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This paper considers cell-free multiple-input multiple-output (MIMO) systems with multi-antenna access points (APs) and multi-antenna users in constrained spaces. Two main effects emerge in such a space-constrained design: spatial correlation and mutual coupling. We analytically study the resulting performance with conjugate beamforming and reducing inter-antenna distance. We derive the closed-form expression for the spectral efficiency (SE) as a function of the spatial correlation and mutual coupling matrices. Additionally, we consider a practical power consumption model to investigate the energy efficiency (EE) with power control coefficients. The obtained ergodic and analytical results will show essential insights into the system performance. The decreasing inter-antenna distance introduces performance loss, while an appropriate number of APs could balance the SE and EE trade-off. The number of users improves the SE performance, while an optimal number of antennas per user can be selected to achieve maximum SE performance. Moreover, more users will introduce a smaller optimal number of antennas per user.

Efficient massive/ultra-massive multiple-input multiple-output (MIMO) detection algorithms with satisfactory performance and low complexity are critical to meet the high throughput and ultra-low latency requirements in 5G and beyond communications, given the extremely large number of antennas. In this paper, we propose a low complexity graph neural network (GNN) enhanced approximate message passing (AMP) algorithm, AMP-GNN, for massive/ultra-massive MIMO detection. The structure of the neural network is customized by unfolding the AMP algorithm and introducing the GNN module for multiuser interference cancellation. Numerical results will show that the proposed AMP-GNN significantly improves the performance of the AMP detector and achieves comparable performance as the state-of-the-art deep learning-based MIMO detectors but with reduced computational complexity. Furthermore, it presents strong robustness to the change of the number of users.

The emerging real-time wireless communication systems are expected to provide deadline assurance for delay-sensitive traffics generated from factory automation, smart grids, and automatic driving, etc. Although there has been cutting edge research on diversity-enabled deadline assurance, real-time communications may suffer from poor energy efficiency. In this paper, we present a paradigm-shift real-time wireless communication method based on matching diversity that is judiciously designed for orthogonal frequency division multi-access (OFDMA) systems. In particular, we adopt bipartite graph to formulate a unified framework for OFDMA, based on which joint subcarrier matching and power adaptation policies are conceived to provide real-time transmissions, also referred to as just-in-time services (JITS). By bridging the average power and the formula of the outage probability, we show that the deadline constraints can be satisfied with a finite average power, when the number of users in the OFDMA system is not greater than that of subcarriers with independent channel gains. Furthermore, we also derive the approximate but yet analytical results on the required average power consumption. Numerical results indicate that a substantial energy efficiency gain can be attained in real-time OFDMA systems if the subcarrier interleaving based frequency diversity is replaced by matching diversity.

Task-oriented communication is an emerging paradigm for next-generation communication networks, which extracts and transmits task-relevant information, instead of raw data, for downstream applications. Most existing deep learning (DL)-based task-oriented communication systems adopt a closed-world assumption, assuming either the same data distribution for training and testing, or the system could have access to a large out-of-distribution (OoD) dataset for retraining. However, in practical open-world scenarios, task-oriented communication systems will be exposed to unknown OoD data. The powerful approximation ability of learning methods may force the task-oriented communication systems to overfit the training data (i.e., in-distribution data). Therefore, these systems tend to provide overconfident judgments when encountering OoD data. Based on the information bottleneck (IB) framework, we propose a class conditional IB (CCIB) approach to address this problem, supported by information-theoretical insights. The idea is to extract distinguishable features from in-distribution data while keeping their compactness and informativeness. It is achieved by imposing the class conditional latent prior distribution and enforcing the latent of different classes to be far away from each other. Simulation results shall demonstrate that the proposed approach detects OoD data more efficiently than the baselines and state-of-the-art approaches, without compromising the rate-distortion tradeoff.

