Power-efficient Resource Allocation Research Articles

On Power-Efficient Low-Complexity Adaptation for D2D Resource Allocation with Interference Cancelation.

This paper presents a detailed framework for adaptive low-complexity and power-efficient resource allocation in decentralized device-to-device (D2D) networks. The adopted system model considers that active devices can directly communicate via specified signaling channels. Each D2D receiver attempts to allocate its D2D resources by selecting a D2D transmitter and one of its spectral channels that can meet its performance target. The process is performed adaptively over successive packet durations with the objective of limiting the transmit power on D2D links while reducing the processing complexity. The proposed D2D link adaptation scheme is modeled and analyzed under generalized channel conditions. It considers the random impact of potential D2D transmitters as well as the random number of co-channel interference sources on each D2D link. Interference cancelation schemes are also addressed to alleviate co-channel interference, which can ease the D2D resource allocation process. Generalized formulations for the statistics of the resulting signal-to-interference plus noise ratio (SINR) of the proposed adaptation scheme are presented. Moreover, generic analytical results were developed for some important performance measures as well as processing load measures. They facilitate tradeoff studies between the achieved performance and the processing complexity of the proposed scheme. Insightful results for the distributions of SINRs on individual D2D links under specific fading models are shown in this paper. The results herein add enhancements to some previous contributions and can handle various practical constraints.

Power efficient UAV placement and resource allocation for adaptive video streaming in wireless networks

Unmanned aerial vehicle (UAV) enabled wireless communication that can transmit/receive data to/from ground users, especially at cell boundaries or in disaster-stricken areas where the ground-based infrastructure has been damaged, is an emerging area of research. These aerial vehicles require energy for communication and are typically energy constrained. The communication system becomes more energy hungry when it comes to video streaming of high quality. Because for higher video quality streaming, higher bitrate and more bandwidth is required. Higher transmission power and rate will result in increased streaming quality. We concentrate on UAV-aided power efficient adaptive video streaming wireless networks with multiple ground users, taking into account the available energy budget and information causality constraints. The objective of this research work is to maximize adaptive video streaming utility per unit of power in a UAV-enabled wireless communication network by optimizing the position of UAV and allocation of communication resources, such as bandwidth, and transmit power, while competing with system-designed constraints, such as energy budget, transmission rate, and allocated bandwidth. The problem that is addressed is non-convex which is approached by applying successive convex approximation, Dinkelbach method and coordinates decent techniques, and an efficient iterative algorithm is devised for its solution. Extensive simulations show that the suggested optimization strategy outperforms the results of the previously proposed works.

A Learning Approach for Joint Design of Event-Triggered Control and Power-Efficient Resource Allocation

In emerging Industrial Cyber-Physical Systems (ICPSs), the joint design of communication and control sub-systems is essential, as these sub-systems are interconnected. In this paper, we study the joint design problem of an event-triggered control and an energy-efficient resource allocation in a fifth generation (5G) wireless network. We formally state the problem as a multi-objective optimization one, aiming to minimize the number of updates on the actuators' input and the power consumption in the downlink transmission. To address the problem, we propose a model-free hierarchical reinforcement learning approach \textcolor{blue}{with uniformly ultimate boundedness stability guarantee} that learns four policies simultaneously. These policies contain an update time policy on the actuators' input, a control policy, and energy-efficient sub-carrier and power allocation policies. Our simulation results show that the proposed approach can properly control a simulated ICPS and significantly decrease the number of updates on the actuators' input as well as the downlink power consumption.

ReCARL: Resource Allocation in Cloud RANs with Deep Reinforcement Learning

Cloud Radio Access Networks (CRANs) have become a key enabling technique for the next generation wireless communications. Resource allocation in CRANs still needs to be further improved to reach the objective of minimizing power consumption and meeting demands of wireless users over a long period. Inspired by the success of Deep Reinforcement Learning (DRL) on solving complicated control problems, we present a novel framework, ReCARL, for power-efficient resource allocation in CRANs with deep reinforcement learning. Specifically, we define the state space, action space and reward function for the DRL agent, apply a Deep Neural Network (DNN) to approximating the action-value function, and formally formulate the resource allocation problem (in each decision epoch) as a convex optimization problem. Under ReCARL, we propose two different DRL agents: one has a regular DNN structure trained with the basic deep Q-learning method (ReCARL-Basic); while the other has a context-aware DNN structure trained with a hybrid deep Q-learning method (ReCARL-Hybrid). We evaluated the performance of ReCARL along with the two DRL agents by comparing them with two widely-used baselines via extensive simulation. The simulation results show that ReCARL achieves significant power savings while meeting user demands, and it can well handle highly dynamic cases.

