Mobile-edge computing (MEC) has recently emerged as a prominent technology to liberate mobile devices from computationally intensive workloads, by offloading them to the proximate MEC server. To make offloading effective, the radio and computational resources need to be dynamically managed, to cope with the time-varying computation demands and wireless fading channels. In this paper, we develop an online joint radio and computational resource management algorithm for multi-user MEC systems, with the objective of minimizing the long-term average weighted sum power consumption of the mobile devices and the MEC server, subject to a task buffer stability constraint. Specifically, at each time slot, the optimal CPU-cycle frequencies of the mobile devices are obtained in closed forms, and the optimal transmit power and bandwidth allocation for computation offloading are determined with the Gauss-Seidel method ; while for the MEC server, both the optimal frequencies of the CPU cores and the optimal MEC server scheduling decision are derived in closed forms. Besides, a delay-improved mechanism is proposed to reduce the execution delay. Rigorous performance analysis is conducted for the proposed algorithm and its delay-improved version, indicating that the weighted sum power consumption and execution delay obey an $\left [{O\left ({1 / V}\right), O\left ({V}\right) }\right ]$ tradeoff with $V$ as a control parameter. Simulation results are provided to validate the theoretical analysis and demonstrate the impacts of various parameters.

Stochastic Joint Radio and Computational Resource Management for Multi-User Mobile-Edge Computing Systems

Recent emphasis on green communications has generated great interest in the investigations of energy harvesting communications and networking. Energy harvesting from ambient energy sources can potentially reduce the dependence on the supply of grid or battery energy, providing many attractive benefits to the environment and deployment. However, unlike the conventional stable energy, the intermittent and random nature of the renewable energy makes it challenging in the realization of energy harvesting transmission schemes. Extensive research studies have been carried out in recent years to address this inherent challenge from several aspects: energy sources and models, energy harvesting and usage protocols, energy scheduling and optimization, implementation of energy harvesting in cooperative, cognitive radio, multiuser and cellular networks, etc. However, there has not been a comprehensive survey to lay out the complete picture of recent advances and future directions. To fill such a gap, in this paper, we present an overview of the past and recent developments in these areas and highlight a number of possible future research avenues.

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Advances in Energy Harvesting Communications: Past, Present, and Future Challenges

Mobile-edge computing (MEC) has recently emerged as a promising paradigm to liberate mobile devices from increasingly intensive computation workloads, as well as to improve the quality of computation experience. In this paper, we investigate the tradeoff between two critical but conflicting objectives in multi-user MEC systems, namely, the power consumption of mobile devices and the execution delay of computation tasks. A power consumption minimization problem with task buffer stability constraints is formulated to investigate the tradeoff, and an online algorithm that decides the local execution and computation offloading policy is developed based on Lyapunov optimization. Specifically, at each time slot, the optimal frequencies of the local CPUs are obtained in closed forms, while the optimal transmit power and bandwidth allocation for computation offloading are determined with the Gauss-Seidel method. Performance analysis is conducted for the proposed algorithm, which indicates that the power consumption and execution delay obeys an $\left[O\left(1\slash V\right),O\left(V\right)\right]$ tradeoff with $V$ as a control parameter. Simulation results are provided to validate the theoretical analysis and demonstrate the impacts of various parameters to the system performance.

Power-Delay Tradeoff in Multi-User Mobile-Edge Computing Systems

To overcome the difficulties of charging the wireless sensors in the wild with conventional energy supply, more and more researchers have focused on the sensor networks with renewable generations. Considering the uncertainty of the renewable generations, an effective energy management strategy is necessary for the sensors. In this paper, we propose a novel energy management algorithm based on the reinforcement learning. By utilizing deep deterministic policy gradient (DDPG), the proposed algorithm is applicable for the continuous states and realizes the continuous energy management. We also propose a state normalization algorithm to help the neural network initialize and learn. With only one day’s real solar data and the simulative channel data for training, the proposed algorithm shows excellent performance in the validation with about 800 days length of real solar data. Compared with the state-of-the-art algorithms, the proposed algorithm achieves better performance in terms of long-term average net bit rate.