Federated learning (FL) is a privacy-preserving collaborative learning framework, and differential privacy can be applied to further enhance its privacy protection. Existing FL systems typically adopt Federated Average (FedAvg) as the training algorithm and implement differential privacy with a Gaussian mechanism. However, the inherent privacy-utility trade-off in these systems severely degrades the training performance if a tight privacy budget is enforced. Besides, the Gaussian mechanism requires model weights to be of high-precision. To improve communication efficiency and achieve a better privacy-utility trade-off, we propose a communication-efficient FL training algorithm with differential privacy guarantee. Specifically, we propose to adopt binary neural networks (BNNs) and introduce discrete noise in the FL setting. Binary model parameters are uploaded for higher communication efficiency and discrete noise is added to achieve the client-level differential privacy protection. The achieved performance guarantee is rigorously proved, and it is shown to depend on the level of discrete noise. Experimental results based on MNIST and Fashion-MNIST datasets will demonstrate that the proposed training algorithm achieves client-level privacy protection with performance gain while enjoying the benefits of low communication overhead from binary model updates.

In synchronous federated learning (SFL), only if all users successfully deliver their local parameters to the central server, the central server can efficiently aggregate a global model. In this case, the user with the worst quality link becomes the bottleneck limiting the training performance of SFL. To cope with this issue for SFL in cell-free massive multiple-input multiple-output (CFmMIMO) network, this paper proposes a joint user cooperation and power control (JUCPC) method. Particularly, the users with good quality links are employed to assist the ones with worse quality links to transfer their parameters to the central server. Then, the power control is employed to coordinate the interference among users to further improve the transmission performance for SFL. To find the joint optimal user cooperation pairing and the power control coefficients, we formulate an optimization problem to minimize the maximum transmission time among users. As the problem is non-convex, we decouple it into two sub-problems. For the first sub-problem, we propose a fairness-based algorithm to select the helping users and also pair them with the ones who required to be assisted. For the second sub-problem, a deep deterministic policy gradient (DDPG)-based algorithm is proposed to find the optimized power control coefficients. Experiments show that the proposed JUCPC method can efficiently reduce the transmission time of the worst user for SFL. Moreover, compared to the traditional optimization method based on convex optimization solver (CVX), our proposed DDPG-based one requires much shorter execution time while achieves approximating result to the CVX-based algorithm. © 2023 IEEE.

Recently, federated learning (FL) has been treated as an important and promising learning scheme in IoT, enabling devices to jointly learn a model without sharing their data sets. Different from centralized training on some collected data sets, FL training suffers a lot of constraints from limited resources in the network. Therein, the bandwidth and package loss restrict interactions in training. Meanwhile, the highly distributed data sets and limited computation could also affect its convergence. To figure out the specific impact, we analyze the convergence rate of FL training considering both communication and training. Further taking in training costs in terms of time and power, the closed-form optimal settings for communication networks are proposed with principles to assist the parameter selection. The results build a bridge between AI and communication, giving us an intuitive knowledge of how the background system could influence the distributed training process.

Short packet transmission techniques have recently attracted substantial attention due to their potential of achieving low latency in emerging Industrial Internet of Things (IIoT). To this end, finite-blocklength coding is expected to play a central role in short packet communications. With the random arrival of short packets, there exists a fundamental tradeoff between reliability and latency in finite-blocklength coding based transmission systems. In this paper, we consider the reliability-latency tradeoff of short packet transmission over AWGN channels. More specifically, an effective bandwidth approach is adopted to obtain the delay violation probability given a hard delay constraint. We present an approximate but closed-form Quality-of-Service (QoS) exponent that bridges the delay violation probability of short packets and the error probability of finite-blocklength coding. Numerical results shall validate our theoretical analysis, which characterizes a performance limit of ultra-reliable and low latency communications (URLLC) over AWGN channels.

The emerging Industrial Internet of Things (IIoT) is driving an ever increasing demand for providing low latency services to massive devices over wireless channels. As a result, how to assure the quality-of-service (QoS) for a large amount of mobile users is becoming a challenging issue in the envisioned sixth-generation (6G) network. In such networks, the delay-optimal wireless access will require a joint channel and queue aware scheduling, whose complexity increases exponentially with the number of users. In this paper, we adopt the mean field approximation to conceive a buffer-aware multi-user diversity or opportunistic access protocol, which serves all backlogged packets of a user if its channel gain is beyond a threshold. A theoretical analysis and numerical results will demonstrate that not only the cross-layer scheduling policy is of low complexity but is also asymptotically optimal for a huge number of devices.

