Efficient power allocation and interference management are critical challenges in dynamic wireless communication systems. To address these challenges, graph neural networks (GNNs) have attracted significant attention, while knowledge graph further enhance this capability by representing structured interactions among entities. This article proposes the Power-focused Knowledge Graph Convolutional Network (PKGCN), a novel framework utilizing knowledge graph driven learning to model and optimize power allocation strategies. By integrating wireless-specific features such as channel conditions and interference metrics, PKGCN effectively captures the complex interactions and dependencies among network nodes. This model employs a message aggregation layer to extract local and global interactions and a power prediction layer to optimize resource allocation. Comprehensive evaluations reveal that PKGCN de-livers higher average user rates, lower interference levels, and greater robustness.

Multi-access edge computing (MEC) emerges as an effective computational paradigm. It meets the low latency and low energy consumption requirements of users by enabling user terminals (UE) to offload their computationally intensive applications to nearby access points (AP). However, the integration of Virtual Reality (VR) applications within MEC environments remains underexplored, primarily due to their structural complexity and high computational requirements for offloading and scheduling. To address these challenges, we use Directed Acyclic Graph (DAG) to model VR applications and 6G-oriented Rate-Splitting Multiple Access (RSMA) to enhance offloading and scheduling processes of VR applications. The objective is to jointly optimize the offloading strategy, transmit power, RSMA decoding order, and UE application scheduling to minimize the total system cost. Recognizing the limitations of traditional learning-based methods, which struggle with convergence in multi-user scenarios, we decompose the optimization problem into two subproblems: application offloading and application scheduling. We then propose the PPOCO algorithm, which integrates reinforcement learning with convex optimization to effectively solve these subproblems independently. Experimental results demonstrate that our proposed method consistently out-performs baseline algorithms in reducing the total system cost across various MEC network configurations.

Immersive communication, including emerging augmented reality, virtual reality, and holographic telepresence, has been identified as a key service for enabling next-generation wireless applications. To align with legacy wireless applications, such as enhanced mobile broadband or ultra-reliable low-latency communication, network slicing has been widely adopted. However, attempting to statistically isolate the above types of wireless applications through different network slices may lead to throughput degradation and increased queue backlog. To address these challenges, we establish a unified QoS-aware framework that supports immersive communication and legacy wireless applications simultaneously. Based on the Lyapunov drift theorem, we transform the original long-term throughput maximization problem into an equivalent short-term throughput maximization weighted by virtual queue length. Moreover, to cope with the challenges introduced by the interaction between large-timescale network slicing and short-timescale resource allocation, we propose an adaptive adversarial slicing (Ad2S) scheme for networks with invarying channel statistics. To track the network channel variations, we also propose a measurement extrapolation-Kalman filter (ME-KF)-based method and refine our scheme into Ad2S-non-stationary refinement (Ad2S-NR). Through extended numerical examples, we demonstrate that our proposed schemes achieve 3.86 Mbps throughput improvement and 63.96% latency reduction with 24.36% convergence time reduction. Within our framework, the trade-off between total throughput and user service experience can be achieved by tuning systematic parameters.

The rapid and substantial fluctuations in wireless network capacity and traffic demand, driven by the emergence of 6G technologies, have exacerbated the issue of traffic-capacity mismatch, raising concerns about wireless network energy consumption. To address this challenge, we propose a model-data dual-driven resource allocation (MDDRA) algorithm aimed at maximizing the integrated relative energy efficiency (IREE) metric under dynamic traffic conditions. Unlike conventional model-driven or data-driven schemes, the proposed MDDRA framework employs a model-driven Lyapunov queue to accumulate long-term historical mismatch information and a data-driven Graph Radial bAsis Fourier (GRAF) network to predict the traffic variations under incomplete data, and hence eliminates the reliance on high-precision models and complete spatial-temporal traffic data. We establish the universal approximation property of the proposed GRAF network and provide convergence and complexity analysis for the MDDRA algorithm. Numerical experiments validate the performance gains achieved through the data-driven and model-driven components. By analyzing IREE and EE curves under diverse traffic conditions, we recommend that network operators shall spend more efforts to balance the traffic demand and the network capacity distribution to ensure the network performance, particularly in scenarios with large speed limits and higher driving visibility.

