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Recently, reconfigurable intelligent surfaces (RISs) have attracted increasing attentions in the design of full-duplex (FD) systems due to their novel capability of propagation environment reconfiguration. However, in conventional RIS-assisted FD systems, the beamforming for self-interference cancellation (SIC) and sum rate maximization (SRM) are highly coupled during RIS optimization, which significantly degrades the system performance. To tackle this issue, we exploit a novel bilayer intelligent omni-surface (BIOS) structure in FD systems. Compared with the conventional RIS designs, the BIOS provides independent beams on both sides, thus enabling more flexible achievement of SRM and SIC. For the BIOS-assisted FD system, we first formulate an optimization problem to achieve SRM and efficient SIC simultaneously. Then, we exploit the relationship between the SRM and mean square error (MSE), and propose a weighted MSE minimization with SIC algorithm to solve the problem. Specifically, we jointly design the beamforming at the base station and the BIOS with manifold optimization while guaranteeing an SIC constraint. Furthermore, we theoretically derive a lower band for the BIOS size required for efficient SIC in FD systems. Simulation results indicate that the BIOS outperforms the conventional RIS designs in FD systems, and verify the accuracy of the derived lower bound for the BIOS size. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Chongqing University of Technology (Natural Science) is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this paper, we consider an unmanned aerial vehicle (UAV)-enabled orthogonal frequency division multiplexing (OFDM) network where the UAV acts as an aerial base station and employs the OFDM to transmit individual data to multiple users on the ground. We propose a $K$ -means clustering-aided power control algorithm to maximize the sum-rate of the network under the total transmit power and the minimum rate constraints. The proposed algorithm categorizes $N_{\mathrm {s}}$ subcarriers into $K$ clusters and optimizes the transmit power for the clustered subcarriers with a power splitting factor to decompose the multi-user power allocation problem. Since the number of clusters is far less than the number of subcarriers, the proposed algorithm requires low-computational complexity compared to the conventional water-filling algorithm. Simulation results show that the proposed algorithm provides near-optimal performance with $K=4$ clusters even for a large number of subcarriers $N_{\mathrm {s}}=1024$ .

Recently, reconfigurable intelligent surfaces (RISs) have attracted increasing attentions in the design of full-duplex (FD) systems due to their novel capability of propagation environment reconfiguration. However, in conventional RIS-assisted FD systems, the beamforming for self-interference cancellation (SIC) and sum rate maximization (SRM) are highly coupled during RIS optimization, which significantly degrades the system performance. To tackle this issue, we exploit a novel bilayer intelligent omni-surface (BIOS) structure in FD systems. Compared with the conventional RIS designs, the BIOS provides independent beams on both sides, thus enabling more flexible achievement of SRM and SIC. For the BIOS-assisted FD system, we first formulate an optimization problem to achieve SRM and efficient SIC simultaneously. Then, we exploit the relationship between the SRM and mean square error (MSE), and propose a weighted MSE minimization with SIC algorithm to solve the problem. Specifically, we jointly design the beamforming at the base station and the BIOS with manifold optimization while guaranteeing an SIC constraint. Furthermore, we theoretically derive a lower band for the BIOS size required for efficient SIC in FD systems. Simulation results indicate that the BIOS outperforms the conventional RIS designs in FD systems, and verify the accuracy of the derived lower bound for the BIOS size.

Vehicle-to-vehicle (V2V) communication has been designed to afford improvements in the traffic congestion and traffic accidents by directly exchanging information between nearby vehicles. However, V2V communication may have difficulty providing service reliability due to interference between V2V links and vehicle-to-cellular user equipment links. In this paper, we consider the transmit power control algorithm to minimize interference among cellular users and vehicles in the context of V2V communication underlaid uplink cellular networks. First, we formulate the problem, which is an NP-hard combinatorial optimization problem with linear constraints. Addressing this problem with traditional optimization methods is ineffective; therefore, we design and train a deep neural network to address this optimization problem. Computational complexity analyses and simulation results reveal that the proposed algorithm outperforms weighted minimum mean squared error (WMMSE), fixed transmit power, and Dinkelbach's methods, and achieves near-global optimum with lower computation complexity than the exhaustive search (ES). [ABSTRACT FROM AUTHOR]

In this paper, a reconfigurable intelligent surfaces (RISs)-aided millimeter wave (mmWave) uplink (UL) rate-splitting multiple access (RSMA) system is investigated which targets to achieve better rate performance and enhanced coverage capability for multiple users. The considered UL RSMA model splits the rate for each user by dividing their message into multiple parts and hence exploits all the necessary degrees of freedom to achieve maximum capacity region and high user fairness. In particular, we focus on the sum-rate maximization for considered UL RSMA system subject to joint optimization of power allocation to the UL users and beamforming design, i.e., active receive beamforming at the base-station (BS) and passive beamforming at multiple RISs. To efficiently mitigate high inter-node interference in multi-user scenario, we first provided a low-complex user pairing scheme based on k-means clustering and then develop an effective low-cost alternating optimization framework to solve the joint optimization problem sub-optimally by decoupling the problem into different sub-problems of power allocation and beamforming design. Specifically, the sub-problems of power allocation and beamforming design are solved using successive convex approximation, Riemannian manifold and fractional programming techniques. Later, the unified solution based on block coordinate descent (BCD) algorithm is proposed. Extensive numerical simulations validate that the user-clustering effectively significantly improves the performance gain and the considered RSMA system outperforms the conventional multiple schemes in terms rate and user-fairness. Also, the exploitation of spatial correlation among each RIS elements i.e., non-diagonal phase-matrices at each RIS achieve better performance that conventional diagonal phase-matrices setting. [ABSTRACT FROM AUTHOR]