Terahertz ultra-massive multiple-input multiple-output (THz UM-MIMO) is envisioned as one of the key enablers of 6G wireless systems. Due to the joint effect of its large array aperture and small wavelength, the near-field region of THz UM-MIMO is greatly enlarged. The high-dimensional channel of such systems thus consists of a stochastic mixture of far and near fields, which renders channel estimation extremely challenging. Previous works based on uni-field assumptions cannot capture the hybrid far- and near-field features, thus suffering significant performance loss. This motivates us to consider hybrid-field channel estimation. We draw inspirations from fixed point theory to develop an efficient deep learning based channel estimator with adaptive complexity and linear convergence guarantee. Built upon classic orthogonal approximate message passing, we transform each iteration into a contractive mapping, comprising a closed-form linear estimator and a neural network based non-linear estimator. A major algorithmic innovation involves applying fixed point iteration to compute the channel estimate while modeling neural networks with arbitrary depth and adapting to the hybrid-field channel conditions. Simulation results verify our theoretical analysis and show significant performance gains over state-of-the-art approaches in the estimation accuracy and convergence rate. © 2022 IEEE.

In limited feedback multi-user multiple-input multiple-output (MU-MIMO) cellular networks, users send quantized information about the channel conditions to the associated base station (BS) for downlink beamforming. However, channel quantization and beamforming have been treated as two separate tasks conventionally, which makes it difficult to achieve global system optimality. In this paper, we propose an augmented deep unfolding (ADU) approach that jointly optimizes the beamforming scheme at the BSs and the channel quantization scheme at the users. In particular, the classic WMMSE beamformer is unrolled and a deep neural network (DNN) is leveraged to preprocess its input to enhance the performance. The variational information bottleneck technique is adopted to further improve the performance when the feedback capacity is strictly restricted. Simulation results demonstrate that the proposed ADU method outperforms all the benchmark schemes in terms of the system average rate. © 2022 IEEE.

The emerging deterministic networking (DetNet) has attracted considerable recent attention due to its potential in supporting services with ultra-low latency, low delay variation, and extremely low loss. In the development of the DetN et, hard delay constraints and extremely low loss are highly desired to be guaranteed. However, the bursty data traffic demands and random channel qualities bring uncertainties for transmission, incurring bit dropping due to the possible delay violation. How to support reliable transmission services by reducing bit dropping rates becomes a critical issue in the envisioned sixth-generation (6G) network. To efficiently reduce the bit dropping rate, a joint channel and buffer aware scheduling is highly desired, whose complexity increases exponentially with the number of users. In this paper, we adopt the mean-field approximation to simplify the design and analysis of the joint channel and buffer aware scheduling under a huge number of users. A threshold-based scheduling policy is proposed, which has low computational complexity. Numerical results will demonstrate the effectiveness of the mean-field approximation based scheduling. © 2022 IEEE.

Mobile edge computing (MEC) has been widely studied to provide new schemes for communication-computing systems such as industrial Internet of Things (IoTs), vehicular networks, smart city applications, etc. In this work, we mainly investigate on the timeliness maintenance of the MEC systems where the freshness of the data and computation tasks plays a significant role. We firstly formulate the average age of information (AoI) minimization problem of the UAV-assisted MEC systems. To maintain the system timeliness, we propose a novel multi-agent reinforcement learning (MARL) approach, called edge federated multi-agent actor-critic (MAAC), for joint trajectory planning, data scheduling and resource management in the investigated MEC systems. Through the proposed online learning method, edge devices and center controller learn the interactive policies through local observations and carry out the model-wise communication. We build up a simulation platform for time sensitive MEC systems as a gym environment module and implement the proposed algorithm. Furthermore, the comparisons with a popular MARL solution, MADDPG, show that the proposed approach achieves better performance in terms of data freshness and system stability. © 2021 IEEE.

In cell-free massive MIMO networks, an efficient distributed detection algorithm is of significant importance. In this paper, we propose a distributed expectation propagation (EP) detector for cell-free massive MIMO. The detector is composed of two modules, a nonlinear module at the central processing unit (CPU) and a linear module at the access point (AP). The turbo principle in iterative decoding is utilized to compute and pass the extrinsic information between modules. An analytical framework is then provided to characterize the asymptotic performance of the proposed EP detector with a large number of antennas. Simulation results will show that the proposed method outperforms the distributed detectors in terms of bit-error rate.