This paper presents a novel two-stage deep reinforcement learning (DRL) algorithm built on a Transformer Encoder-based Deep Deterministic Policy Gradient (TEDDPG) framework, named TS-TEDDPG, which jointly optimizes the learning latency, energy consumption and model accuracy of Asynchronous Federated Learning (AFL) systems with prescribed security. The CPU configuration of local training and the transmit power of model uploading are learnt by the TEDDPG in the first stage. A linear programming-based device scheduling and cooperative jamming strategy is designed to efficiently optimize the rest of the decisions in the second stage and evaluates the immediate reward to train the TEDDPG. Experimental results based on a CNN model and the MNIST dataset demonstrate that the proposed TS-TEDDPG can reduce the training latency and energy consumption by 68.6% compared to its benchmarks, when the required test accuracy is 0.9.

This paper studies the potential of tightly coupling the Internet-of-Things (IoT) and smart grids for effective management of energy. A new approach is presented to minimize energy costs for IoT devices and edge servers, and reduce reliance on non-renewable energy by diversifying power supply. Rechargeable batteries at end devices are considered for holistic energy management of the system. We jointly optimize the transmit powers and battery (dis)charging decisions of the devices, the receive beamformer of the edge servers, and the dynamic generation of different energy types. The alternating direction method of multipliers (ADMM) is applied to support distributed optimization of (dis)charging decisions at individual devices. The Karush–Kuhn–Tucker (KKT) conditions are applied to deliver semi-closed-form power control of the devices. Simulations demonstrate significant improvement of the algorithm in renewable energy utilization and cost saving, compared to the existing techniques.

In order to provide design guidelines for energy efficient 6G networks, we propose a novel radial basis function (RBF) based optimization framework to maximize the integrated relative energy efficiency (IREE) metric. Different from the conventional energy efficient optimization schemes, we maximize the transformed utility for any given IREE using spectrum efficiency oriented RBF network and gradually update the IREE metric using proposed Dinkelbach’s algorithm. The existence and uniqueness properties of RBF networks are provided, and the convergence conditions of the entire framework are discussed as well. Through some numerical experiments, we show that the proposed IREE outperforms many existing SE or EE oriented designs and find a new Jensen-Shannon (JS) divergence constrained region, which behaves differently from the conventional EE-SE region. Meanwhile, by studying IREE-SE trade-offs under different traffic requirements, we suggest that network operators shall spend more efforts to balance the distributions of traffic demands and network capacities in order to improve the IREE performance, especially when the spatial variations of the traffic distribution are significant.

Federated learning (FL) is a viable technique to train a shared machine learning model without sharing data. Hierarchical FL (HFL) system has yet to be studied regrading its multiple levels of energy, computation, communication, and client scheduling, especially when it comes to clients relying on energy harvesting to power their operations. This paper presents a new two-phase deep deterministic policy gradient (DDPG) framework, referred to as “TP-DDPG”, to balance online the learning delay and model accuracy of an FL process in an energy harvesting-powered HFL system. The key idea is that we divide optimization decisions into two groups, and employ DDPG to learn one group in the first phase, while interpreting the other group as part of the environment to provide rewards for training the DDPG in the second phase. Specifically, the DDPG learns the selection of participating clients, and their CPU configurations and the transmission powers. A new straggler-aware client association and bandwidth allocation (SCABA) algorithm efficiently optimizes the other decisions and evaluates the reward for the DDPG. Experiments demonstrate that with substantially reduced number of learnable parameters, the TP-DDPG can quickly converge to effective polices that can shorten the training time of HFL by 39.4% compared to its benchmarks, when the required test accuracy of HFL is 0.9.

