LoRa is considered one of the most promising low-power wide-area techniques. Given that end devices (EDs) are typically battery-powered, energy efficiency (EE) is a critical factor to consider. In this paper, we aim to improve the system EE of the LoRa network by jointly allocating transmission parameters such as the channel (CH), transmission power (TP) and spreading factor (SF) for each ED. Owing to the low duty cycle and sporadic traffic of LoRa networks, evaluating the system EE under various parameter settings proves to be time-consuming. Consequently, we propose an analytical model aimed at calculating the system EE while fully considering the impact of multiple gateways, quasi-orthogonal SFs and capture effects. On this basis, we investigate a joint CH, SF and TP allocation problem to optimize the system EE for uplink transmissions. Given the NP-hard complexity of the problem, the original problem is decomposed into two subproblems: CH assignment and SF/TP assignment. First, a matching-based algorithm is introduced to tackle the CH assignment subproblem. Then, an attention-based multiagent reinforcement learning technique is employed to address the SF/TP assignment subproblem for EDs allocated to the same CH. The simulation outcomes indicate that the proposed approach converges quickly and obtains significantly better system EE than baseline algorithms.

Long Range (LoRa) wireless technology, characterized by low power consumption and a long communication range, is regarded as one of the enabling technologies for the Industrial Internet of Things (IIoT). However, as the network scale increases, the energy efficiency (EE) of LoRa networks decreases sharply due to severe packet collisions. To address this issue, it is essential to appropriately assign transmission parameters such as the spreading factor and transmission power for each end device (ED). However, due to the sporadic traffic and low duty cycle of LoRa networks, evaluating the system EE performance under different parameter settings is time-consuming. Therefore, we first formulate an analytical model to calculate the system EE. On this basis, we propose a transmission parameter allocation algorithm based on multiagent reinforcement learning (MALoRa) with the aim of maximizing the system EE of LoRa networks. Notably, MALoRa employs an attention mechanism to guide each ED to better learn how much “attention” should be given to the parameter assignments for relevant EDs when seeking to improve the system EE. Simulation results demonstrate that MALoRa significantly improves the system EE compared with baseline algorithms with an acceptable degradation in packet delivery rate (PDR).

Multi-access edge computing (MEC) and ultra-dense networking (UDN) are recognized as two promising paradigms for future mobile networks that can be utilized to improve the spectrum efficiency and the quality of computational experience (QoCE). In this paper, we study the task offloading problem in an MEC-enabled UDN architecture with the aim to minimize the task duration while satisfying the energy budget constraints. Due to the dynamics associated with the environment and parameter uncertainty, designing an optimal task offloading algorithm is highly challenging. Consequently, we propose an online task offloading algorithm based on a state-of-the-art deep reinforcement learning (DRL) technique: asynchronous advantage actor-critic (A3C). It is worthy of remark that the proposed method requires neither instantaneous channel state information (CSI) nor prior knowledge of the computational capabilities of the base stations. Simulations show that the our method is able to learn a good offloading policy to obtain a near-optimal task allocation while meeting energy budget constraints of mobile devices in UDN environment.

Multi-access edge computing (MEC) and ultra-dense networking (UDN) are recognized as two promising paradigms for future mobile networks that can be utilized to improve the spectrum efficiency and the quality of computational experience (QoCE) . In this paper, we study the task offloading problem in an MEC-enabled UDN architecture with the aim to minimize the task duration while satisfying the energy budget constraints. Due to the dynamics associated with the environment and parameter uncertainty, designing an optimal task offloading algorithm is highly challenging. Consequently, we propose an online task offloading algorithm based on a state-of-the-art deep reinforcement learning (DRL) technique: asynchronous advantage actor-critic (A3C) . It is worthy of remark that the proposed method requires neither instantaneous channel state information (CSI) nor prior knowledge of the computational capabilities of the base stations. Simulations show that the our method is able to learn a good offloading policy to obtain a near-optimal task allocation while meeting energy budget constraints of mobile devices in the UDN environment.

Ultrareliable and low-latency communication (URLLC) is a prerequisite for the successful implementation of the Internet of Controllable Things. In this article, we investigate the potential of deep reinforcement learning (DRL) for joint subcarrier-power allocation to achieve low latency and high reliability in a general form of device-to-device (D2D) networks, where each subcarrier can be allocated to multiple D2D pairs and each D2D pair is permitted to utilize multiple subcarriers. We first formulate the above problem as a Markov decision process and then propose a double deep $Q$ -network (DQN)-based resource allocation algorithm to learn the optimal policy in the absence of full instantaneous channel state information (CSI). Specifically, each D2D pair acts as a learning agent that adjusts its own subcarrier-power allocation strategy iteratively through interactions with the operating environment in a trial-and-error fashion. Simulation results demonstrate that the proposed algorithm achieves near-optimal performance in real time. It is worth mentioning that the proposed algorithm is especially suitable for cases where the environmental dynamics are not accurate and the CSI delay cannot be ignored.

Device-to-device communications underlaying cellular networks have been recognized as one of the key technologies for the fifth generation (5G) cellular system to improve the spectrum efficiency and system capacity. In this paper, we investigate the potential of deep reinforcement learning (DRL) for joint subcarrier assignment and power allocation in a general form of D2D networks, where a subcarrier can be assigned to multiple D2D pairs and each D2D pair is permitted to utilize multiple subcarriers. We first formulate the above problem as a Markov decision process, and then propose a double deep Q-network (DQN)-based subcarrier-power allocation algorithm to learn the optimal policy in the absence of full instantaneous channel state information (CSI). Specifically, each D2D pair acts as a learning agent that adjusts its own subcarrier-power allocation strategy iteratively through interactions with the operating environment in a trial-and-error fashion. Simulation results confirm that the proposed algorithm achieves near optimal performance in real time. It is worth mentioning that the proposed algorithm is especially suitable for the case where the environmental dynamics is not accurate and the CSI delay cannot be ignored.