Channel estimation and beamforming play critical roles in frequency-division duplexing (FDD) massive multiple-input multiple-output (MIMO) systems. However, these two modules have been treated as two stand-alone components, which makes it difficult to achieve a global system optimality. In this paper, we propose a deep learning-based approach that directly optimizes the beamformers at the base station according to the received uplink pilots, thereby, bypassing the explicit channel estimation. Different from the existing fully data-driven approach where all the modules are replaced by deep neural networks (DNNs), a neural calibration method is proposed to improve the scalability of the end-to-end design. In particular, the backbone of conventional time-efficient algorithms, i.e., the least-squares (LS) channel estimator and the zero-forcing (ZF) beamformer, is preserved and DNNs are leveraged to calibrate their inputs for better performance. The permutation equivariance property of the formulated resource allocation problem is then identified to design a low-complexity neural network architecture. Simulation results will show the superiority of the proposed neural calibration method over benchmark schemes in terms of both the spectral efficiency and scalability in large-scale wireless networks.

Ultra-reliable and low latency communications (URLLC) has recently attracted much attention because it holds the promise of supporting mission-critical applications in task-driven radio access networks. In URLLC, finite-blocklength coding plays an important role. In this paper, we are interested in the performance limit of finite-blocklength coded wireless URLLC with burst traffics, which may induce severe delay in practice. A large deviation technique is exploited to derive the delay violation probability given a hard delay constraint. Specifi-cally, an approximate QoS exponent is conceived to characterize the reliability-latency tradeoff, i.e., tradeoff between the error probability and delay violation probability. Further, we present closed-form upper and lower bounds of the QoS exponent, which are shown to become tight in the high signal-to-noise ratio (SNR) regime. Our theoretical analysis is also validated by numerical results.

This paper proposes a deep Q network (DQN)-based solution framework to minimize UAV's energy consumption in UAV-assisted wireless powered sensor network under the age of information (AoI) constraint, where a UAV wirelessly charges ground sensors and then the sensors use harvested energy to upload their freshly collected information to the UAV. The corresponding non-convex energy-minimization problem is first modeled as a Markov process, and then the state spaces, action spaces and reward function are designed. Simulation results show that the proposed DQN achieves much smaller energy consumption than traditional greedy-based scheme, and when the number of sensors is more than 8, traditional greedy-based scheme becomes very difficult to solve the problem, while our presented DQN method can still find an optimal solution. Moreover, the UAV's energy consumption increases with the decrease of AoI or the increment of sensors' amount, and with the rotation angle constraint, UAV's trajectory becomes smooth. © 2021 IEEE.

Over-the-air computation (AirComp) is a disruptive technique for fast wireless data aggregation in Internet of Things (IoT) networks via exploiting the waveform superposition property of multiple-access channels. However, the performance of AirComp is bottlenecked by the worst channel condition among all links between the IoT devices and the access point. In this paper, a reconfigurable intelligent surface (RIS) assisted AirComp system is proposed to boost the received signal power and thus mitigate the performance bottleneck by reconfiguring the propagation channels. With an objective to minimize the AirComp distortion, we propose a joint design of AirComp transceivers and RIS phase-shifts, which however turns out to be a highly intractable non-convex programming problem. To this end, we develop a novel alternating minimization framework in conjunction with the successive convex approximation technique, which is proved to converge monotonically. To reduce the computational complexity, we transform the subproblem in each alternation as a smooth convex-concave saddle point problem, which is then tackled by proposing a Mirror-Prox method that only involves a sequence of closed-form updates. Simulations show that the computation time of the proposed algorithm can be two orders of magnitude smaller than that of the state-of-the-art algorithms, while achieving a similar distortion performance.

