To realize orthogonal frequency division multiplexing (OFDM)-based grant-free access for wideband systems under frequency-selective fading, existing device activity detection and channel estimation methods need substantial accuracy improvement or computation time reduction. In this paper, we aim to resolve this issue. First, we present an exact time-domain signal model for OFDM-based grant-free access under frequency-selective fading. Then, we present a maximum a posteriori (MAP)-based device activity detection problem and two minimum mean square error (MMSE)-based channel estimation problems. The MAP-based device activity detection problem and one of the MMSE-based channel estimation problems are formulated for the first time. Next, we build a new factor graph that captures the exact statistics of time-domain channels and device activities. Based on it, we propose two approximate message passing (AMP)-based algorithms,AMP-A-ECandAMP-A-AC, to approximately solve the MAP-based device activity detection problem and two MMSE-based channel estimation problems. Both proposed algorithms alleviate the AMP’s inherent convergence problem when the pilot length is smaller or comparable to the number of active devices. Then, we analyzeAMP-A-EC’s error probability of activity detection and mean square error (MSE) of channel estimation via state evolution and show thatAMP-A-AChas the lower computational complexity (in dominant term). Finally, numerical results show the two proposed AMP-based algorithms’ superior performance and respective preferable regions, revealing their significant values for OFDM-based grant-free access.

Optimal frequency band allocation for multi-cell Internet of Things (IoT) networks that operate on an unlicensed spectrum and adopt grant-free ALOHA random access is crucial for effectively supporting massive IoT devices but has yet to be closely studied. This paper systematically investigates optimal frequency band allocation under frequency reuse for a slotted multi-cell IoT network that operates on an unlicensed wide band with multiple frequency bands and adopts the slotted multi-band ALOHA scheme. Specifically, we consider three cases of frequency band allocation, i.e., adjustment cost-free case, adjustment cost-constrained case, and adjustment cost-penalized case, and respectively study the average rate maximization, the average rate maximization under a frequency band adjustment cost constraint, and the maximization of a weighted sum of the average rate and frequency band adjustment cost, which are challenging large-scale integer programming (IP) problems. Then, in each case, we propose an efficient algorithm with parallel updates per iteration and a low-complexity greedy algorithm with finite iterations to obtain optimal and approximate optimal points, respectively. We also analyze the convergence results of the proposed optimization algorithms and the differences and connections of the three problems. We compare the proposed methods with existing frequency band allocation methods. Analytical and numerical results offer design insight and validate the values of the proposed formulations and algorithms.

Leveraging communications signals for localization has attracted increasing attention. However, most existing studies face two limitations. First, timing-frequency synchronization (TiFSync), essential for reliable demodulation, is often ignored in modeling, leading to over-optimistic bounds and degraded accuracy in localization. Second, joint position and velocity estimation (PaVE) has not been fully investigated, including the analysis of the requirement for local identifiability and the corresponding low-complexity algorithm design. To address these issues, we propose an uplink orthogonal frequency division multiplexing multiple-input-multiple-output (OFDM-MIMO) PaVE framework under imperfect TiFSync and multipath conditions. An equivalent system model is first established to demonstrate the potential of PaVE. We then derive the Fisher information matrix (FIM) and Cramér-Rao lower bound (CRLB). By analyzing the FIM rank, we examine the local identifiability of our PaVE framework. The results show that four synchronous base stations (BSs) are required to ensure the local identifiability of PaVE of an asynchronous mobile device. We also develop a decentralized two-step method for PaVE: (i) estimating the channel parameters through the multi-dimensional discrete Fourier transform (DFT)-based interpolation; (ii) performing PaVE based on a weighted least squares (WLS) estimator, which is solved by the Gauss-Newton method with a good initial guess. This algorithm only requires the exchange of channel parameter estimates and TiFSync adjustments among BSs, resulting in a very low communications overhead. Simulation results demonstrate that our proposed PaVE framework achieves fast convergence and near-CRLB accuracy. Comparisons with existing works confirm its reliability in practical scenarios.

Existing optimization and deep learning approaches for resource management optimization cannot achieve satisfactory performance and computation time trade-offs for interference networks with arbitrary device locations. This paper aims to address this issue. Specifically, we investigate an orthogonal frequency-division multiplexing (OFDM)-based wide-band device-to-device (D2D) network with multiple multi-antenna transceiver pairs accessing various subcarriers. We optimize resource management (RM), i.e., beamforming and power control, to maximize the worst-case achievable rate among all transceiver pairs under the beamforming and power constraints. The RM problem is a challenging large-scale non-convex problem. We propose joint and separate designs offering different worst-case achievable rate and computation time trade-offs by solving the RM problem and a resultant power control (PC) problem for separately optimized beamforming using optimization and deep learning techniques. First, we propose two parallel successive convex approximation (PSCA)-based parallel iterative algorithms to obtain stationary points of the RM and PC problems. Both can achieve completely parallel and closed-form per-iteration updates, significantly reducing the computation times. Next, we propose two data and model-driven deep learning methods that optimally select good channel state information (CSI)-adaptive initial points for RM-PSCA and PC-PSCA and effectively unroll the two PSCA-based algorithms into neural networks with the algorithm parameters as tunable parameters. Both further reduce the underlying algorithms’ computation times while achieving appealing worst-case achievable rates based on training over vast samples. Numerical results demonstrate the proposed approaches’ superior advantages over the state of the art.

Multi-target detection and estimation present critical challenges in 5G NR-based multi-input multi-output (MIMO) integrated sensing and communications (ISAC) systems. In this paper, we investigate a 5G NR-based MIMO-ISAC system, leveraging 5G NR pilots, in a practical multi-target scenario with an unknown target number. We propose a novel joint target detection and 4-dimensional (4D) estimation (azimuth, elevation, distance, and velocity) algorithm, JTD4DE, by exploiting MIMO radar gain. First, we derive the Cramér–Rao lower bounds (CRLBs) for 4D estimation to establish theoretical estimation limits. Then, we propose a low-complexity virtual array-based 4D estimation method, 4DE, which successfully improves the estimation accuracy. Furthermore, we apply the generalized maximum likelihood (GML) rule to effectively determine the target number. Numerical results demonstrate that our proposed algorithm accurately detects the number of targets and significantly improves the 4D estimation accuracy, highlighting its substantial value for 5G NR-based MIMO-ISAC systems.

Existing primal decomposition algorithms for nonconvex problems cannot handle nonlinear equality constraints and do not fully exploit the original primal decomposition structures, limiting their effectiveness and efficiency. To address these limitations, we propose a new primal decomposition method and a new primal decomposition algorithm for a nonconvex problem with coupling variables in nonconvex inequality and nonlinear equality constraints. Specifically, they remove the dependence on the subproblems’ globally optimal points and the master problem’s convexity, successfully generalizing the classic ones for convex problems to nonconvex problems. Besides, the proposed primal decomposition algorithm allows parallel and distributed implementations and is shown to converge to the stationary points of the original nonconvex problem. We also customize the proposed primal decomposition algorithm for a new optimal Zero-Forcing (ZF) transmit and receive beamforming problem, which is more general and challenging than the existing ZF transmit beamforming problem. Numerical results demonstrate the superior advantages of the proposed primal decomposition algorithm.

Nowadays, localization functionality is becoming increasingly critical in Internet of Things (IoT) applications. However, the localization accuracy of long-range IoT systems is fundamentally constrained by the limitations of narrowband transmission, low power consumption, and imperfect timing-frequency synchronization. To overcome these problems, this paper proposes a frequency-hopping (FH)-assisted localization system based on the existing LoRa technique under imperfect synchronization. The core idea is that localization accuracy can benefit from an equivalent wider bandwidth by transmitting symbols over multiple frequency bands and jointly processing the collectively received symbols. Specifically, we first develop a system model for the FH-assisted LoRa localization, incorporating the effects of imperfect timing-frequency synchronization. We then derive the Cramér-Rao Lower Bound (CRLB) for localization accuracy in the continuous-time domain, quantifying the theoretical performance limits with an infinitely wide sampling bandwidth. Furthermore, we propose a low-complexity 2D discrete Fourier transform (DFT)-based channel estimation algorithm. The estimation results are then used to localize the target device in a time-difference-of-arrival (TDoA)-based trilateration scheme. Numerical results demonstrate that our proposed FH-assisted LoRa localization system can achieve tens-of-meter-level accuracy in both LoS and multipath environments, significantly outperforming current LoRa localization systems with an accuracy of around 200 m.

For massive grant-free access, maximum a posterior (MAP) estimation-based device activity detection is known to achieve superior detection accuracy in the scenario of known activity probabilities. Unfortunately, no comparable Bayesian statistical estimation method exists in the scenario of unknown activity probabilities. This paper investigates MAP estimation-based device activity detection in this scenario. First, we formulate the hierarchical MAP estimation problem for device activities, which is a very challenging non-convex problem. For tractability, we obtain its approximate non-convex problem and prove its equivalence to the MAP estimation problem of device activities and activity probabilities. Next, we propose a block coordinate descent (BCD)-based algorithm to obtain the approximate MAP estimation problem's stationary points. Finally, we numerically show that the proposed algorithm achieves a superior detection accuracy and short computation time tradeoff compared with the state-of-the-art device activity detection methods.

Existing optimization and deep neural network (DNN) approaches for beamforming and power control cannot achieve satisfactory performance and computation time trade-offs for interference networks with arbitrary device locations, due to optimization algorithms' expensive computation costs and DNNs' universal approximation and lack of generality. To address this issue, we propose new beamforming and power control approaches by elegantly leveraging optimization and deep learning techniques. We investigate the maximization of the worst-case achievable rate of multiple transceiver pairs under power constraints in orthogonal frequency-division multiplexing (OFDM)-based wideband device-to-device (D2D) networks with multiple subcarriers and arbitrary device locations. First, we obtain each transceiver's transmit and receive beamforming using the standard singular value decomposition (SVD) method. Then, we propose an iterative algorithm named PSCA to obtain joint power control for all transceivers using the parallel successive convex approximation (PSCA) method. PSCA allows parallel and closed-form per-iteration updates and has a short per-iteration computation time. Next, we propose two PSCA-driven deep unrolling neural networks, namely PSCANet and S-PSCANet to reduce the overall computation time. PSCANet and S-PSCANet marry PSCA's parallel computation mechanism with the parallelizable neural network architecture and effectively optimize PSCA's algorithm parameters based on vast samples of random channel fading coefficients and device locations. Moreover, S-PSCANet successfully resolves the training problem due to vanishing or exploding gradients when unrolling many PSCA iterations. Numerical results demonstrate the superior advantages of PSCANet and S-PSCANet over the existing approaches.