Extended reality (XR) is one of the most important applications of beyond 5G and 6G networks. Real-time XR video transmission presents challenges in terms of data rate and delay. In particular, the frame-by-frame transmission mode of XR video makes real-time XR video very sensitive to dynamic network environments. To improve the users’ quality of experience (QoE), we design a cross-layer transmission framework for real-time XR video. The proposed framework allows the simple information exchange between the base station (BS) and the XR server, which assists in adaptive bitrate and wireless resource scheduling. We utilize the cross-layer information to formulate the problem of maximizing user QoE by finding the optimal scheduling and bitrate adjustment strategies. To address the issue of mismatched time scales between two strategies, we decouple the original problem and solve them individually using a multi-agent-based approach. Specifically, we propose the multi-step Deep Q-network (MS-DQN) algorithm to obtain a frame-priority-based wireless resource scheduling strategy and then propose the Transformer-based Proximal Policy Optimization (TPPO) algorithm for video bitrate adaptation. The experimental results show that the TPPO+MS-DQN algorithm proposed in this study can improve the QoE by 3.6% to 37.8%. More specifically, the proposed MS-DQN algorithm enhances the transmission quality by 49.9%-80.2%.

Channel estimation is crucial to modern wireless systems and becomes increasingly challenging when the ultra-sized antenna is configured in sub-6GHz wireless communication systems. In an ultra-massive multiple-input multiple-output (U-MIMO) orthogonal frequency division multiplex (OFDM) system, the channel demonstrates spatial non-stationarity. Additionally, the limited pilot location in the OFDM system further complicates the channel estimation process. In this paper, we propose a model-data dual-driven (MDD) scheme to jointly perform the model-driven non-stationary channel denoising and the data-driven channel interpolation in an end-to-end way, which is followed by a low-complexity channel refinement module to improve the robustness of the proposed scheme. Specifically, image contour extraction (ICE) is utilized to effectively eliminate the non-stationary noises in the channel matrices before being sent to the downstream interpolation network. An enhanced convolutional neural network (CNN)-based residual network (eCNN-RN) is developed to perform non-linear interpolations for recovering the U-MIMO-OFDM channels. Based on ICE, the proposed online refinement module can improve the generalizability of the learned model to a practical environment. Numerical experiments demonstrate the efficiency and the effectiveness of the cross-fertilization of the model-driven and data-driven approaches.

The advent of online video services has resulted in a remarkable surge in Internet traffic, prompting the need for mobile edge computing (MEC) as a crucial element in augmenting the quality of adaptive streaming media services amidst the time-varying wireless channels. MEC reduces network backhaul traffic by providing video transcoding and adaptive streaming services closer to users. Nonetheless, the process of video transcoding introduces additional latency and energy consumption. In order to effectively tackle this challenge and uphold the optimal quality of experience (QoE), we propose the Joint Bitrate Transcoding and Parallel Cooperative Transmission (JBTPCT) model, which operates at the edge of mobile networks and handles multiple video chunks simultaneously. Within the JBTPCT model, the Asynchronous Advantage Actor-Critic (A3C) algorithm framework is employed to jointly account for radio access network conditions and MEC resources, leveraging a parallel execution strategy for transmission and transcoding. This integrated approach aims to minimize both latency and energy consumption while enhancing the QoE of video streaming. We evaluate the average QoE of JBTPCT in different network scenarios, and the experimental results demonstrate that JBTPCT consistently achieves higher average QoE compared to competing algorithms.

As a core performance metric for green communications, the conventional energy efficiency (EE) definition has successfully resolved many issues in the energy-efficient wireless network design. In the past several generations of wireless communication networks, the traditional EE measure plays an important role to guide many energy-saving techniques for slow varying traffic profiles. However, for the next-generation wireless networks, the traditional EE fails to capture the traffic and capacity variations of wireless networks in temporal or spatial domains, which is shown to be quite popular, especially with ultrascale multiple antennas and space–air–ground integrated network (SAGIN). In this article, we present a novel EE metric named integrated relative EE (IREE), which is able to jointly measure the traffic profiles and the network capacities from the EE perspective. On top of that, the IREE-based green tradeoffs have been investigated and compared with the conventional energy-efficient design. Moreover, we apply the IREE-based green tradeoffs to evaluate several candidate technologies for 6G networks, including reconfigurable intelligent surfaces and SAGIN. Through some analytical and numerical results, we show that the proposed IREE metric is able to capture the wireless traffic and capacity mismatch property, which is significantly different from the conventional EE metric. Since the IREE-oriented design or deployment strategy is able to consider the network capacity improvement and the wireless traffic matching simultaneously, it can be regarded as a useful guidance for future energy-efficient network design.

