Multi-access edge computing (MEC) has been one promising solution to reduce the computation delay of wireless devices. Due to the high spectrum efficiency of non-orthogonal multiple access (NOMA), this paper studies the single-user multi-edge-server MEC system based on downlink NOMA, aiming to minimize task computation delay by jointly optimizing the NOMA-based transmission duration (TD) and workload offloading allocation (WOA) among edge computing servers. This task computation delay minimization (CDM) problem is formulated as a nonconvex optimization problem. To solve the CDM problem efficiently, we decompose it into the sub-problem of determining the optimal WOA with a given TD and the top-problem of optimizing the TD. For the sub-problem, we first derive its some important properties and then design an efficient channel quality ranking based algorithm to obtain the optimal WOA. We solve the top-problem for the static-channel and dynamic-channel scenarios, respectively. For the static-channel scenario, we design an optimal algorithm which only apply once the golden section search method to obtain the optimal TD of first task and directly obtain the optimal offloading solution for any consequently arrived task with different workloads. For the dynamic-channel scenario where the channel qualities from the wireless device to the edge-computing servers are varying, it is critical to quickly determine the current task’s offloading solution under the current channel state and task workload, which is very challenging for the traditional optimization methods. In order to conquer this challenge, we propose the deep reinforcement learning (DRL) based algorithm, which can obtain the near-optimal offloading solution instantly after enough learning. Finally, we validate through simulations the advantages of NOMA over frequency division multiple access (FDMA).

The advanced Internet of Things (IoT) enables more and more interactions between people and machines in the emerging applications, which rely on real-time communication and computing. However, the limited battery capacity and low computing capacity of IoT nodes can hardly support high-performance computing applications. The integration of wireless power transmission (WPT) and mobile edge computing (MEC) is a feasible and promising solution to address the energy shortage and computing capacity limitation of IoT nodes by harvesting radio frequency signal’s energy and offloading the nodes’ computation tasks to edge computing servers (ECSs). In this work, we focus on the wireless powered MEC network with an ECS and multiple edge devices (EDs), and study the joint optimization of WPT duration, transmission time allocation of each ED and partial offloading decision to maximize the sum computation rate. First, we formulate this as a non-convex problem which is hard to solve. Second, to conquer this problem, we decompose the original offloading problem into the sub-problem of optimizing the offloading time allocation among EDs and the proportion of harvested energy allocated for offloading at each ED under a given WPT duration and the top-problem of optimizing the WPT duration. Finally, we design an online DRL-based framework where one DNN together with its exploration strategy and training strategy is adopted to learn the near-optimal WPT duration and an efficient optimal algorithm is designed to solve the sub-problem. Numerical results show that the DRL-based offloading algorithm achieves the near-maximal sum computation rate while greatly reducing the processing time by at least three orders of magnitude compared with using the solver CVX for the sub-problem and the DNN for the top-problem.

This article focuses on the issue of cooperative spectrum sensing (CSS) in a mobile energy-harvesting cognitive radio network (EH-CRN), where secondary transmitters (STs) are powered by the radio-frequency (RF) signal emitted from primary transmissions. Only the STs with sufficient energy participate in CSS, and send their local sensing decisions to a fusion center (FC), which makes a final decision on the state of the spectrum by a general k-out-of-M(k) fusion rule. The target of this article is to develop an optimal CSS strategy in terms of final decision threshold k that maximizes the expected achievable throughput of the EH-CRN, subject to a collision constraint and an energy causality constraint. We first show that the EH-CRN can be divided into an energy-deficit state and a spectrum-deficit state depending on the final decision threshold. The final decision threshold has a negative correlation with the number of STs participating in CSS in the energy-deficit state, and has no impact on that in the spectrum-deficit state. We then derive the collision probability and the expected achievable throughput of the EH-CRN, both of which are indicated to be determined by the active probability of a ST, the state of the spectrum, and the spectrum access opportunity. By tackling the tradeoff between the active probability and spectrum access opportunity introduced by the final decision threshold, we derive the optimal final decision threshold that maximizes the expected achievable throughput of the EH-CRN while protecting primary transmissions to a predefined extent. Extensive numerical simulations are conducted to illustrate the performance versus the final decision threshold. One of the main findings indicates that the optimal range of final decision threshold in the energy-deficit state could be acquired by the number of reporting received at the FC.

