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.

Long Range (LoRa) technology, characterized by low power consumption and a long transmission range, is regarded as a key technology for the Industrial Internet of Things (IIoT). However, with an increasing network scale, existing LoRa networks begin to exhibit low energy efficiency (EE) due to severe collisions. Most existing methods assume perfect SF orthogonality and ignore the harmful effects of the interference between signals using different spreading factors (SF) in the same channel (CH). In this article, we investigate a joint SF, CH, and transmit power (TP) allocation problem with the objective of optimizing the system uplink EE while fully considering the quasi-orthogonal SFs, which is an NPhard optimization problem. To enhance tractability, the original problem is decomposed into two subproblems: CH/SF assignment and TP allocation. First, an attention-based deep neural network is designed to generate a near-optimal CH/SF assignment based on the channel state information and channel activity detection based historical channel occupancy information. Second, given the CH/SF assignment, the optimal TP allocation is determined by solving a convex optimization problem. Experimental results on a real LoRa network demonstrate that the proposed method significantly improves the system's uplink EE. Index Terms

Long-range (LoRa) communication technology, distinguished by its low power consumption and long communication range, is widely used in the Internet of Things. However, the LoRa MAC layer adopts pure ALOHA for medium access control, which may lead to severe packet collisions as the network scale expands, consequently reducing the system energy efficiency (EE). To address this issue, it is critical to carefully allocate transmission parameters such as the channel (CH), transmission power (TP) and spreading factor (SF) to each end device (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. Therefore, we propose an analytical model to calculate the system EE while fully considering the impacts of multiple gateways, duty cycling, quasi-orthogonal SFs and capture effects. On this basis, we investigate a joint CH, SF and TP allocation problem with the objective of optimizing the system EE for uplink transmissions. Due to the NP-hard complexity of this optimization problem, it is decomposed into two subproblems: CH assignment and SF/TP assignment. First, a matching-based algorithm is introduced to address 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, which reduces the number of learning agents to achieve fast convergence. The simulation outcomes indicate that the proposed approach converges quickly under various parameter settings and obtains significantly better system EE than the baseline algorithms.

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.

Long-range (LoRa) technology has emerged as a promising solution for Internet of Things applications due to its low power consumption and long communication range. However, its pure ALOHA-based MAC layer leads to severe packet collisions as the network scale expands, significantly degrading the system energy efficiency (EE). While careful allocation of transmission parameters such as channel (CH), transmission power (TP), and spreading factor (SF) could mitigate this issue, the complex interference patterns in multi-gateway scenarios and the time-consuming nature of EE evaluation pose significant challenges. Therefore, we propose an analytical model to calculate the system EE while fully considering the impacts of multiple gateways, duty cycling, quasi-orthogonal SFs and capture effects. Based on this model, we formulate a joint CH, SF, and TP allocation problem to optimize the system EE. To solve this NP-hard optimization problem, we decompose it into CH assignment and SF/TP assignment subproblems. A two-phase optimization framework is then designed. In the first phase, a matching-based algorithm is designed for CH assignment. In the second phase, a multi-agent reinforcement learning approach that incorporates a two-stage attention mechanism and graph convolutional networks is proposed for SF/TP assignment, which effectively captures and weights inter-ED interactions in multi-gateway scenarios. Simulation results indicate that the proposed approach well-suited for complex multi-GW LoRa network topologies and outperforms state-of-the-art algorithms.

Long-range (LoRa) technology has emerged as a promising solution for Internet of things applications due to its low power consumption and long communication range. However, its pure ALOHA-based MAC layer leads to severe packet collisions as the network scale expands, significantly degrading the system energy efficiency (EE). While careful allocation of transmission parameters such as channel (CH), transmission power (TP), and spreading factor (SF) could mitigate this issue, the complex interference patterns in multi-gateway scenarios and the time-consuming nature of EE evaluation pose significant challenges. Therefore, we propose an analytical model to calculate the system EE while fully considering the impacts of multiple gateways, duty cycling, quasi-orthogonal SFs and capture effects. Based on this model, we formulate a joint CH, SF, and TP allocation problem to optimize the system EE. To solve this NPhard optimization problem, we decompose it into CH assignment and SF/TP assignment subproblems. A two-phase optimization framework is then designed. In the first phase, a matching-based algorithm is designed for CH assignment. In the second phase, a multi-agent reinforcement learning approach that incorporates a two-stage attention mechanism and graph convolutional networks is proposed for SF/TP assignment, which effectively captures and weights inter-ED interactions in multi-gateway scenarios. Simulation results indicate that the proposed approach wellsuited for complex multi-GW LoRa network topologies and outperforms state-of-the-art algorithms.