With the rapid expansion of electric vehicle (EV) charging piles, anomaly detection in EV charging piles has become critical to ensure charging reliability and safety. The high-dimensional data, complex data patterns, and scarcity of anomaly samples pose significant challenges for effective detection. Unfortunately, existing machine learning and deep learning-based methods exhibit detection accuracy or computational efficiency flaws. To address these issues, we propose a successive light gradient boosting machine (SLGBM)-based anomaly detection method for EV charging piles. SLGBM comprises multiple light gradient boosting machine (LGBM) models arranged in a layered structure and successively improves detection accuracy by gradually capturing nuanced feature differences. We propose a method to successively add and pre-train a new layer of LGBMs. We also propose a method to jointly train all component LGBMs to further improve detection accuracy. The number of layers controls the detection accuracy and computation efficiency tradeoff, demonstrating SLGBM’s flexibility for anomaly detection in EV charging piles. Numerical results on a Baidu EV charging piles dataset demonstrate the proposed SLGBM’s superior advantages over existing methods such as support vector machine (SVM), random forest (RF), and convolutional neural network-long short-term memory (CNN-LSTM) in large-sample and small-sample scenarios. The significant gains show SLGBM’s practical importance for anomaly detection in EV charging piles.

With the rapid deployment of electric vehicle (EV) charging infrastructure, the detection of anomalies in EV charging piles has become crucial for charging reliability and safety. However, the high data dimensionality, complex pattern structures, and scarcity of anomaly samples present significant detection challenges. Existing machine learning and deep learning methods often suffer from limitations in detection accuracy or computation complexity. To resolve these problems, we propose a successive Support Vector Machine (SSVM)-based anomaly detection approach for EV charging piles. SSVM consists of multiple Support Vector Machine (SVM) modules in a layered architecture, with each layer incrementally improving detection accuracy by capturing subtle feature differences. We also propose a training method for optimizing SSVM's parameters and hyperparameters, significantly reducing the training time. Experiments on a real-world EV charging pile dataset demonstrate that SSVM achieves a superior trade-off between detection performance and inference time across different training sample sizes, compared to existing methods such as SVM, random forest (RF), and convolutional neural network-long short-term memory (CNN-LSTM). The significant improvements highlight the practical value of SSVM in EV charging pile anomaly detection.

Abstract The sixth-generation (6G) mobile network implements the social vision of digital twins and ubiquitous intelligence. Contrary to the fifth-generation (5G) mobile network that focuses only on communications, 6G mobile networks must natively support new capabilities such as sensing, computing, artificial intelligence (AI), big data, and security while facilitating Everything as a Service. Although 5G mobile network deployment has demonstrated that network automation and intelligence can simplify network operation and maintenance (O&M), the addition of external functionalities has resulted in low service efficiency and high operational costs. In this study, a technology framework for a 6G autonomous radio access network (RAN) is proposed to achieve a high-level network autonomy that embraces the design of native cloud, native AI, and network digital twin (NDT). First, a service-based architecture is proposed to re-architect the protocol stack of RAN, which flexibly orchestrates the services and functions on demand as well as customizes them into cloud-native services. Second, a native AI framework is structured to provide AI support for the diverse use cases of network O&M by orchestrating communications, AI models, data, and computing power demanded by AI use cases. Third, a digital twin network is developed as a virtual environment for the training, pre-validation, and tuning of AI algorithms and neural networks, avoiding possible unexpected losses of the network O&M caused by AI applications. The combination of native AI and NDT can facilitate network autonomy by building closed-loop management and optimization for RAN.

The costs of channel estimation, reflection adjustment, and computation have severe impacts on intelligent reflection surface (IRS)-aided physical layer security (PLS) wireless communication systems in practice but are usually overlooked for simplicity in most existing works. This paper considers a multi-antenna base station serving a single-antenna legitimate user with the assistance of a multi-element IRS under the surveillance of a single-antenna eavesdropper. Firstly, we introduce a partial instantaneous CSI and statistical CSI (PICSI-SCSI)-adaptive beamforming and SCSI-adaptive reflection design. Secondly, we maximize the achievable ergodic secrecy rate (ESR) with respect to the PICSI-SCSI-adaptive beamforming and SCSI-adaptive reflection design, resulting in a two-timescale stochastic non-convex problem. Thirdly, we present two stochastic iterative algorithms to reach stationary and approximate stationary points. Moreover, we show that the two proposed designs achieve lower computational complexities and adjustment costs for reflection than the existing PICSI-SCSI-adaptive beamforming and reflection design. Lastly, we numerically demonstrate the two proposed designs’ notable gains over baselines. To our knowledge, this is the first work providing an optimization-based PICSI-SCSI-adaptive beamforming and SCSI-adaptive reflection design in an IRS-aided PLS wireless communication system, achieving promising secure performance at the minimum adjustment cost for reflection.

Rate Splitting (RS) is a versatile and powerful technique for multi-antenna transmission. In this paper, we study the precoding optimization for RS, which is critically important for improving the system performance but often challenging to address. We first investigate the non-convex sum rate maximization problem under perfect Channel State Information at the Transmitter (CSIT). By constructing a separable structure for the sum-of-functions-of-ratios problem and jointly leveraging the Convex Concave Procedure (CCP) and the Alternating Direction Method of Multipliers (ADMM), we obtain a fast algorithm that substantially reduces the overall computational time through the parallel computation and explicit closed-form solutions. We then investigate the average sum rate maximization problem under imperfect CSIT, which is known as a more challenging non-convex stochastic problem. To obtain a fast algorithm for practical use, we carefully approximate the non-convex stochastic problem to a non-convex deterministic one with acceptable performance loss and tailor the fast algorithm derived from perfect CSIT for imperfect CSIT with modest changes. Numerical results show that compared to the state-of-the-art algorithms, the proposed algorithms achieve comparable sum rates or average sum rates but short computation times for large problem sizes, owing to the unique parallel computation structures and few matrix inverse operations.

Deep reinforcement learning (DRL) demonstrates its promising potential in adaptive video streaming and has recently received increasing attention. However, existing DRL-based methods for adaptive video streaming mainly use application (APP) layer information, adopt heuristic training methods, and are not robust against continuous network fluctuations. This paper aims to boost the quality of experience (QoE) of adaptive wireless video streaming by using cross-layer information, deriving a rigorous training method, and adopting effective online tuning methods with real-time data. First, we formulate a more comprehensive and accurate adaptive wireless video streaming problem as an infinite stage discounted Markov decision process (MDP) problem by additionally incorporating past and lower-layer information. This formulation allows a flexible tradeoff between QoE and computational and memory costs for solving the problem. In the offline scenario (only with pre-collected data), we propose an enhanced asynchronous advantage actor-critic (eA3C) method by jointly optimizing the parameters of parameterized policy and value function. Specifically, we build an eA3C network consisting of a policy network and a value network that can utilize cross-layer, past, and current information and jointly train the eA3C network using pre-collected samples. In the online scenario (with additional real-time data), we propose two continual learning-based online tuning methods for designing better policies for a specific user with different QoE and training time tradeoffs. The proposed online tuning methods are robust against continuous network fluctuations and more general and flexible than the existing online tuning methods. Finally, experimental results show that the proposed offline policy can improve the QoE by 6.8% to 14.4% compared to the state-of-the-arts in the offline scenario, and the proposed online policies can achieve$6.3\%$to 55.8% gains in QoE over the state-of-the-arts in the online scenario.

Fast and accurate device activity detection is the critical challenge in grant-free access for supporting massive machine-type communications (mMTC) and ultra-reliable low-latency communications (URLLC) in 5G and beyond. The state-of-the-art methods have unsatisfactory error rates or computation times. To address these outstanding issues, we propose new maximum likelihood estimation (MLE) and maximum a posterior estimation (MAPE) based device activity detection methods for known and unknown pathloss that achieve superior error rate and computation time tradeoffs using optimization and deep learning techniques. Specifically, we investigate four non-convex optimization problems for MLE and MAPE in the two pathloss cases, with one MAPE problem being formulated for the first time. For each non-convex problem, we develop an innovative parallel iterative algorithm using the parallel successive convex approximation (PSCA) method. Each PSCA-based algorithm allows parallel computations, uses up to the objective function’s second-order information, converges to the problem’s stationary points, and has a low per-iteration computational complexity compared to the state-of-the-art algorithms. Then, for each PSCA-based iterative algorithm, we present a deep unrolling neural network implementation, called PSCA-Net, to further reduce the computation time. Each PSCA-Net elegantly marries the underlying PSCA-based algorithm’s parallel computation mechanism with the parallelizable neural network architecture and effectively optimizes its step sizes based on vast data samples to speed up the convergence. Numerical results demonstrate that the proposed methods can significantly reduce the error rate and computation time compared to the state-of-the-art methods, revealing their significant values for grant-free access.

Fast and accurate device activity detection is the critical challenge in grant-free access for supporting massive machine-type communications (mMTC). The state-of-the-art methods have an unsatisfactory error rate or computation time. To address these outstanding issues, we propose new maximum likelihood estimation (MLE)-based device activity detection methods that achieve superior tradeoffs between the error rate and computation time using optimization and deep learning techniques. Specifically, we propose an innovative parallel successive convex approximation (PSCA) algorithm for solving the noncon-vex MLE problem. We show that it allows parallel computations, uses up to the objective function’s second-order information, converges to the problem’s stationary points, and has a low per-iteration computational complexity compared to the state-of-the-art algorithms for the MLE problem. Then, we propose a PSCA-driven deep unrolling neural network implementation, called PSCANet, to reduce the overall computation time of PSCA. PSCANet elegantly marries PSCA’s parallel computation mechanism with the parallelizable neural network architecture and effectively optimizes its step sizes based on vast data samples. Numerical results demonstrate that PSCA and PSCANet can significantly reduce the error rate and computation time compared to the state-of-the-art methods, revealing their significant values for mMTC in 5G and beyond.

Deep reinforcement learning (DRL) demonstrates its promising potential in the realm of adaptive video streaming. However, existing DRL-based methods for adaptive video streaming use only application (APP) layer information and adopt heuristic training methods. This paper aims to boost the quality of experience (QoE) of adaptive wireless video streaming by using lower-layer information and deriving a rigorous training method. First, we formulate a more comprehensive and accurate adaptive wireless video streaming problem as an infinite stage discounted Markov decision process (MDP) problem by additionally incorporating past and lower-layer information, allowing a flexible tradeoff between QoE and computational and memory costs for solving the problem. Then, we propose an enhanced asynchronous advantage actor-critic (eA3C) method by jointly optimizing the parameters of parameterized policy and value function. Specifically, we build an eA3C network consisting of a policy network and a value network that can utilize cross-layer, past, and current information and jointly train the eA3C network using pre-collected samples. Finally, experimental results show that the proposed eA3C method can improve the QoE by 6.8%$\sim$14.4% compared to the state-of-the-arts.

The optimal implementation of federated learning (FL) in practical edge computing systems has been an outstanding problem. In this paper, we propose an optimization-based quantized FL algorithm, which can appropriately fit a general edge computing system with uniform or nonuniform computing and communication resources at the workers. Specifically, we first present a new random quantization scheme and analyze its properties. Then, we propose a general quantized FL algorithm, namely GQFedWAvg. Specifically, GQFedWAvg applies the proposed quantization scheme to quantize wisely chosen model update-related vectors and adopts a generalized mini-batch stochastic gradient descent (SGD) method with the weighted average local model updates in global model aggregation. Besides, GQFedWAvg has several adjustable algorithm parameters to flexibly adapt to the computing and communication resources at the server and workers. We also analyze the convergence of GQFedWAvg. Next, we optimize the algorithm parameters of GQFedWAvg to minimize the convergence error under the time and energy constraints. We successfully tackle the challenging non-convex problem using general inner approximation (GIA) and multiple delicate tricks. Finally, we interpret GQFedWAvg’s function principle and show its considerable gains over existing FL algorithms using numerical results.

