considered when maximizing the sum throughput. The probabilistic mixture problem can be converted into a non-probability problem, and thus, PA under imperfect CSI can be optimized with the proposed IPA algorithm. The simulation results demonstrate that the proposed scheme outperforms the existing schemes and can be effective in harsh environments.

A Green Downlink Power Allocation Scheme for Cell-Free Massive MIMO Systems

In this article, we consider the problem of downlink power allocation in a cell-free massive multiple-input multiple-output (m-MIMO) communication system under spectral efficiency (SE) constraints for the users. From the perspective of green communications, the power allocation is formulated as an optimization problem where the aim is to maximize the sum SE as the objective function, while limiting the transmission power of each access point (AP) and imposing lower and upper bounds on the achievable SEs of different users. The resulting optimization problem is non-convex since the objective function is non-concave and the upper bounding constraints on user SEs are non-convex. To address these difficulties, we first derive a closed-form lower bound on the sum SE (objective function) and prove that it is a quasi-concave function. Then, we relax the unwieldy upper bounding constraints on the user SEs by replacing them with linear functions, which renders the optimization problem convex. An optimal solution to the relaxed problem is finally obtained by solving a sequence of convex feasibility programs. We evaluate the performance of the proposed downlink power allocation scheme through Monte Carlo simulations under the uncorrelated and correlated shadow fading models. The results show that for both models, the proposed algorithm can lead to a significant reduction in total power consumption compared to a benchmark approach, while accurately allocating power to the APs so that the SE constraint of each user is satisfied within the imposed bounds.

Efficient Downlink Power Allocation Algorithms for Cell-Free Massive MIMO Systems

Cell-free Massive MIMO systems consist of a large number of geographically distributed access points (APs) that serve the users by coherent joint transmission. The spectral efficiency (SE) achieved by each user depends on the power allocation: which APs that transmit to which users and with what power. In this article, we revisit the max-min and sum-SE power allocation policies, which have previously been approached using high-complexity general-purpose solvers. We develop and compare several different high-performance low-complexity power allocation algorithms that are appropriate for use in large systems. We propose two new algorithms for sum-SE power optimization inspired by weighted minimum mean square error (WMMSE) minimization and fractional programming (FP). Further, one new FP-based algorithm is proposed for max-min fair power allocation. The alternating direction method of multipliers (ADMM) is used to solve specific convex subproblems in the proposed algorithms. Our ADMM reformulations lead to multiple small-sized subproblems with closed-form solutions. The proposed algorithms find global or local optimal power allocation solutions for large-scale systems but with reduced computational time compared to previous work.

Spectral Efficiency of Dynamic Licensed Shared Access

In licensed shared access (LSA) the radio spectrum is dynamically shared between an incumbent and one or more licensee systems. Protective measures are applied to the licensees’ communication activity to protect the normal operation of the incumbent system. Such measures are therefore crucial components of the LSA, and thus fundamentally affect the achievable spectrum efficiency. In this paper, we investigate a vertical LSA including an airport traffic control system, as the incumbent, and a mobile network as the licensee. While some previous works only consider the licensee uplink, we analytically obtain the interference received by the incumbent from the licensee's transmission both in the uplink and downlink. We then obtain optimal uplink and downlink power allocation in the licensee using an optimisation problem with the objective of maximizing licensee's spectral efficiency (SE) subject to the incumbent interference threshold. Furthermore, we investigate the effect of the number of users and cell size on the SE. Our results provide quantitative insights for practical system design and deployment of LSA system. We then examine the whole LSA spectrum utilization by characterising the availability of the LSA spectrum using a tandem queue setting. Using this model we obtain an expression for the spectral utilization as a function of the licensee's achievable spectral efficiency and the statistics of the LSA spectrum availability. Simulation results show more than a seven-fold improvement in the licensee SE using the optimal power allocation. It is also seen that a higher SE gain is achieved with the proposed optimal power allocation in cases where the number of user equipment in the eNodeB coverage area is very small. Furthermore, higher spectrum utilization efficiency is achieved as a result of shorter busy period and higher achievable SE for distant cells.

