Sum-power Minimization Problem Research Articles

Latency-Aware Highly-Reliable mmWave Systems via Multi-Point Connectivity

The sensitivity of millimeter-wave (mmWave) radio channel to blockage is a fundamental challenge in achieving low-latency and ultra-reliable connectivity. In this paper, we explore the viability of using coordinated multi-point (CoMP) transmission for a delay bounded and reliable mmWave communication. We propose a novel blockage-aware algorithm for the sum-power minimization problem under the user-specific latency requirements in a dynamic mobile access network. We use the Lyapunov optimization framework, and provide a dynamic control algorithm, which efficiently transforms a time-average stochastic problem into a sequence of deterministic subproblems. A robust beamformer design is then proposed by exploiting the queue backlogs and channel information, that efficiently allocates the required radio and cooperation resources, and proactively leverages the multi-antenna spatial diversity according to the instantaneous needs of the users. Further, to adapt to the uncertainties of the mmWave channel, we consider a pessimistic estimate of the rates over link blockage combinations and an adaptive selection of the CoMP serving set from the available remote radio units (RRUs). Moreover, after the relaxation of coupled and non-convex constraints via the Fractional Program (FP) techniques, a low-complexity closed-form iterative algorithm is provided by solving a system of Karush-Kuhn-Tucker (KKT) optimality conditions. The simulation results manifest that, in the presence of random blockages, the proposed methods outperform the baseline scenarios and provide power-efficient, high-reliable, and low-latency mmWave communication.

Semi-Distributed Resource Management in UAV-Aided MEC Systems: A Multi-Agent Federated Reinforcement Learning Approach

Recently, unmanned aerial vehicle (UAV)-enabled multi-access edge computing (MEC) has been introduced as a promising edge paradigm for the future space-aerial-terrestrial integrated communications. Due to the high maneuverability of UAVs, such a flexible paradigm can improve the communication and computation performance for multiple user equipments (UEs). In this paper, we consider the sum power minimization problem by jointly optimizing resource allocation, user association, and power control in an MEC system with multiple UAVs. Since the problem is nonconvex, we propose a centralized multi-agent reinforcement learning (MARL) algorithm to solve it. However, the centralized method ignores essential issues like distributed framework and privacy concern. We then propose a multi-agent federated reinforcement learning (MAFRL) algorithm in a semi-distributed framework. Meanwhile, we introduce the Gaussian differentials to protect the privacy of all UEs. Simulation results show that the semi-distributed MAFRL algorithm achieves close performances to the centralized MARL algorithm and significantly outperform the benchmark schemes. Moreover, the semi-distributed MAFRL algorithm costs 23<inline-formula><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> lower opeartion time than the centralized algorithm.

Energy Efficient Resource Allocation in UAV-Enabled Mobile Edge Computing Networks

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 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. The numerical results show that the proposed algorithm achieves better performance than the conventional approaches.

QoS-Aware User Association and Resource Allocation in LAA-LTE/WiFi Coexistence Systems

The licensed-assisted access-based long term evolution (LAA-LTE) is a promising solution to provide enhanced LTE services by sharing unlicensed bands with WiFi systems. However, the intense contention with the incumbent WiFi system makes it challenging for the LAA-LTE system to support guaranteed quality-of-service (QoS) for the users. This paper is interested in the QoS-aware LAA-LTE/WiFi coexistence system. We first propose a flexible coexistence framework using the listen-before-talk mechanism, based on which the QoS metrics of LAA-LTE and WiFi systems are quantified. Then, a joint user association and resource allocation problem is formulated, which aims to maximize the number of QoS-preferred users supported by LAA-LTE, while protecting the WiFi users. The considered optimization problem is equivalently decomposed into two subproblems, the sum-power minimization problem and the user association problem. For the first subproblem, the deep-cut ellipsoid method is adopted to optimize the LAA-LTE transmission time, subcarrier assignment, and power allocation. For the latter one, an efficient algorithm called successive user removal is proposed. The simulation results have demonstrated the effectiveness of the proposed scheme, based on which the tradeoff among different QoS metrics in the coexistence system is observed.