Most existing studies on massive grant-free access, proposed to support massive machine-type communications (mMTC) for the Internet of things (IoT), assume Rayleigh fading and perfect synchronization for simplicity. However, in practice, line-of-sight (LoS) components generally exist, and time and frequency synchronization are usually imperfect. This paper systematically investigates maximum likelihood estimation (MLE)-based device activity detection under Rician fading for massive grant-free access with perfect and imperfect synchronization. We assume that the large-scale fading powers, Rician factors, and normalized LoS components can be estimated offline. We formulate device activity detection in the synchronous case and joint device activity and offset detection in three asynchronous cases (i.e., time, frequency, and time and frequency asynchronous cases) as MLE problems. In the synchronous case, we propose an iterative algorithm to obtain a stationary point of the MLE problem. In each asynchronous case, we propose two iterative algorithms with identical detection performance but different computational complexities. In particular, one is computationally efficient for small ranges of offsets, whereas the other one, relying on fast Fourier transform (FFT) and inverse FFT, is computationally efficient for large ranges of offsets. The proposed algorithms generalize the existing MLE-based methods for Rayleigh fading and perfect synchronization. Numerical results show that the proposed algorithm for the synchronous case can reduce the detection error probability by up to 50.4% at a 78.6% computation time increase, compared to the MLE-based state-of-the-art, and the proposed algorithms for the three asynchronous cases can reduce the detection error probabilities and computation times by up to 65.8% and 92.0%, respectively, compared to the MLE-based state-of-the-arts.

The impacts of channel estimation, beamforming adjustment, phase shift adjustment, and computation costs on an intelligent reflecting surface (IRS)-assisted secure wireless communication system are severe in practice but are usually ignored for simplicity. In this paper, we consider a multi-antenna BS serving a single-antenna legitimate user with the help of a multi-element IRS in the presence of an eavesdropper. To maximally reduce the implementation cost, we investigate the no-instantaneous channel state information (ICSI) case with the legitimate user’s and eavesdropper’s statistical CSI (SCSI). First, we present a SCSI-adaptive (quasi-static) beamforming and phase shift design, also referred to as a quasi-static design, which has low channel estimation, beamforming adjustment, and phase shift adjustment costs. Then, we formulate the maximization of the achievable ergodic secrecy rate with respect to the quasi-static design as a challenging stochastic non-convex problem. Next, we propose two parallel iterative algorithms to obtain a stationary point and an approximate stationary point and present their respective quasi-static designs. Furthermore, we show that the quasi-static designs derived from the stationary point and approximate stationary point achieve lower implementation costs than existing designs. Finally, we numerically verify the analytical results and demonstrate notable gains of the two proposed quasi-static designs over existing designs.

This paper investigates optimal implementations of federated learning (FL) in practical edge computing systems with possibly distinct computing and communication resources at the server and workers. First, we present a new random quantization scheme and analyze its properties. Then, we propose a general quantized FL algorithm, namely HQFedWAvg, and analyze its convergence. HQFedWAvg adopts the proposed quantization scheme and a generalized mini-batch stochastic gradient descent (SGD) method and has several adjustable algorithm parameters to maximally adapt to the computing and communication resources at the server and workers. Next, we optimize the algorithm parameters of HQFedWAvg. The resulting challenging non-convex optimization problem is successfully tackled using several optimization techniques. Numerical results demonstrate HQFedWAvg's considerable performance gains over existing FL algorithms and interpret its function principle.

The analysis and optimization of single intelligent reflecting surface (IRS)-assisted systems have been extensively studied, whereas little is known regarding multiple-IRS-assisted systems. This paper investigates the analysis and optimization of a double-IRS cooperatively assisted downlink system (D-IRS-C), where a multi-antenna base station (BS) serves a single-antenna user with the help of two multi-element IRSs, connected by an inter-IRS channel. The channel between any two nodes is modeled with Rician fading. The BS adopts the instantaneous CSI-adaptive maximum-ratio transmission (MRT) beamformer, and the two IRSs adopt a cooperative quasi-static phase shift design. The goal is to maximize the average achievable rate, which can be reflected by the average channel power of the equivalent channel between the BS and user at low channel estimation and phase adjustment costs and computational complexity. First, we obtain tractable expressions of the average channel power of the equivalent channel in the general (Rician factor), pure line of sight (LoS), and pure non-line of sight (NLoS) regimes, respectively. Then, we jointly optimize the phase shifts of the two IRSs to maximize the average channel power of the equivalent channel in these regimes. The optimization problems are challenging non-convex problems. We obtain globally optimal closed-form solutions for some cases and propose computationally efficient iterative algorithms to obtain stationary points for the other cases. Next, we compare the computational complexity for optimizing the phase shifts and the optimal average channel power of D-IRS-C with those of a counterpart double-IRS non-cooperatively assisted system (D-IRS-NC) and a counterpart single-IRS-assisted system (S-IRS) at a large number of reflecting elements in the three regimes. Finally, we numerically demonstrate notable gains of the proposed solutions over the existing solutions at different system parameters. To our knowledge, this is the first work that optimizes the quasi-static phase shift design of D-IRS-C and characterizes its advantages over the optimal quasi-static phase shift design of the counterpart D-IRS-NC and S-IRS.

Existing works on grant-free access, proposed to support massive machine-type communication (mMTC) for the Internet of Things (IoT), mainly concentrate on narrow band systems under flat fading. In contrast, this paper investigates massive grant-free access in a wideband system under frequency-selective fading. First, we present an orthogonal frequency division multiplexing (OFDM)-based massive grant-free access scheme. Then, we propose two different but equivalent models for the received pilot signal. Specifically, one directly models the received signal for actual devices, whereas the other can be interpreted as a signal model for virtual devices. The two signal models are insightful and essential for designing various device activity detection and channel estimation methods for OFDM-based massive grant-free access. Next, we systematically investigate statistical device activity detection under frequency-selective Rayleigh fading based on the two signal models. In particular, in the case without prior knowledge of device activities, we model device activities as deterministic but unknown binary constants and propose three maximum likelihood (ML) estimation-based device activity detection methods with different detection accuracies and computation times. In the case with prior knowledge of device activities, we model device activities as realizations of Bernoulli random variables with a known joint distribution, which appropriately incorporates the prior knowledge, and propose three maximum a posterior probability (MAP) estimation-based device activity methods, which further enhance the accuracies of the corresponding ML estimation-based methods at the cost of increased computational complexities. The proposed methods can meet diverse practical needs for OFDM-based massive grant-free access.

In this paper, we consider the service caching and the computing resource allocation in edge computing (EC) enabled networks. We introduce a random service caching design considering multiple types of latency sensitive services and the base stations (BSs)’ service caching storage. We then derive a successful service probability (SSP). We also formulate a SSP maximization problem subject to the service caching distribution and the computing resource allocation. Then, we show that the optimization problem is nonconvex and develop a novel algorithm to obtain the stationary point of the SSP maximization problem by adopting the parallel successive convex approximation (SCA). Moreover, to further reduce the computational complexity, we also provide a low complex algorithm that can obtain the near-optimal solution of the SSP maximization problem in high computing capability region. Finally, from numerical simulations, we show that proposed solutions achieve higher SSP than baseline schemes. Moreover, we show that the near-optimal solution achieves reliable performance in the high computing capability region. We also explore the impacts of target delays, a BSs’ service cache size, and an EC servers’ computing capability on the SSP.

The optimal design of federated learning (FL) algorithms for solving general machine learning (ML) problems in practical edge computing systems with quantized message passing remains an open problem. This paper considers an edge computing system where the server and workers have possibly different computing and communication capabilities and employ quantization before transmitting messages. To explore the full potential of FL in such an edge computing system, we first present a general FL algorithm, namely GenQSGD, parameterized by the numbers of global and local iterations, mini-batch size, and step size sequence. Then, we analyze its convergence for an arbitrary step size sequence and specify the convergence results under three commonly adopted step size rules, namely the constant, exponential, and diminishing step size rules. Next, we optimize the algorithm parameters to minimize the energy cost under the time constraint and convergence error constraint, with the focus on the overall implementing process of FL. Specifically, for any given step size sequence under each considered step size rule, we optimize the numbers of global and local iterations and mini-batch size to optimally implement FL for applications with preset step size sequences. We also optimize the step size sequence along with these algorithm parameters to explore the full potential of FL. The resulting optimization problems are challenging non-convex problems with non-differentiable constraint functions. We propose iterative algorithms to obtain KKT points using general inner approximation (GIA) and tricks for solving complementary geometric programming (CGP). Finally, we numerically demonstrate the remarkable gains of GenQSGD with optimized algorithm parameters over existing FL algorithms and reveal the significance of optimally designing general FL algorithms.

Immersive video, such as virtual reality (VR) and multi-view videos, is growing in popularity. Its wireless streaming is an instance of general multicast, extending conventional unicast and multicast, whose effective design is still open. This paper investigates general rate splitting for general multicast. Specifically, we consider a multi-carrier single-cell wireless network where a multi-antenna base station (BS) communicates to multiple single-antenna users via general multicast. We consider linear beamforming at the BS and joint decoding at each user in the slow fading and fast fading scenarios. In the slow fading scenario, we consider the maximization of the weighted sum average rate, which is a challenging nonconvex stochastic problem with numerous variables. To reduce computational complexity, we decouple the original nonconvex stochastic problem into multiple nonconvex deterministic problems, one for each system channel state. Then, we propose an iterative algorithm for each deterministic problem to obtain a Karush-Kuhn-Tucker (KKT) point using the concave-convex procedure (CCCP). In the fast fading scenario, we consider the maximization of the weighted sum ergodic rate. This problem is more challenging than the one for the slow fading scenario, as it is not separable. First, we propose a stochastic iterative algorithm to obtain a KKT point using stochastic successive convex approximation (SSCA) and the exact penalty method. Then, we propose two low-complexity iterative algorithms to obtain feasible points with promising performance for two cases of channel distributions using approximation and CCCP. The proposed optimization framework generalizes the existing ones for rate splitting for various types of services. Finally, we numerically show substantial gains of the proposed solutions over existing schemes in both scenarios and reveal the design insights of general rate splitting for general multicast.

Grant-free access is recently proposed as an essential technique for supporting massive machine-type communications (mMTC) for the Internet of Things (IoT). However, high accuracy and low complexity device activity detection under imperfect frequency synchronization remains open. To address this issue, this paper proposes a maximum likelihood estimation (MLE)- based device activity detection method for the frequency asynchronous case. First, we formulate the estimation of device activities together with the device carrier frequency offsets (CFOs) as an MLE problem. Then, to tackle this challenging nonconvex problem, we propose a computationally efficient iterative algorithm using the block coordinate descent (BCD) method and fast computation enabled by fast Fourier transform (FFT). Analytical and numerical results demonstrate the notable gains of the proposed method over the existing solutions and offer important design insights into practical massive grant-free access for mMTC.

