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This paper investigates the user clustering and power control in the uplink multiple-input single-output non-orthogonal multiple access (MISO-NOMA) networks. A joint optimization problem is formulated to minimize the system transmit power. The formulated optimization problem is prohibitively complicated, especially when the number of users is large. Alternatively, a two-step user clustering and power control algorithm is proposed. First, a K-means-based algorithm is proposed for user clustering, where both channel gain and channel correlation among users are taken into account for the distance measurement to reduce the intra- and inter-cluster interference. Then, a semi-orthogonal user selection (SUS) algorithm is designed, with which the optimal cluster number and cluster centers can be dynamically obtained. Further, the closed-form expression of the optimal intra-cluster power control is derived, and the resulting inter-cluster power control problem is solved by designing an efficient iterative algorithm. Simulation results show that the proposed K-means-based iterative power control scheme outperforms other reference methods, and can approach the optimal performance in terms of power consumption and energy efficiency at a much lower computational complexity. [ABSTRACT FROM AUTHOR]

This paper proposes a multi-agent double deep Q network (DDQN)-based approach to jointly optimize the beamforming vectors and power splitting (PS) ratio in multi-user multiple-input single-output (MU-MISO) simultaneous wireless information and power transfer (SWIPT)-enabled heterogeneous networks (HetNets), where a macro base station (MBS) and several femto base stations (FBSs) serve multiple macro user equipments (MUEs) and femto user equipments (FUEs). The PS receiver architecture is deployed at FUEs. An optimization problem is formulated to maximize the achievable sum information rate of FUEs under the constraints of the achievable information rate requirements of MUEs and FUEs and the energy harvesting (EH) requirements of FUEs. Since the optimization problem is challenging to handle due to the high dimension and time-varying environment, an efficient multi-agent DDQN-based algorithm is presented, which is trained in a centralized manner and runs in a distributed manner, where two sets of deep neural network parameters are jointly updated and trained to tackle the problem and avoid overestimation. To facilitate the presented multi-agent DDQN-based algorithm, the action space, the state space and the reward function are designed, where the codebook matrix is employed to deal with the complex transmit beamforming vectors. Simulation results validate the proposed algorithm. Notable performance gains are achieved by the proposed algorithm due to considering the beam directions in the action space and the adaptability to the Doppler frequency shifts. Besides, the proposed algorithm is shown to be superior to other benchmark ones numerically. [ABSTRACT FROM AUTHOR]

As for Generative Adversarial Networks (GANs), its interpretability may be closely related to optimization objective functions, that is, information metrics play important roles in networks training and data generation. In terms of original GAN, the objective function based on Kullback-Leibler (KL) divergence has limitations on the performance of data training and generation. Therefore, it is significant to investigate objective functions for the optimization in GANs to bring gains on the efficiency of network learning from the perspective of metrics. In this paper, the objective function with exponential form, referred from the Message Importance Measure (MIM), is adapted to replace that with logarithm form in the optimization for adversarial networks. This approach named MIM-based GAN, may provide more hidden information in terms of interpretability on training process and probability events generation. Specifically, we first analyze the intrinsic relationship between the proposed approach and other classical GANs. Moreover, compared with the original GAN, LSGAN and WGAN, we discuss its advantages on training performance in theory including sensitivity, convergence rate and so on. In addition, we do simulations on the datasets to confirm why the MIM-based GAN achieves state-of-the-art performance on training process and data generation. [ABSTRACT FROM AUTHOR]

A UAV-aided wireless power transfer and data collection network is studied, where it is assumed that when the harvested energy at the sensor node (SN) cannot surpass its circuit activation threshold or the received data rate at UAV falls below a minimal required rate threshold, the information outage occurs. The closed-form expressions of energy outage probability and rate outage probability are derived at first, and then the overall outage probability and coverage performance of the system are analyzed. Based on which, an optimization problem is formulated to minimize the overall outage probability by optimizing UAV’s elevation angle and the time splitting (TS) factor. Since the problem is non-convex and has no known solution, an alternating optimization (AO)-based algorithm with Golden-section (GS) based linear search method is designed to find the global optimal solution. In order to explore the maximum coverage area of the UAV for a given tolerable outage probability, another optimization problem is also formulated to maximize the coverage range by optimizing UAV’s elevation angle. By using Karush-Kuhn-Tucker (KKT) conditions, the closed-form solution of the optimal elevation angle for maximizing the coverage area is derived. Monte Carlo simulations verify the accuracy of the derived closed-form expression of the overall outage probability and the semi-closed-form expressions of the optimum UAV’s elevation angle and TS factor. It shows that there exist a unique optimum elevation angle and the TS factor to achieve the minimum overall outage probability, and significant performance gain can be obtained by using our proposed optimization scheme. The developed theoretical results can be useful to the design of UAV-aided wireless communication systems with wireless power transfer. [ABSTRACT FROM AUTHOR]

