In this paper, a communication-efficient federated learning (FL) framework is proposed for improving the convergence rate of FL under a limited uplink capacity. The central idea of the proposed framework is to transmit the values and positions of the top-S entries of a local model update for uplink transmission. A lossless encoding technique is considered for transmitting the positions of these entries, while a linear transformation followed by the Lloyd-Max scalar quantization is considered for transmitting their values. For an accurate reconstruction of the top-S values, a linear minimum mean squared error method is developed based on the Bussgang decomposition. Moreover, an error feedback strategy is introduced to compensate for both compression and reconstruction errors. The convergence rate of the proposed framework is analyzed for a non-convex loss function with consideration of the compression and reconstruction errors. From the analytical result, the key parameters of the proposed framework are optimized for maximizing the convergence rate for the given capacity. Simulation results on the MNIST and CIFAR-10 datasets demonstrate that the proposed framework outperforms state-ofthe-art FL frameworks in terms of classification accuracy under the limited uplink capacity.

In this paper, we consider the sum power minimization problem via jointly optimizing user association, power control, computation capacity allocation and location planning in a mobile edge computing (MEC) network with multiple unmanned aerial vehicles (UAVs). To solve the nonconvex problem, we propose a low-complexity algorithm with solving three subproblems iteratively. For the user association subproblem, the compressive sensing based algorithm is accordingly is proposed. For the computation capacity allocation subproblem, the optimal solution is obtained in closed form. For the location planning subproblem, the optimal solution is effectively obtained via one-dimensional search method. To obtain a feasible solution for this iterative algorithm, a fuzzy c-means clustering based algorithm is proposed.Numerical results show that the proposed algorithm achieves better performance than conventional approaches. ). 2 I. INTRODUCTION With high mobility and the explosive growth of data traffic, unmanned aerial vehicles (UAVs) assisted wireless communications have attracted considerable attention [1]. Compared to conventional wireless communications, UAV-enabled wireless communications can provide higher wireless connectivity in areas without infrastructure coverage. Besides, high throughput can always be achieved in UAV-enabled wireless communications due to the higher probability of line-of-sight (LoS) communication links between user equipments (UEs) and UAVs [2]-[5]. Due to the above distinctions, UAVs can be utilized in many applications, such as UAVenabled relaying [6]-[9], UAV-enabled data collection [10]-[13], UAV-enabled device-to-device communication networks [14], [15], UAV-enabled wireless power transfer networks [16] and UAV-enabled caching networks [17], [18].To fully exploit the design degrees of freedom for UAV-enabled communications, it is crucial to investigate the location and trajectory optimization in UAV-enabled wireless communication networks. In [19], the altitude of UAV was optimized to provide maximum radio coverage on the ground. To maximize the number of covered users using the minimum transmit power, an optimal location and altitude placement algorithm was investigated in [20] for UAV-base stations (BSs). With different quality-of-service (QoS) requirements of users, authors in [21] studied the three-dimension UAV-BS placement that maximizes the number of covered users.Considering the adjustable UAVs' locations, the UAV number minimization was considered in [22]. In [23] and [24], the UAV's trajectory was optimized by jointly considering both the communication throughput and the UAV's energy consumption. Further optimizing user-UAV association, [25] investigated the sum power minimization problem of the UAV. Different from [19]-[25] with fixed-beamwidth antenna, the beamwidth of the directional antenna was optimized in [26] with fixed bandwidth allocation to improve the system throughput. Through jointly optimizing beamwidth and bandwidth, the sum power was further minimized in [27]. Deployi...

This paper investigates the problem of resource allocation for a wireless communication network with distributed reconfigurable intelligent surfaces (RISs). In this network, multiple RISs are spatially distributed to serve wireless users and the energy efficiency of the network is maximized by dynamically controlling the on-off status of each RIS as well as optimizing the reflection coefficients matrix of the RISs. This problem is posed as a joint optimization problem of transmit beamforming and RIS control, whose goal is to maximize the energy efficiency under minimum rate constraints of the users. To solve this problem, two iterative algorithms are proposed for the singleuser case and multi-user case. For the single-user case, the phase optimization problem is solved by using a successive convex approximation method, which admits a closed-form solution at each step. Moreover, the optimal RIS on-off status is obtained by using the dual method. For the multi-user case, a low-complexity greedy searching method is proposed to solve the RIS on-off optimization problem. Simulation results show that the proposed scheme achieves up to 33% and 68% gains in terms of the energy efficiency in both single-user and multi-user cases compared to the conventional RIS scheme and amplify-and-forward relay scheme, respectively.

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4]- [8]. More specifically, UAVs can be utilized in various applications, such as data collection [9]- [12], wireless power transfer [13]-[20], relaying [21]-[24], device-to-device communications [25], caching [26], [27], and mobile edge computing [28].One line of work in the existing literature about UAV communication is UAV-aided ubiquitous coverage [29]-[34], where the UAVs are deployed to assist existing terrestrial communication infrastructure. To fully exploit the degrees of freedom for designing UAV-enabled communications, it is crucial to investigate resource allocation in UAV-enabled wireless communication networks. In [29], the altitude of UAV was optimized to provide maximum coverage on the ground. To maximize the coverage using the minimum transmit power, an optimal location and altitude placement algorithm was investigated in [30] for UAV-base stations (BSs). With different quality-of-service (QoS) requirements of users, authors in [31] studied the three-dimension (3D) UAV-BS placement that maximizes the number of users in the coverage. Exploiting the flexibility of UAV placement, the number of UAVs required for serving a certain area was considered in [32]. To further consider network delay, the optimal placement and distribution of cooperative UAVs was presented in [35]. In delay-constrained communication scenarios, [36] investigated the fundamental throughput-delay tradeoff in UAV-enabled communications. The authors in [37] solved the mission completion time optimization for multi-UAV-enabled data collection. The UAV trajectory was optimized in [38] for parameter estimation in wireless sensor networks.On the other hand, energy saving is critical for UAV communications especially in Internet of Things applications [9]. In order to prolong the lifetime of a sensor network, wireless energy consumption was minimized in [10]. Under a more practical energy consumption model of the UAV, it was pointed out that the propulsion energy is much larger than the communication-related energy [39]. Therefore, to minimize the dominating component of energy consumption, the authors in [11] minimized the total flight time of a UAV while allowing sensors to successfully upload a certain amount of data. Further considering the energy consumption of both user and UAV, the tradeoff between the propulsion energy and the wireless energy of the served user was investigated in [12]. There are two major differences between this paper and [12]. One difference is that this paper investigates the total energy minimization for the rotary-wing UAV, while the fixed-wing UAV was adopted in [12]. The other difference is that this paper considers the general multiuser case, while only single user was investigated in [12].Recently, energy harvesting [40]-[45] has received a great deal of attention in prolonging the lifetime of low-power devices. Different from conventional wireless powered communication network (WPCN), UAV-enabled WPCN can exploit the mobility of UAVs to further improve the system performance [13]- [17]. In [13] and [...

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