This treatise investigates multistream hybrid beamforming (HBF) for a millimeter-wave non-orthogonal multiple access (NOMA) system deployed in a downlink of an urban microcell environment. Maximization problem is developed to optimize sum-rate. For the sake of ensuring high correlation of users’ channels, user’s clustering and ordering are based on the angle of arrivals and users’ channel-weights, respectively. An optimized analog combiner of each user is obtained from the first $N_{s}$ column vectors of the left unitary matrix derived from the singular value decomposition of the channel of each user. Analog beamformer is matched to the phase of the strong users ’ composite intermediate analog channel to maximize the beamforming gain. Both the analog combiner and precoder for sub-connected structure (SCS) are formulated via a novel dominant sub-array matrix elements’ extractor. Conventional zero-forcing processing is contemplated for digital precoding. Memory space complexity is evaluated to corroborate the simplicity of the proposed schemes’ computational complexity. Results obtained from the simulation exhibit that HBF-NOMA attain superior sum-rate than HBF-orthogonal multiple access and conventional multiuser schemes in line of sight link. Lastly, the proposed SCS-HBF-NOMA precoding scheme performs higher than fully connected counterpart in terms of energy efficiency.

This paper investigates unmanned aerial vehicles (UAVs)-assisted full-duplex (FD) non-orthogonal multiple access (NOMA) system based cellular network, aiming to improve overall sum-rate throughput of the system through dynamic user clustering, optimal UAV placement and power allocation. Since each UAV operates in FD mode, self-interference (SI), co-channel interference (CCI), inter-UAV interference (IUI) and intra-node interference (INI) dominate the system’s performance. Consequently, we propose an unconventional two-stage dynamic user clustering for user nodes (UNs) to reduce the cross-interference in multi-UAV aided FD-NOMA system. Particularly, all UNs are initially clustered into $K$ clusters using k-means clustering in the first stage where each cluster is served by an UAV. Furthermore, each cluster is further divided into sub-clusters and each sub-clusters are operated in FD-NOMA scheme. Finally, to control interferences, a sum-rate throughput maximization problem is formulated for each UAV to jointly optimize uplink and downlink power allocation and UAV placement. The joint optimization problem is non-convex and difficult to solve directly, for which we decoupled the original problem by addressing UAV placement and power allocation separately. We first fix the UAV position and then solve the problem iteratively using successive convex approximation (SCA) method. By utilizing brute-force search algorithm, an optimal UAV placement is later performed which corresponds to maximum possible sum-rate throughput. Simulation results demonstrate that the proposed solution for the considered FD-NOMA system outperforms the conventional schemes. [ABSTRACT FROM AUTHOR]

Copyright of Journal on Communication / Tongxin Xuebao is the property of Journal on Communications Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In order to address the shortcomings of orthogonal frequency division multiplexing (OFDM) and extend the lifetime of energy-constrained Internet-of-Things (IoT) devices, the combination of filter bank multi-carrier (FBMC) and simultaneous wireless information and power transfer (SWIPT) is investigated in this paper. Specifically, a multi-user FBMC-based SWIPT system is proposed in which user nodes (UNs) have the capability for both energy harvesting (EH) and information decoding (ID) with the aid of separate antennas. A practical non-linear EH model, which considers the saturation effects of the EH circuit, is considered. The information receiver at both the UNs and base station (BS) adopts an iterative interference cancellation (IIC) receiver to cancel the intrinsic interference in the demodulated FBMC signal. A sum-rate maximization problem is solved to jointly optimize parameters such as time, power, and weight allocations. Sub-optimal schemes are proposed for comparison. Numerical results show that the optimal solution significantly outperforms the sub-optimal methods in terms of achievable sum-rate and amount of harvested energy. Moreover, the results show that the proposed algorithm converges within a few iterations.

The distributed antenna systems (DAS) and non-orthogonal multiple access (NOMA) are the key technologies to boost the data rate of the cellular system in terms of small cell and multiple access, respectively. To meet the high data rate requirements for 5G and beyond, we suggest a framework of using NOMA in DAS. In the proposed scheme, RRUs which are geographically distributed in the cell serve the cell-edge users within their own coverage. Meanwhile, the macro BS, which covers the entire cell region with relatively high transmit power, supports both the cell-center and the cell-edge users with identical resources by using NOMA. Compared to the conventional DAS where the macro BS and the RRU serve the cell-center and the cell-edge users, respectively, the proposed framework also boosts the data rate of the cell-center user by improving the reliability of successive interference cancellation (SIC) at the cell-center user. In the proposed framework, this paper also proposes the optimal power allocation rules maximizing the user fairness in two different cases of instantaneous channel gain information (CGI) and channel distribution information (CDI) known at the transmitter. Also, the power allocation methods maximizing the sum-rate with a minimum rate constraint and the weighted sum-rate are presented in the case of CGI known at the transmitter. Simulation results show that the proposed framework of using NOMA in DAS can boost data rates more in a variety of system environments compared to the conventional NOMA or DAS.