Mobile edge computing (MEC) offers an opportunity for devices relying on wireless power transfer (WPT), to accomplish computationally demanding tasks. Such WPT-powered MEC systems have yet to be optimized for long-term efficiency, due to random and changing task demands and wireless channel states of the devices. This paper presents an augmented two-staged deep Q-network (DQN), referred to as “TS-DQN,” for online optimization of WPT-powered MEC systems, where the WPT, offloading schedule, channel allocation, and the CPU configurations of the edge server and devices are jointly optimized to minimize the long-term average energy requirement of the systems. The key idea is to design a DQN for learning the channel allocation and task admission, while the WPT, offloading time and CPU configurations are efficiently optimized to precisely evaluate the reward of the DQN and substantially reduce its action space. Another important aspect is that a new action generation method is developed to expand and diversify the actions of the DQN, further accelerating its convergence. As validated by simulations, the proposed TS-DQN is much more energy efficient and converges much faster, than its potential alternative directly using the state-of-the-art Deep Deterministic Policy Gradient algorithm to learn all decision variables.

Pre-distortion is a key technique to compensate for the nonlinear distortions caused by the transmitter in wireless communication systems. Generally, pre-distortion can be classified into digital pre-distortion (DPD) and analog pre-distortion (APD), which focus on optimizing and assessing the nonlinearity in their own areas. In this paper, we propose a new DPD approach to optimize the performance metric of the analog RF-domain (i.e., inter-modulation distortion (IMD) or adjacent channel power ratio (ACPR)) and that of the digital IF-domain (i.e., mean square error (MSE)) simultaneously. To make the joint design feasible, we derive a new hybrid performance metric, where the analog preferred metric is defined in the form of digital signals to bridge the gap between digital and analog signal processing. On top of that, an effective DPD scheme is developed based on a new dual time-delayed neural network (TDNN) learning architecture. The coefficients of the TDNN for power amplifier (PA) modeling can be trained offline with a PA dataset, while those of the TDNN for pre-distortion are obtained adaptively by optimizing the proposed joint design metric. Experimental results show that the proposed scheme is able to significantly improve the IMD/ACPR performance without compromising the MSE, compared to conventional DPD schemes.

The high computational complexity and high energy consumption of artificial intelligence (AI) algorithms hinder their application in augmented reality (AR) systems. This paper considers the scene of completing video-based AI inference tasks in the mobile edge computing (MEC) system. We use multiply-and-accumulate operations (MACs) for problem analysis and optimize delay and energy consumption under accuracy constraints. To solve this problem, we first assume that offloading policy is known and decouple the problem into two subproblems. After solving these two subproblems, we propose an iterative-based scheduling algorithm to obtain the optimal offloading policy. We also experimentally discuss the relationship between delay, energy consumption, and inference accuracy.

This paper presents a new two-stage deep Q-network (DQN), referred to as "TS-DQN", for online optimization of wireless power transfer (WPT)-powered mobile edge computing (MEC) systems, where the WPT, offloading schedule, channel allocation, and the CPU configurations of the edge server and devices are jointly optimized to minimize the long-term average energy requirement of the systems. The key idea is to design a DQN to learn the channel allocation and task admission, while the WPT, offloading time and CPU configurations are efficiently optimized to precisely evaluate the reward of the DQN and substantially reduce its action space. A new action generation method is developed to expand and diversify the actions of the DQN, hence further accelerating its convergence. Simulation shows that the gain of the TS-DQN in energy saving is nearly 60% compared to its potential alternatives.

