Power Allocation Framework Research Articles

On Optimal Resource Allocation in Multi-RAT Wireless Networks With Receiver Characteristic Awareness

Next-generation wireless systems are expected to use multiple coexisting radio access technologies with different receive and transmit characteristics. This will make the radio frequency front-ends susceptible to unprecedented adjacent-channel interference (ACI), which can jeopardize communication performance. In this paper, we propose a novel ACI-aware joint channel and power allocation framework that takes into account the receiver imperfections arising due to: 1) imperfect image frequency rejection and 2) analog-to-digital converter aliasing. The objective is to minimize the number of allocated channels and the aggregate power transmitted while satisfying the rate demands of different links in a multi-radio access technology environment. As the overall problem is NP-hard, we develop an efficient algorithm to solve it through decomposing the problem into two subproblems: channel allocation and power assignment. Then, it solves the decomposed problem by iteration using a two-phase structure. In Phase I, a particle swarm optimization-based algorithm is tailored to obtain good solutions for the channel allocation subproblem. Phase II solves the power allocation subproblem optimally given the channel allocation solution obtained in Phase I. The results demonstrate: 1) the criticality of receiver-characteristic awareness when designing resource allocation schemes and 2) the superiority of the proposed algorithm in solving the overall problem efficiently.

Scaled accuracy based power allocation for multi-target tracking with colocated MIMO radars

A colocated MIMO radar system can offer enhanced multi-target tracking (MTT) performance due to its agile multibeam working mode, in which multiple transmit beams are synthesized simultaneously using probing signals from individual colocated transmitters. To ensure that the limited transmitting power resource of a colocated MIMO radar is exploited effectively, in this paper, a scaled accuracy based power allocation framework (SA-PAF) is proposed for MTT. For a given total power budget, an overall MTT performance objective is optimized by adjusting the transmit power of each beam based on the feedback information in the tracking recursion cycle. An overall cost function (OCF), which is a combination of individual target cost functions (ITCF), is designed as the overall performance objective of the optimization problem. The necessity of ITCF is due to the fact that the impact of the state estimation error on the objective function should be different for each target due to its unique importance (or threat level) and the corresponding tracking accuracy requirement among multiple targets. The proposed OCF can quantify the global MTT performance while properly taking into account different target priorities through ITCFs. To formulate the OCF based framework as an optimization problem, we use the exponential function that employs the posterior Cramér–Rao lower bound (PCRLB) as the tracking performance measure as the instantiation of ITCF. It is shown that the proposed scheme is non-convex in the presence of a clutter. Then, the Zoutendijk method of feasible directions (ZMFD) is used to find the suboptimal power allocation solutions. Simulation results verify the superiority of the proposed power allocation algorithm in terms of achieving the effective overall performance of MTT while taking into account the differences targets priorities.

On Maximum of Energy-Efficiency in Amplify-and-Forward Relay Networks Under Channel Estimation Errors

In this paper, we investigate the energy-efficiency for amplify-and-forward relay networks in the presence of channel estimation errors. Two kinds of transmission strategies, i.e., direct transmission (DT) and relay transmission (RT), are dealt with for the energy-efficient communication system design. The energy cost function in DT or RT is defined as the total energy consumption for transmitting one bit data successfully, in which the channel estimation error, the channel estimation cost (i.e., energy consumed for pilot transmission) and the outage probability are included. As the power allocation plays a crucial role in implementing relay networks in an energy-efficient manner, we propose a theoretical framework for energy-efficient power allocation in DT and RT strategies to minimize the energy cost. By simulation, we compare the two transmission strategies according to the energy cost under various network scenarios. Our results provide several insights into the choice between DT and RT strategies if some transmission parameters are known.

A Game Theoretic Framework for Power Allocation in Full-Duplex Wireless Networks

A Game Theoretic Framework for Power Allocation in Full-Duplex Wireless Networks

Joint interference alignment and power allocation for NOMA-based multi-user MIMO systems

Interference alignment (IA) and non-orthogonal multiple access (NOMA) are key technologies for achieving the capacity scaling required by next generation networks to overcome the unprecedented growth of data network traffic. Each of these technologies was proved to present excellent performance for MIMO systems. In this article, we propose a joint IA and power allocation (PA) framework for NOMA-based multi-user MIMO (MU-MIMO) systems. Different approaches for applying IA in downlink NOMA-based MU-MIMO systems will be addressed while implementing a PA technique that fully exploits the characteristics of NOMA-based systems. The proposed framework aims to maximize the sum-rate of the NOMA-based MU-MIMO system through combining IA with PA. The process begins by initially grouping the system users into clusters for optimum implementation of NOMA. The sum-rate maximization is carried out under cluster power budget, user quality-of-service (QoS), and robust successive interference cancellation (SIC) constraints. Meanwhile, it uses the power domain multiplexing strategy to allow the users within each cluster to share the data streams without exerting interference to one another. Three iterative joint IA and PA algorithms are proposed for NOMA-based MU-MIMO systems. Moreover, these algorithms are compared with orthogonal multiple access (OMA)-based MU-MIMO counterpart as well as the state-of-the-art techniques presented for NOMA-based MU-MIMO systems. Numerical simulations verify that the proposed framework can greatly improve the performance of NOMA-based MU-MIMO systems in terms of the achievable sum-rate when compared with OMA-based MU-MIMO and the state-of-the-art NOMA-based MU-MIMO systems.