Immersive video, such as virtual reality (VR) and multi-view videos, is growing in popularity. Its wireless streaming is an instance of general multicast, extending conventional unicast and multicast, whose effective design is still open. This paper investigates the optimization of general rate splitting with linear beamforming for general multicast. Specifically, we consider a multi-carrier single-cell wireless network where a multi-antenna base station (BS) communicates to multiple single-antenna users via general multicast. Linear beamforming is adopted at the BS, and joint decoding is adopted at each user. We consider the maximization of the weighted sum rate, which is a challenging nonconvex problem. Then, we propose an iterative algorithm for the problem to obtain a KKT point using the concave-convex procedure (CCCP). The proposed optimization framework generalizes the existing ones for rate splitting for various types of services. Finally, we numerically show substantial gains of the proposed solutions over existing schemes and reveal the design insights of general rate splitting for general multicast.

Most existing random access schemes for machine-type communications (MTC) simply adopt a uniform preamble selection distribution, irrespective of the underlying device activity distributions. Hence, they may yield unsatisfactory access efficiency. In this paper, we model device activities for MTC as multiple Bernoulli random variables following an arbitrary multivariate Bernoulli distribution which can reflect both dependent and independent device activities. Then, we optimize preamble selection and access barring for random access in MTC according to the underlying joint device activity distribution. Specifically, we investigate three cases of the joint device activity distribution, i.e., the cases of perfect, imperfect, and unknown joint device activity distributions, and formulate the average, worst-case average, and sample average throughput maximization problems, respectively. The problems in the three cases are challenging nonconvex problems. In the case of perfect joint device activity distribution, we develop an iterative algorithm and a low-complexity iterative algorithm to obtain stationary points of the original problem and an approximate problem, respectively. In the case of imperfect joint device activity distribution, we develop an iterative algorithm and a low-complexity iterative algorithm to obtain a Karush-Kuhn-Tucker (KKT) point of an equivalent problem and a stationary point of an approximate problem, respectively. Finally, in the case of unknown joint device activity distribution, we develop an iterative algorithm to obtain a stationary point. The proposed solutions are widely applicable and outperform existing solutions for dependent and independent device activities.

Edge computing technology has great potential to improve various computation-intensive applications in vehicular networks by providing sufficient computation resources for vehicles. However, inappropriate task offloading to roadside units (RSUs) can lead to large energy consumption, which will result in negative economic, environmental, and performance impacts. Therefore, in this paper, we develop the energy-efficient cooperative offloading scheme for edge computing-enabled vehicular networks. We first establish a novel cooperative offloading model to multiple RSUs for given batch of moving vehicles, different from existing works that consider single vehicle only or static users. Then, we consider the total energy minimization by optimizing the task splitting ratio, computation resource, and communication resource, which is a challenging non-convex problem, and provide optimal solutions for multi-vehicle case and single-vehicle case, respectively. Furthermore, we extend our proposed scheme to the one for a more realistic scenario (i.e., online scenario), where batches of vehicles sequentially approach the RSUs. Finally, through numerical results, the impact of network parameters on the total energy consumption is analyzed, and we verify that our proposed solution consumes lower energy than baseline schemes.

The impacts of channel estimation errors, inter-cell interference, phase adjustment cost, and computation cost on an intelligent reflecting surface (IRS)-assisted system are severe in practice but have been ignored for simplicity in most existing works. In this paper, we investigate a multi-antenna base station (BS) serving a single-antenna user with the help of a multi-element IRS in a multi-cell network with inter-cell interference. We consider imperfect channel state information (CSI) at the BS, i.e., imperfect CSIT, and focus on the robust optimization of the BS’s instantaneous CSI-adaptive beamforming and the IRS’s quasi-static phase shifts in two scenarios. In the scenario of coding over many slots, we formulate a robust optimization problem to maximize the user’s ergodic rate. In the scenario of coding within each slot, we formulate a robust optimization problem to maximize the user’s average goodput under the successful transmission probability constraints. The robust optimization problems are challenging two-timescale stochastic non-convex problems. In both scenarios, we obtain closed-form robust beamforming designs for any given phase shifts and more tractable stochastic non-convex approximate problems only for the phase shifts. Besides, we propose an iterative algorithm to obtain a Karush-Kuhn-Tucker (KKT) point of each of the stochastic problems for the phase shifts. It is worth noting that the proposed methods offer closed-form robust instantaneous CSI-adaptive beamforming designs which can promptly adapt to rapid CSI changes over slots and robust quasi-static phase shift designs of low computation and phase adjustment costs in the presence of imperfect CSIT and inter-cell interference. Numerical results further demonstrate the notable gains of the proposed robust joint designs over existing ones and reveal the practical values of the proposed solutions.

Device activity detection is one main challenge in grant-free massive access, which is recently proposed to support massive machine-type communications (mMTC). Existing solutions for device activity detection fail to consider inter-cell interference generated by massive IoT devices or important prior information on device activities and inter-cell interference. In this paper, given different numbers of observations and network parameters, we consider both non-cooperative device activity detection and cooperative device activity detection in a multi-cell network, consisting of many access points (APs) and IoT devices. Under each activity detection mechanism, we consider the joint maximum likelihood (ML) estimation and joint maximum a posterior probability (MAP) estimation of both device activities and interference powers, utilizing tools from probability, stochastic geometry, and optimization. Each estimation problem is a challenging non-convex problem, and a coordinate descent algorithm is proposed to obtain a stationary point. Each proposed joint ML estimation extends the existing one for a single-cell network by considering the estimation of interference powers, together with the estimation of device activities. Each proposed joint MAP estimation further enhances the corresponding joint ML estimation by exploiting prior distributions of device activities and interference powers. The proposed joint ML estimation and joint MAP estimation under cooperative detection outperform the respective ones under non-cooperative detection at the costs of increasing backhaul burden, knowledge of network parameters, and computational complexities. Numerical results show the substantial gains of the proposed designs over well-known existing designs and reveal the importance of explicit consideration of inter-cell interference, the value of prior information, and the advantage of AP cooperation in device activity detection.

Device activity detection and channel estimation for grant-free massive access under frequency-selective fading have unfortunately been an outstanding problem. This paper aims to address the challenge. Specifically, we present an orthogo-nal frequency division multiplexing (OFDM)-based grant-free massive access scheme for a wideband system with one M- antenna base station (BS),$N$single-antenna Internet of Things (IoT) devices, and$P$channel taps. We obtain two different but equivalent models for the received pilot signals under frequency-selective Rayleigh fading. Based on each model, we formulate device activity detection as a non-convex maximum likelihood estimation (MLE) problem and propose an iterative algorithm to obtain a stationary point using optimal techniques. The two proposed MLE-based methods have the identical computational complexity order O(NPL2), irrespective of M, and degrade to the existing MLE-based device activity detection method when P = 1. Conventional channel estimation methods can be readily applied for channel estimation of detected active devices under frequency-selective Rayleigh fading, based on one of the derived models for the received pilot signals. Numerical results show that the two proposed methods have different preferable system parameters and complement each other to offer promising device activity detection design for grant-free massive access under frequency-selective Rayleigh fading.

The impacts of channel estimation errors, inter-cell interference, phase adjustment cost, and computation cost on an intelligent reflecting surface (IRS)-assisted system are severe in practice but have been ignored for simplicity in most existing works. In this paper, we investigate a multi-antenna base station (BS) serving a single-antenna user with the help of a multi-element IRS in the presence of channel estimation errors and inter-cell interference. We consider imperfect channel state information (CSI) at the BS, i.e., imperfect CSIT, and focus on the robust optimization of the BS's instantaneous CSI-adaptive beamforming and the IRS's quasi-static phase shifts. First, we formulate the robust optimization of the BS's instantaneous channel state information (CSI)-adaptive beamforming and IRS's quasi-static phase shifts for the ergodic rate maximization as a very challenging two-timescale stochastic non-convex problem. Then, we obtain a closed-form beamformer for any given phase shifts and a more tractable single-timescale stochastic non-convex problem only for phase shifts. Next, we propose a low-complexity stochastic algorithm to obtain quasi-static phase shifts which correspond to a KKT point of the single-timescale stochastic problem. It is worth noting that the proposed method offers a closed-form robust instantaneous CSI-adaptive beamforming design that can promptly adapt to rapid CSI changes over slots and a robust quasi-static phase shift design of low computation and phase adjustment costs in the presence of channel estimation errors and inter-cell interference. Finally, numerical results demonstrate the notable gains of the proposed robust joint design over existing ones and reveal the practical values of the proposed solutions.

Optimal algorithm design for federated learning (FL) remains an open problem. This paper explores the full potential of FL in practical edge computing systems where workers may have different computation and communication capabilities, and quantized intermediate model updates are sent between the server and workers. First, we present a general quantized parallel mini-batch stochastic gradient descent (SGD) algorithm for FL, namely GenQSGD, which is parameterized by the number of global iterations, the numbers of local iterations at all workers, and the mini-batch size. We also analyze its convergence error for any choice of the algorithm parameters. Then, we optimize the algorithm parameters to minimize the energy cost under the time constraint and convergence error constraint. The optimization problem is a challenging non-convex problem with non-differentiable constraint functions. We propose an iterative algorithm to obtain a KKT point using advanced optimization techniques. Numerical results demonstrate the significant gains of GenQSGD over existing FL algorithms and reveal the importance of optimally designing FL algorithms.

Existing gradient coding schemes introduce identical redundancy across the coordinates of gradients and hence cannot fully utilize the computation results from partial stragglers. This motivates the introduction of diverse redundancies across the coordinates of gradients. This paper considers a distributed computation system consisting of one master and$N$workers characterized by a general partial straggler model and focuses on solving a general large-scale machine learning problem with$L$model parameters. We show that it is sufficient to provide at most$N$levels of redundancies for tolerating$0,1, \cdots, N-1$stragglers, respectively. Consequently, we propose an optimal block coordinate gradient coding scheme based on a stochastic optimization problem that optimizes the partition of the$L$coordinates into$N$blocks, each with identical redundancy, to minimize the expected overall runtime for collaboratively computing the gradient. We obtain an optimal solution using a stochastic projected subgradient method and propose two low-complexity approximate solutions with closed-from expressions, for the stochastic optimization problem. We also show that under a shifted-exponential distribution, for any$L$, the expected overall runtimes of the two approximate solutions and the minimum overall runtime have sub-linear multiplicative gaps in$N$. To the best of our knowledge, this is the first work that optimizes the redundancies of gradient coding introduced across the coordinates of gradients.

In this paper, we study the optimal transmission of a multi-quality tiled 360 virtual reality (VR) video from a multi-antenna server (e.g., access point or base station) to multiple single-antenna users in a multiple-input multiple-output (MIMO)-orthogonal frequency division multiple access (OFDMA) system. We minimize the total transmission power with respect to the subcarrier allocation constraints, rate allocation constraints, and successful transmission constraints, by optimizing the beamforming vector and subcarrier, transmission power and rate allocation. The formulated resource allocation problem is a challenging mixed discrete-continuous optimization problem. We obtain an asymptotically optimal solution in the case of a large antenna array, and a suboptimal solution in the general case. As far as we know, this is the first work providing optimization-based design for 360 VR video transmission in MIMO-OFDMA systems. Finally, by numerical results, we show that the proposed solutions achieve significant improvement in performance compared to the existing solutions.