Resource allocation in sparse code multiple access‐based systems for cloud‐radio access network in 5G networks

AbstractIn this paper, downlink joint user association, codebook assignment and power allocation for a Sparse Code Multiple Access (SCMA)‐based in Cloud Radio Access Network (C‐RAN) are investigated. The main aim of this paper is to accomplish a resource allocation to achieve the maximum total sum rate subject to SCMA limitation, total available power in Radio Remote Head (RRH), fronthaul constraint, users association, user power constraint, and Quality of Service requirements for each user with low complexity. To solve the recommended problem an iterative algorithm considering Successive Convex Approximation is utilized. The main problem is separated into two subproblems of joint user association and codebook assignment and power allocation. By defining a new binary variable for user association and codebook assignment and a new constraint for the user association, the joint user association and codebook assignment subproblem becomes an Integer Linear Programming. Then user association and codebook assignment subproblem solved with the help of the CVX toolbox and MOSEK solver. Furthermore, to solve the power allocation subproblem, Successive Convex Approximation with Low Complexity is applied. Then the power allocation subproblem solved by subgradient method for the Lagrange dual problem. In the simulation results, the effectiveness of the recommended optimization algorithm and resource allocation method are shown and compared to Non‐Orthogonal Multiple Access and Orthogonal Frequency Division Multiple Access.

Max-Min Power Control in Downlink Massive MIMO With Distributed Antenna Arrays

In this paper, we investigate optimal downlink power allocation in massive multiple-input multiple-output (MIMO) networks with distributed antenna arrays (DAAs) under correlated and uncorrelated channel fading. In DAA massive MIMO, the base station (BS) consists of multiple antenna sub-arrays. Notably, the antenna sub-arrays are deployed in arbitrary locations within a DAA massive MIMO cell. Consequently, the distance-dependent large-scale propagation coefficients are different from a user to these different antenna sub-arrays, which makes power control a challenging problem. We assume that the network operates in time-division duplex mode, where each BS obtains the channel estimates via uplink pilots. Based on the channel estimates, the BSs perform maximum-ratio transmission in the downlink. We then derive a closed-form signal-to-interference-plus-noise ratio (SINR) expression, where the channels are subject to correlated fading. Based on the SINR expression, we propose a networkwide max-min power control algorithm to ensure that each user in the network receives a uniform quality of service. Numerical results demonstrate the performance advantages offered by DAA massive MIMO. For some specific scenarios, DAA massive MIMO can improve the average per-user throughput up to 55%. Furthermore, we demonstrate that channel fading covariance is an important factor in determining the performance of DAA massive MIMO.

New framework for interference and energy analysis of soft frequency reuse in 5G networks

Cellular networks are expanding massively due to high data requirements from mobile devices. This has motivated base station densification as an essential requirement for the 5G network. The implication is obvious benefits in enhanced system capacity, but also increased challenges in terms of interference. One important interference management technique which has been widely adopted in cellular networks is frequency reuse. In this article, an analysis is presented based on network interference and energy expended by base stations in downlink communication when Soft frequency reuse (SFR) is deployed. A framework is presented that captures the bandwidth overlaps in SFR across base station assignments, computes the interference probabilities arising and derives new performance equations which are verified using simulations. Results show an improvement of over previous SFR implementations that do not consider the interference probabilities. Thus, a more in-depth and accurate modelling of SFR in 5G networks is achieved. Furthermore, the downlink power allocation is investigated as against other parameters like the center ratio and edge bandwidth. The result shows that signal-to-interference-noise ratio (SINR) and spectral efficiency give different performance under energy consideration. A framework is developed on how to tune a base station to achieve desired network performance in user SINR or cell spectral efficiency depending on the operator’s preference.

Joint uplink and downlink power allocation for maximizing the energy efficiency in ultra-dense networks

Ultra-dense network (UDN) is a promising solution to meet the huge data requirements of future 5G wireless communication systems. With many irregularly deployed small cell base stations (SBSs), it is very difficult to accurately measure network energy efficiency (EE) in UDN. Considering the necessity of saving energy in modern-day networks, in this paper, I present the EE evaluation of UDN based on the random spatial network model. In this considered system, the SBSs and the macro cell users (MUEs) are modeled as a homogeneous Poisson point process (PPP). Unlike the existing literature, I derive the simple closed-form expressions for the successful transmission probabilities of MUEs and small cells by Laplace transformation. Then, based on the obtained successful transmission probabilities, I derive the closed form expression for EE of the small cells in both uplink and downlink under some practical constraints. Further, to obtain the optimal SBS density in UDN, I propose a joint uplink and downlink power allocation (JUDPA) algorithm. The JUDPA algorithm obtains the maximum EE in small cells by iteratively optimizing the uplink and downlink powers. Our theoretical analysis and simulated results demonstrate the accuracy of the proposed model with better power allocation and less complexity than conventional solutions.