Unified Power Allocation for Receive Spatial Modulation Based on Approximate Optimization

In this paper, a novel unified power allocation (PA) framework is proposed for receive (pre-coding aided) spatial modulation (RSM). We find that the PA matrix design can be formulated as a non-convex quadratically constrained quadratic program (QCQP) problem, whose solution is generally intractable. To tackle this problem, we propose a pair of solvers having different trade-offs in terms of bit-error-rate and complexity. Specifically, we first propose a successive convex approximation (SCA) method, to convert the non-convex QCQP problem under consideration into a series of linear convex subproblems, where the latter can be easily solved by the classic polynomial-time-based optimization method, i.e., the interior point method. To further reduce the computational complexity, we propose an augmented Lagrangian multiplier (ALM) method, which transforms the challenging non-convex constrained PA optimization problem into its unconstrained counterpart, which can be efficiently solved by an iterative manner. Our simulation results show that both the proposed SCA and ALM methods are capable of substantially improving the system error performance compared with the conventional RSM system without PA as well as conventional PA-aided RSM schemes.

Self-Coexistence among IEEE 802.22 Networks: Distributed Allocation of Power and Channel.

Ensuring self-coexistence among IEEE 802.22 networks is a challenging problem owing to opportunistic access of incumbent-free radio resources by users in co-located networks. In this study, we propose a fully-distributed non-cooperative approach to ensure self-coexistence in downlink channels of IEEE 802.22 networks. We formulate the self-coexistence problem as a mixed-integer non-linear optimization problem for maximizing the network data rate, which is an NP-hard one. This work explores a sub-optimal solution by dividing the optimization problem into downlink channel allocation and power assignment sub-problems. Considering fairness, quality of service and minimum interference for customer-premises-equipment, we also develop a greedy algorithm for channel allocation and a non-cooperative game-theoretic framework for near-optimal power allocation. The base stations of networks are treated as players in a game, where they try to increase spectrum utilization by controlling power and reaching a Nash equilibrium point. We further develop a utility function for the game to increase the data rate by minimizing the transmission power and, subsequently, the interference from neighboring networks. A theoretical proof of the uniqueness and existence of the Nash equilibrium has been presented. Performance improvements in terms of data-rate with a degree of fairness compared to a cooperative branch-and-bound-based algorithm and a non-cooperative greedy approach have been shown through simulation studies.

Joint Rate Control and Power Allocation for Non-Orthogonal Multiple Access Systems

This paper investigates the optimal resource allocation of a downlink non-orthogonal multiple access (NOMA) system consisting of one base station and multiple users. Unlike existing short-term NOMA designs that focused on the resource allocation for only the current transmission timeslot, we aim to maximize a long-term network utility by jointly optimizing the data rate control at the network layer and the power allocation among multiple users at the physical layer, subject to practical constraints on both the short-term and long-term power consumptions. To solve this problem, we leverage the recently-developed Lyapunov optimization framework to convert the original long-term optimization problem into a series of online rate control and power allocation problems in each timeslot. The power allocation problem, however, is shown to be non-convex in nature and thus cannot be solved with a standard method. However, we explore two structures of the optimal solution and develop a dynamic programming based power allocation algorithm, which can derive a globally optimal solution, with a polynomial computational complexity. Extensive simulation results are provided to evaluate the performance of the proposed joint rate control and power allocation framework for NOMA systems, which demonstrate that the proposed NOMA design can significantly outperform multiple benchmark schemes, including orthogonal multiple access (OMA) schemes with optimal power allocation and NOMA schemes with non-optimal power allocation, in terms of average throughput and data delay.

A coalitional game‐based integrated framework for optimal power allocation in multirelay cooperative environment

SummaryCooperative relaying has emerged as an efficient transmit strategy in wireless networks affected by fading. In multirelay scenarios, the performance of cooperative systems is heavily influenced by resource allocation among the available relays. However, in practical scenarios, a relay may not help unless the cooperation improves its own utility. To address the above problem, this paper introduces a novel integrated game‐theoretic framework that involves incentive‐based optimal power allocation among the cooperating relays. Coalition formation game has been used to model the cooperation among relays that is integrated into a Stackelberg game, for considering the benefits of source and relays jointly. The game theoretic framework has been implemented with an objective to find an optimal and stable coalition set of relays that not only maximizes their utilities but also helps the source to allocate the optimal power to the relays at the optimal price, thereby increasing its utility too. Simulation results have confirmed that the proposed game theoretic solutions achieve comparable performance in system throughput as compared with the centralized approach.