There are numerous scenarios in source coding where not only the code length but the importance of each value should also be taken into account. Different from the traditional coding theorems, by adding the importance weights for the length of the codes, we define the average cost of the weighted codeword length as an importance-aware measure of the codes. This novel information theoretical measure generalizes the average codeword length by assigning importance weights for each symbol according to users’ concerns through focusing on user’s selections. With such definitions, coding theorems of the bounds are derived and the outcomes are shown to be extensions of traditional coding theorems. [ABSTRACT FROM AUTHOR]

This paper investigates the optimal resource allocation in wireless powered communication network with user cooperation, where two single-antenna users first harvest energy from the signals transmitted by a multi-antenna hybrid access point (H-AP) and then cooperatively send information to the H-AP using their harvested energy. To explore the system information transmission performance limit, an optimization problem is formulated to maximize the weighted sum-rate (WSR) by jointly optimizing energy beamforming vector, time assignment, and power allocation. Besides, another optimization problem is also formulated to minimize the total transmission time for given amount of data required to be transmitted at the two sources. Because both problems are non-convex, we first transform them to be convex by using proper variable substitutions and then apply semi-definite relaxation to solve them. We theoretically prove that our proposed methods guarantee the global optimum of both problems. Simulation results show that system WSR and transmission time can be significantly enhanced by using energy beamforming and user cooperation. It is observed that when the total amount of information of two users is fixed, with the increase of the information amount of the user relatively farther away from the H-AP, the transmission time of the user cooperation scheme decreases while that of the direct transmission increases. Besides, the effects of user position on the system performances are also discussed, which provides some useful insights. [ABSTRACT FROM PUBLISHER]

This paper investigates the multicell coordinated beamforming (MCBF) design for high-speed railway communications, where a high-mobility train goes through a multicell multiuser system in urban areas. All passengers in the train are aggregated to be a high-mobility “big user.” In order to explore the downlink system performance limit, we formulate an optimization problem to maximize the information rate from the serving base station to the high-mobility train while guaranteeing the minimal required achievable information rates of all low-mobility users outside the train by optimizing MCBF vectors. The interchannel interference (caused by Doppler effect to the train), the intercell interference and the interuser interference are jointly considered. Since the problem is nonconvex and cannot be solved directly, we first introduce an auxiliary variable (AV), and for a given AV, we find the optimal MCBF vectors via solving a second-order cone programming. Then, by updating the AV with the bisection method, the corresponding global optimal MCBF vectors are numerically obtained. Simulation results show that our proposed MCBF design greatly enhances the information throughput of the high-mobility train without degrading the required data rates of low-mobility users. [ABSTRACT FROM PUBLISHER]

This paper investigates the simultaneous wireless information and power transfer (SWIPT) in cooperative relay networks, where a relay harvests energy from the radio frequency (RF) signals transmitted by a source and then uses the harvested energy to assist the information transmission from the source to its destination. Both source and relay transmissions use rateless codes (RCs), which allow the destination to employ any of the two information receiving strategies, i.e., the mutual information accumulation (IA) and the energy accumulation (EA). The SWIPT-enabled relay employs three different SWIPT receiver architectures, the ideal receiver, and two practical receivers (i.e., the power splitting (PS) receiver and the time switch (TS) receiver). Accordingly, three relaying protocols, namely, the ideal protocol, PS protocol, and TS protocol, are presented. To explore the system performance limits with these three protocols, optimization problems are formulated to maximize their achievable information rates. For the ideal protocol, explicit expressions of the optimal solutions are derived. For the PS protocol, a linear-search algorithm is designed to solve the nonconvex problems. For the TS protocol, two solving methods are presented. Numerical experiments are carried out to validate our analysis and algorithms, which show that, with the same SWIPT receiver, the IA-based system outperforms the EA-based system, whereas with the same information receiving strategy, the PS protocol outperforms the TS protocol. Moreover, compared with nonrateless-coded systems, the proposed protocols exhibit considerable performance gains. Moreover, the effects of the relay position on system performance are also discussed, which provides insights on SWIPT-enabled relay systems. [ABSTRACT FROM AUTHOR]