Power-Efficient Resource Allocation in C-RANs With SINR Constraints and Deadlines

In this paper, we address the problem of power-efficient resource management in Cloud Radio Access Networks (C-RANs). Specifically, we consider the case where Remote Radio Heads (RRHs) perform data transmission, and signal processing is executed in a virtually centralized Base-Band Units (BBUs) pool. Users request to transmit at different time instants; they demand minimum signal-to-noise-plus-interference ratio (SINR) guarantees, and their requests must be accommodated within a given deadline. These constraints pose significant challenges to the management of C-RANs and, as we will show, considerably impact the allocation of processing and radio resources in the network. Accordingly, we analyze the power consumption of the C-RAN system, and we formulate the power consumption minimization problem as a weighted joint scheduling of processing and power allocation problem for C-RANs with minimum SINR and finite horizon constraints. The problem is a Mixed Integer Non-Linear Program (MINLP), and we propose an optimal offline solution based on Dynamic Programming (DP). We show that the optimal solution is of exponential complexity, thus we propose a sub-optimal greedy online algorithm of polynomial complexity. We assess the performance of the two proposed solutions through extensive numerical results. Our solution aims to reach an appropriate trade-off between minimizing the power consumption and maximizing the percentage of satisfied users. We show that it results in power consumption that is only marginally higher than the optimum, at significantly lower complexity.

Optimal Resource Allocation for Power-Efficient MC-NOMA With Imperfect Channel State Information

In this paper, we study power-efficient resource allocation for multicarrier non-orthogonal multiple access (MC-NOMA) systems. The resource allocation algorithm design is formulated as a non-convex optimization problem which jointly designs the power allocation, rate allocation, user scheduling, and successive interference cancellation (SIC) decoding policy for minimizing the total transmit power. The proposed framework takes into account the imperfection of channel state information at transmitter (CSIT) and quality of service (QoS) requirements of users. To facilitate the design of optimal SIC decoding policy on each subcarrier, we define a channel-to-noise ratio outage threshold. Subsequently, the considered non-convex optimization problem is recast as a generalized linear multiplicative programming problem, for which a globally optimal solution is obtained via employing the branch-and-bound approach. The optimal resource allocation policy serves as a system performance benchmark due to its high computational complexity. To strike a balance between system performance and computational complexity, we propose a suboptimal iterative resource allocation algorithm based on difference of convex programming. Simulation results demonstrate that the suboptimal scheme achieves a close-to-optimal performance. Also, both proposed schemes provide significant transmit power savings than that of conventional orthogonal multiple access (OMA) schemes.

Power-Efficient Transceiver Design for Full-Duplex MIMO Multi-Cell Systems With CSI Uncertainty

With increasing emphasis on incorporating energy awareness in future communication systems, it is desirable to explore power-efficient resource allocation techniques. Therefore, in this paper, we consider the sum-power minimization of base stations (BSs) and users in a full-duplex (FD) multiple-input multiple-output multi-cell system. In particular, we assume that BSs operating in FD transmission mode serve multiple FD mobile users at the same time over the same frequency band. To guarantee a certain quality of service (QoS), we enforce the maintenance of a minimum signal-to-interference-plus-noise ratio for each user. Concerning these design constraints together with realistic FD self-interference models, we investigate the transmit and receive beamforming designs that minimize the joint transmission power of BSs and users. However, the resulting optimization problem is NP-hard. We therefore divide this optimization problem into separate receive and transmit beamforming design steps, which can be solved iteratively. In addition, the non-convex precoder design problem is posed as a difference of convex function programming, which can be efficiently solved via successive convex approximation. In order to account for practical aspects in our design, we also take into account imperfect channel state information by way of stochastic and bounded uncertainties. Numerical results suggest that the FD systems generally outperform the half-duplex ones under a wide range of QoS constraints and transceiver distortions.

Power-Efficient Resource Allocation in a Heterogeneous Network With Cellular and D2D Capabilities

This paper focuses on a heterogeneous scenario in which cellular and wireless local area technologies coexist and mobile devices are enabled with device-to-device (D2D) communication capabilities. In this context, this paper assumes a network architecture in which a given user equipment (UE) can receive mobile service either by connecting directly to a cellular base station (BS) or by connecting through another UE that acts as an access point (AP) and relays the traffic from a cellular BS. This paper investigates the optimization of the connectivity of different UEs with the target to minimize the total transmission power. An optimization framework is presented, and a distributed strategy based on Q-learning and softmax decision-making is proposed as a means to solve the considered problem with reduced complexity. The proposed strategy is evaluated under different conditions, and it is shown that the strategy achieves a performance very close to the optimum. Moreover, significant transmission power reductions of approximately 40% are obtained with respect to the classical approach, in which all UEs are connected to the cellular infrastructure. For multicell scenarios, in which the optimum solution cannot be easily known a priori , the proposed approach is compared against a centralized genetic algorithm. The proposed approach achieves similar performance in terms of total transmitted power while exhibiting much lower computational requirements.