In this paper, a generalized uplink cell-free massive multiple-input multiple-output (mMIMO) system with hardware imperfections is investigated. Specifically, the hardware impairments consist of multiplicative phase drifts, additive distortion, and amplified thermal noise. Leveraging on a generic hardware impairment model, closed-form expressions for the channel estimation and achievable rate are presented. Based on these tractable results, we explore the uplink achievable rate surrounding the key design parameters, i.e., the channel estimation error, the number of antenna arrays, the local oscillator (LO) setup, and the variance of phase drifts. Aside from this, we also design a power optimization algorithm to maximize the sum-rate under variant channel use. The weighted minimum mean square error (MMSE) methodology is introduced to iteratively determine the solution in closed form. Numerical simulations validate the analytical analyses and examine the effectiveness of the proposed power control algorithm. [ABSTRACT FROM AUTHOR]

Enhanced mobile broadband (eMBB) and ultra-reliable and low-latency communications (URLLC) are the two main expected services in the next generation of wireless networks. Accommodation of these two services on the same wireless infrastructure leads to a challenging resource allocation problem due to their heterogeneous specifications. To address this problem, slicing has emerged as an architecture that enables a logical network with specific radio access functionality to each of the supported services on the same network infrastructure. The allocation of radio resources to each slice according to their requirements is a fundamental part of the network slicing that is usually executed at the radio access network (RAN). In this work, we formulate the RAN resource allocation problem as a sum-rate maximization problem subject to the orthogonality constraint (i.e., service isolation), latency-related constraint and minimum rate constraint while maintaining the reliability constraint with the incorporation of adaptive modulation and coding (AMC). However, the formulated problem is not mathematically tractable due to the presence of a step-wise function associated with the AMC and a binary assignment variable. Therefore, to solve the proposed optimization problem, first, we relax the mathematical intractability of AMC by using an approximation of the non-linear AMC achievable throughput, and next, the binary constraint is relaxed to a box constraint by using the penalized reformulation of the problem. The result of the above two-step procedure provides a close-to-optimal solution to the original optimization problem. Furthermore, to ease the complexity of the optimization-based scheduling algorithm, a low-complexity heuristic scheduling scheme is proposed for the efficient multiplexing of URLLC and eMBB services. Finally, the effectiveness of the proposed optimization and heuristic schemes is illustrated through extensive numerical simulations.

Device-to-device (D2D) communication affords the benefits of improved network spectral efficiency, throughput, energy efficiency, and delay performance. However, D2D communication underlaid cellular uplinks are degraded by interference between D2D user equipment (DUE) and cellular user equipment (CUE) that use the same frequency band. In this context, researchers have proposed the optimization of the transmit power of the devices to mitigate interference. This power allocation problem is generally modeled as a NP-hard combinatorial optimization problem with linear constraints; therefore, traditional optimization methods are ineffective in addressing the problem. In this paper, we apply a deep reinforcement learning (DRL) algorithm to optimize the transmit power for both DUEs and CUEs in the context of D2D communication underlaid uplink cellular networks, which can solve the optimization problem via optimal decision-making together with efficient deep network training. Our simulation results show that the proposed algorithm affords a near-global-optimum solution with lower computation complexity than exhaustive search, and outperforms the fixed-power, weighted minimum mean square error (WMMSE), gradient descent method, and Newton-Raphson approaches. [ABSTRACT FROM AUTHOR]

In this paper, the sum-rate maximization problem is studied for wireless networks that use downlink rate splitting multiple access (RSMA). In the considered model, the base station (BS) divides the messages that can be transmitted to its users into a “private” part and a “common” part. Here, the common message is a message that multiple users want to receive and the private message is a message that is dedicated to only a specific user. The RSMA mechanism enables a BS to adjust the split of common and private messages so as to control the interference by decoding and treating interference as noise and, thus optimizing the data rate of users. To maximize the users’ sum-rate, the network can determine the rate allocation for the common message to meet the rate demand, and adjust the transmit power for the private message to reduce the interference. This problem is formulated as an optimization problem whose goal is to maximize the sum-rate of all users. To solve this nonconvex maximization problem with a single-antenna BS, the optimal power used for transmitting the private message to the users is first obtained in closed form for a given rate allocation and common message power. Based on the optimal private message transmit power, the optimal rate allocation is then derived under a fixed common message transmit power. Subsequently, an iterative algorithm is proposed to obtain a suboptimal solution of common message transmit power. To solve this nonconvex maximization problem with a multiple-antenna BS, a successive convex approximation method is utilized. Simulation results show that the RSMA can achieve up to 15.6% and 21.5% gains in terms of data rate compared to non-orthogonal multiple access (NOMA) and orthogonal frequency-division multiple access (OFDMA), respectively. [ABSTRACT FROM AUTHOR]

This letter proposes a new optimization framework for power-domain non-orthogonal multiple access (PD-NOMA) ambient backscatter communication (AmBC) system with imperfect successive interference cancellation (SIC) decoding. In particular, we jointly optimize the transmit power of the source and the reflection coefficient of the backscatter tag to enhance the sum-rate of the system. Closed-form solutions are derived for the considered joint optimization problem via iteratively computing the Lagrange multipliers by the sub-gradient method. Simulation results validate the superiority of the proposed joint optimization framework over other benchmark frameworks, namely suboptimal PD-NOMA AmBC, optimal pure PD-NOMA, and orthogonal multiple access. [ABSTRACT FROM AUTHOR]

For a multi-cell, multi-user, cellular network downlink sum-rate maximization through power allocation is a nonconvex and NP-hard optimization problem. In this article, we present an effective approach to solving this problem through single- and multi-agent actor-critic deep reinforcement learning (DRL). Specifically, we use finite-horizon trust region optimization. Through extensive simulations, we show that we can simultaneously achieve higher spectral efficiency than state-of-the-art optimization algorithms like weighted minimum mean-squared error (WMMSE) and fractional programming (FP), while offering execution times more than two orders of magnitude faster than these approaches. Additionally, the proposed trust region methods demonstrate superior performance and convergence properties than the Advantage Actor-Critic (A2C) DRL algorithm. In contrast to prior approaches, the proposed decentralized DRL approaches allow for distributed optimization with limited CSI and controllable information exchange between BSs while offering competitive performance and reduced training times. [ABSTRACT FROM AUTHOR]