As interference becomes one of the key factors restricting the performance of wireless network, many interference cancellation schemes are studied. However, most of these schemes have disadvantages in one way or another, such as high complexity and cost of the accurate feedback. In this case, blind interference cancellation schemes are proposed, which can eliminate the interference according to the received signal without any prior information, but with a very high searching complexity. To solve above issues, we propose a semi-blind interference parameter detection (Semi-BIPD) and signal restoration scheme in this paper. Firstly, a semi-blind interference detection module is investigated to detect the parameters related to strong inter-ference with the help of the received demodulation reference signal (DM-RS) sequence. Then, the channel parameters of both target user and interfering users are estimated. Finally, the signal restoration based on Semi-BIPD is conducted in the data sequence to eliminate the interference. Simulation results demonstrate that the proposed scheme can achieve better mean square error (MSE) with a low searching complexity in 5G uplink orthogonal frequency division multiplexing (OFDM) systems.

Edge infrastructures, including edge computing servers, are increasingly powered by renewable energy and smart grid combined. Two-way energy trading allows the surplus or shortfall of renewable energy to be traded between a server and the smart grid, but is non-trivial due to randomly varying computation demands and renewables. This paper proposes a new online policy, namely, distributed online resource allocation and load management (DORL), which enables such an edge server and its serving devices to minimize their energy cost and energy consumption, respectively, in a fully distributed manner. The key idea is that we employ the stochastic dual-subgradient method to interpret the battery of the server as a virtual queue. Based on the virtual queue and task queues, the CPU frequencies of the devices and the edge server, the offloading transmit rates of the devices (to the server) and the energy trading decisions of the server (with the smart grid) are decoupled over time and among devices, and optimized on an ongoing basis. Furthermore, we prove that the DORL yields a feasible and asymptotically optimal solution with a cost-backlog tradeoff of$[\eta, 1/\eta]$. Simulations show that the DORL reduces the system cost by nearly 50%, as compared to existing benchmarks.

In this paper, we jointly design resource allocation and load management in a mobile-edge computing (MEC) system with wireless power transfer (WPT), to minimize the total energy consumption of the BS, while meeting computation latency requirements. For the first time, the cooling energy, which is non-negligible, is considered to minimize the energy consumption of the MEC system. By orchestrating the alternative optimization technique, Lagrange duality method and subgradient method, we decompose the original optimization problem and obtain the optimal solution in a semi-closed form. Extensive numerical tests corroborate the merits of the proposed algorithm over existing benchmarks in terms of energy saving.

Heterogeneous network (HetNet) has been proposed as a promising solution for handling the wireless traffic explosion in future fifth-generation (5G) system. In this paper, a joint subchannel and power allocation problem is formulated for HetNets to maximize the energy efficiency (EE). By decomposing the original problem into a classification subproblem and a regression subproblem, a convolutional neural network (CNN) based approach is developed to obtain the decisions on subchannel and power allocation with a much lower complexity than conventional iterative methods. Numerical results further demonstrate that the proposed CNN can achieve similar performance as the Exhaustive method, while needs only 6.76% of its CPU runtime.

In vehicular communications, intracell interference and the stringent latency requirement are challenging issues. In this paper, a joint spectrum reuse and power allocation problem is formulated for hybrid vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications. Recognizing the high capacity and low-latency requirements for V2I and V2V links, respectively, we aim to maximize the weighted sum of the capacities and latency requirement. By decomposing the original problem into a classification subproblem and a regression subproblem, a convolutional neural network (CNN) based approach is developed to obtain real-time decisions on spectrum reuse and power allocation. Numerical results further demonstrate that the proposed CNN can achieve similar performance as the Exhaustive method, while needs only 3.62% of its CPU runtime.