In this paper, we investigate unconstrained and constrained sample-based federated optimization, respectively. For each problem, we propose a privacy preserving algorithm using stochastic successive convex approximation (SSCA) techniques, and show that it can converge to a Karush-Kuhn-Tucker (KKT) point. To the best of our knowledge, SSCA has not been used for solving federated optimization, and federated optimization with nonconvex constraints has not been investigated. Next, we customize the two proposed SSCA-based algorithms to two application examples, and provide closed-form solutions for the respective approximate convex problems at each iteration of SSCA. Finally, numerical experiments demonstrate inherent advantages of the proposed algorithms in terms of convergence speed, communication cost and model specification.

In this article, we investigate jointly sparse signal recovery and jointly sparse support recovery in Multiple Measurement Vector (MMV) models for complex signals, which arise in many applications in communications and signal processing. Recent key applications include channel estimation and device activity detection in MIMO-based grant-free random access which is proposed to support massive machine-type communications (mMTC) for Internet of Things (IoT). Utilizing techniques in compressive sensing, optimization and deep learning, we propose two model-driven approaches, based on the standard auto-encoder structure for real numbers. One is to jointly design the common measurement matrix and jointly sparse signal recovery method, and the other aims to jointly design the common measurement matrix and jointly sparse support recovery method. The proposed model-driven approaches can effectively utilize features of sparsity patterns in designing common measurement matrices and adjusting model-driven decoders, and can greatly benefit from the underlying state-of-the-art recovery methods with theoretical guarantee. Hence, the obtained common measurement matrices and recovery methods can significantly outperform the underlying advanced recovery methods. We conduct extensive numerical results on channel estimation and device activity detection in MIMO-based grant-free random access. The numerical results show that the proposed approaches provide pilot sequences and channel estimation or device activity detection methods which can achieve higher estimation or detection accuracy with shorter computation time than existing ones. Furthermore, the numerical results explain how such gains are achieved via the proposed approaches.

This paper investigates adaptive streaming of one or multiple tiled 360 videos from a multi-antenna base station (BS) to one or multiple single-antenna users, respectively, in a multi-carrier wireless system. We aim to maximize the video quality while keeping rebuffering time small via encoding rate adaptation at each group of pictures (GOP) and transmission adaptation at each (transmission) slot. To capture the impact of field-of-view (FoV) prediction, we consider three cases of FoV viewing probability distributions, i.e., perfect, imperfect, and unknown FoV viewing probability distributions, and use the average total utility, worst average total utility, and worst total utility as the respective performance metrics. In the single-user scenario, we optimize the encoding rates of the tiles, encoding rates of the FoVs, and transmission beamforming vectors for all subcarriers to maximize the total utility in each case. In the multi-user scenario, we adopt rate splitting with successive decoding and optimize the encoding rates of the tiles, encoding rates of the FoVs, rates of the common and private messages, and transmission beamforming vectors for all subcarriers to maximize the total utility in each case. Then, we separate the challenging optimization problem into multiple tractable problems in each scenario. In the single-user scenario, we obtain a globally optimal solution of each problem using transformation techniques and the Karush-Kuhn-Tucker (KKT) conditions. In the multi-user scenario, we obtain a KKT point of each problem using the concave-convex procedure (CCCP). Finally, numerical results demonstrate that the proposed solutions achieve notable gains in quality, quality variation, and rebuffering time over existing schemes in all three cases. To the best of our knowledge, this is the first work revealing the impact of FoV prediction on the performance of adaptive streaming of tiled 360 videos.

In this paper, we would like to investigate the optimal wireless streaming of a multi-quality tiled 360 virtual reality (VR) video from a server to multiple users. To this end, we propose to maximally exploit potential multicast opportunities by effectively utilizing characteristics of multi-quality tiled 360 VR videos and computation resources at the users' side. In particular, we consider two requirements for quality variation in one field-of-view (FoV), i.e., the absolute smoothness requirement and the relative smoothness requirement, and two video playback modes, i.e., the direct-playback mode (without user transcoding) and transcode-playback mode (with user transcoding). Besides natural multicast opportunities, we introduce two new types of multicast opportunities, namely, relative smoothness-enabled multicast opportunities, which allow a flexible tradeoff between viewing quality and communications resource consumption, and transcoding-enabled multicast opportunities, which allow a flexible tradeoff between computation and communications resource consumptions. Then, we establish a novel mathematical model that reflects the impacts of natural, relative smoothness-enabled, and transcoding-enabled multicast opportunities on the average transmission energy and transcoding energy. Based on this model, we optimize the transmission resource allocation, playback quality level selection, and transmission quality level selection to minimize the energy consumption in the four cases with different requirements for quality variation and video playback modes. By comparing the optimal values in the four cases, we prove that the energy consumption reduces when more multicast opportunities can be utilized. Finally, numerical results show substantial gains of the proposed solutions over existing schemes, and demonstrate the importance of exploiting of the three types of multicast opportunities.

In this paper, we study the optimal wireless streaming of a multi-quality tiled 360 virtual reality (VR) video from a multi-antenna server to multiple single-antenna users in a multiple-input multiple-output (MIMO)-orthogonal frequency division multiple access (OFDMA) system. In the scenario without user transcoding, we jointly optimize beamforming and subcarrier, transmission power, and rate allocation to minimize the total transmission power. This problem is a challenging mixed discrete-continuous optimization problem. We obtain a globally optimal solution for small multicast groups, an asymptotically optimal solution for a large antenna array, and a suboptimal solution for the general case. In the scenario with user transcoding, we jointly optimize the quality level selection, beamforming, and subcarrier, transmission power, and rate allocation to minimize the weighted sum of the average total transmission power and the transcoding power. This problem is a two-timescale mixed discrete-continuous optimization problem, which is even more challenging than the problem for the scenario without user transcoding. We obtain a globally optimal solution for small multicast groups, an asymptotically optimal solution for a large antenna array, and a low-complexity suboptimal solution for the general case. Finally, numerical results demonstrate the significant gains of proposed solutions over the existing solutions.

In this paper, we consider a large-scale edge computing (EC)-enabled network. We consider multiple latency-sensitive services. We adopt a random service caching scheme and a computation resource allocation scheme at base stations (BSs). We first derive the successful service probability (SSP). Using tools from stochastic geometry and queuing theory, we formulate the SSP maximization problem with respect to (w.r.t.) the service caching distribution and computation resource allocation, which is a challenging non-convex problem due to the complicated form of the SSP. Using parallel successive convex approximation (SCA), we develop an efficient iterative algorithm to obtain a stationary point of the non-convex problem. Finally, by numerical simulations, we show that the proposed solution achieves a higher SSP than the baseline schemes. We also show the impacts of the cache size and service rate of EC servers.

In this paper, we investigate wireless streaming of multi-quality tiled 360 virtual reality (VR) videos from a multi-antenna server to multiple single-antenna users in a multicarrier system. To capture the impact of field-of-view (FoV) prediction, we consider three cases of FoV viewing probability distributions, i.e., perfect, imperfect and unknown FoV viewing probability distributions, and use the average total utility, worst average total utility and worst total utility as the respective performance metrics. We adopt rate splitting with successive decoding for efficient transmission of multiple sets of tiles of different 360 VR videos to their requesting users. In each case, we optimize the encoding rates of the tiles, minimum encoding rates of the FoVs, rates of the common and private messages and transmission beamforming vectors to maximize the total utility. The problems in the three cases are all challenging nonconvex optimization problems. We successfully transform the problem in each case into a difference of convex (DC) programming problem with a differentiable objective function, and obtain a suboptimal solution using concave-convex procedure (CCCP). Finally, numerical results demonstrate the proposed solutions achieve notable gains over existing schemes in all three cases. To the best of our knowledge, this is the first work revealing the impact of FoV prediction and its accuracy on the performance of streaming of multi-quality tiled 360 VR videos.

In this paper, we study an intelligent reflecting surface (IRS)-assisted system where a multi-antenna base station (BS) serves a single-antenna user with the help of a multi-element IRS in the presence of interference generated by a multi-antenna BS serving its own single-antenna user. The signal and interference links via the IRS are modeled with Rician fading. To reduce phase adjustment cost, we adopt quasi-static phase shift design where the phase shifts do not change with the instantaneous channel state information (CSI). Maximum Ratio Transmission (MRT) is adopted at the two BSs to enhance the receive signals at their own users. First, we obtain a tractable expression of the ergodic rate. Then, we maximize the ergodic rate with respect to the phase shifts, corresponding to a non-convex optimization problem. We obtain a globally optimal solution under certain system parameters, and propose an iterative algorithm based on parallel coordinate descent (PCD), to obtain a stationary point under arbitrary system parameters. Finally, we numerically verify the analytical results and demonstrate the notable gains of the proposed solutions. To the best of our knowledge, this is the first work that studies the analysis and optimization of the ergodic rate of an IRS-assisted system in the presence of interference.

Device activity detection is one main challenge in grant-free random access, which is recently proposed to support massive access for massive machine-type communications (mMTC). Existing solutions fail to consider interference generated by massive Internet of Things (IoT) devices, or important prior information on device activities and interference. In this paper, we consider device activity detection at an access point (AP) in the presence of interference generated by massive devices from other cells. We consider the joint maximum likelihood (ML) estimation and the joint maximum a posterior probability (MAP) estimation of both the device activities and interference powers, jointly utilizing tools from probability, stochastic geometry and optimization. Each estimation problem is a difference of convex (DC) programming problem, and a coordinate descent algorithm is proposed to obtain a stationary point. The proposed ML estimation extends the existing ML estimation by considering the estimation of interference powers together with the estimation of device activities. The proposed MAP estimation further enhances the proposed ML estimation by exploiting prior distributions of device activities and interference powers. Numerical results show the substantial gains of the proposed joint estimation designs, and reveal the importance of explicit consideration of interference and the value of prior information in device activity detection.

In this paper, combining techniques in compressed sensing, parallel optimization and deep learning, an autoencoder-based approach is proposed to jointly design the common measurement matrix and jointly sparse signal recovery method for complex sparse signals. The encoder achieves noisy linear compression for jointly sparse signals, with a common measurement matrix. The decoder realizes jointly sparse signal recovery based on an iterative parallel-coordinate descent algorithm which is proposed to solve GROUP LASSO in a parallel manner. In particular, the decoder consists of an approximation part which unfolds (several iterations of) the proposed iterative algorithm to obtain an approximate solution of GROUP LASSO and a correction part which reduces the difference between the approximate solution and the actual jointly sparse signals. To our knowledge, this is the first time that an optimization-based jointly sparse signal recovery method is implemented using a neural network. The proposed approach achieves higher recovery accuracy with less computation time than the classic GROUP LASSO method, and the gain significantly increases in the presence of extra structures in sparse patterns. The common measurement matrix obtained by the proposed approach is also suitable for the classic GROUP LASSO method. We consider an application example, i.e., channel estimation in Multiple-Input Multiple-Output (MIMO)-based grant-free massive access for massive machine-type communications (mMTC). By numerical results, we demonstrate the substantial gains of the proposed approach over GROUP LASSO and AMP when the number of jointly sparse signals is not very large.