Cooperative Asynchronous Non-Orthogonal Multiple Access With Power Minimization Under QoS Constraints

Recent studies have demonstrated the superiority of non-orthogonal multiple access (NOMA) over orthogonal multiple access (OMA) in cooperative communication networks. In this paper, we propose a novel half-duplex cooperative asynchronous NOMA (C-ANOMA) framework with user relaying, where a timing mismatch is intentionally added in the broadcast signal. We derive the expressions for the throughputs of the strong user (acts as relay) which employs the block-wise successive interference cancellation (SIC) and the weak user which combines the symbol-asynchronous signal with the interference-free signal. We analytically prove that in the C-ANOMA systems with a sufficiently large block length, the strong user attains the same throughput to decode its own message while both users can achieve a higher throughput to decode the weak user’s message compared with those in the cooperative NOMA (C-NOMA) systems. Besides, we obtain the optimal timing mismatch when the block length goes to infinity. Furthermore, to exploit the trade-off between the power consumption of the base station and that of the relay user, we solve a weighted sum power minimization problem under quality of services (QoS) constraints. Numerical results show that the C-ANOMA system can consume less power compared with the C-NOMA system to satisfy the same QoS requirements.

Fast Water-Filling Method for Sum-Power Minimization in OFDMA Networks

In this letter, we investigate the problem of sum-power minimization for the users in orthogonal frequency division multiple access (OFDMA) networks subject to the target rate constraint. To address this problem, two fast water-filling (WF) methods are developed under two scenarios: without and with considering the peak-power constraint on the subchannels, which are referred to as FWF and FWF-PP, respectively. We show that our proposed methods converge to the optimal solution, while the computational complexity is reduced compared to the existing WF methods. Our results are also confirmed via simulations.

Joint Transceiver Designs for Full-Duplex $K$ -Pair MIMO Interference Channel With SWIPT

In this paper, we propose joint transceiver design algorithms for the full-duplex $K$ -pair multiple-input multiple-output interference channel with simultaneous wireless information and power transfer. To mitigate and exploit the complex interference, we consider two important utility optimization problems, i.e., the sum power minimization problem and the sum-rate maximization problem. In the first problem, our aim is to minimize the total transmission power under both transmission rate and energy harvesting (EH) constraints. An iterative algorithm based on alternating optimization (AO) and with guaranteed monotonic convergence is proposed to successively optimize the transceiver coefficients. The algorithm consists of three main steps, where the concave–convex procedure (CCCP), the minimum mean-square error (MMSE) criterion, and the semidefinite relaxation technique are, respectively, employed to compute the vectors of power splitting ratios, the receiving matrices, and the transmitting beamforming vectors. Two simplified algorithms based on fixed beamformers, namely, the maximum ratio transmission and the maximum signal-to-interference-leakage beamformers are also proposed. In the second problem, our aim is to maximize the sum-rate under additional power and EH constraints. Due to the highly non-convex nature of this problem, we first reformulate it into an equivalent-weighted MMSE problem by introducing suitable weight factors, such that the global optima of the two problems are identical. Then, by utilizing the concept of AO and CCCP, we show that the equivalent problem can be efficiently solved. Again, with the aid of the fixed beamformers, two simplified algorithms are provided to reduce the computational complexity. Simulation results are presented to validate the effectiveness of the proposed algorithms.

Beamforming Design for Full-Duplex MIMO Interference Channels–QoS and Energy-Efficiency Considerations

We consider a K link multiple-input multiple-output interference channel where each link consists of two full-duplex (FD) nodes. Two transmit beamforming design problems are solved: 1) sum-power minimization problem subject to rate constraints and 2) energy-efficiency maximization problem subject to individual power constraints. To tackle the sum-power minimization problem, we first generalize the well-known relationship between weighted-sum-rate (WSR) and weighted minimum-mean-squared-error (WMMSE) problems, originally used to solve the sum-rate maximization problems, and then propose a low complexity centralized algorithm, which converges to a stationary point. To decrease the exchange of a huge amount of data and excessive signaling traffic among the nodes, a distributed algorithm is also proposed. For the energy-efficiency maximization problem, the original fractional form optimization problem is first transformed into an equivalent subtractive-form optimization problem by exploiting the properties of fractional programming, and then performs a dual-layer optimization scheme. In the outer layer, the energy-efficiency parameter is searched using a simple 1-D search, and in the inner layer, the relationship between WSR and WMMSE is exploited to solve the subtractive form optimization problem. Since the proposed algorithms require perfect channel-state-information, which is difficult to acquire in practice, we also propose a robust design, by taking the imperfect channel knowledge into consideration. It is shown in the simulations that the sum-power achieved in FD mode depends heavily on the transmitter/receiver distortion. Also the energy-efficiency of FD systems is lower than that of half-duplex systems, as FD nodes need to overcome self-interference and increased inter-user interference, which leads to high power consumption.