Recently, federated learning (FL) has been treated as an important and promising learning scheme in IoT, enabling devices to jointly learn a model without sharing their data sets. Different from centralized training on some collected data sets, FL training suffers a lot of constraints from limited resources in the network. Therein, the bandwidth and package loss restrict interactions in training. Meanwhile, the highly distributed data sets and limited computation could also affect its convergence. To figure out the specific impact, we analyze the convergence rate of FL training considering both communication and training. Further taking in training costs in terms of time and power, the closed-form optimal settings for communication networks are proposed with principles to assist the parameter selection. The results build a bridge between AI and communication, giving us an intuitive knowledge of how the background system could influence the distributed training process.

Short packet transmission techniques have recently attracted substantial attention due to their potential of achieving low latency in emerging Industrial Internet of Things (IIoT). To this end, finite-blocklength coding is expected to play a central role in short packet communications. With the random arrival of short packets, there exists a fundamental tradeoff between reliability and latency in finite-blocklength coding based transmission systems. In this paper, we consider the reliability-latency tradeoff of short packet transmission over AWGN channels. More specifically, an effective bandwidth approach is adopted to obtain the delay violation probability given a hard delay constraint. We present an approximate but closed-form Quality-of-Service (QoS) exponent that bridges the delay violation probability of short packets and the error probability of finite-blocklength coding. Numerical results shall validate our theoretical analysis, which characterizes a performance limit of ultra-reliable and low latency communications (URLLC) over AWGN channels.

The emerging Industrial Internet of Things (IIoT) is driving an ever increasing demand for providing low latency services to massive devices over wireless channels. As a result, how to assure the quality-of-service (QoS) for a large amount of mobile users is becoming a challenging issue in the envisioned sixth-generation (6G) network. In such networks, the delay-optimal wireless access will require a joint channel and queue aware scheduling, whose complexity increases exponentially with the number of users. In this paper, we adopt the mean field approximation to conceive a buffer-aware multi-user diversity or opportunistic access protocol, which serves all backlogged packets of a user if its channel gain is beyond a threshold. A theoretical analysis and numerical results will demonstrate that not only the cross-layer scheduling policy is of low complexity but is also asymptotically optimal for a huge number of devices.

Proactive caching has emerged as a promising solution to reduce the content access latency in radio access networks (RANs), thereby attracting considerable attention in the era of 6G research. It allows base stations to push popular content items to mobile users’ devices proactively. Therefore, a cached-enabled RAN may serve a user by either on-demand transmission or proactive content placement, which share a common radio spectrum. How to efficiently schedule proactive caching and on-demand transmission then becomes a challenging issue that remains open. In this paper, we present a unified framework for joint scheduling of caching and on-demand transmission. In particular, we formulate a Markovian queueing model to analyze the average delay and power consumption of the proposed scheduling policy, which are then jointly minimized via linear programming (LP). Furthermore, a low-complexity heuristic scheduling policy is conceived to strike a sub-optimal tradeoff between delay and power based on greedy algorithms. Simulation results shall demonstrate that the overall service latency of a RAN can be substantially reduced by judiciously designing joint scheduling of caching and on-demand transmission.

Intelligent reflecting surfaces (IRSs) are revolutionary enablers for next-generation wireless communication networks, with the ability to customize the radio propagation environment. To fully exploit the potential of IRS-assisted wireless systems, reflective elements have to be jointly optimized with conventional communication techniques. However, the resulting optimization problems pose significant algorithmic challenges, mainly due to the large-scale non-convex constraints induced by the passive hardware implementations. In this paper, we propose a low-complexity algorithmic framework incorporating alternating optimization and gradient-based methods for large-scale IRS-assisted wireless systems. The proposed algorithm provably converges to a stationary point of the optimization problem. Extensive simulation results demonstrate that the proposed framework provides significant speedups compared with existing algorithms, while achieving a comparable or better performance.

This paper explores the age-energy region in a wireless powered communication network (WPCN), where an Internet of things (IoT) device works in a 'harvest-then-transmit' protocol, in which the IoT device first harvests energy from radio-frequency (RF) signals transmitted by a hybrid access point (HAP) via wireless power transfer (WPT), and then transmits real-time update using the harvested energy to the HAP. For such a system, achievable age-energy region and age-energy function are defined as metrics to measure the trade-off between the age of information (AoI) and the storable energy at the IoT device. Moreover, the optimal transmission policy is designed by solving an AoI minimization problem formulated in terms of the storable energy at the IoT device, which also characterizes the boundaries of the system's age-energy region. As the problem is non-convex, it is first transformed into equivalent ones, and then solved with the closed-form solution. Numerical results show that the transmit power at the HAP and the distance between the HAP and the IoT device have greater effects on the storable energy than the system AoI, and the data size of the update packet has a greater effect on the system AoI than the storable energy. The minimal signal-to-noise-ratio (SNR) threshold has a bilateral influence on the age-energy region, while the impact of the maximal achievable transmit power of the IoT device on the age-energy region is not obvious. © 2020 IEEE.