Deep Deterministic Policy Gradient (DDPG)-Based Energy Harvesting Wireless Communications

Internet of Things (IoT) is facing the shortage of spectrum resources due to the rapid growth of IoT terminals and big data services. Fifth generation (5G) network owns sufficient spectrum resources and supplies large data volume business, which can help to expand the communication resources of the IoT by combing IoT with 5G network. In this paper, a 5G-based IoT is designed to transfer both 5G and IoT information simultaneously. Two simultaneous transfer models including time switching model and power splitting model are proposed to carry out 5G and IoT communications using different time slots and power streams, respectively. For these two models, we have formulated joint optimization problem of allocation factors and node powers to maximize the 5G transmission rate while the IoT transmission rate and the total power are constrained. An alternative optimization problem is also proposed to maximize the IoT transmission rate while guaranteeing the minimal 5G transmission rate. A joint optimization algorithm based on the Lagrange dual optimization is proposed to obtain the solution to the optimization problems. An energy efficiency model is proposed to minimize the consumed total power of the IoT while keeping the minimal 5G and IoT transmission rates. Simulation results are given to evaluate the performance of our proposed models from diverse perspectives.

Rate and Energy Efficiency Improvements for 5G-Based IoT With Simultaneous Transfer

In this paper, we develop optimal energy scheduling algorithms for N-user fading multiple-access channels with energy harvesting to maximize the channel sum-rate, assuming that the side information of both the channel states and energy harvesting states for K time slots is known a priori, and the battery capacity and the maximum energy consumption in each time slot are bounded. The problem is formulated as a convex optimization problem with O (NK) constraints making it hard to solve using a general convex solver since the computational complexity of a generic convex solver becomes impractically high when the number of constraints is large. This paper gives an efficient energy scheduling algorithm, called the iterative dynamic water-filling algorithm, that has a computational complexity of O(NK
 2
) per iteration. For the single-user case, a dynamic water-filling method is shown to be optimal. Unlike the traditional water-filling algorithm, in dynamic water-filling, the water level is not constant but changes when the battery overflows or depletes. An iterative version of the dynamic water-filling algorithm is shown to be optimal for the case of multiple users. Even though in principle the optimality is achieved under large number of iterations, in practice convergence is reached in only a few iterations. Moreover, a single iteration of the dynamic water-filling algorithm achieves a sum-rate that is within (N-1)K nats of the optimal sum-rate.

Iterative Dynamic Water-Filling for Fading Multiple-Access Channels With Energy Harvesting

We consider power allocation for an access-controlled transmitter with energy harvesting capability based on causal observations of the channel fading state. We assume that the system operates in a time-slotted fashion and the channel gain in each slot is a random variable which is independent across slots. Further, we assume that the transmitter is solely powered by a renewable energy source and the energy harvesting process can practically be predicted. With the additional access control for the transmitter and the maximum power constraint, we formulate the stochastic optimization problem of maximizing the achievable rate as a Markov decision process (MDP) with continuous state. To effi- ciently solve the problem, we define an approximate value function based on a piecewise linear fit in terms of the battery state. We show that with the approximate value function, the update in each iteration consists of a group of convex problems with a continuous parameter. Moreover, we derive the optimal solution to these con- vex problems in closed-form. Further, we propose power allocation algorithms for both the finite- and infinite-horizon cases, whose computational complexity is significantly lower than that of the standard discrete MDP method but with improved performance. Extension to the case of a general payoff function and imperfect energy prediction is also considered. Finally, simulation results demonstrate that the proposed algorithms closely approach the optimal performance.