Differential evolution-based optimal power allocation scheme for NOMA-VLC systems.

This paper investigates a downlink power allocation scheme for application of non-orthogonal multiple access (NOMA) in visible light communication (VLC) systems. Aiming to achieve a flexible tradeoff between sum rate and user fairness depending on administrators' subjective setting and acquire the optimal solution for any setting, we formulate two optimization problems, which are closely aligned with the practical application. Specifically, one is user fairness-guaranteed sum rate maximization, the other is sum rate-guaranteed user fairness maximization. Moreover, through classified discussion around the threshold guaranteeing user fairness or sum rate, we present rigorous mathematical derivation and optimization analysis, and two corresponding algorithms are proposed. Furthermore, through numerical simulation, the superiority of our proposed algorithms over conventional schemes is verified in terms of meeting the practical demand and performance gain in sum rate, user fairness, and coverage probability.

A Robust Game-Based Algorithm for Downlink Joint Resource Allocation in Hierarchical OFDMA Femtocell Network System

Femtocell is a promising technology for wireless service networks to facilitate sustainable and efficient services for users. This paper deals with a downlink joint channel assignment and power allocation problem with multiple channels, users, constraints, and uncertainties in the orthogonal frequency division of a multiple-access hierarchical femtocell network system. Specifically, a hierarchical robust Stackelberg game, which aims to achieve robust equilibrium, is first proposed for resource allocation with uncertainties. Then, a low-complexity, low-interference, high-efficiency, and high-performance algorithm is presented to handle the complex robust joint allocation problem. Considering the demand capacity of macro-base stations, an efficient fitness function in conjunction with particle swarm optimization-constriction factor, is utilized to yield the best response in the upper game to satisfy multiple constraints. Meanwhile, an iterative waterfilling algorithm is exploited to achieve the best response of femto-base stations in the lower game. Lastly, a stop protocol is established for two-tier users to accomplish an efficient robust Stackelberg equilibrium. Comparative results demonstrated that the proposed algorithm is superior to the existing game-based algorithms.

Uplink Fractional Power Control and Downlink Power Allocation for Cell-Free Networks

This letter proposes respective policies for uplink power control and downlink power allocation in cell-free wireless networks. Both policies rely only on large-scale quantities and are expressed in closed form, being therefore scalable. The uplink policy, which generalizes the fractional power control employed extensively in cellular networks, features a single parameter; by adjusting this parameter, the SIR distribution experienced by the users can be compressed or expanded, trading average performance for fairness. The downlink policy dualizes the uplink solution, featuring two parameters that again allow effecting a tradeoff between average performance and fairness.

Joint backhaul bandwidth and power allocation in heterogeneous cellular networks: A two‐level approach

SummaryWe investigate the problem of joint downlink wireless backhaul bandwidth (WBB) and power allocation in heterogeneous cellular networks (HCNs). A WBB partitioning scheme is considered, which allocates the whole bandwidth between the macrocell and small cells for data transmission and backhauling. We formulate an optimization problem to maximize the weighted sum logarithmic utility function by jointly optimizing WBB portion and fronthaul power allocation of each base station with consideration of the backhaul capacity limitation on each small cell. In order to solve this joint optimization problem, we propose a hierarchical two‐level approach and decompose the original problem into two independent subproblems: the WBB allocation at the macrocell base station (MBS) and the power allocation at both the MBS and small cell base stations (SBSs). Accordingly, the optimal WBB portion and power allocation solutions are obtained, respectively. Furthermore, we develop a distributed algorithm to implement the joint WBB and power allocation. Numerical results verify the effectiveness of the proposed approach and analyze the impact of the weighted coefficient and backhaul capacity limitation on the network performance. In addition, significant performance gains can be achieved by the proposed approach over the benchmark.

Mixed Uplink, Downlink Channel Allocation and Power Allocation Schemes for 5G Networks

Device-to-device (D2D) communication plays a significant role for the next generation networks i.e. (5G). The D2D communication leads to an efficient spectral utilization which results in a potential reduction in the overhead at the evolved node B side. However, without an efficient interference management scheme in place, cellular users experience a degradation in the quality of service, which makes interference mitigation a major deployment challenge. In this paper, we have put forward an efficient interference mitigation scheme in order to maximize the system throughput. This scheme comprises of three subparts. Firstly, we introduce a cell sub-division concept under which we virtually subdivide the cell into three regions, namely center, mid and outer regions. Secondly, we formulate the power allocation problem as a robust particle swarm optimization algorithm for the mid-cell region. This enables the scheme in handling the uncertainties in the allocated power, thus reaching a robust optimal solution. The third part consists of a resource allocation scheme built around a cell subdivision concept. Extensive simulations are performed under the scenarios of simultaneous uplink and downlink transmission for the center cell region and the outer cell region. We further draw the comparison of our scheme along with the schemes mentioned in the literature.