Rate splitting (RS) is a potentially powerful and flexible technique for multi-antenna downlink transmission. In this paper, we address several technical challenges towards its practical implementation for beyond 5G systems. To this end, we focus on a single-cell system with a multi-antenna base station (BS) and K single-antenna receivers. We consider RS in its most general form with 2K-1 streams, and joint decoding to fully exploit the potential of RS. First, we investigate the achievable rates under joint decoding and formulate the precoder design problems to maximize a general utility function, or to minimize the transmit power under pre-defined rate targets. Building upon the concave-convex procedure (CCCP), we propose precoder design algorithms for an arbitrary number of users. Our proposed algorithms approximate the intractable non-convex problems with a number of successively refined convex problems, and provably converge to stationary points of the original problems. Then, to reduce the decoding complexity, we consider the optimization of the precoder and the decoding order under successive decoding. Further, we propose a stream selection algorithm to reduce the number of precoded signals. With a reduced number of streams and successive decoding at the receivers, our proposed algorithm can even be implemented when the number of users is relatively large, whereas the complexity was previously considered as prohibitively high in the same setting. Finally, we propose a simple adaptation of our algorithms to account for the imperfection of the channel state information at the transmitter. Numerical results demonstrate that the general RS scheme provides a substantial performance gain as compared to state-of-the-art linear precoding schemes, especially with a moderately large number of users.

In this paper, the scheduling of downlink file transmission in one cell with the assistance of cache nodes with finite cache space is studied. Specifically, requesting users arrive randomly and the base station (BS) reactively multicasts files to the requesting users and selected cache nodes. The latter can offload the traffic in their coverage areas from the BS. We consider the joint optimization of the abovementioned file placement and delivery within a finite lifetime subject to the cache space constraint. Within the lifetime, the allocation of multicast power and symbol number for each file transmission at the BS is formulated as a dynamic programming problem with a random stage number. Note that there are no existing solutions to this problem. We develop an asymptotically optimal solution framework by transforming the original problem to an equivalent finite-horizon Markov decision process (MDP) with a fixed stage number. A novel approximation approach is then proposed to address the curse of dimensionality, where the analytical expressions of approximate value functions are provided. We also derive analytical bounds on the exact value function and approximation error. The approximate value functions depend on some system statistics, e.g., requesting users’ distribution. One reinforcement learning algorithm is proposed for the scenario where these statistics are unknown.

Existing decentralized coded caching solutions cannot guarantee small loads in the general scenario with arbitrary file sizes and cache sizes. In this paper, we propose an optimization framework for decentralized coded caching in the general scenario to minimize the worst-case load and average load (under an arbitrary file popularity), respectively. Specifically, we first propose a class of decentralized coded caching schemes for the general scenario, which are specified by a general caching parameter and include several known schemes as special cases. Then, we optimize the caching parameter to minimize the worst-case load and average load, respectively. Each of the two optimization problems is a challenging nonconvex problem with a nondifferentiable objective function. For each optimization problem, we develop an iterative algorithm to obtain a stationary point using techniques for solving Complementary Geometric Programming (GP). We also obtain a low-complexity approximate solution by solving an approximate problem with a differentiable objective function which is an upper bound on the original nondifferentiable one, and characterize the performance loss caused by the approximation. Finally, we present two information-theoretic converse bounds on the worst-case load and average load (under an arbitrary file popularity) in the general scenario, respectively. To the best of our knowledge, this is the first work that provides optimization-based decentralized coded caching schemes and information-theoretic converse bounds for the general scenario.

Multi-view videos (MVVs) provide immersive viewing experience, at the cost of traffic load increase for wireless networks. In this paper, we would like to optimize MVV transmission in a multiuser wireless network by exploiting both natural multicast opportunities and view synthesis-enabled multicast opportunities. Specifically, we first establish a mathematical model to specify view synthesis at the server and each user, and characterize its impact on multicast opportunities. This model is highly nontrivial and fundamentally enables the optimization of view synthesis-based multicast opportunities. For given video quality requirements of all users, we consider the optimization of view selection, transmission time and power allocation to minimize the average weighted sum energy consumption for view transmission and synthesis. In addition, under the energy consumption constraints at the server and each user respectively, we consider the optimization of view selection, transmission time and power allocation and video quality selection to maximize the total utility. These two optimization problems are challenging mixed discrete-continuous optimization problems. For the first problem, we propose an algorithm to obtain an optimal solution with reduced computational complexity by exploiting optimality properties. For each problem, to reduce computational complexity, we also propose a low-complexity algorithm to obtain a suboptimal solution, using Difference of Convex (DC) programming. Finally, numerical results show the advantage of the proposed solutions over existing ones, and demonstrate the importance of the optimization of view synthesis-enabled multicast opportunities in MVV transmission.

In this article, we study an intelligent reflecting surface (IRS)-assisted system where a multi-antenna base station (BS) serves a single-antenna user with the help of a multi-element IRS in the presence of interference generated by a multi-antenna BS serving its own single-antenna user. The signal and interference links via the IRS are modeled with Rician fading. To reduce phase adjustment cost, we adopt quasi-static phase shift design where the phase shifts do not change with the instantaneous channel state information (CSI). We investigate two cases of CSI at the BSs, namely, the instantaneous CSI case and the statistical CSI case, and apply Maximum Ratio Transmission (MRT) based on the complete CSI and the CSI of the Line-of-sight (LoS) components, respectively. Different costs on channel estimation and beamforming adjustment are incurred in the two CSI cases. First, we obtain a tractable expression of the average rate in the instantaneous CSI case and a tractable expression of the ergodic rate in the statistical CSI case. We also provide sufficient conditions for the average rate in the instantaneous CSI case to surpass the ergodic rate in the statistical CSI case, at any phase shifts. Then, we maximize the average rate and ergodic rate, both with respect to the phase shifts, leading to two non-convex optimization problems. For each problem, we obtain a globally optimal solution under certain system parameters, and propose an iterative algorithm based on parallel coordinate descent (PCD) to obtain a stationary point under arbitrary system parameters. Next, in each CSI case, we provide sufficient conditions under which the optimal quasi-static phase shift design is beneficial, compared to the system without IRS. Finally, we numerically verify the analytical results and demonstrate notable gains of the proposal solutions over existing ones. To the best of our knowledge, this is the first work that considers optimal quasi-static phase shift design for an IRS-assisted system in the presence of interference.

In this paper, we would like to investigate fundamental impacts of multicast opportunities on efficient transmission of a 360 VR video to multiple users in the cases with and without transcoding at each user. We establish a novel mathematical model that reflects the impacts of multicast opportunities on the average transmission energy in both cases and the transcoding energy in the case with user transcoding, and facilitates the optimal exploitation of transcoding-enabled multicast opportunities. In the case without user transcoding, we optimize the transmission resource allocation to minimize the average transmission energy by exploiting natural multicast opportunities. The problem is nonconvex. We transform it to an equivalent convex problem and obtain an optimal solution using standard convex optimization techniques. In the case with user transcoding, we optimize the transmission resource allocation and the transmission quality level selection to minimize the weighted sum of the average transmission energy and the transcoding energy by exploiting both natural and transcoding- enabled multicast opportunities. The problem is a challenging mixed discrete-continuous optimization problem. We transform it to a Difference of Convex (DC) programming problem and obtain a suboptimal solution using a DC algorithm. Finally, numerical results demonstrate the importance of effective exploitation of transcoding-enabled multicast opportunities in the case with user transcoding.

In this paper, we investigate the optimization of task operation sequences for designing practical multi-user mobile edge computing (MEC) systems. First, we consider a computation task model with non-negligible sizes of computation results and arbitrary task arrival times and deadlines. Based on it, we further establish a computation offloading model considering non-negligible executing durations, allowing parallel transmissions and executions for different tasks, and reflecting the impact of task operation sequences. Then, we formulate the weighted sum energy consumption minimization problem to optimize the task operation sequences and starting times for uploading, executing and downloading as well as uploading and downloading time durations. The problem is a challenging mixed discrete-continuous optimization problem. By analyzing structural properties of task operation sequences, we develop an algorithm to obtain an optimal solution. In addition, using several optimization techniques, we transform the problem to an equivalent Difference of Convex (DC) problem, and develop a low-complexity algorithm to obtain a suboptimal solution using Penalty Convex Concave Procedure (CCP). Finally, numerical results demonstrate the advantages of the suboptimal solution over some optimized schemes with typical choices for task operation sequences.

The tradeoff between bandwidth efficiency and quality of experience (QoE) is a key issue in 360 video transmission. In this paper, we propose a QoE-oriented saliency-aware 360 video transmission framework to balance this tradeoff. The target is to reduce the bandwidth demand without declining the QoE. Specifically, the proposed model is based on the decision-making process. We use Lyapunov optimization to solve the decisionmaking problem. Furthermore, we integrate saliency information into the model to influence the decision policy, so that the model has the advantage of bandwidth efficiency. The simulation results show that the tradeoff parameter of Lyapunov optimization can balance the tradeoff between QoE and bandwidth efficiency, and 360 video saliency entropy limits the upper and lower bounds of QoE and bandwidth efficiency.

Sparse support recovery arises in many applications in communications and signal processing. Existing methods tackle sparse support recovery problems for a given measurement matrix, and cannot flexibly exploit the properties of sparsity patterns for improving performance. In this letter, we propose a data-driven approach to jointly design the measurement matrix and support recovery method for complex sparse signals, using auto-encoder in deep learning. The proposed architecture includes two components, an auto-encoder and a hard thresholding module. The proposed auto-encoder successfully handles complex signals using standard auto-encoder for real numbers. The proposed approach can effectively exploit properties of sparsity patterns, and is especially useful when these underlying properties do not have analytic models. In addition, the proposed approach can achieve sparse support recovery with low computational complexity. Experiments are conducted on an application example, device activity detection in grant-free massive access for massive machine type communications (mMTC). Numerical results show that the proposed approach achieves significantly better performance with much less computation time than classic methods, in the presence of extra structures in sparsity patterns.

Efficient dissemination of videos is an important problem for mobile telecom carriers. In this paper, to facilitate massive video dissemination, we study joint caching and multicasting for multi-quality videos encoded using two video encoding techniques, namely, scalable video coding (SVC) and HEVC or H.264 as in dynamic adaptive streaming over HTTP (DASH) respectively, in a large-scale wireless network. First, for each type of video, we propose a random caching and multicasting scheme, carefully reflecting the relationship between layers of an SVC-based video or descriptions of a DASH-based video. Then, for each type of video, we derive tractable expressions for the successful transmission probability in the general and high user density regions, respectively, utilizing tools from stochastic geometry. The analytical results reveal that in the high user density region, the marginal increase of the successful transmission probability with respect to the caching probability of a video with a certain quality reduces when the caching probability increases. Next, for each type of video, we consider the maximization of the successful transmission probability in the high user density region, which is a convex problem with an exceedingly large number of optimization variables. We propose a two-stage optimization method to obtain a low-complexity near-optimal solution by solving a relaxed convex problem and a related packing problem. The optimization results reveal the impact of the caching gain of a layer for an SVC-based video or a description for a DASH-based video on its caching probability. Finally, we show that the proposed solutions for SVC-based and DASH-based videos achieve significant performance gains over baseline schemes in the general and high user density regions, and demonstrate their respective operating regions, using numerical results based on real video sequences.