Power Allocation for Energy Harvesting Transmitter With Causal Information

In this paper, we consider the energy-bandwidth allocation for a network with multiple broadcast channels, where the transmitters each powered by an energy harvester, access the network orthogonally on the assigned frequency band and each transmitter communicates with multiple receivers orthogonally or non-orthogonally. We assume that the energy harvesting state and channel gain of each transmitter can be predicted for $K$ time slots a priori . To maximize the weighted throughput, we formulate an optimization problem with $\mathcal{O}(MK)$ constraints, where $M$ is the total number of receivers, and optimize over the energy and bandwidth allocation variables. To solve the problem efficiently, an iterative algorithm is proposed that alternatively solves the two subproblems of energy allocation and bandwidth allocation in each iteration. We show that this algorithm converges to the optimal solution. Also, we propose efficient algorithms to solve the two subproblems, so that the optimal energy-bandwidth allocation can be obtained with an overall complexity of ${\mathcal O}(MK^{2})$ , even though the problem is non-convex when the broadcast channel is non-orthogonal. Simulation results show that the proposed algorithms can make efficient use of the harvested energy and the available bandwidth, and achieve significantly better performance as compared to some heuristic policies for energy and bandwidth allocation. Moreover, it is seen that with energy-harvesting transmitters, the non-orthogonal broadcast channel offers limited gain over the orthogonal broadcast channel.

Joint Energy-Bandwidth Allocation in Multiple Broadcast Channels With Energy Harvesting

We consider multiple transmission links in a frequency-selective fading channel, where the transmitters are powered by renewable energy sources that provide variable amount of energy at different times. We formulate the problem of joint energy and subchannel allocation for all transmitters over a scheduling period, as a mixed integer program. Assuming that the harvested energy and subchannel gains can be predicted, we propose an algorithm to efficiently obtain the energy-subchannel allocations for all links over the scheduling period based on controlled water-filling. The proposed algorithm is shown to be asymptotically optimal when the bandwidth of the subchannel goes to zero. A causal algorithm is also proposed based on the Q-learning technique that makes use of the statistics of the energy harvesting and channel fading processes. Simulation results demonstrate that the performance of the proposed noncausal algorithm is close to the upper-bound on the optimal performance and the proposed causal algorithm outperforms various heuristic allocation policies.

Transmission With Energy Harvesting Nodes in Frequency-Selective Fading Channels

In this paper, we develop optimal energy-bandwidth allocation algorithms in fading channels for multiple energy harvesting transmitters. We first assume that the side information of both the channel states and the energy harvesting states is known for $K$ time slots a priori , and the battery capacity and the maximum transmission power in each time slot are bounded. The network consists of $N$ transmitter-receiver pairs and the objective is to maximize the sum-rate of all communication links over the $K$ time slots by assigning the transmission power and bandwidth for each transmitter in each slot. The problem is formulated as a convex optimization problem with ${\cal O}(NK)$ constraints, where $N$ is the number of the receivers, making it hard to solve with a generic convex solver. An iterative algorithm is proposed based on efficiently solving two subproblems in each iteration, that has an overall complexity of ${\cal O}(NK^2)$ . The convergence and the optimality of this algorithm are also shown. Moreover, a heuristic algorithm is also proposed for energy-bandwidth allocation based on causal information of channel and energy harvesting states. Simulation results show that the proposed causal and noncausal algorithms can make efficient use of the harvested energy and the available bandwidth. And they achieve significantly higher rates than some heuristic policies for energy and bandwidth allocation.

Zhe Wang

Papers

Iterative Dynamic Water-Filling for Fading Multiple-Access Channels With Energy Harvesting

Power Allocation for Energy Harvesting Transmitter With Causal Information

Joint Energy-Bandwidth Allocation in Multiple Broadcast Channels With Energy Harvesting

Transmission With Energy Harvesting Nodes in Frequency-Selective Fading Channels

Optimal Energy-Bandwidth Allocation for Energy-Harvesting Networks in Multiuser Fading Channels