In multi-user downlink small cell networks, cooperative resource allocation (RA) within a small cell cluster is a key technique to enhance network capacity. However, capacity-maximizing RA in frequency-selective fading channels requires global channel state information (CSI) of users within a small cell cluster, which makes it infeasible in practical networks with limited direct link capacity. To circumvent this global CSI assumption, most of the existing studies on RA have been based on several CSI assumptions such as local CSI and local CSI at the transmitters (CSIT). Nevertheless, cost functions with local CSI or local CSIT in the literature rely on heuristic formulations, because the sum-rate cannot be computed if without global CSI. In this paper, we propose a deep reinforcement learning-based RA algorithm to maximize the sum-rate for any given limited information on instantaneous CSI or sum-rate at the previous period. The proposed scheme is not restricted to certain CSI assumptions, but attempts to find the best RA for any given information such as quantized local CSI and quantized local CSIT; thus, it is applicable to any given direct link capacity. The proposed algorithm is self-adaptive in time-varying channels, since it is not divided into training and test phases. We modify the target neural network (TNN) scheme to enhance the sum-rate and the convergence speed. Numerical simulations confirm that: i) the proposed algorithm outperforms the conventional algorithms even under the same CSI assumption such as local CSI and local CSIT; ii) a flexible trade-off between the amount of CSI and the sum-rate is realizable in practical systems. [ABSTRACT FROM AUTHOR]

This paper proposes a new sum-rate maximization based relay selection (RS) scheme for cooperative non-orthogonal multiple access (NOMA) networks over Nakagami-m fading, where one base station communicates with two mobile users by means of multiple relays. The outage probability of the proposed scheme is derived in a closed-form expression, and the diversity order is also obtained. Simulation results are shown to compare the outage performance of the proposed scheme with that of the existing RS schemes. [ABSTRACT FROM AUTHOR]

Multi-tier cloud-radio access networks (C-RANs) have been suggested as a promising network model in fifth generation wireless networks to provide high data rate, high spectral efficiency, and high energy efficiency at low cost. However, to achieve the potential benefits of multi-tier C-RANs, it is important to design efficient resource allocation algorithms provided that the data rate of users is guaranteed. Therefore, in this paper, we considered the joint optimization of remote-radio-heads (RRH) association, sub-channel assignment, and power allocation for network sum-rate maximization in single-carrier frequency division multiple access (SC-FDMA)-based multi-tier C-RAN. Due to the exclusivity and adjacency constraints of SC-FDMA, the resource allocation problem becomes harder to solve, thus, the optimal solution is difficult for reasonably sized network. Therefore, we propose an iterative algorithm that solves this non-linear mixed-integer problem in two steps wherein the first step, power allocation and subchannel assignment are carried out, while the second step of the proposed algorithm is concerned with the RRH-association. The iterative algorithm converges to efficient solution. The simulation results verify the effectiveness of our proposed algorithm.

In this paper, we propose a distributed energy beamforming and information transfer (DEBIT) scheme for realizing simultaneous wireless information and power transfer (SWIPT) in multiway relay networks (MWRNs), where multiple users exchange messages with the help of an energy-constrained relay node. We investigate the sum-rate maximization as well as the harvested power maximization problems for the proposed scheme. The resultant sum-rate maximization problem is non-convex and the global optimal solution can be obtained through a 3-D search in combination with convex optimization. To reduce the computation complexity, a suboptimal DEBIT scheme is also proposed, for which the optimization problem becomes linear programming. The achievable sum-rate performance is analyzed and a closed-form lower bound is derived for the MWRN with a large number of users. Furthermore, we consider the harvestedpower maximization problem under a target sum-rate constraint and derive a lower bound of the average harvested power for MWRNs with a large number of users. Numerical results show that the DEBIT scheme significantly outperforms the conventional SWIPT and the derived lower bounds are tight.

The future wireless networks promise to provide ubiquitous connectivity to a multitude of devices with diversified traffic patterns wherever and whenever needed. For the sake of boosting resilience against faults, natural disasters, and unexpected traffic, the unmanned aerial vehicle (UAV)-assisted wireless communication systems can provide a unique opportunity to cater for such demands in a timely fashion without relying on the overly engineered cellular network. However, for UAV-assisted communication, issues of capacity, coverage, and energy efficiency are considered of paramount importance. The case of non-orthogonal multiple access (NOMA) is investigated for aerial base station (BS). NOMA's viability is established by formulating the sum-rate problem constituting a function of power allocation and UAV altitude. The optimization problem is constrained to meet individual user-rates arisen by orthogonal multiple access (OMA) bringing it at par with NOMA. The relationship between energy efficiency and altitude of a UAV inspires the solution to the aforementioned problem considering two cases, namely, altitude fixed NOMA and altitude optimized NOMA. The latter allows exploiting the extra degrees of freedom of UAV-BS mobility to enhance the spectral efficiency and the energy efficiency. Hence, it saves joules in the operational cost of the UAV. Finally, a constrained coverage expansion methodology, facilitated by NOMA user rate gain is also proposed. Results are presented for various environment settings to conclude NOMA manifesting better performance in terms of sum-rate, coverage, and energy efficiency.

In this paper, we study an energy-harvesting cellular two-way relay network with massive multiple-input multiple-output, where multiple energy-harvesting mobile stations (MSs) communicate with a base station (BS) via an energy-harvesting relay station (RS). A signal space alignment (SSA) based simultaneous wireless information and power transfer (SWIPT) protocol is proposed for efficient energy harvesting and information exchange. We analyze the performance of the proposed SSA-SWIPT scheme, and derive closed-form expressions for the asymptotic signal-to-interference-plus-noise ratios and the achievable sum-rate when the numbers of antennas at the BS and the RS go large simultaneously. We also investigate the optimal power splitting ratios at the RS and the MSs to maximize the achievable sum-rate. Numerical results are provided to verify the analysis and it is shown that employing massive antennas can improve the efficiencies of wireless energy harvesting and information transmission.

