Distributed Power Allocation Algorithm Research Articles

Energy-Efficient Resource Allocation for NOMA HetNets in Millimeter Wave Communications

The integration of millimeter-wave (mmWave) and non-orthogonal multiple access (NOMA) has attracted academic and industrial interest as a promising enabling approach due to their massive bandwidth support and greater spectral efficiency. In this paper, we consider NOMA heterogeneous network (HetNet) in mmWave communications, consisting of the macro-cell and small cell tiers connected through the wireless backhaul. Following this, we propose a user grouping algorithm to significantly simplify the user clustering process where each cluster consists of highly correlated users to suppress the inter-cluster interference. We then propose a hybrid analog/digital precoding scheme at the macro base station. The goal is to maximize the system’s total energy-efficiency (EE) in NOMA HetNet by jointly optimizing the transmission of hybrid precoding, power allocation, and bandwidth partition. Next, we formulate an optimization problem within the proposed framework under the minimum data rate and total maximum transmission power constraints. Due to the non-convexity nature of the formulated problem, it is challenging to obtain the optimal solution. As a result, we transform the original problem into a quasi-convex equivalent by analyzing its structure. Then we propose energy-efficient distributed power allocation algorithms to optimize the resource allocation of macro-cell and small cells. Finally, we exhibit the simulation results to demonstrate the proposed algorithm’s performance gains. The simulation results reveal that the proposed algorithmic approach surpasses state-of-the-art and traditional mmWave orthogonal multiple access systems in both capacity and EE.

Some power allocation algorithms for cognitive uplink satellite systems

Cognitive satellite communication (SatCom) is rapidly emerging as a promising technology to overcome the scarcity of the exclusive licensed band model in order to fulfill the increasing demand for high data rate services. The paper addresses power allocation methods for multi-operator multi-beam uplink satellite communication systems co-existing with a Ka-band terrestrial network, using cognitive radio paradigm. Such a scenario is especially challenging because of (i) the coexisting multiple SatCom operators over the cognitive band need to coordinate the use of their resources under limited inter-operator information exchange, and (ii) nonlinear onboard high power amplifier (HPA) which leads to nonlinear interference between users and beams. In order to tackle the first challenge, we propose distributed power allocation algorithms including the standard Alternate Direction Multiplier Method (ADMM); Regarding the HPA nonlinear impairment, we propose nonlinear-aware power allocation based on Signomial Programming. The proposed solutions outperform state-of-the-art in both cases.

Buffer-Aided Relaying in NOMA-Based MTC Networks With Finite Blocklength and Statistical QoS Constraints

Machine-type communication (MTC) is one of the main enabling technologies to support various applications with diverse quality of service (QoS) requirements. Finite blocklength transmission has great potential in meeting the strict delay requirements of delay-sensitive MTC devices (MTCDs), while also causing loss of network capacity due to the decoding error probability. Aiming at this problem, we introduce uplink non-orthogonal multiple access (NOMA) and buffer-aided relaying to assist the finite blocklength transmission with delay requirements for improving the achievable effective capacity (EC), which is defined as the maximum short-packet constant arrival rate under specific statistical QoS constraints. To solve the EC maximization problem, we derive the closed-form expression of time allocation coefficient. Then we establish a concave lower bound of EC using successive convex approximation (SCA) for power allocation of MTCDs, and formulate a non-cooperative game based distributed power allocation algorithm for relay. Furthermore, a joint time and power allocation (JTPA) algorithm is proposed to implement joint resource allocation. Simulation results show that under finite blocklength and statistical QoS constraints, adopting buffer-aided relaying can improve EC by 41.82% compared with no-buffer relaying. Moreover, the achievable EC of JTPA algorithm is only 3.12% lower than that of exhaustive search while reducing complexity.