Power Allocation in 5G mmWave Networks with Massive MIMO and Block Diagonalization

In this paper, we consider an energy-efficient downlink power allocation problem of a massive multiple-input multiple –output (MIMO) system in cellular networks. We propose a new technique that uses block diagonalization (BD) precoding with massive MIMO in 5G networks that employs milli-meter wave (mmWave) channel matrix to reduce the level of interference. A power allocation algorithm is then introduced by increasing the energy efficiency (EE) of the system to obtain optimal downlink power values using sequential quadratic programming (SQP) algorithm. The results reveal that using BD reduces the interference level and hence, increases the energy.

Improved Joint Spectrum Sensing and Power Allocation for Cognitive Radio Networks Using Probabilistic Spectrum Access

In this paper, by employing a probabilistic spectrum access (PSA) scheme, we perform optimal disjoint and joint spectrum sensing and downlink power allocation methods in a cognitive radio (CR) network. For the spectrum sensing, we consider a cooperative framework, in which cognitive sensors cooperate via energy-detection-based soft spectrum sensing. The proposed optimization problems related to disjoint and joint spectrum sensing and power allocation are formulated with two main objectives: first, minimizing the false-alarm probability under constant probability of detection; and second, maximizing the average opportunistic CR data rate under limited detection probability and limited CR power budget. These optimization problems are in fact the functional-based optimization problems, in which the optimization parameter is a functional of the measured energy by CR sensors, where the optimal solution is obtained using Euler-Lagrange equation. Spectrum sensing is then performed by using the optimal functional inside a PSA framework. The advantages of the proposed approach are confirmed through several numerical results.

Downlink Power Allocation Strategy for Next-Generation Underwater Acoustic Communications Networks

The increasing interest in next-generation underwater acoustic communications networks is due to vast investigation of oceans for oceanography, commercial operations in maritime areas, military surveillance, and more. A surface buoy or underwater base station controller (UBSC) communicates with either transceivers or underwater base stations (UBSs) via acoustic links. Transceivers further communicate with underwater sensor nodes using acoustic links. In this paper, we employ a downlink (DL) power allocation (PA) strategy using an orthogonal frequency-division multiple access (OFDMA) technique for underwater acoustic communications (UAC) networks. First, we present an approach to power offsets using three kinds of pilot spacing and apply the power boosting (PB) concept on orthogonal frequency-division multiplexing (OFDM) symbols for the UAC network. Secondly, we draw the block error rate (BLER) curves from link-level simulation (LLS) and analyze the signal-to-noise ratio (SNR) for both PA and non-PA strategies. Lastly, we adopt the best PB for system-level simulation (SLS) and compare the throughput and outage performance for PA and non-PA strategies. Hence, the simulation results confirm the effectiveness of the DL PA strategy for UAC networks.

A Survey on Recent Trends and Open Issues in Energy Efficiency of 5G.

The rapidly increasing interest from various verticals for the upcoming 5th generation (5G) networks expect the network to support higher data rates and have an improved quality of service. This demand has been met so far by employing sophisticated transmission techniques including massive Multiple Input Multiple Output (MIMO), millimeter wave (mmWave) bands as well as bringing the computational power closer to the users via advanced baseband processing units at the base stations. Future evolution of the networks has also been assumed to open many new business horizons for the operators and the need of not only a resource efficient but also an energy efficient ecosystem has greatly been felt. The deployment of small cells has been envisioned as a promising answer for handling the massive heterogeneous traffic, but the adverse economic and environmental impacts cannot be neglected. Given that 10% of the world’s energy consumption is due to the Information and Communications Technology (ICT) industry, energy-efficiency has thus become one of the key performance indicators (KPI). Various avenues of optimization, game theory and machine learning have been investigated for enhancing power allocation for downlink and uplink channels, as well as other energy consumption/saving approaches. This paper surveys the recent works that address energy efficiency of the radio access as well as the core of wireless networks, and outlines related challenges and open issues.