While the problems of sum-rate maximization and sum-power minimization subject to quality of service (QoS) constraints in the multiple input multiple output interference broadcast channel (MIMO IBC) have been widely studied, most of the proposed solutions have neglected the user scheduling aspect assuming that a feasible set of users has been previously selected. However, ensuring QoS for each user in the MIMO IBC involves the joint optimization of transmit/receive beamforming vectors, transmit powers, and user scheduling variables. To address the full problem, we propose a novel formulation of a rate-constrained sum-utility maximization problem which allows to either deactivate users or minimize the QoS degradation for some scheduled users in infeasible scenarios. Remarkably, this is achieved avoiding the complexity of traditional combinatorial formulations, but rather by introducing a novel expression of the QoS constraints that allows to solve the problem in a continuous domain. We propose centralized and decentralized solutions, where the decentralized solutions focus on practical design and low signaling overhead. The proposed solutions are then compared with benchmarking algorithms, where we show the effectiveness of the joint scheduling and transceiver design as well as the flexibility of the proposed solution performing advantageously in several MIMO IBC scenarios. [ABSTRACT FROM AUTHOR]

In recent years, intelligent transportation systems are a vital part of the development of smart cities. In intelligent transportation systems, it is necessary for each vehicle to know the states of all other running vehicles within a range, depends on the road speed limit. The states include location, travelling direction, speed, and possibly other useful information. Knowing the states of surrounding vehicles is critical in high-risk locations, such as traffic intersections and roundabouts. The development of 5G and its technologies, such as massive multiple-input multiple-output antenna, enables a new communication model, termed vehicle-to-infrastructure edge network (V2IEN), to achieve the above communication goal. In this paper, we study the optimization of the data throughput of the proposed V2IENs in 5G with the Time Division Duplex scheme. We show that due to the nature of information flow in the proposed V2IENs, the uplink and downlink traffic loads are asymmetric. This asymmetric characteristic allows maximizing the degrees of freedom of the V2IENs by optimizing the time-slot resources for uplink and downlink transmission. In this work, we present the sum-DoF capacity of the V2IENs with proof. We demonstrate that a significant DoF gain is achieved for the V2IENs by carefully allocate the system transmission time resources. We further propose an iterative algorithm for network sum-rate maximization via the optimization of the vehicle and base station precoders. Numerical results demonstrate that a careful design of the precoders at the base station and at the vehicles can considerably improve the V2IENs performance. [ABSTRACT FROM AUTHOR]

We study the sum-rate maximization problem, under a total power budget, for asynchronous single-carrier bi-directional relay networks, consisting of two transceivers and multiple amplify-and-forward relays. When different transceiver-relay links cause significantly different propagation delays in the signal they convey, the end-to-end channel is not amenable to a frequency-flat modeling; rather, a multi-path channel model is appropriate. Such a multi-path channel model results in inter-symbol-interference at the transceivers. Aiming to maximize the sum-rate of this channel over the relay weights and transceivers' powers, we rigorously prove that such a sum-rate maximization problem leads to a relay selection scheme, where only those relays, which contribute to one of the taps of the end-to-end channel impulse response (CIR), are turned on. Indeed, we prove that the optimal end-to-end CIR has only one non-zero tap, rendering the end-to-end channel frequency-flat. Our proof shows that the mean-squared-error (MSE) optimal joint post-channel equalization, network beamforming, and power allocation scheme is sum-rate-optimal. The equivalence of MSE-optimal and sum-rate-optimal solutions is interesting, as MSE minimization promotes end-to-end reliability, while sum-rate maximization advocates for multiplexing gain. These approaches often pull the design of communication systems in different directions. For the aforementioned scenario, these approaches are identical as we prove.

This letter studies an unmanned aerial vehicle (UAV)-enabled two-user interference channel, in which two ground users send individual messages to their respective UAVs in the sky. By considering probabilistic line-of-sight (LoS) channel models, we jointly optimize the two UAVs’ three-dimensional (3D) positioning locations and the two users’ power control, to maximize their sum-rate throughput. Although this problem is non-convex and difficult to solve in general, we obtain the optimal solution by decoupling the positioning optimization and power control. The optimal solution shows that if the signal-to-noise-ratio (SNR) $\rho$ is high, then the two users should take turns to transmit at the maximum power in an on-off manner and each UAV is positioned exactly above the associated user. By contrast, if $\rho$ is low and/or the two users’ distance $D$ is large, then the two users should transmit simultaneously at the maximum power, and each UAV should be positioned away from the two users for interference mitigation. [ABSTRACT FROM AUTHOR]

Integrating unmanned aerial vehicles (UAVs) into the cellular network as new aerial users is a promising solution to meet their ever-increasing communication demands in a plethora of applications. Due to the high UAV altitude, the channels between UAVs and the ground base stations (GBSs) are dominated by the strong line-of-sight (LoS) links, which brings both opportunities and challenges. On one hand, a UAV can communicate with a large number of GBSs at the same time, leading to a higher macro-diversity gain as compared to terrestrial users. However, on the other hand, severe interference may be generated to/from the GBSs in the uplink/downlink, which renders the interference management with coexisting terrestrial and aerial users a more challenging problem to solve. To deal with the above new trade-off, this paper studies the uplink communication from a multi-antenna UAV to a set of GBSs in its signal coverage region. Among these GBSs, we denote available GBSs as the ones that do not serve any terrestrial users at the assigned resource block (RB) of the UAV, and occupied GBSs as the rest that are serving their respectively associated terrestrial users in the same RB. We propose a new cooperative interference cancellation strategy for the multi-beam UAV uplink communication, which aims to eliminate the co-channel interference at each of the occupied GBSs and in the meanwhile maximize the sum-rate to the available GBSs. Specifically, the multi-antenna UAV sends multiple data streams to selected available GBSs, which in turn forward their decoded data streams to their backhaul-connected occupied GBSs for interference cancellation. To draw useful insights and facilitate our proposed design, the maximum degrees-of-freedom (DoF) achievable by the multi-beam UAV communication for sum-rate maximization in the high signal-to-noise ratio (SNR) regime is first characterized, subject to the stringent constraint that all the occupied GBSs do not suffer from any interference in the UAV’s uplink transmission. Then, based on the DoF-optimal design, the achievable sum-rate at finite SNR is maximized, subject to given maximum allowable interference power constraints at each of the occupied GBSs. The numerical examples validate the DoF and sum-rate performance of our proposed designs, as compared to benchmark schemes with fully cooperative, local, or no interference cancellation at the GBSs. [ABSTRACT FROM AUTHOR]

