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In this paper, in order to reduce the energy consumption and time of data transmission, the non-orthogonal multiple access (NOMA) and mobile edge caching technologies are jointly considered in mobile edge computing (MEC) networks. As for the cache-assisted vehicular NOMA–MEC networks, a problem of minimizing the energy consumed by vehicles (mobile devices, MDs) is formulated under time and resource constraints, which jointly optimize the computing resource allocation, subchannel selection, device association, offloading and caching decisions. To solve the formulated problem, we develop an effective joint computation offloading and task-caching algorithm based on the twin-delayed deep deterministic policy gradient (TD3) algorithm. Such a TD3-based offloading (TD3O) algorithm includes a designed action transformation (AT) algorithm used for transforming continuous action space into a discrete one. In addition, to solve the formulated problem in a non-iterative manner, an effective heuristic algorithm (HA) is also designed. As for the designed algorithms, we provide some detailed analyses of computation complexity and convergence, and give some meaningful insights through simulation. Simulation results show that the TD3O algorithm could achieve lower local energy consumption than several benchmark algorithms, and HA could achieve lower consumption than the completely offloading algorithm and local execution algorithm. [ABSTRACT FROM AUTHOR]

Cell-free massive multiple input multiple outputs (CF-mMIMO) is considered a promising technology for sixth-generation (6G) telecommunication systems. In the CF-mMIMO system, an extensive array of distributed small base stations (BSs) is deployed across the network, which enables us to facilitate seamless collaboration among BSs. To achieve this goal, the baseband signal from these BSs needs to be transmitted to a central server via fronthaul networks. Due to the large number of BSs, the data that needs to be transmitted is usually huge, which brings severe requirements on fronthaul networks. Time and wavelength division multiplexed passive optical networks (TWDM-PON) can be a potential solution for CF-mMIMO fronthaul due to their large capacity and high flexibility. However, how to efficiently allocate both optical and wireless resources in a TWDM-PON-enabled CF-mMIMO system is still a problem to be addressed. This paper proposes a joint scheduling method of wavelength, antenna, radio unit (RU), and radio resource block (RB) resources in the TWDM-PON-enabled CF-mMIMO system. Furthermore, an integer linear programming (ILP) model for joint resource allocation is proposed to minimize the fronthaul resource occupancy, thereby increasing network scalability. Considering the complexity of the ILP model, two heuristic algorithms are also presented to solve this model. We compare the ILP with heuristic algorithms under different scenarios. Simulation results show that the proposed algorithm can reduce the fronthaul resource occupancy to improve the network scalability of the CF-mMIMO system. [ABSTRACT FROM AUTHOR]

This paper studies an uplink nonorthogonal multiple access (NOMA)-enabled mobile-edge computing network. Specifically, we focus on the practical design of secure offloading without knowing the eavesdropper's channel state information. The aim is to maximize the minimum antieavesdropping ability (AEA) for uplink NOMA users subject to the worst-case secrecy rate requirements and limited transmission power budgets. The formulated problem is nonconvex and difficult to be solved directly. In order to tackle this issue, we propose an efficient iterative algorithm by jointly designing the secrecy rate, local computing bits, and power allocation. The local computing bits and the power allocation are derived in closed form. Numerical results are provided to demonstrate the efficiency of our proposed scheme in terms of AEA. [ABSTRACT FROM AUTHOR]

In this paper, we devote to maximize the secrecy energy efficiency (SEE) for a cloud radio access network where exists an eavesdropper wiretapping information from remote radio head. We first establish the system model and then formulate the power optimization problem to maximize the SEE of the system subject to the fronthaul capacity and transmit power constraints. To address this nonconvex problem, we first convert the fractional objective function into a subtractive form and two algorithms are then proposed to solve it. Simulation results indicate that the proposed the iterative optimization algorithms can obtain higher SEE and present the merit of AN in enhancing the system performance when compared with conventional methods. [ABSTRACT FROM AUTHOR]

Abstract Underwater acoustic sensor networks play a significant role in ocean observation, pollution monitoring and underwater remote control. Different from conventional sensor networks, transmission efficiency and lifetime for nodes in underwater acoustic sensor networks is vital since it is difficult to change batteries of buoy relays and underwater sensors. In this paper, two resource allocation algorithms for energy harvesting time and transmit power allocation in underwater acoustic sensor networks with single and multiple wireless energy harvesting buoy nodes are proposed to maximize the sum rate and energy efficiency. Both of the circuit power consumption and transmission power consumption are considered as the power consumption of the system. We first propose an optimal algorithm to allocate the time of energy harvesting slot for sum rate maximization. From the derivation, we show that the sum rate of the network grows as the transmission power increases. However, when the transmission power is extremely large, the energy efficiency of the network starts to decrease. Thus a joint algorithm with energy harvesting time and transmit power allocation is further provided for energy efficiency maximization. Simulation results validate the sum rate improvement under our proposed sum rate maximization algorithm and energy efficiency improvement under the proposed energy efficiency maximization algorithm. [ABSTRACT FROM AUTHOR]

In this paper, we address the downlink resource (subcarriers and power jointly) allocation problem for energy harvesting-based device-to-device communication in a railway carriage communication network to improve the energy efficiency (EE) of the system. The considered problem is formulated as maximizing the weighted EE and is solved by leveraging a game-theoretic learning approach. Specifically, we first propose a new performance metric for evaluating the EE and optimize its lower bound. However, there exists an intractable issue of mixing the integer nature into the feasible region. To this end, we decompose the optimization problem into two subproblems by fixing the subcarrier and power allocations alternately. These two subproblems are formulated as two exact potential games, and the optimal properties of their solutions are analyzed. Accordingly, we respectively design a virtual distributed learning algorithm for the power control to find the optimum solution, i.e., Nash equilibrium (NE) point, based on the derived conditions of the uniqueness of NE, which can effectively accelerate convergence, and a fully distributed Max-logit algorithm for the subcarrier allocations to obtain the best NE with an arbitrarily high probability in which only local information needs to be exchanged. Through the alternation of two algorithms and iterative operation, the optimal solution to the problem is achieved. Finally, numerical results verify the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

With the increase of access point (AP) density and the exponential growth of mobile devices supported by ultradense networks (UDNs), user-centric overlapped clustering relying on AP cooperation is becoming a promising design principle for guaranteeing the quality of service (QoS) required by each user equipment. This ambitious goal can be achieved by the joint design of user-centric overlapped clustering and resource allocation. In this context, both the traffic-load balancing and the limited availability of orthogonal resource blocks (RBs) have to be carefully considered in UDNs. To tackle these challenges, we formulate a joint user-centric overlapped clustering and resource allocation problem with the goal of maximizing the system's spectral efficiency. To efficiently solve this problem, it is decoupled into two subproblems, which can be solved independently with the aid of our graph–theoretical framework. We first develop a novel distributed three-stage user-centric clustering solution, which is aware of the APs’ traffic-loads. Then, based on the overlapped clusters constructed, we propose a novel two-stage graph-based user-centric resource allocation scheme for mitigating the resultant inter-cluster interference, despite the limited availability of orthogonal RBs. Our numerical results confirm the benefits of the proposed framework and show that it outperforms the benchmark solutions in terms of both its per area aggregated user rate and user rate. [ABSTRACT FROM PUBLISHER]