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Register a new userEnergy Optimization in Massive MIMO UAV-Aided MEC-Enabled Vehicular Networks
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This paper presents a novel unmanned aerial vehicle (UAV) aided mobile edge computing (MEC) architecture for vehicular networks. It is considered that the vehicles should complete latency critical computation intensive tasks either locally with on-board computation units or by offloading part of their tasks to road side units (RSUs) with collocated MEC servers. In this direction, a hovering UAV can serve as an aerial RSU (ARSU) for task processing or act as an aerial relay and further offload the computation tasks to a ground RSU (GRSU). In order to significantly reduce the delay during data offloading and downloading, this architecture relies on the benefits of massive multiple input multiple output (MIMO). Therefore, it is considered that the vehicles, the ARSU, and the GRSU employ large scale antennas. A three dimensional (3D) geometrical representation of the MEC enabled network is introduced and an optimization method is proposed that minimizes the weighted total energy consumption (WTEC) of the vehicles and ARSU subject to transmit power allocation, task allocation, and timeslot scheduling. The numerical results verify the theoretical derivations, emphasize on the effectiveness of the massive MIMO transmission, and provide useful engineering insights.
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Mobile edge computing (MEC)-enabled Internet of Things (IoT) networks have been deemed a promising paradigm to support massive energy-constrained and computation-limited IoT devices. IoT with mobility has found tremendous new services in the 5G era and the forthcoming 6G eras such as autonomous driving and vehicular communications. However, mobility of IoT devices has not been studied in the sufficient level in the existing works. In this paper, the offloading decision and resource allocation problem is studied with mobility consideration. The long-term average sum service cost of all the mobile IoT devices (MIDs) is minimized by jointly optimizing the CPU-cycle frequencies, the transmit power, and the user association vector of MIDs. An online mobility-aware offloading and resource allocation (OMORA) algorithm is proposed based on Lyapunov optimization and Semi-Definite Programming (SDP). Simulation results demonstrate that our proposed scheme can balance the system service cost and the delay performance, and outperforms other offloading benchmark methods in terms of the system service cost.
In this paper, we investigate joint vehicle association and multi-dimensional resource management in a vehicular network assisted by multi-access edge computing (MEC) and unmanned aerial vehicle (UAV). To efficiently manage the available spectrum, computing, and caching resources for the MEC-mounted base station and UAVs, a resource optimization problem is formulated and carried out at a central controller. Considering the overlong solving time of the formulated problem and the sensitive delay requirements of vehicular applications, we transform the optimization problem using reinforcement learning and then design a deep deterministic policy gradient (DDPG)-based solution. Through training the DDPG-based resource management model offline, optimal vehicle association and resource allocation decisions can be obtained rapidly. Simulation results demonstrate that the DDPG-based resource management scheme can converge within 200 episodes and achieve higher delay/quality-of-service satisfaction ratios than the random scheme.
Spectrum sharing is a method to solve the problem of frequency spectrum deficiency. This paper studies a novel AI based spectrum sharing and energy harvesting system in which the freshness of information (AoI) is guaranteed. The system includes a primary user with access rights to the spectrum and a secondary user. The secondary user is an energy harvesting sensor that intends to use the primary user spectrum opportunistically. The problem is formulated as partially observable Markov decision processes (POMDPs) and solved using two methods: a deep Q-network (DQN) and dueling double deep Q-Network (D3QN) to achieve the optimal policy. The purpose is to choose the best action adaptively in every time slot based on its situation in both overlay and underlay modes to minimize the average AoI of the secondary user. Finally, simulation experiments are performed to evaluate the effectiveness of the proposed scheme compared to the overlay mode. According to the results, the average AoI in the proposed system is less than that of the existing models, including only overlay mode. The average user access improved from 30% in the overlay mode to 45% in the DQN and 48% in the D3QN.
Cell-free (CF) massive multiple-input multiple-output (MIMO) is a promising solution to provide uniform good performance for unmanned aerial vehicle (UAV) communications. In this paper, we propose the UAV communication with wireless power transfer (WPT) aided CF massive MIMO systems, where the harvested energy (HE) from the downlink WPT is used to support both uplink data and pilot transmission. We derive novel closed-form downlink HE and uplink spectral efficiency (SE) expressions that take hardware impairments of UAV into account. UAV communications with current small cell (SC) and cellular massive MIMO enabled WPT systems are also considered for comparison. It is significant to show that CF massive MIMO achieves two and five times higher 95%-likely uplink SE than the ones of SC and cellular massive MIMO, respectively. Besides, the large-scale fading decoding receiver cooperation can reduce the interference of the terrestrial user. Moreover, the maximum SE can be achieved by changing the time-splitting fraction. We prove that the optimal time-splitting fraction for maximum SE is determined by the number of antennas, altitude and hardware quality factor of UAVs. Furthermore, we propose three UAV trajectory design schemes to improve the SE. It is interesting that the angle search scheme performs best than both AP search and line path schemes. Finally, simulation results are presented to validate the accuracy of our expressions.
This paper investigates the achievable rate maximization problem of a downlink unmanned aerial vehicle (UAV)-enabled communication system aided by an intelligent omni-surface (IOS). Different from the state-of-the-art reconfigurable intelligent surface (RIS) that only reflects incident signals, the IOS can simultaneously reflect and transmit the signals, thereby providing full-dimensional rate enhancement. To tackle such a problem, we formulate it by jointly optimizing the IOSs phase shift and the UAV trajectory. Although it is difficult to solve it optimally due to its non-convexity, we propose an efficient iterative algorithm to obtain a high-quality suboptimal solution. Simulation results show that the IOS-assisted UAV communications can achieve more significant improvement in achievable rates than other benchmark schemes.
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