Reinforcement Learning for Distributed Energy Efficiency Optimization in Underwater Acoustic Communication Networks

To solve the problems of poor quality of service and low energy efficiency of nodes in underwater multinode communication networks, a distributed power allocation algorithm based on reinforcement learning is proposed. The transmitter with reinforcement learning capability can select the power level autonomously to achieve the goal of getting higher user experience quality with lower power consumption. Firstly, we propose a distributed power optimization model based on the Markov decision process. Secondly, we further give a reward function suitable for multiobjective optimization. Finally, we present a distributed power allocation algorithm based on Q-learning and use it as an adaptive mechanism to enable each transmitter in the network to adjust the transmit power according to its own environment. The simulation results show that the proposed algorithm not only increases the total channel capacity of the system but also improves the energy efficiency of each transmitter.

Energy Efficiency Optimization for Multi-Cell Massive MIMO: Centralized and Distributed Power Allocation Algorithms

This paper investigates the energy efficiency (EE) optimization in downlink multi-cell massive multiple-input multiple-output (MIMO). In our research, the statistical channel state information (CSI) is exploited to reduce the signaling overhead. To maximize the minimum EE among the neighbouring cells, we design the transmit covariance matrices for each base station (BS). Specifically, optimization schemes for this max-min EE problem are developed, in the centralized and distributed ways, respectively. To obtain the transmit covariance matrices, we first find out the closed-form optimal transmit eigenmatrices for the BS in each cell, and convert the original transmit covariance matrices designing problem into a power allocation one. Then, to lower the computational complexity, we utilize an asymptotic approximation expression for the problem objective. Moreover, for the power allocation design, we adopt the minorization maximization method to address the non-convexity of the ergodic rate, and use Dinkelbach's transform to convert the max-min fractional problem into a series of convex optimization subproblems. To tackle the transformed subproblems, we propose a centralized iterative water-filling scheme. For reducing the backhaul burden, we further develop a distributed algorithm for the power allocation problem, which requires limited inter-cell information sharing. Finally, the performance of the proposed algorithms are demonstrated by extensive numerical results.

Robust joint resource allocation algorithm for hierarchical underwater acoustic communication networks based on outage probability minimization

AbstractThis study investigates a joint resource allocation (channel selection (CS) and power allocation (PA)) problem with multiple channels, users, constraints, and uncertainties in the underwater acoustic communication networks (UACNs). A hierarchical joint resource allocation model is formulated considering different user requirements. This model aims to minimize the maximum outage probability of lower users while satisfying the outage requirements of upper users. This study presents a robust distributed power allocation (RDPA) algorithm to solve complex PA problems with nonconvex mixed integers and uncertainties in UACNs. The residual energy of nodes is considered in the proposed RDPA algorithm to balance their energy, thereby extending the working time of the entire network. The proposed robust CS algorithm considers the actual and potential interferences of all neighboring nodes to decrease the interferences of nodes and the entire network, thereby reducing the outage probability of the network. Simulation results are presented to demonstrate the effectiveness of the proposed algorithm.

Distributed Deep Learning for Power Control in D2D Networks With Outdated Information

Traditional D2D power control methods require instantaneous interference information and so are difficult to implement in a real network due to the backhaul delay and high computational requirements. To overcome this challenge, we propose a distributed power allocation algorithm called interference feature extractor aided recurrent neural network (IFE-RNN). The core design ideas are described as follows. First, we design linear filters with various sizes termed IFEs to extract the different local interference patterns from outdated channel information. This feature extraction process enables our network to precisely learn the interference patterns around D2D links, so as to provide more effective power allocation strategies. Second, we propose to predict the real-time interference pattern based on the outputs of the IFEs and further make power decision. The prediction and decision can be modelled as a Markov decision problem (MDP) and solved by using a recurrent neural network. Third, an input reduction process is also designed to reduce the input size from O(N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) to O(1), which speeds up the operation time and reduces the system overhead. Finally, extensive simulation results show that the proposed algorithm achieves an encouraging performance compared to the state-of-the-art power allocation algorithm.