Decentralized Linear Transceiver Design and Signaling Strategies for Sum Power Minimization in Multi-Cell MIMO Systems

This paper considers linear downlink transceiver design for the sum power minimization problem with per-user rate constraints in a multi-cell multi-user MIMO system. This non-convex problem is divided into transmit and receive beamforming optimization steps which are iteratively repeated such that the sum power converges. The transmit beamformers are solved using a successive convex approximation method (SCA), whereas the receive beamformers are computed via the linear minimum mean square error (MMSE) criterion. In addition to centralized design, we propose two decentralized algorithms where the transmit and receive beamforming designs are facilitated by a combination of pilot and backhaul signaling. In the first algorithm, sum power is minimized by computing the transmit beamformers at each base station (BS) and the receive beamformers at each user via iterative over-the-air optimization process. BS side processing requires local effective channel state information (CSI) acquired from precoded uplink pilots. In the second algorithm, the transmit and receive beamformers are iteratively optimized at each BS requiring only local CSI obtained from antenna-specific uplink pilots. In both algorithms, transmit beamforming optimization is decoupled among the BSs via primal decomposition method, which requires scalar backhaul information exchange for decentralized processing. The performance of the proposed algorithms is evaluated via numerical examples under different system settings in static and time-correlated channel conditions.

Robust MMSE Beamforming for Multiantenna Relay Networks

In this paper, we propose a robust minimum mean square error (MMSE)-based beamforming technique for multiantenna relay broadcast channels, where a multiantenna base station transmits signal to single antenna users with the help of a multiantenna relay. The signal transmission from the base station to the single antenna users is completed in two time slots, where the relay receives the signal from the base station in the first time slot, and it then forwards the received signal to different users based on amplify and forward (AF) protocol. We propose a robust beamforming technique for a sum-power minimization problem with an imperfect channel state information (CSI) between the relay and the users. This robust scheme is developed based on the worst-case optimization framework and the Nemirovski Lemma by incorporating uncertainties in the CSI. The original optimization problem is divided into three subproblems due to joint nonconvexity in terms of beamforming vectors at the base station, the relay amplification matrix, and receiver coefficients. These subproblems are formulated into a convex optimization framework by exploiting the Nemirovski Lemma, and an iterative algorithm is developed by alternatively optimizing each of them with channel uncertainties. In addition, we provide an optimization framework to evaluate the achievable worst-case mean square error (MSE) of each user for a given set of design parameters. Simulation results are provided to validate the convergence of the proposed algorithm.

Joint Beamforming and Power Allocation for MIMO Relay Broadcast Channel With Individual SINR Constraints

In this paper, system design for the multi-input multi-output (MIMO) relay broadcast channel with individual signal-to-interference-plus-noise ratio (SINR) constraints at the mobile stations (MS) is considered. By exploring the structure of downlink (DL) uplink (UL) duality at either the base station (BS) or the relay station (RS), we propose two schemes of joint power allocation and beamforming design at the BS and the RS. The problem of existence of feasible solutions under practical power constraints at the BS and the RS with given SINR targets is considered first. Then the problem of sum power minimization is considered. Each design problem can be solved efficiently using optimal joint power allocation and beamforming under the framework of convex optimization. We also show that with subchannel pairing at the RS, the transmission power can be reduced by channel compensation at either hop. Finally, an extension to more general multi-hop applications is provided to further improve the power efficiency.