In 5G heterogeneous networking, it is promising to integrate different wireless networks to provide higher data rate. This paper models the integrated system as a receiver frequency division relay channel (RFDRC) and studies how to improve the transmission rate by combining decode-forward (DF), compress-forward (CF), and amplify-forward (AF) schemes. First, we establish clear criterions on how to select a relay scheme among DF, CF, and AF schemes and prove that the CF outperforms AF for all possible configurations. Based on the scheme selection criterions, we propose a hybrid DF–CF scheme which takes advantage of both DF and CF schemes in RFDRC. A near-optimal resource allocation is presented for the DF–CF-based system, leading to a new achievable rate for RFDRC. For ease of implementation, we further put forward a hybrid DF–AF scheme and reconsider the joint bandwidth and power allocation. Two suboptimal resource allocation solutions are established. In particular, when source frequency band and relay frequency band have equivalent bandwidth, we show that the proposed hybrid DF–AF scheme can achieve the concave envelope of the maximum between DF rate and AF rate. Numerical results show that the proposed schemes bring significant gains for RFDRC. [ABSTRACT FROM AUTHOR]

For a two-way relay network (TWRN) with three nodes, we discuss the performance optimization of digital network coding (DNC) and physical network coding (PNC) schemes under the energy harvesting (EH) constraints and peak power constraints. We also consider the energy transfer between nodes, which is referred to as energy co-operation. To find the maximal achievable performance, we first consider the case of offline scheduling, formulate the corresponding optimization problems, find the optimal solutions, as well as present some useful theoretical properties on optimality. Then we move to the online scheduling, and propose both dynamic programming and some intuitive policies to approach the performance of its offline counterpart. Numerical results show that PNC outperforms DNC due to the higher spectrum efficiency and the intrinsic coding gains, under the same conditions. Furthermore, it is observed that if the relay harvests much more energy and shares it with the two source nodes, DNC with energy co-operation scheme has the potential to perform comparable to or even better than PNC without energy co-operation scheme, which validates the importance of energy co-operation in contemporary communication systems. [ABSTRACT FROM AUTHOR]

In this paper, we investigate a multisource multicast network with the aid of an arbitrary number of relays, where it is assumed that no direct link is available at each S-D pair. The aim is to find the fundamental limit on the maximal common multicast throughput of all source nodes if resource allocations are available. A transmission protocol employing the relaying strategy, i.e., compute-and-forward (CPF), is proposed. We also adjust the methods in the literature to obtain the integer network-constructed coefficient matrix (i.e., a naive method, a local optimal method, and a global optimal method) to fit the general topology with an arbitrary number of relays. Three transmission scenarios are addressed. The first scenario is delay-stringent transmission, where each message must be delivered within one slot. The second scenario is delay-tolerant transmission where no delay constraint is imposed. The associated optimization problems to maximize the short- and long-term common multicast throughputs are formulated and solved, and the optimal allocation of power and time slots are presented. The third case (a general $N$-slot-delay-tolerant scenario) is also discussed, and a suboptimal algorithm is presented. Performance comparisons show that the CPF strategy outperforms conventional decode-and-forward (DF) strategy. It is also shown in the simulation that with more relays, the CPF strategy performs even better due to the increased diversity. Finally, by simulation, it is observed that, with CPF, the $N$-slot-delay-constraint case can perform close to that of the delay-tolerant case in terms of throughput, given that $N$ is relatively large. [ABSTRACT FROM PUBLISHER]

High-speed railways (HSRs) have been widely deployed all over the world in recent years. Different from traditional cellular communications, the high mobility of HSR communication makes it essential to implement power allocation (PA) along time. In the HSR case, the transmission rate greatly depends on the distance between the base station (BS) and the train. As a result, the train receives a time-varying data rate service when passing by a BS. It is clear that the most efficient PA will spend all the power when the train is nearest the BS, which will cause great unfairness along time. On the other hand, channel inversion allocation achieves the best fairness in terms of constant rate transmission. However, its power efficiency is much lower. Therefore, power efficiency and fairness along time are two incompatible objects. For the HSR cellular system considered in this paper, a tradeoff between the two is achieved by proposing a temporal proportional-fair PA scheme. In addition, a near-optimal closed-form solution and one algorithm finding \epsilon-optimal allocation are presented. [ABSTRACT FROM PUBLISHER]