This paper investigates the optimal design of wireless powered fog computing networks, where a hybrid access point integrated with fog computing function (F-HAP) first charges an Internet of Things (IoT) device via wireless power transfer (WPT), and then the IoT device uses the harvested energy to compute real-time updates locally or offload the updates to the F-HAP for computing. For such a system, an age-energy aware cost function is defined to evaluate the system performance, based on which, an optimization problem is formulated to explore the minimum age-energy aware cost by jointly optimizing the time assignment, the transmit power, the computing frequency, as well as the computing mode selection, such that the given data processing task can be completed. Since the problem is non-convex with the discrete binary variable, variable substitution and Karush-Kuhn-Tucker (KKT) conditions are applied to solve it and some closed-form results on the optimal solution are derived. Numerical results show that the transmit power has much greater effects on the age-energy performance of the fog offloading mode than on that of the local computing mode. Moreover, when the IoT device is relatively close to the F-HAP, fog offloading is a better choice; Otherwise, local computing should be selected.

Network adaptation is essential for the efficient operation of Cloud-RANs. Unfortunately, it leads to highly intractable mixed-integer nonlinear programming problems. Existing solutions typically rely on convex relaxation, which yield performance gaps that are difficult to quantify. Meanwhile, global optimization algorithms such as branch-and-bound can find optimal solutions but with prohibitive computational complexity. In this paper, to obtain near-optimal solutions at affordable complexity, we propose to approximate the branch-and-bound algorithm via machine learning. Specifically, the pruning procedure in branch-and-bound is formulated as a sequential decision problem, followed by learning the oracle's action via imitation learning. A unique advantage of this framework is that the training process only requires a small dataset, and it is scalable to problem instances with larger dimensions than the training setting. This is achieved by identifying and leveraging the problem-size independent features. Numerical simulations demonstrate that the learning based framework significantly outperforms competing methods, with computational complexity much lower than the traditional branch-and-bound algorithm. © 2018 IEEE.

Memory caches are being aggressively used in today's data-parallel frameworks such as Spark, Tez and Storm. By caching input and intermediate data in memory, compute tasks can witness speedup by orders of magnitude. To maximize the chance of in-memory data access, existing cache algorithms, be it recency-or frequency-based, settle on cache hit ratio as the optimization objective. However, unlike the conventional belief, we show in this paper that simply pursuing a higher cache hit ratio of individual data blocks does not necessarily translate into faster task completion in data-parallel environments. A data-parallel task typically depends on multiple input data blocks. Unless all of these blocks are cached in memory, no speedup will result. To capture this all-or-nothing property, we propose a more relevant metric, called effective cache hit ratio. Specifically, a cache hit of a data block is said to be effective if it can speed up a compute task. In order to optimize the effective cache hit ratio, we propose the Least Effective Reference Count (LERC) policy that persists the dependent blocks of a compute task as a whole in memory. We have implemented the LERC policy as a memory manager in Spark and evaluated its performance through Amazon EC2 deployment. Evaluation results demonstrate that LERC helps speed up data-parallel jobs by up to 37% compared with the widely employed least-recently-used (LRU) policy.

This paper considers the channel state information (CSI) acquisition and exploitation problem in cloud radio access networks (Cloud-RAN). A novel CSI acquisition method, called compressive CSI acquisition, is adopted to effectively reduce the CSI signaling overhead by obtaining instantaneous coefficients of only a subset of all the channel links. To deal with the uncertainty in available CSI, we propose a stochastic power control (SPC) framework, which is a highly intractable joint chance constrained program (JCCP). In contrast to conventional works, which can only find sub-optimal solutions, we propose a novel stochastic power control algorithm with optimality guarantee. Simulation results will show that the proposed compressive CSI acquisition method can reduce the overhead significantly. Moreover, the proposed SPC algorithm provides performance close to the full CSI case while only partial CSI is required. © 2016 IEEE.