Multi-Objective Optimization for Robust Power Efficient and Secure Full-Duplex Wireless Communication Systems

In this paper, we investigate the power efficient resource allocation algorithm design for secure multiuser wireless communication systems employing a full-duplex (FD) base station (BS) for serving multiple half-duplex (HD) downlink (DL) and uplink (UL) users simultaneously. We propose a multi-objective optimization framework to study two conflicting yet desirable design objectives, i.e., total DL transmit power minimization and total UL transmit power minimization. To this end, the weighed Tchebycheff method is adopted to formulate the resource allocation algorithm design as a multi-objective optimization problem (MOOP). The considered MOOP takes into account the quality-of-service (QoS) requirements of all legitimate users for guaranteeing secure DL and UL transmission in the presence of potential eavesdroppers. Thereby, secure UL transmission is enabled by the FD BS and would not be possible with an HD BS. The imperfectness of the channel state information of the eavesdropping channels and the inter-user interference channels is incorporated for robust resource allocation algorithm design. Although the considered MOOP is non-convex, we solve it optimally by semidefinite programming (SDP) relaxation. Simulation results not only unveil the trade-off between the total DL transmit power and the total UL transmit power, but also confirm the robustness of the proposed algorithm against potential eavesdroppers.

Power headroom report-based uplink power control in 3GPP LTE-A HetNet

In a 3rd Generation Partnership Project Long Term Evolution-Advanced (3GPP LTE-A) uplink, user equipment (UE) has a maximum transmission power limit defined by the UE power class. Generally, the cell edge UE has a higher probability to be constrained by the maximum transmission power level owing to the compensation of the large pathloss. When the UE transmission power is constrained by the maximum level, allocating a higher number of physical resource blocks (PRBs) than the UE power capability can afford will reduce the transmission power to be allocated per PRB, resulting in inefficient use of power resources. To avoid this power inefficiency, the uplink transmission power can be controlled according to the number of PRBs allocated using the power headroom report-based power efficient resource allocation (PHR-PERA) scheme proposed in this paper. Furthermore, adaptive open-loop power control (OL-PC) based on the signal-to-interference-plus-noise ratio (SINR) and the uplink interference is used to improve the cell capacity. By the uplink power control employing the proposed PHR-PERA scheme, the macro and femto UE throughputs were increased by 49.9 and 5 %, respectively, compared to the case of conventional fractional power control (FPC). Additional gains of 21.9 and 4.8 % for macro and femto UE throughputs, respectively, were achieved by adaptive OL-PC. The performance of fast closed-loop power control (CL-PC) based on the received SINR is also evaluated in this paper. The simulation results demonstrate that CL-PC supports OL-PC by compensating the fading effect for the UE uplink SINR to meet the target SINR.

Power-Efficient Resource Allocation and Quantization for TDMA Using Adaptive Transmission and Limited-Rate Feedback

Power-efficient scheduling and resource allocation are critical tasks for wireless sensor networks as well as commercial and tactical radios relying on IEEE access standards for power-limited communications. Tailored for such applications, this paper formulates and solves analytically novel convex optimization problems offering globally optimal user scheduling, as well as rate and power allocation for time-division multiple access (TDMA) in time-division-duplex or frequency-division-duplex operation. Through a limited-rate feedback link the access point provides quantized channel state information to the transmitters (Q-CSIT) based on which users adapt their modulation and code choices to the intended fading channel. When the quantizer needed to form the Q-CSIT is not prescribed, a joint allocation-quantization scheme is devised to minimize average transmit power subject to average rate and bit error rate constraints. The novel design couples adaptive transmission modes with quantization regions which are constructed to attain at least a local minimum of the average transmit power. Fairness in resource allocation is guaranteed by design. Transmit power and quantization region books are efficiently obtained offline while the online Q-CSIT based operation turns out to entail only a few feedback bits. Analysis and simulations include a perfect CSIT benchmark and reveal substantial power savings (as high as 15 dB) with low-overhead feedback.