Distributed Interference and Delay Aware Design for D2D Communication in Large Wireless Networks With Adaptive Interference Estimation

We investigate distributed flow control and power allocation strategies for delay-aware device-to-device (D2D) communication underlaying large wireless networks, where D2D pairs reuse the resource blocks of interior cellular users (CUEs). We consider a distributed D2D power allocation framework, where the D2D pairs individually attempt to maximize their own time-average throughput utility, while collectively guaranteeing the time-average coverage probability of CUEs in multiple cells. We design a novel method to compute the individual budget of interference from each D2D pair to CUEs based on stochastic geometry tools. Then, accounting for time-varying channel fading and dynamic D2D traffic arrival, we design a distributed interference-and-delay-aware (DIDA) flow control and power allocation strategy based on Lyapunov optimization and several interference estimation methods. We also analytically derive the performance bounds of D2D pairs, and prove that the coverage probability of CUEs can be guaranteed regardless of the interference estimation error at D2D receivers. Finally, simulation results suggest that adaptive interference estimation methods are preferred and demonstrate that the DIDA strategy achieves substantial performance improvement against alternative strategies.

A Unified Power-Allocation Framework for Bidirectional Cognitive Radio Communication

In this paper, we consider the power-allocation problem in orthogonal frequency-division multiplexing (OFDM)-based bidirectional cognitive radio (CR) relay networks. A joint optimization under the amplify-and-forward (AF) relaying protocol is formulated to maximize the throughput of secondary users (SUs) via power loading over different subcarriers at the three (two transceivers and a relay) nodes. For compatibility with practical systems, separate power constraints are considered at each transmitting node. Furthermore, to provide high-level protection to primary users (PUs) in underlay CR transmission, per-subcarrier-based interference constraints are assumed. For tractability of the solution, we adopt a decomposition framework where the joint power optimization (JPO) is split into two subproblems. Later, each subproblem is solved by exploiting the convex optimization techniques. Moreover, a low-complexity suboptimal solution of the joint power allocation at all nodes is also proposed. Simulation results are presented to validate the effectiveness of the proposed schemes.

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.

Resource Optimization in Multi-Tier HetNets Exploiting Multi-Slope Path Loss Model

Current resource allocation techniques in cellular networks are largely based on single-slope path loss model, which falls short in accurately capturing the effect of physical environment. The phenomenon of densification makes cell patterns more irregular; therefore, the multi-slope path loss model is more realistic to approximate the increased variations in the links and interferences. In this paper, we investigate the impacts of multi-slope path loss models, where different link distances are characterized by different path loss exponents. We propose a framework for joint user association, power and subcarrier allocation on the downlink of a heterogeneous network (HetNet). The proposed scheme is formulated as a weighted sum rate maximization problem, ensuring the users’ quality-of-service requirements, namely users’ minimum rate, and the base stations’ (BSs) maximum transmission power. We then compare the performance of the proposed approach under different path loss models with demonstrate the effectiveness of dual-slope path loss model in comparison to the single-slope path loss model. Simulation results show that the dual-slope model leads to significant improvement in network’s performance in comparison to the standard single-slope model by accurately approximating the path loss exponent dependence on the link distance. Moreover, it improves the user offloading from macrocell BS to small cells by connecting the users to nearby BSs with minimal attenuation. It has been shown that the path loss exponents significantly influence the user association lying across the critical radius in the case of the dual-slope path loss model.

A Stochastic Optimization Framework for Adaptive Spectrum Access and Power Allocation in Licensed-Assisted Access Networks

Licensed-assisted access (LAA) has been becoming a promising technology to the supplementary utilization of the unlicensed spectrum. However, due to the densification of small base stations (SBSs) and the dynamic variation of the number of Wi-Fi nodes in the overlapping areas, the licensed channel interference and the unlicensed channel collision could seriously influence the quality of service and the energy consumption. In this paper, jointly considering time-variant multi-wireless-channel conditions and random numbers of Wi-Fi nodes, we address an adaptive spectrum access and power allocation problem that enables the minimization of the system power consumption under a certain queue stability constraint in the LAA-enabled SBSs and Wi-Fi networks. The complex stochastic optimization problem has been decomposed as a modified Hungarian algorithm and a difference of two convex algorithm in the framework of Lyapunov optimization. We also characterize the performance bounds of the proposed algorithm with a tradeoff of [O(1/V), O(V)] between power consumption and delay theoretically. The numerical results verify the tradeoff and show that our scheme can reduce the power consumption over the existing scheme by up to 73.3% under the same traffic delay.