Joint Power and Sub-Channel Allocation for Secure Transmission in NOMA-Based mMTC Networks

In this paper, we consider the physical layer security for non-orthogonal multiple access (NOMA)-based uplink massive machine type communication (mMTC) networks. Aiming at the maximization of the system secrecy capacity, with the presence of eavesdroppers, we propose a joint power and sub-channel allocation for secrecy capacity (JPSASC) algorithm to obtain the suboptimal solution of the joint problem. Particularly, the power allocation problem is modeled as a non-cooperative game with a distributed perspective, where each MTC device selfishly optimizes its power allocation over multi-channels to maximize its own secrecy capacity. The existence of Nash equilibrium (NE) is proved and a sufficient condition to ensure the uniqueness of NE is given. Moreover, the distributed power allocation and preference secrecy capacity maximum (PSCM) algorithms are proposed for power allocation and sub-channel allocation problem, respectively. Simulation results verify that JPSASC algorithm outperforms other algorithms in maximizing secrecy capacity. Furthermore, the secrecy capacity in NOMA-based mMTC is improved compared with that in orthogonal multiple access schemes.

Reinforcement Learning for Self Organization and Power Control of Two-Tier Heterogeneous Networks

Self-organizing networks (SONs) can help to manage the severe interference in dense heterogeneous networks (HetNets). Given their need to automatically configure power and other settings, machine learning is a promising tool for data-driven decision making in SONs. In this paper, a HetNet is modeled as a dense two-tier network with conventional macrocells overlaid with denser small cells (e.g. femto or pico cells). First, a distributed framework based on the multi-agent Markov decision process is proposed that models the power optimization problem in the network. Second, we present a systematic approach for designing a reward function based on the optimization problem. Third, we introduce Q-learning-based distributed power allocation algorithm (Q-DPA) as a self-organizing mechanism that enables the ongoing transmit power adaptation as new small cells are added to the network. Furthermore, the sample complexity of the Q-DPA algorithm to achieve $\epsilon $ -optimality with high probability is provided. We demonstrate, at the density of several thousands femtocells per km2, the required quality of service of a macrocell user can be maintained via the proper selection of independent or cooperative learning and appropriate Markov state models.

Resource Optimization in Heterogeneous Cloud Radio Access Networks

This letter tackles the problem of sum-rate maximization via joint user association and power allocation in heterogeneous cloud radio access networks, while guaranteeing the quality-of-service requirements of all users. A generalized Stackelberg game is applied to this problem with the coupled strategy set of power allocation, and a centralized-distributed method is designed to achieve the optimal solution. Specifically, the user association problem is efficiently solved in a centralized manner and a distributed power allocation algorithm is proposed by using variational inequality theory.

Distributed Power Allocation for Nonorthogonal Multiple Access Heterogeneous Networks

In this letter, we investigate the power allocation problem in nonorthogonal multiple access (NOMA) heterogeneous networks (HetNets), where the macrocell base station (MBS) and small cell base station (SBS) compete to maximize their own throughput with minimum rate requirement of each user. The throughput of MBS and SBS is formulated based on the equivalent channel gain, and the power allocation problem is captured as a Stackelberg game. A distributed power allocation algorithm is proposed to achieve the Stackelberg equilibrium. Simulation results demonstrate the convergence of the proposed algorithm. Meanwhile, it is verified that the combination of NOMA and HetNets possesses great potential to improve the spectrum efficiency compared to the orthogonal multiple access based HetNets.

Distributed Power Allocation for Cognitive Radio Networks with Time Varying Channel and Delay: H∞ State Feedback Control Approach

On the basis of ${\mathcal{ H_\infty }}$ control approach instead of using the conventional optimization method, we present a distributed power allocation algorithm for a cognitive radio network (CRN), where underlying secondary users (SUs) share same licensed spectrum with primary users (PUs) and the channel gain is time-varying between two time slots. Based on the target signal-to-interference-plus-noise ratio (target-SINR) tracking power control (TPC) algorithm in the conventional network and the dynamic description of the channel gain fluctuation in the CRN as a first-order Markov model, we formulate the power allocation in the CRN into a state-space system model with exogenous input. In this time, the core for us becomes to design a ${\mathcal{ H_\infty }}$ state feedback controller obtained by solving a linear matrix inequality (LMI) for this system to realize power allocation for SUs. The SINR requirement of SUs and the interference temperature (IT) constraint of all PUs can be guaranteed. According to this controller design principle, we also give a ${\mathcal{ H_\infty }}$ delay-independent state feedback controller to treat time-delay for the protection of the communication performance. Simulation results demonstrate the validity, effectiveness, and advantages of this approach compared with the algorithms obtained by the optimization theory for the power allocation in CRNs.