This paper proposes a new fully decentralized approach to online placement and optimization of virtual machines (VMs) for network functions virtualization (NFV). The approach is of practical value, as network services comprising a chain of virtual network functions (VNFs) are proposed to be queued on the basis of leading unexecuted VNFs at every server, rather than on the typical basis of services, reducing queues per server and facilitating queue management and signaling. It is also non-trivial because the VNFs of network services must be executed correctly in order at different VMs, coupling the optimal decisions of VMs on processing or offloading. Exploiting Lyapunov optimization techniques, we decouple the optimal decisions by deriving and minimizing the instantaneous upper bound of the NFV cost in a distributed fashion, and achieve the asymptotically minimum time-average cost. We also reduce the queue length by allowing individual VMs to (un)install VNFs based on local knowledge, achieving stable redeployment of VNFs, adapting to the network topology and the temporal and spatial variations of services. Simulations show that the proposed approach is able to reduce the time-average cost of NFV by 71% and reduce the queue length (or delay) by 74%, as compared with existing approaches.

Network Function Virtualization (NFV) can cost-efficiently provide network services by running different virtual network functions (VNFs) at different virtual machines (VMs) in a correct order. This can result in strong couplings between the decisions of the VMs on the placement and operations of VNFs. This paper presents a new fully decentralized online approach for optimal placement and operations of VNFs. Building on a new stochastic dual gradient method, our approach decouples the real-time decisions of VMs, asymptotically minimizes the time-average cost of NFV, and stabilizes the backlogs of network services with a cost-backlog tradeoff of [ε, 1/ε], for any ε > 0. Our approach can be relaxed into multiple timescales to have VNFs (re)placed at a larger timescale and hence alleviate service interruptions. While proved to preserve the asymptotic optimality, the larger timescale can slow down the optimal placement of VNFs. A learn-and-adapt strategy is further designed to speed the placement up with an improved tradeoff [ε, log2(ε)/ε]. Numerical results show that the proposed method is able to reduce the time-average cost of NFV by 23 percent and reduce the queue length (or delay) by 74 percent, as compared to existing benchmarks.

This paper proposes a new fully decentralized approach for online placement and optimization of virtual machines (VMs) for network functions virtualization (NFV). The approach is non-trivial as the virtual network functions (VNFs) constituting network services must be executed correctly in order at different VMs, coupling the optimal decisions of VMs on processing or forwarding. Leveraging Lyapunov optimization techniques, we decouple the optimal decisions by minimizing the instantaneous NFV cost in a distributed fashion, and achieve the asymptotically minimum time-average cost. We also reduce the queue length by allowing individual VMs to (un)install VNFs based on local knowledge, adapting to the network topology and the temporal and spatial variations of services. Simulations show that the proposed approach is able to reduce the time-average cost of NFV by 71% and reduce the queue length (or delay) by 74%, as compared to existing approaches.

Future fifth-generation (5G) cellular networks, equipped with energy harvesting devices, are uniquely positioned to closely interoperate with smart grid. New interoperable functionalities are discussed in stochastic two-way energy trading and online planning to improve efficiency and productivity. Challenges lie in the unavailability of a-priori knowledge on future wireless channels, energy pricing and harvesting. Lyapunov optimization techniques are utilized to address the challenges and stochastically optimize energy trading and planning. Particularly, it is able to decouple the optimization of energy trading and planning during individual time slots, hence eliminating the need for joint optimization across a large number of slots.

Decoupling network services from underlying hardware, network function virtualization (NFV) is expected to significantly improve agility and reduce network cost. However, network services, sequences of network functions, need to be processed in specific orders at specific types of virtual machines (VMs), which couples decisions of VMs on processing or routing network services. Built on a new stochastic dual gradient method, our approach suppresses the couplings, minimizes the time-average cost of NFV, stabilizes queues at VMs, and reduces the backlogs of unprocessed services through online learning and adaptation. Asymptotically optimal decisions are instantly generated at individual VMs, with a cost-delay tradeoff [ε,log2(ε)/√ε]. Numerical results show that the proposed method is able to reduce the time-average cost of NFV by 30% and reduce the queue length (or delay) by 83%, as compared to existing non-stochastic approaches.