The decentralized coded caching scheme of Maddah-Ali and Niesen for the shared link network achieves an order-optimal memory-load tradeoff when the file size goes to infinity. It is then successively shown by Shanmugam et al. that, in the practical operating regime where the file size is finite, such a scheme yields a much less attractive coded caching gain. In this paper, we focus on designing decentralized coded caching schemes that can achieve low worst case loads of the shared link when the file size is finite and maintain order-optimal memory-load tradeoffs when the file size grows to infinity. First, we propose a decentralized coded caching design framework for designing decentralized coded caching schemes that can achieve significantly lower worst case loads than Maddah-Ali-Niesen's decentralized coded caching scheme in the finite file size regime while maintaining order-optimal memory-load tradeoffs when the file size grows to infinity. Then, within the proposed framework, we propose a decentralized coded caching scheme, which is simple and tractable, and can achieve a low worst case load in both the finite and infinite file size regimes. We analyze the worst case load of the proposed scheme and show that it outperforms Maddah-Ali-Niesen's and Shanmugam et al.'s decentralized schemes in the finite file size regime when the number of users is not too small. We also analyze the asymptotic worst case load of the proposed scheme when the file size goes to infinity and show that the proposed scheme achieves an order-optimal memory-load tradeoff. Finally, we analytically characterize the behavior of the worst case coded caching gain of the proposed scheme as a function of the required file size when the file size is large.

Joint pushing and caching is recognized as an efficient remedy to the problem of spectrum scarcity incurred by tremendous mobile data traffic. In this paper, we design the optimal joint pushing and caching policy to maximize bandwidth utilization, which is of fundamental importance to mobile telecom carriers. In particular, we consider a multiuser wireless network with multicast opportunities where each user is equipped with a cache of limited size. First, we formulate the stochastic optimization problem as an infinite horizon average cost Markov decision process. By the structural analysis, we show that how the optimal policy achieves a balance between the current transmission cost and the future average transmission cost. We also show that the optimal average transmission cost decreases with the cache sizes, revealing a tradeoff between storage and bandwidth. Then, due to the fact that obtaining a numerical optimal solution suffers the curse of dimensionality and implementing it requires a centralized controller and global system information, we develop a low-complexity decentralized policy (LDP) by using a linear approximation of the value function and transforming challenging discrete optimization problems into difference of convex (DC) problems, which can be efficiently solved by using DC algorithms. We also obtain an upper bound on the performance gap between the average cost of LDP and the minimum average cost, which can be easily evaluated. Next, we propose an online decentralized algorithm to implement the proposed LDP, when priori knowledge of user demand processes is not available. Finally, using numerical results, we demonstrate the advantage of the proposed solutions over some existing designs. The results in this paper offer useful guidelines for designing practical cache-enabled multiuser wireless networks.

Most of the existing caching solutions for wireless networks rest on the ideal assumption that the file popularity distribution is perfectly known. In this paper, we consider optimal random caching designs for perfect, imperfect, and unknown file popularity distributions in a large-scale multi-tier wireless network. First, in the case of perfect file popularity distribution, we formulate the successful transmission probability (STP) optimization problem, which is nonconvex. We propose an efficient parallel iterative algorithm to obtain a stationary point based on parallel successive convex approximation (SCA). Then, in the case of imperfect file popularity distribution, we formulate the worst-case STP maximization problem. To solve this challenging robust optimization problem, we transform it into an equivalent complementary geometric programming (CGP) and propose an efficient iterative algorithm to obtain a stationary point based on the SCA. To the best of our knowledge, this is the first work explicitly considering the estimation error of file popularity distribution in the optimization of caching design. Next, in the case of unknown file popularity distribution, we formulate the stochastic STP (i.e., the STP in the stochastic form) maximization problem. This is a challenging nonconvex stochastic optimization problem, and we propose an efficient iterative algorithm to obtain a stationary point based on stochastic parallel SCA. As far as we know, this is the first work considering stochastic optimization in a large-scale wireless network. Finally, by numerical results, we show that the proposed solutions achieve notable gains over existing schemes in all three cases and reveal the values of the robust caching optimization and stochastic caching optimization in the cases of imperfect file popularity distribution and unknown file popularity distribution, respectively.

To improve signal-to-interference ratio (SIR) and make better use of file diversity provided by random caching, we consider two types of linear receivers, i.e., maximal ratio combining (MRC) receiver and partial zero forcing (PZF) receiver, at users in a large-scale cache-enabled single-input multi-output network. First, for each receiver, by utilizing tools from stochastic geometry, we derive a tractable expression and a tight upper bound for the successful transmission probability (STP). In the case of the MRC receiver, we also derive a closed-form expression for the asymptotic outage probability in the low SIR threshold regime. Then, for each receiver, we maximize the STP. In the case of the MRC receiver, we consider the maximization of the tight upper bound on the STP by optimizing the caching probabilities, which is a non-convex problem. We obtain a stationary point, by solving an equivalent difference of convex programming problem using concave-convex procedure. We also obtain a closed-form asymptotically optimal solution in the low SIR threshold regime. In the case of the PZF receiver, we consider the maximization of the tight upper bound on the STP by optimizing the caching probabilities and the degrees of freedom allocation (for boosting signal power and canceling interference), which is a mixed discrete-continuous problem. Based on structural properties, we obtain a low-complexity near optimal solution by using an alternating optimization approach. The analysis and optimization results reveal the impact of antenna resource at users on random caching.

A 360 virtual reality (VR) video, recording a scene of interest in every direction, provides VR users with immersive viewing experience. However, transmission of a 360 VR video which is of a much larger size than a traditional video to mobile users brings a heavy burden to a wireless network. In this paper, we consider multi-quality multicast of a 360 VR video from a single server to multiple users using time division multiple access (TDMA). To improve transmission efficiency, tiling is adopted, and each tile is pre-encoded into multiple representations with different qualities. We optimize the quality level selection, transmission time allocation and transmission power allocation to maximize the total utility of all users under the transmission time and power allocation constraints as well as the quality smoothness constraints for mixed-quality tiles. The problem is a challenging mixed discrete-continuous optimization problem. We propose two low-complexity algorithms to obtain two suboptimal solutions, using continuous relaxation and DC programming, respectively. Finally, numerical results demonstrate the advantage of the proposed solutions.

In this paper, the downlink file transmission in one cell with the assistance of cache nodes is studied. Specifically, the base station (BS) reactively delivers files to cache nodes and a requesting user in a multicast manner. Therefore, one file transmission may lead to the cache status update, which further affects the future file transmissions. We consider the joint optimization of file placement and delivery. In particular, we first formulate the optimization of transmission power and time in one finite file lifetime as a Markov Decision Process (MDP) with a random number of stages, where the objective is to minimize the transmission resource at the BS. It is shown that the optimal solution can be obtained via a revised Bellman's equation. Due to the curse of dimensionality, a novel approximation approach is proposed, where the value functions of the Bellman's equation can be calculated from analytical expressions. Hence, iterative algorithms, which appear in the general approximate MDP solutions, can be avoided. Moreover, an bound on the approximation error is also provided.

Multi-view videos (MVVs) provide immersive viewing experience, at the cost of heavy load to wireless networks. Except for further improving viewing experience, view synthesis can create multicast opportunities for efficient transmission of MVVs in multiuser wireless networks, which has not been recognized in existing literature. In this paper, we would like to exploit view synthesis-enabled multicast opportunities for energy-efficient MVV transmission in a multiuser wireless network. Specifically, we first establish a mathematical model to characterize the impact of view synthesis on multicast opportunities and energy consumption. Then, we consider the optimization of view selection, transmission time and power allocation to minimize the weighted sum energy consumption for view transmission and synthesis, which is a challenging mixed discrete-continuous optimization problem. We propose an algorithm to obtain an optimal solution with reduced computational complexity by exploiting optimality properties. To further reduce computational complexity, we also propose two low-complexity algorithms to obtain two suboptimal solutions, based on continuous relaxation and Difference of Convex (DC) programming, respectively. Finally, numerical results demonstrate the advantage of the proposed solutions.

To improve signal-to-interference ratio (SIR) and make better use of file diversity provided by random caching, we consider the maximal ratio combining (MRC) receiver at each user in a large-scale cache- enabled single-input multi-output (SIMO) network. First, by utilizing tools from stochastic geometry, we derive a tractable expression and closed-form upper and lower bounds for the successful transmission probability in the general SIR threshold regime. We also derive a closed-form expression for the asymptotic outage probability in the low SIR threshold regime. The analytical results reveal that the successful transmission probability increases with the number of receive antennas M at each user in the general SIR threshold regime, the coefficient of the asymptotic outage probability decreases with M and the order gain of the asymptotic outage probability does not depend on M. Then, we consider the successful transmission probability maximization. In the general SIR threshold regime, we consider the maximization of the tight upper bound on the successful transmission probability by optimizing the caching distribution, which is a non-convex problem. We obtain a stationary point, by solving an equivalent difference of convex (DC) programming problem using concave-convex procedure (CCCP). We also obtain a closed-form asymptotically optimal solution in the low SIR threshold regime. The optimization results indicate that files of higher popularity get more storage resources, and the caching distribution becomes more flat when M is larger.

In this paper, to make better use of file diversity provided by random caching and improve the successful transmission probability (STP) of a file, we consider retransmissions with random discontinuous transmission (DTX) in a large-scale cache-enabled heterogeneous wireless network (HetNet) employing random caching. We analyze the STP in two mobility scenarios, i.e., the high mobility scenario and the static scenario. In each scenario, by using tools from stochastic geometry and series expansion of some special functions, we obtain the closed-form expressions for the STP in the general and low signal-to-interference ratio (SIR) threshold regimes, respectively. It shows that a larger caching probability corresponds to a higher STP in both scenarios; random DTX can improve the STP in the static scenario and its benefit gradually diminishes when mobility increases. In addition, the asymptotic analysis shows that the diversity gain is jointly affected by random caching and random DTX in both scenarios.

Existing coded caching schemes fail to simultaneously achieve efficient content placement for non-uniform file popularity and efficient content delivery in the presence of common requests, and hence may not achieve desirable average load under a non-uniform, possibly very skewed, popularity distribution. In addition, existing coded caching schemes usually require the splitting of a file into a large number of subfiles, i.e., high subpacketization, and hence may cause huge implementation complexity. To address the above two challenges, we first present a class of centralized coded caching schemes consisting of a general content placement strategy specified by a file partition parameter, enabling efficient and flexible content placement, and a specific content delivery strategy, enabling load reduction by exploiting common requests of different users. Then we consider two cases, namely, the case without considering the subpacketization issue and the case considering the subpacke-tization issue. In the first case, we formulate the coded caching optimization problem over the considered class of schemes with N2Kvariables to minimize the average load under an arbitrary file popularity. Imposing some conditions on the file partition parameter, we transform the original optimization problem into a linear optimization problem with N(K + 1) variables under an arbitrary file popularity and a linear optimization problem with K +1 variables under the uniform file popularity. We also show that Yu et al.'s centralized coded caching scheme corresponds to an optimal solution of our problem. In the second case, taking into account the subpacketization issue, we first formulate the coded caching optimization problem over the considered class of schemes to minimize the average load under an arbitrary file popularity subject to a subpacketization constraint involving the ℓ0-norm. By imposing the same conditions and using an exact DC (difference of two convex functions) reformulation method, we convert the original problem with N2Kvariables into a simplified DC problem with N(K + 1) variables. Then, we use a DC algorithm to solve the simplified DC problem.