Distributed Power Allocation Based on LQG Regulator With Adaptive Weight and Switching Scheme for Cognitive Radio Networks

We present a distributed power allocation algorithm for a cognitive radio network (CRN), where the underlying secondary users (SUs) share the same licensed spectrum with the primary users (PUs). Based on the target signal-to-interference-plus-noise ratio (target-SINR) tracking power control (TPC) algorithm in a conventional network, the power allocation problem is modeled as a statespace model with exogenous input that includes varying channel state information (CSI) and some parameter measurement errors. This power allocation algorithm is actually a state feedback controller from the linear quadratic Gaussian (LQG) solution, which is used to guarantee the SINR requirement of the SUs and keep the interference temperature (IT) constraint of all PUs under a given threshold. Considering the quality of service (QoS) of the SU, an adaptive control weight and a switching safety margin of the IT threshold are introduced in this algorithm to achieve better performance. Analysis and simulation results show the effectiveness and advantages of the proposed power allocation scheme, designed based on control theory, compared with those of other traditional algorithms designed based on optimization.

Interference-Constrained Pricing for D2D Networks

The concept of device-to-device (D2D) communications underlaying cellular networks opens up potential benefits for improving system performance but also brings new challenges such as interference management. In this paper, we propose a pricing framework for interference management from the D2D users to the cellular system, where the base station (BS) protects itself (or its serving cellular users) by pricing the crosstier interference caused from the D2D users. A Stackelberg game is formulated to model the interactions between the BS and D2D users. Specifically, the BS sets prices to a maximize its revenue (or any desired utility) subject to an interference temperature constraint. For given prices, the D2D users competitively adapt their power allocation strategies for individual utility maximization. We first analyze the competition among the D2D users by noncooperative game theory and an iterative based distributed power allocation algorithm is proposed. Then, depending on how much network information the BS knows, we develop two optimal algorithms, one for uniform pricing with limited network information and the other for differentiated pricing with global network information. The uniform pricing algorithm can be implemented by a fully distributed manner and requires minimum information exchange between the BS and D2D users, and the differentiated pricing algorithm is partially distributed and requires no iteration between the BS and D2D users. Then a suboptimal differentiated pricing scheme is proposed to reduce complexity and it can be implemented in a fully distributed fashion. Extensive simulations are conducted to verify the proposed framework and algorithms.

Sum-Rate Maximization for Energy Harvesting Nodes With a Generalized Power Consumption Model

This paper considers a network of energy harvesting wireless nodes transmitting simultaneously in a Gaussian interference channel and investigates a distributed power allocation algorithm that maximizes the sum-rate. The power consumption model is based on a series of step functions that allow to model, among others, radio frequency circuits being on/off and the startup power consumption of the transmitter. After showing that the sum-rate maximization problem is nonsmooth, nonconvex, and NP-hard, the Iterative Smooth and Convex approximation Algorithm (ISCA) is proposed, which successively approximates the step functions by proper smooth functions to obtain a sequence of smooth nonconvex problems that can be solved by means of the successive convex approximation method. It is demonstrated that the ISCA distributedly converges to a stationary solution of the sum-rate maximization problem. For the particular case of point to point communications, the numerical results show that the ISCA is able to avoid bad stationary solutions, performing close to the globally optimal solution. The performance of the ISCA is also evaluated in the interference channel and with real solar energy harvesting data.