In this paper, a novel two-scale stochastic control framework is put forth for smart-grid powered coordinated multi-point (CoMP) systems. Taking into account renewable energy sources (RES), dynamic pricing, two-way energy trading facilities and imperfect energy storage devices, the energy management task is formulated as an infinite-horizon optimization problem minimizing the time-averaged energy transaction cost, subject to the users' quality of service (QoS) requirements. Leveraging the Lyapunov optimization approach and the stochastic subgradient method, a two-scale online control (TS-OC) approach is developed to make online control decisions at two timescales. It is analytically established that the TS-OC is capable of yielding a feasible and asymptotically near-optimal solution.

An infinite time-horizon resource allocation problem is formulated to maximize the time-averaged multi-input multi-output (MIMO) downlink throughput, subject to a time-averaged energy cost budget. By using the advanced time decoupling technique, a novel stochastic subgradient based online control (SGOC) approach is developed for the resultant smart-grid powered communication system. It is analytically established that even without a-priori knowledge of the underlying random processes, the proposed online algorithm is capable of yielding a feasible and asymptotically optimal solution.

Benefiting from technological advances in the smart grid era, next-generation multi-input multi-output (MIMO) communication systems are expected to be powered by renewable energy sources (RES) integrated in the distribution grid, thus realizing the vision of “green communications.” However, penetration of renewables introduces variabilities in the traditional power system, making RES benefits achievable only after appropriately mitigating their inherently high variability, which challenges existing resource allocation strategies. Aligned with this goal, an infinite time-horizon resource allocation problem is formulated to maximize the time-average MIMO downlink throughput, subject to a time-average energy cost budget. By using the advanced time decoupling technique, a novel stochastic subgradient-based online control approach is developed for the resultant smart-grid powered communication system. It is established analytically that even without a priori knowledge of the independently and identically distributed (i.i.d.) processes involved such as channel coefficients, renewables, and electricity prices, the proposed online control algorithm is still able to yield a feasible and asymptotically optimal solution. Numerical results further demonstrate that the proposed algorithm also works well in non-i.i.d. scenarios, where the underlying randomness is highly correlated over time.

In this paper, a new dynamic string tautening algorithm is proposed to generate the most energy-efficient off-line schedule for delay-limited traffic of transmitters with non-negligible circuit power. The algorithm is based on two key findings that we derive through judicious convex formulation and resultant optimality conditions, specifies a set of simple but optimal rules, and generates the optimal schedule with a low complexity of O(N2) in the worst case. The proposed algorithm is also extended to on-line scenarios, where the transmit schedule is generated on-the-fly. Simulation shows that the proposed algorithm requires substantially lower average complexity by almost two orders of magnitude to retain optimality than general convex solvers. The effective transmit region, specified by the tradeoff of the data arrival rate and the energy harvesting rate, is substantially larger using our algorithm than using other existing alternatives. Significantly more data or less energy can be supported in the proposed algorithm.

This paper develops novel approaches for energy-harvesting powered transmissions of delay-limited busty data packets under both ideal and non-ideal circuit power consumption. It is shown that the problems can be formulated as convex programs. Relying on the specific structure of the optimality conditions, we put forth efficient algorithms to find the optimal transmission strategies with a low computational complexity. It is revealed that the optimal energy departure curve for the ideal circuit power case can be yielded by a vivid calculus method. On the other hand, the optimal energy departure for the general non-ideal circuit power case can be obtained by simply adjusting the ideal-case energy departure in accordance to an energy efficient (EE) maximizing power value.

Energy harvesting has been widely considered in many wireless applications, especially the wireless sensor networks. This paper develops a novel approach to energy-harvesting powered transmissions under arbitrary packet arrival process and strict deadline constraints over time-varying channels. It is shown that the problem can be formulated as a convex program. Relying on the specific structure of the optimality conditions, we put forth an efficient algorithm with a low computational complexity to find the optimal rate control strategy. An insightful visualization is also provided to depict the construction of the optimal policy. Numerical results are presented to demonstrate the merit of the proposed scheme.