The curse of big data, propelled by the explosive growth of mobile devices, places overwhelming pressures on wireless communications. Network densification is a promising approach to improve the area spectral efficiency, but to acquire massive channel state information (CSI) for effective interference management becomes a formidable task. In this paper, we propose a novel interference management method which only requires the network connectivity information, i.e., the knowledge of the presence of strong links, and statistical information of the weak links. To acquire such mixed network connectivity information incurs significant less overhead than complete CSI, and thus this method is scalable to large network sizes. To maximize the sum-rate with the mixed network connectivity information, we formulate a rank minimization problem to cancel strong interference and suppress weak interference, which is then solved by a Riemannian trust-region algorithm. Such algorithm is robust to initial points and has a fast convergence rate. Simulation result shows that our approach achieves a higher data rate than the state-of-the-art methods. © 2016 IEEE.

Underlaying device-to-device (D2D) communication links to a cellular network is a promising way to improve spectrum efficiency, for which the cross- link interference should be carefully controlled. Resource allocation has been widely utilized for managing interference in D2D networks. However, most previous works made simple assumptions by either ignoring the reliability requirement of D2D links or not allowing multiple D2D links to share the same channel. In this paper, we propose effective channel assignment algorithms to maximize the weighted sum-rate in a cellular network with underlaying D2D communications, where multiple D2D links are allowed to share the same channel. Meanwhile, the minimum Signal-to- Interference-plus-Noise Ratio (SINR) requirements for both cellular and D2D links are guaranteed. We first provide an optimal algorithm based on dynamic programming (DP) to serve as the performance benchmark, which enjoys much lower complexity compared to exhaustive search. To further reduce complexity, we then propose a cluster-based near-optimal channel assignment algorithm. Simulation results will demonstrate the advantage of allowing multiple D2D links to share the same channel in dense D2D networks, as well as verifying the effectiveness of the proposed algorithms.

As a promising technique to meet the drastically growing demand for both high throughput and uniform coverage in the fifth generation (5G) wireless networks, massive multiple-input multiple-output (MIMO) systems have attracted significant attention in recent years. However, in massive MIMO systems, as the density of mobile users (MUs) increases, conventional uplink training methods will incur prohibitively high training overhead, which is proportional to the number of MUs. In this paper, we propose a selective uplink training method for massive MIMO systems, where in each channel block only part of the MUs will send uplink pilots for channel training, and the channel states of the remaining MUs are predicted from the estimates in previous blocks, taking advantage of the channels' temporal correlation. We propose an efficient algorithm to dynamically select the MUs to be trained within each block and determine the optimal uplink training length. Simulation results show that the proposed training method provides significant throughput gains compared to the existing methods, while much lower estimation complexity is achieved. It is observed that the throughput gain becomes higher as the MU density increases.

In this work, we address the delay optimal scheduling problem for wireless transmission with fixed modulation over multi-state fading channels. We propose a stochastic scheduling policy which schedules the source to transmit with probability jointly based on the buffer and channel states, with an average power constraint at the transmitter. Our objective is to derive the optimal transmission probabilities such that the average queueing delay is minimized subject to the power constraint. Using Markov chain modeling, we formulate a power-constrained delay minimization problem, and then transform it into a Linear Programming one. By analyzing its property, we can derive the optimal threshold-based scheduling policy together with the corresponding transmission probabilities. Our theoretical analysis is corroborated by simulation results.

Energy harvesting (EH) has recently been attracting significant attention because of its ability to scavenge environmentally friendly energy. In this paper, we investigate the use of EH relay nodes to improve the quality of service (QoS) for relaying networks. To simplify the hardware design, we adopt a half-duplex selective decode-and-forward (SDF) relay. We propose a joint link selection and relay power allocation strategy to minimize the average outage probability. Both offline and online policies, i.e., with non-causal or causal side information about the energy state and the decoding result at the relay, are investigated by utilizing deterministic and stochastic dynamic programming (DP) algorithms, respectively. Furthermore, to reduce the complexity of the optimal online solution, we propose two low-complexity suboptimal online policies. Simulation results will show that the proposed suboptimal policies outperform the existing policies and achieve near optimal performance. © 2014 IEEE.