An energy efficient framework for user association and power allocation in HetNets with interference and rate-loss constraints

Dense deployment of femtocells has proved to be an effective solution to handle increasing demands of indoor mobile data. A femtocell not only helps reducing operational and capital expenditure but also improves the energy efficiency of the network. Femtocells are able to increase spectrum efficiency by manyfold by reusing the available spectrum for indoor users. However, it has been seen that traditional cell selection schemes limit the user count under femtocell. Additionally, dense deployment of femtocells comes with the cost of increased interference to the neighbouring femtocell and macrocell users. In this paper, we first analyse various cell selection schemes to improve user association and resource utilization in femtocells. Then, we focus on improving energy efficiency and throughput of femtocell based cellular networks. For this, we formulate an optimization problem that efficiently reuses macrocell spectrum in femtocells with power control while satisfying macrocell users’ interference and rate-loss constraints.

JOINT POWER ALLOCATION FOR DF CONCATENATED MIMO SUCCESSIVE RELAYING SCHEME UNDER NETWORK POWER CONSTRAINTS

Power efficiency is a vital consideration in wireless system. In this paper, we propose a framework for efficient power allocation in decode and forward multiple input multiple output successive relaying systems under network power constraints. Our aim is to maximize the information rate at each link by an optimal power allocation scheme via the primal dual algorithm. Then, we jointly allocate power to the source and transmitting relay under network power constraints. The simulated results show that the proposed joint power allocation scheme under network power constraint can outperform the uniform power allocation under an aggregate power constraints.

Radio resource allocation framework for quality of experience optimization in wireless networks

This article presents a QoE-aware resource assignment and power allocation framework aiming at maximizing the minimum MOS of the users in a wireless network. It considers a satisfaction factor, that is, a requirement related to the minimum number of users that should be satisfied. This requirement is an important aspect from the operators’ point of view and has not yet been studied in this context, as far as we know. The proposed method is evaluated by means of simulations, and the obtained results show that it outperforms existing solutions, and performs close to the optimal solution as well. The performance evaluation demonstrates that the satisfaction factor is a good solution to deal with heavily loaded systems.

Energy-Efficient Power Allocation for Underlay Cognitive Radio Systems

We present a power allocation framework for spectrum sharing cognitive radio (CR) systems based on maximizing the energy efficiency (EE). First, we show that the relation between the EE and the spectral efficiency (SE) is strictly increasing in contrast with the SE–EE tradeoff discussed in the literature. We also solve a nonconvex problem and explicitly derive the optimal power for the proposed average EE under either a peak or an average power constraint. We apply our results to the underlay CR systems where the power is limited by an additional interference constraint. When the instantaneous channel is not available, we provide a necessary and sufficient condition for the optimal power and present a simple suboptimal power. In the numerical results, we show that the proposed EE corresponds to a higher SE at midrange and high-power regime compared to the classical EE. We also show that the suboptimal solution is very close to the optimal solution. In addition, we deduce that the absence of instantaneous CSI affects the EE and the SE at high power regime compared to full CSI. In the CR context, we show that the interference threshold has a minimal effect on the EE compared to the SE.

Uplink Decentralized Joint Bandwidth and Power Allocation for Energy-Efficient Operation in a Heterogeneous Wireless Medium

In this paper, energy efficient uplink communications are investigated for battery-constrained mobile terminals (MTs) with service quality requirements and multi-homing capabilities. A heterogeneous wireless medium is considered, where MTs communicate with base stations (BSs) and access points (APs) of different networks with overlapped coverage. Different from the existing works, we develop a quality of service (QoS)-based optimization framework for joint uplink bandwidth and power allocation to maximize energy efficiency for a set of MTs with multi-homing capabilities. The proposed framework is implemented in a decentralized architecture, through coordination among BSs/APs of different networks and MTs, which is a desirable feature when different networks are operated by different service providers. A suboptimal framework is presented with a reduced computational complexity as compared with the optimal framework. Simulation results demonstrate the improved performance of both the optimal and suboptimal frameworks over a state-of-the-art benchmark.

Pricing-Based Interference Coordination for D2D Communications in Cellular Networks

A pricing-based joint spectrum and power allocation framework is proposed for decentralized interference coordination among device-to-device (D2D) communications and cellular users (CUs), with the quality-of-service guarantee. The interlayer interference from D2D pairs to CUs is controlled by the base station through setting a price for each D2D channel usage. The intralayer interference among D2D pairs is mitigated distributively using a game-theoretic approach, where the D2D pairs compete for the spectrum until a Nash equilibrium is achieved. The effectiveness of the proposed strategy, including a practical scheme with limited signaling overhead, is demonstrated through comparing with a centralized scheme.