In this letter, we propose a novel simultaneous uplink (UL) and downlink (DL) transmission scheme aiming to achieve a higher spectral efficiency for wireless power communication networks (WPCNs). Specifically, by exploiting full-duplex (FD) radio and wireless power transfer techniques, an FD hybrid access point is able to communicate with a wireless powered UL user and a DL user over the same time-frequency resource. The optimization problem of interest is to maximize the sum rate of the scheme under a given power budget, which is formulated as a nonconvex problem. To this end, we propose a low-complexity path following algorithm for its solution. Numerical results reveal that the proposed scheme indeed achieves substantial sum-rate improvement for the WPCNs. [ABSTRACT FROM AUTHOR]

The ever increasing demands for mobile network access have resulted in a significant increase in bandwidth usage. By improving the system spectral efficiency, multi-way relay networks (MWRNs) provide promising approaches to address this challenge. In this paper, we propose a novel linear beamforming design, namely partial zero-forcing (PZF), for MWRNs with a multiple-input-multiple-output relay. Compared to zero-forcing (ZF), PZF relaxes the constraints on the relay beamforming matrix such that only partial user-interference, instead of all, is canceled at the relay. The users eliminate the remaining interferences through self-interference and successive interference cancellation. A sum-rate maximization problem is formulated and solved to exploit the extra degrees-of-freedom resulted from PZF. Simulation results show that the proposed PZF relay beamforming design achieves significantly higher network sum-rates than the existing linear beamforming designs. [ABSTRACT FROM AUTHOR]

In this paper, we study a wireless powered communication network (WPCN) in a general $N$ -user interference channel (IFC), where a hybrid access-point (H-AP) in each cell supports its corresponding user. In this multi-cell environment, the H-AP first sends the energy signal to charge users in the downlink (DL) phase, while in the subsequent uplink (UL) phase, each user transmits its information signal to the corresponding H-AP utilizing the previously harvested energy. For the WPCN in this IFC scenario, cross-link interference occurs due to asynchronous time allocation of the DL and the UL among $N$ cells which significantly affects the overall performance. To handle the interference issue efficiently, we jointly optimize the DL and UL time allocation of each cell as well as the transmit power allocation at the H-APs and the users so that the weighted sum-rate of UL information transmission is maximized. To tackle non-convexity of the weighted sum-rate maximization problem, we propose an iterative algorithm where the time allocation and the transmit power are updated based on the weighted minimum mean square error criteria and the gradient projection method, respectively. Furthermore, we consider two simple protocols where the DL time allocation of each cell is synchronized and present resource allocation method, respectively. In simulation results, we verify that the proposed algorithm for the asynchronous protocol outperforms conventional schemes. [ABSTRACT FROM AUTHOR]

This paper is concerned with the sum-rate maximization problem in multiple-input multiple-output interference channels. We design the transmit covariance matrix, associated with each transmitter to maximize the sum-rate of the network when the number of data symbols is unknown. The design problem is nonconvex and then hard to be solved. We devise a semidistributed design methodology according to majorization–minimization technique to deal with the optimization problem. This leads us to a convex quadratically constrained quadratic program at each iteration with a semiclosed form solution. The proposed method is computationally efficient and converges to stationary points of the problem. The fact that the method is based on a distributed processing framework along with relatively low computational burden ( $\mathcal{O}(\max \lbrace M,N\rbrace ^{2.3})$ per iteration with $M$ and $N$ being the number of antennas at the transmitter and receiver, respectively) for the user terminals can make it a potential candidate for practical implementations. Finally, we extend the proposed method for the case with channel estimation errors. Simulation results verify the efficiency of the proposed method, and in a typical scenario we have around 60 times lower run-time when compared to the counterparts of the method. [ABSTRACT FROM AUTHOR]

Load balancing is an effective approach to address the spatial–temporal fluctuation problem of mobile data traffic for cellular networks. The existing schemes that focus on channel borrowing from neighboring cells cannot be directly applied to the future 5G wireless networks, because the neighboring cells will reuse the same spectrum band in 5G systems. In this paper, we consider an orthogonal frequency division multiple access ultra-dense small cell network, where device-to-device (D2D) communication is advocated to facilitate load balancing without extra spectrum. Specifically, the data traffic can be effectively offloaded from a congested small cell to other underutilized small cells by D2D communications. The problem is naturally formulated as a joint resource allocation and D2D routing problem that maximizes the system sum-rate. To efficiently solve the problem, we decouple the problem into a resource allocation subproblem and a D2D routing subproblem. The two subproblems are solved iteratively as a monotonic optimization problem and a complementary geometric programming problem, respectively. Simulation results show that the data sum-rate in the neighboring small cells increases 20% on average by offloading the data traffic in the congested small cell to the neighboring small cell base stations. [ABSTRACT FROM AUTHOR]