Ambient backscatter (AB) communications and radio frequency (RF)-powered cognitive radio networks (CRNs) address the concerns of energy and spectrum scarcities from different perspectives, and the integration of them has potential benefits for throughput. Motivated by this fact, we study the RF-powered AB-assisted hybrid underlay CRN (ABHU-CRN), and optimize the long-term secondary throughput. Based on the channel states, secondary users choose to perform different actions, and two action spaces are accordingly designed. Due to dynamic and unpredictable environment states, we jointly control the time scheduling and energy management of secondary users, and propose two algorithms, i.e., adjusted-deep deterministic policy gradient (A-DDPG) and combination of A-DDPG and convex optimization (C-ADCO), for the long-term secondary throughput. A-DDPG, a deep reinforcement learning algorithm with continuous spaces, is extended from DDPG to adapt to the design of two action spaces. C-ADCO utilizes the convex optimization that can find the optimal solution to assist A-DDPG to accelerate the convergence. In simulations, the ABHU-CRN under A-DDPG and C-ADCO achieves higher throughput than the optimal throughput of AB-assisted overlay CRN and AB-assisted underlay CRN, which indicates the advantage of the hybrid transmission mode in the ABHU-CRN.

Wireless power transfer (WPT) and edge computing have been validated as effective ways to solve the energy-limited problem and computation-capacity-limited problem of wireless devices (WDs), respectively. This paper studies the wireless-powered multi-access edge computing (WP-MEC) network, where WDs conduct either local computing or task offloading for their individable computation tasks. We aim to minimize total computation delay (TCD) when each WD has a computation task to execute, referred to as the total computation delay minimization (TCDM) problem, by jointly optimizing the offloading-decision, WPT duration, and transmission durations of offloading WDs. The TCDM problem is a mixed integer programming (MIP) problem that is challenging to efficiently obtain the optimal or near-optimal solution. To tackle this challenge, we decompose the TCDM problem into the sub-problem of optimizing the WPT duration and transmission durations, and the top-problem of optimizing the offloading decision. For the nonconvex sub-problem, we design a worst-WD-adjusting (WDA) algorithm to efficiently obtain its optimal solution. For the top-problem, under the time-varying channel conditions, traditional optimization methods are hard to determine the optimal or near-optimal offloading decision within the channel coherence duration. To fast obtain the near-optimal offloading decision, we propose a deep neural networks (DNN)-based deep reinforcement learning (DRL) model, which takes the sub-problem solving as one component for utility evaluation. Finally, numerical results demonstrate that the proposed online DRL-based offloading algorithm achieves the near-minimal TCD with low computational complexity, and is suitable for the fast-fading WP-MEC network.

In the ongoing 5G and upcoming 6G eras, the intelligent Internet of Things (IoT) network will take increasingly important responsibility for industrial production, daily life and so on. The IoT devices with limited battery size and computing ability cannot meet many applications brought out by the data-driven artificial intelligence technique. The combination of wireless power transfer (WPT) and edge computing is regarded as an effective solution to this dilemma. IoT devices can collect radio frequency energy provided by hybrid access points (HAPs) to process data locally or offload data to the edge servers of HAPs. However, how to efficiently make offloading decisions and allocate resource is challenging, especially for the networks with multiple HAPs. In this paper, we consider the sum computation rate maximization problem for a WPT empowered IoT network with multiple HAPs and IoT devices. The problem is formulated as a mixed-integer nonlinear programming problem. To solve this problem efficiently, we decompose it into a top-problem of optimizing offloading decisions and a sub-problem of optimizing time allocation under the given offloading decisions. We propose a deep reinforcement learning (DRL) based algorithm to output the near-optimal offloading decision and design an efficient algorithm based on Lagrangian duality method to obtain the consequent optimal time allocation. Simulations verified that the proposed DRL-based algorithm can achieve more than 95 percent of the maximal computation rate with low complexity. Compared with the common actor-critic algorithm, the proposed algorithm has the substantial advantage in convergence speed, achieved computation rate and running time.