Contract‐auction based distributed resource allocation for cooperative communications

Cooperative communication significantly improves the performance of wireless systems. Transmission of signal in such a system can be accomplished by the help of intermediate relay nodes. However, due to the selfish nature of network nodes, an incentive mechanism is required to stimulate relay nodes to cooperate. On the other hand, the authors assume that the source nodes are ill-informed about channel conditions of the relay nodes, which may result in asymmetry of information. In this study, the authors propose a distributed power allocation and price assignment algorithm over cooperative wireless networks. The proposed solution aims to achieve optimum power allocation to the source nodes and best price of power at the relay nodes, in the presence of asymmetric channel state information. To this end, the authors combine contract theory and auction mechanism in order to provide the highest possible utility for both the source and the relay nodes. The proposed distributed approach benefits the source nodes by preventing the relay nodes from cheating behaviour. Additionally, it favours the relay nodes by letting them assign the final price of power. Finally, the authors present simulation results in order to demonstrate efficiency of the proposed distributed multi-user algorithm.

Resource Allocation in Spectrum-Sharing OFDMA Femtocells With Heterogeneous Services

Femtocells are being considered a promising technique to improve the capacity and coverage for indoor wireless users. However, the cross-tier interference in the spectrum-sharing deployment of femtocells can degrade the system performance seriously. The resource allocation problem in both the uplink and the downlink for two-tier networks comprising spectrum-sharing femtocells and macrocells is investigated. A resource allocation scheme for cochannel femtocells is proposed, aiming to maximize the capacity for both delay-sensitive users and delay-tolerant users subject to the delay-sensitive users' quality-of-service constraint and an interference constraint imposed by the macrocell. The subchannel and power allocation problem is modeled as a mixed-integer programming problem, and then, it is transformed into a convex optimization problem by relaxing subchannel sharing; finally, it is solved by the dual decomposition method. Subsequently, an iterative subchannel and power allocation algorithm considering heterogeneous services and cross-tier interference is proposed for the problem using the subgradient update. A practical low-complexity distributed subchannel and power allocation algorithm is developed to reduce the computational cost. The complexity of the proposed algorithms is analyzed, and the effectiveness of the proposed algorithms is verified by simulations.

A Refined Convergence Condition for Iterative Waterfilling Algorithm

In a multi-user spectrum optimization problem, iterative waterfilling is normally used as a distributed power allocation algorithm. In this letter, we derive a new sufficient condition for its convergence, which is more relaxed than previous conditions. Our result strengthens the fact that the convergence of iterative waterfilling is dependent on signal-to-noise ratio (SNR), and that convergence is much easier in the low SNR regime.

Multi-channel power allocation based on market competitive equilibrium in cognitive radio networks

Cognitive radios such as intelligent phones and Bluetooth devices have been considered essential goods in next-generation communication systems. Such devices will have to share the same frequency band owing to the limited bandwidth resource. To improve spectrum efficiency, we formulate multi-channel power allocation as a market competitive equilibrium (CE) problem, and prove that its solution exists and is unique under the condition of weak interference. We then propose two distributed power allocation algorithms achieving CE, namely the fast convergent power allocation algorithm (FCPAA) and the social-fairness-aware FCPAA (SFAF). Theoretical analysis and simulation results demonstrate that the proposed algorithms lead to better system performance in terms of the guaranteed interference temperature constraint using the price mechanism instead of a strategy based on the Nash equilibrium. Moreover, it is shown that the FCPAA maximizes total utility, and converges more quickly than the method addressed in prior research with the help of improved round-robin rules. However, the FCPAA cannot ensure the same social fairness among secondary users as the SFAF scheme in both the non-fading channel and Rayleigh fading channel; the SFAF balances the individual utility by adjusting each user’s budget at the expense of a small quantity of system total throughput.

Robust Power Allocation for Energy-Efficient Location-Aware Networks

In wireless location-aware networks, mobile nodes (agents) typically obtain their positions using the range measurements to the nodes with known positions. Transmit power allocation not only affects network lifetime and throughput, but also determines localization accuracy. In this paper, we present an optimization framework for robust power allocation in network localization with imperfect knowledge of network parameters. In particular, we formulate power allocation problems to minimize localization errors for a given power budget and show that such formulations can be solved via conic programming. Moreover, we design a distributed power allocation algorithm that allows parallel computation among agents. The simulation results show that the proposed schemes significantly outperform uniform power allocation, and the robust schemes outperform their non-robust counterparts when the network parameters are subject to uncertainty .