The analog beamforming and analog combining in millimeter-wave (mm-Wave) massive MIMO communications are investigated. Based on a hierarchical codebook, the weighted sum-rate maximization is studied by jointly considering the duration for channel training and the receiving signal-to-noise ratio (SNR). An algorithm employing exhaustive search at the starting level of the codebook and the multi-sectional search at the other levels is proposed. At each iteration, the channel gain of the dominant path is estimated and the level of the codebook achieving the weighted sum-rate maximization is predicted by the receiver. Then the predicted level is fed back to the transmitter. If the predicted level is reached by the algorithm, the transmitter begins the data transmission with the obtained analog beamformer and the receiver uses the obtained analog combiner for data receiving. Simulation results show that the proposed algorithm outperforms multi-sectional search, where the former can achieve the weighted sum-rate almost twice of the latter when each block is consisted of 35 symbols and the channel SNR is 20 dB. [ABSTRACT FROM PUBLISHER]

This paper studies zero-forcing (ZF) precoding designs for multi-user multiple-input single-output visible light communication (VLC) broadcast channels. In such broadcast systems, the main challenging issue arises from the presence of multi-user interference (MUI) among non-coordinated users. In order to completely suppress the MUI, ZF precoding, which is originally designed for radio frequency (RF) communications, is adopted. Different from RF counterpart, VLC signal is inherently non-negative and has a limited linear range, which leads to an amplitude constraint on the input data signal. Unlike the average power constraint, obtaining the exact capacity for an amplitude-constrained channel is more cumbersome. In this paper, we first investigate lower and upper bounds on the capacity of an amplitude-constrained Gaussian channel, which are especially tight in the high signal-to-noise regime. Based on the derived bounds, optimal beamformer designs for the max-min fairness sum-rate and the maximum sum-rate problems are formulated as convex optimization problems, which then can be efficiently solved by using standard optimization packages. [ABSTRACT FROM AUTHOR]

In this paper, we consider precoder designs for multiuser multiple-input-single-output broadcasting channels. Instead of using a traditional linear zero-forcing (ZF) precoder, we propose a generalized ZF (GZF) precoder in conjunction with successive dirty-paper coding (DPC) for data transmissions, namely, the GZF-DP precoder, where the suffix “DP” stands for “dirty-paper.” The GZF-DP precoder is designed to generate a band-shaped and lower triangular effective channel F , such that only the entries along the main diagonal and the $\nu $ first lower-diagonals can take non-zero values. Utilizing the successive DPC, the known non-causal inter-user interferences from the other (up to) $\nu $ users are canceled through successive encoding. We analyze optimal GZF-DP precoder designs both for sum-rate and minimum user-rate maximizations. Utilizing Lagrange multipliers, the optimal precoders for both cases are solved in closed-forms in relation to optimal power allocations. For the sum-rate maximization, the optimal power allocation can be found through water filling, but with modified water levels depending on the parameter $\nu $ , while for the minimum user-rate maximization that measures the quality of the service, the optimal power allocation is directly solved in closed-form, which also depends on $\nu $ . Moreover, we propose two low-complexity user-ordering algorithms for the GZF-DP precoder designs for both maximizations, respectively. We show, through numerical results, that the proposed GZF-DP precoder with a small $\nu $ value (≤ 3) renders significant rate increments compared with the previous precoder designs, such as the linear ZF and the user-grouping-based DPC precoders. [ABSTRACT FROM PUBLISHER]

This paper analyzes the ratio of transmit antennas to receive antennas at the base station (BS) in full-duplex multiuser multiple-input–multiple-output (MU-MIMO) systems with large-array BS and half-duplex multiantenna users. We consider a full-duplex BS with a block diagonalization (BD) precoder for downlink transmission and a BD receive filter for uplink reception. We derive the approximated downlink sum-rate considering the inter-user interference, and the uplink sum-rate considering the self-interference (SI), for large numbers of BS antennas. Based on the analysis, we formulate an optimization problem in terms of the ratio of transmit antennas to receive antennas to maximize the sum-rate. The analysis shows that the antenna ratio for maximizing the sum rate converges to the ratio of downlink streams to uplink streams as the number of total BS antennas goes to infinity. Simulation results confirm the analysis and show that the BS using the derived antenna ratio in the full-duplex MU-MIMO system can achieve about a 10∼20 b/s/Hz performance gain compared with the BS using an equal number of transmit and receive antennas. [ABSTRACT FROM PUBLISHER]

This paper studies the optimization of power control for the two-user interference channel in which the terminals are time-switched between the communication and energy-harvesting phases. The objective is to maximize the sum rate, subject to the minimum data and harvested energy constraints at the receivers, assuming a fixed time-switching coefficient. Our key contribution is a geometric approach that analyzes the feasible region governed by the constraints, which gives rise to the optimal power control solution. We assume that perfect channel state information is available at both transmitters to determine the solution. [ABSTRACT FROM PUBLISHER]

In this paper, we consider a multiuser multiple-input multiple-output (MIMO) decode-and-forward (DF) relay broadcasting channel (BC) with single source, multiple energy harvesting (EH) relays, and multiple destinations. All the nodes are equipped with multiple antennas. The EH and information decoding tasks at the relays and destinations are separated over the time, which is termed as the time switching scheme. As optimal solutions for the sum-rate maximization problems of BC channels and the MIMO interference channels are hard to obtain, the end-to-end sum rate maximization problem of a multiuser MIMO DF relay BC channel is even harder. In this paper, we propose to tackle a simplified problem, where we employ the block diagonalization (BD) procedure at the source, and we mitigate the interference between the relay-destination channels using an algorithm similar to the BD method. In order to show the relevance of our low complex proposed solution, we compare it with the minimum mean-square error (mmse) solution that was shown in the literature to be equivalent to the solution of the sum-rate maximization in the MIMO broadcasting interfering channels. We also investigate the time division multiple access (TDMA) solution, which separates all the information transmissions from the source to the relays and from the relays to the destinations over time. We provide the numerical results to show the relevance of our proposed solution, in comparison with the no co-channel interference case, the TDMA-based solution, and the mmse-based solution. [ABSTRACT FROM PUBLISHER]