MMSE-Based Beamforming Techniques for Relay Broadcast Channels

We propose minimum mean square error (MMSE)-based beamforming techniques for a multiantenna relay network, where a base station (BS) equipped with multiple antennas communicates with a number of single-antenna users through a multiantenna relay. We specifically solve three optimization problems, namely, 1) the sum-power minimization problem, 2) the mean-square-error (MSE) balancing problem, and 3) the mixed quality-of-service (QoS) problem. Unfortunately, these problems are not jointly convex in terms of beamforming vectors at the BS and the relay amplification matrix. To circumvent this nonconvexity issue, the original problems are divided into two subproblems, where the beamforming vectors and the relay amplification matrix are alternately optimized, whereas the other is fixed. Three iterative algorithms are developed based on convex optimization techniques and general MSE duality. Simulation results are provided to validate the convergence of the proposed algorithms.

MSE-Based Robust Precoder Design in Multicell Downlink Systems

SUMMARY To mitigate the inter-cell interference in mul-ticell downlink systems, this letter consider the robust precoderdesign for multicell cooperation where the knowledge of chan-nel state available at the base station is imperfect. Assumingthat imperfect channel state information (CSI) can be exchangedamong cells but with no data sharing, we investigate the worst-case performance optimization problem with bounded CSI error.Our objective is to minimize the weighted sum mean-square-error(MSE) subject to per-base-station power constraints. A dis-tributed solution is obtained by reformulating the upper bound ofMSE and exploiting the Lagrangian method for the optimal prob-lem. Simulation results demonstrate that the proposed algorithmis robust to guarantee the worst-case sum rate performance andhas lower computational complexity than the SINR-based design. key words: Multi-cell cooperation, robust precoder, upper boundof MSE, weighted sum MSE minimization. 1. IntroductionMulticell cooperation communication between base sta-tions (BSs) has emerged as a promising approach toimprove the performance of systems and reduce the in-tercell interference with universal frequency reuse. De-pending on the BS coordinated level, it can be per-formed by two mode[1],[2]. In the first mode, the BSsshare both data and channel state information (CSI),where the system is termed as “network multiple inputmultiple output (MIMO)” or “joint processing (JP)”.In the second mode, the cooperative BSs exchange CSIonly, and the system is called “multicell system” or “co-ordinated beamforming (CBF)”. In this paper, we focuson the latter.One crucial processing for the cooperative multi-cell system is how to optimize the cooperation trans-mission between the cells. Without data sharing, [3]consider the sum-power minimization problem subjectto signal-to-interference plus noise ratio (SINR) con-straint. In [4], the weighted sum-rate maximizationproblem is solved through virtual SINR maximization,but the distributed solution is only suitable for highsignal-to-noise ratio (SNR) regime. To achieve a fair-ness basedrate optimality, [5] proposeda distributedal-gorithm to approach the performance of the centralized

Energy-efficient multiuser SIMO: achieving probabilistic robustness with gaussian channel uncertainty

This paper addresses the joint optimization of power control and receive beamforming vectors for a multiuser singleinput multiple-output (SIMO) antenna system in the uplink in which mobile users are single-antenna transmitters and the base station receiver has multiple antennas. Channel state information at the receiver (CSIR) is exploited but the CSIR is imperfect with its uncertainty being modeled as a random Gaussian matrix. Our objective is to devise an energy-efficient solution to minimize the individual users' transmit power while meeting the users' signal-to-interference plus noise ratio (SINR) constraints, under the consideration of CSIR and its error characteristics. This is achieved by solving a sum-power minimization problem, subject to a collection of users' outage probability constraints on their target SINRs. Regarding the signal power minus the sum of inter-user interferences (SMI) power as Gaussian, an iterative and convergent algorithm which is proved to reach the global optimum for the joint power allocation and receive beamforming solution, is proposed, though the optimization problem is indeed non-convex. A systematic scheme to detect feasibility and find a feasible initial solution, if there exists any, is also devised. Simulation results verify the use of Gaussian approximation and robustness of the proposed algorithm in terms of users' probability constraints, and indicate a significant performance gain as compared to the zero-forcing (ZF) and minimum mean square- error (MMSE) beamforming systems.