As the capacity demand of mobile applications keeps increasing, the backhaul network is becoming a bottleneck to support high quality of experience (QoE) in next-generation wireless networks. Content caching at base stations (BSs) is a promising approach to alleviate the backhaul burden and reduce user-perceived latency. In this paper, we consider a wireless caching network where all the BSs are connected to a central controller via backhaul links. In such a network, users can obtain the required data from candidate BSs if the data are pre-cached. Otherwise, the user data need to be first retrieved from the central controller to local BSs, which introduces extra delay over the backhaul. In order to reduce the download delay, the caching placement strategy needs to be optimized. We formulate such a design problem as the minimization of the average download delay over user requests, subject to the caching capacity constraint of each BS. Different from existing works, our model takes BS cooperation in the radio access into consideration and is fully aware of the propagation delay on the backhaul links. The design problem is a mixed integer programming problem and is highly complicated, and thus we relax the problem and propose a low-complexity algorithm. Simulation results will show that the proposed algorithm can effectively determine the near-optimal caching placement and provide significant performance gains over conventional caching placement strategies. © 2015 IEEE.

Deploying femtocells over the conventional macrocell network is a promising way to increase network capacity, whereas the main bottleneck is the interference between the femtocell and macrocell tiers. Recent research has proposed many effective methods for cross-tier interference mitigation, but unfortunately, the optimal spectrum access scheme remains unknown. In this paper, a cognitive spectrum access scheme is proposed, where each femtocell can dynamically explore and access the idle macro-tier spectrum besides its dedicated femto-tier spectrum. The optimum probabilities for each femtocell to access the femto-tier and idle macro-tier spectrum which maximize the area spectral efficiency (ASE) are investigated. With perfect spectrum sensing, the ratio between the optimum probabilities to access the femto-tier and macro-tier spectrum is equal to the amounts of available subchannels in the femto-tier and macro-tier spectrum for most cases. With non-perfect spectrum sensing, a lower bound of the optimum probability to access the femto-tier spectrum is determined, which is a piecewise function of the femtocell intensity. Simulation results validate the accuracy of our analytical results and reveal the advantages of the proposed scheme over existing schemes.

Deploying femtocells over the conventional macrocell network is a promising way to increase network capacity, whereas the main bottleneck is the interference between the femtocell and macrocell tiers. Recent research has proposed many effective methods for cross-tier interference mitigation, but unfortunately, the optimal spectrum access scheme remains unknown. In this paper, a cognitive spectrum access scheme is proposed, where each femtocell can dynamically explore and access the idle macro-tier spectrum besides its dedicated femto-tier spectrum. The optimum probabilities for each femtocell to access the femto-tier and idle macro-tier spectrum which maximize the area spectral efficiency (ASE) are investigated. With perfect spectrum sensing, the ratio between the optimum probabilities to access the femto-tier and macro-tier spectrum is equal to the amounts of available subchannels in the femto-tier and macro-tier spectrum for most cases. With non-perfect spectrum sensing, a lower bound of the optimum probability to access the femto-tier spectrum is determined, which is a piecewise function of the femtocell intensity. Simulation results validate the accuracy of our analytical results and reveal the advantages of the proposed scheme over existing schemes.

One major objective in smart grids is to improve the system efficiency by reducing the peak-to-average ratio (PAR) of the power consumption, i.e., to balance the supply and demand in the power system. In this paper, we propose to control the power adjustable appliances to reduce PAR in residential buildings. By considering the indoor temperature control appliances, whose operating power is continuously adjustable, we formulate the power management problem as a convex optimization problem to minimize the electricity cost under a comfortable temperature range. An optimal algorithm is developed, which can reduce the cost by about 25% and the PAR by about 63% given non-causal prior knowledge of the power consumption profile of the unadjustable appliances. A practical sub-optimal algorithm that requires only causal knowledge is also developed, which can reduce the cost by about 10% and the PAR by about 28%.