This paper presents a location-oriented multiplexing transmission (LOT) strategy for use with a capillary machine-to-machine (M2M) system based on a millimeter wave (mmWave) wireless personal area network (WPAN). Omni-directional or directional antennas are used depending on the multicast groups and the data rate that is achievable in order to maximize the normalized throughput. Specifically, the devices that are closer to the access point (AP) are served with a high-rate omni-directional antenna. In contrast, devices that are distant to the AP are treated with a high-rate directional-antenna. We exploit the optimal transmission strategy with sum rate maximization to show that the normalized throughput can be maximized. The IEEE 802.15.3c WPAN standard is used to demonstrate that the LOT strategy improves the normalized throughput by 39-62 % relative to other multicast schemes. [ABSTRACT FROM AUTHOR]

This paper considers a network of energy harvesting wireless nodes transmitting simultaneously in a Gaussian interference channel and investigates a distributed power allocation algorithm that maximizes the sum-rate. The power consumption model is based on a series of step functions that allow to model, among others, radio frequency circuits being on/off and the startup power consumption of the transmitter. After showing that the sum-rate maximization problem is nonsmooth, nonconvex, and NP-hard, the Iterative Smooth and Convex approximation Algorithm (ISCA) is proposed, which successively approximates the step functions by proper smooth functions to obtain a sequence of smooth nonconvex problems that can be solved by means of the successive convex approximation method. It is demonstrated that the ISCA distributedly converges to a stationary solution of the sum-rate maximization problem. For the particular case of point to point communications, the numerical results show that the ISCA is able to avoid bad stationary solutions, performing close to the globally optimal solution. The performance of the ISCA is also evaluated in the interference channel and with real solar energy harvesting data. [ABSTRACT FROM PUBLISHER]

This work studies the robust design of linear precoding and linear/ non-linear equalization for multi-cell MIMO systems in the presence of imperfect channel state information (CSI). A worst-case design approach is adopted whereby the CSI error is assumed to lie within spherical sets of known radius. First, the optimal robust design of linear precoders is tackled for a MIMO interference broadcast channel (MIMO-IBC) with general unicast/multicast messages in each cell and operating over multiple time-frequency resources. This problem is formulated as the maximization of the worst-case sum-rate assuming optimal detection at the mobile stations (MSs). Then, symbol-by-symbol non-linear equalization at the MSs is considered. In this case, the problem of jointly optimizing linear precoding and decision-feedback (DF) equalization is investigated for a MIMO interference channel (MIMO-IC) with the goal of minimizing the worst-case sum-mean squared error (MSE). Both problems are addressed by proposing iterative algorithms with descent properties. The algorithms are also shown to be implementable in a distributed fashion on processors that have only local and partial CSI by means of the Alternating Direction Method of Multipliers (ADMM). From numerical results, it is shown that the proposed robust solutions significantly improve over conventional non-robust schemes in terms of sum-rate or symbol error rate. Moreover, it is seen that the proposed joint design of linear precoding and DF equalization outperforms existing separate solutions. [ABSTRACT FROM AUTHOR]

Cognitive radio (CR) systems have been proposed for efficient usage of spare spectrum licensed to primary systems. This leads to the issue of providing as much spectrum to CR users as possible while not degrading the quality of service (QoS) of primary users of the spectrum. This paper proposes a novel cooperation scheme between primary and CR users to guarantee QoS of primary users up to the cell edge while making the licensed spectrum available for opportunistic access by the CR users. We suggest that the primary users at the cell edge, who have poor QoS, should allow secondary users to access their spectrum, while at the same time, the secondary users would help to enhance the primary users QoS using superposition coding on the primary users transmissions. Thus the proposed method can provide a so called 'win-win strategy' by benefiting both primary and CR users. The proposed cooperative access scheme in cognitive radios solves efficiently the sum-rate maximization problem on cognitive Gaussian Multiple Access Channels (GMACs) for power allocation of primary systems that cooperates with CR systems in a distributed fashion. We solved the problem using iterative Jacobian method in a distributed manner. A totally asynchronous distributed power allocation for sum-rate maximization on cognitive GMACs is suggested. Numerical results show that the QoS of primary users at the cell edge is improved by the proposed cooperative access scheme. [ABSTRACT FROM AUTHOR]

A two-way active parallel channel refers to a communication link between two transceivers, where the subchannel gains between the two transceivers can be adjusted such that a given performance criterion is optimized. A two-way active channel can have reciprocal subchannels, meaning that the subchannel gains in both communication directions are identical. Otherwise, if the gains of each subchannel in the two communication directions are different, the active channel is referred to as non-reciprocal. In this paper, we consider the problem of sum-rate maximization for reciprocal and non-reciprocal two-way active channels under two constraints on the transceivers’ transmit powers and a third constraint on the channel power (i.e., the sum of squared of the subchannel gains). We prove rigorously that for such active channels, in order to maximize the sum-rate, only a subset of the subchannels will have to be active. We also provide the optimal values of the subchannel gains and the optimal values of the transceivers’ transmit powers over different subchannels in closed forms. Simulation results show that parallel active channels can outperform their passive counterparts, where the subchannel gains are fixed, and thus, they cannot be adjusted. [ABSTRACT FROM PUBLISHER]