Caching and multicasting are two promising methods to support massive content delivery in multi-tier wireless networks. In this paper, we consider a random caching and multicasting scheme with caching distributions in the two tiers as design parameters, to achieve efficient content dissemination in a two-tier large-scale cache-enabled wireless multicasting network. First, we derive tractable expressions for the successful transmission probabilities in the general region as well as the high signal-to-noise ratio (SNR) and high user density region, respectively, utilizing tools from stochastic geometry. Then, for the case of a single operator for the two tiers, we formulate the optimal joint caching design problem to maximize the successful transmission probability in the asymptotic region, which is nonconvex in general. By using the block successive approximate optimization technique, we develop an iterative algorithm, which is shown to converge to a stationary point. Next, for the case of two different operators, one for each tier, we formulate the competitive caching design game where each tier maximizes its successful transmission probability in the asymptotic region. We show that the game has a unique Nash equilibrium (NE) and adopt an iterative algorithm, which is shown to converge to the NE under a mild condition. Finally, by numerical simulations, we show that the proposed designs achieve significant gains over existing schemes.

In this paper, we consider multi-quality multicast beamforming of a video stream from a multi-antenna base station to multiple single-antenna users receiving different qualities of the same video stream, via scalable video coding (SVC). Leveraging the layered structure of SVC and exploiting superposition coding as well as successive interference cancellation, we propose a layer-based multi-quality multicast beamforming scheme. To reduce the computational complexity, we also propose a quality-based multi-quality multicast beamforming scheme, which further utilizes the layered structure of SVC and quality information of all users. Under each scheme, for given quality requirements of all users, we formulate the corresponding optimal beamforming design as a non-convex power minimization problem, and obtain a globally optimal solution for a class of special cases as well as a locally optimal solution for the general case. Then, we show that the minimum total transmission power of the layer-based power minimization problem is the same as that of the quality-based power minimization problem, although the latter incurs a lower computational complexity. Next, we consider the optimal joint layer selection and quality-based multi-quality multicast beamforming design to maximize the total utility representing the satisfaction with the received video quality for all users under a given maximum transmission power budget, which is NP-hard in general. Based on the optimal solution of the quality-based power minimization problem, we develop a greedy algorithm to obtain a near optimal solution. Finally, numerical results show that the proposed solutions achieve better performance than existing solutions.

Base station cooperation in heterogeneous wireless networks (HetNets) is a promising approach to improve the network performance, but it also imposes a significant challenge on backhaul. On the other hand, caching at small base stations (SBSs) is considered as an efficient way to reduce backhaul load in HetNets. In this paper, we jointly consider SBS caching and cooperation in a downlink large-scale HetNet. We propose two SBS cooperative transmission schemes under random caching at SBSs with the caching distribution as a design parameter. Using tools from stochastic geometry and adopting appropriate integral transformations, we first derive a tractable expression for the successful transmission probability under each scheme. Then, under each scheme, we consider the successful transmission probability maximization by optimizing the caching distribution, which is a challenging optimization problem with a non-convex objective function. By exploring optimality properties and using optimization techniques, under each scheme, we obtain a locally optimal solution in the general case and a globally optimal solution in some special cases. Compared with some existing caching designs in the literature, e.g., the most popular caching, the i.i.d. caching and the uniform caching, the optimal random caching under each scheme achieves a promising successful transmission probability. The analysis and optimization results provide valuable design insights for practical HetNets.

To make better use of file diversity provided by random caching and improve the successful transmission probability (STP) of a file, we consider retransmissions with random discontinuous transmission (DTX) in a large-scale cache-enabled heterogeneous wireless network employing random caching. We analyze and optimize the STP in two mobility scenarios, i.e., the high mobility scenario and the static scenario. First, in each scenario, by using tools from stochastic geometry, we obtain a closed-form expression for the STP in the general signal-to-interference ratio (SIR) threshold regime. The analysis shows that a larger caching probability corresponds to a higher STP in both scenarios; random DTX can improve the STP in the static scenario and its benefit gradually diminishes when mobility increases. In each scenario, we also derive a closed-form expression for the asymptotic outage probability in the low SIR threshold regime. The asymptotic analysis shows that the diversity gain is jointly affected by random caching and random DTX in both scenarios. Then, in each scenario, we consider the maximization of the STP with respect to the caching probability and the BS activity probability, which is a challenging non-convex optimization problem with a complex objective function. In particular, in the high mobility scenario, we obtain a globally optimal solution. In the static scenario, we develop a low-complexity iterative algorithm to obtain a stationary point. Finally, numerical results show that the proposed solutions achieve significant gains over existing baseline schemes and can well adapt to the changes of the system parameters to wisely utilize storage resources and transmission opportunities.

Millimeter-wave (mm-wave) communication opens up tens of giga-hertz spectrum in the mm-wave band for use by next-generation wireless systems, thereby solving the problem of spectrum scarcity. Maintaining connectivity stands out as a key design challenge for mm-wave networks deployed in urban regions due to the blockage effect characterizing mm-wave propagation. In this paper, we set out to investigate the blockage effect on the connectivity of mm-wave networks in a Manhattan-type urban region modeled using a random regular lattice, while base stations (BSs) are Poisson distributed in the plane. In particular, we analyze the connectivity probability that a typical user is within the transmission range of a BS and connected by a line-of-sight. First, we consider a single-tier network. By jointly applying the random lattice and stochastic geometry theories, a lower bound on the connectivity probability is derived as a function of building parameters (e.g., size and site occupancy probability) and BS parameters (e.g., transmission range and BS density). For the case of dense buildings, the bound is derived in a simpler form. Next, the preceding lower bounds are tightened based on the geometric technique of partitioning the irregular blockage-free region around the typical user. Moreover, the analysis is generalized to mm-wave channels with both LoS and NLoS paths. Finally, the results are extended to a K-tier heterogeneous network (HetNet), where building heights are random, and depending on its height, a building can block the signals transmitted by a subset of BS tiers but not all. The analysis shows that the connectivity probability of the K-tier HetNet increases linearly with the number of tiers. In general, our work quantifies the relation between the coverage of an mmwave network and the parameters of building and BS processes, providing useful guidelines for deploying practical networks in a Manhattan-type region.

Existing designs for content dissemination do not fully explore and exploit potential caching and computation capabilities in advanced wireless networks. In this paper, we propose two partition-based caching designs, i.e., a coded caching design based on random linear network coding and an uncoded caching design. We consider the analysis and optimization of the two caching designs in a large-scale successive interference cancelation (SIC)-enabled wireless network. First, under each caching design, by utilizing tools from stochastic geometry, we derive a tractable expression for the successful transmission probability in the general file size regime. We also derive closed-form expressions in the small and large file size regimes, respectively. Then, under each caching design, we consider the successful transmission probability maximization in the general file size regime, which is an NP-hard problem. By exploring structural properties, we obtain a near optimal solution with 1/2 approximation guarantee and polynomial complexity. We also obtain closed-form asymptotically optimal solutions. The analysis and optimization results show the advantage of the coded caching design over the uncoded caching design, and reveal the impact of caching and SIC capabilities. Finally, we numerically show that the two proposed caching designs achieve significant performance gains over some baseline caching designs.

In this paper, a radically new anticipatory perspective is taken into account when designing the user-to-access point (AP) associations for indoor visible light communications (VLC) networks, in the presence of users' mobility and wireless-traffic dynamics. In its simplest guise, by considering the users' future locations and their predicted traffic dynamics, the novel anticipatory association prepares the APs for users in advance, resulting in an enhanced locationand delayawareness. This is technically realized by our contrived design of an efficient approximate dynamic programming algorithm. More importantly, this paper is in contrast to most of the current research in the area of indoor VLC networks, where a static network environment was mainly considered. Hence, this paper is able to draw insights on the performance tradeoff between delay and throughput in dynamic indoor VLC networks. It is shown that the novel anticipatory design is capable of significantly outperforming the conventional benchmarking designs, striking an attractive performance trade-off between delay and throughput. Quantitatively, the average system queue backlog is reduced from 15 to 8 [ms], when comparing the design advocated to the conventional benchmark at the peruser throughput of 100 [Mbps], in a 15 × 15 × 5 [m3] indoor environment associated with 8 × 8 APs and 20 users walking at 1 [m/s].

Recent studies have preliminarily investigated and proven the feasibility and effectiveness of applying Content Centric Networking (CCN) principles to Mobile Ad-hoc Networks (MANETs) for content-oriented wireless communications in infrastructure-less mobile environments. However, existing designs may not achieve the full potential of CCN-MANETs. In this paper, we develop a content-centric communication system, named CC-OffGrid, for efficient content delivery (over possibly long distances) in infrastructure-less mobile environments, by deploying a CCN layer directly on top of the media access control (MAC) and physical (PHY) layers. In CC-OffGrid, each user is equipped with a mobile installing a designed APP and an integrated chip with Bluetooth4.0, MSP430 and Lora™ module. We propose a MAC frame structure to achieve unicast, broadcast as well as multi-hop transmissions. We also design an interface protocol for the communication between the APP and the MAC layer. In addition, to effectively alleviate the broadcast storm of Interest Packets, we propose two optimization-based next-hop broadcasting node selection algorithms for Interest Packet broadcasting, based on one-hop and two-hop neighbor information, respectively, obtained using global positioning system (GPS). By adding a hop counter in Interest Packets and a data dissemination limit (DDL) counter in Data Packets, we can obtain node speed-based DDL and use it for alleviating the broadcast storm of Data Packets in mobile environments. We also propose an efficient caching algorithm for data caching by effectively exploiting content popularity information. Finally, we evaluate the performance of CC-OffGrid in ns-3 and test CC-OffGrid on hardware testbeds.

In this paper, we consider multi-quality multicast of a video stream from a multi-antenna base station (BS) to multiple single-antenna users requiring the video at different quality levels, using scalable video coding (SVC). Leveraging the layered structure of SVC and exploiting superposition coding (SC) as well as successive interference cancelation (SIC), we propose a power-efficient layer-based multi- quality multicast beamforming scheme. To reduce the computational complexity, we also propose a power- efficient quality-based multi-quality multicast beamforming scheme, which further utilizes the layered structure of SVC and quality requirements of all users. Under each scheme, for given quality requirements of all users, we formulate the corresponding beamforming design as a non-convex power minimization problem, and obtain a globally optimal solution for a class of special cases as well as a locally optimal solution for the general case. Then, we show that the minimum total transmission power of the quality-based optimization problem is the same as that of the layer-based optimization problem, although the former requires a lower computational complexity. Finally, numerical results show that the proposed solutions achieve better performance than existing solutions.

Designing mobile edge computing (MEC) systems by jointly optimizing communication and computation resources, which can help increase mobile batteries' lifetime and improve quality of experience for computation-intensive and latency-sensitive applications, has received significant interest. In this paper, we consider energy-efficient resource allocation schemes for a multi-user mobile edge computing system with inelastic computation tasks and non-negligible task execution durations. First, we establish a mathematical model to characterize the offloading of a computation task from a mobile to the base station (BS) equipped with MEC servers. This computation-offloading model consists of three stages, i.e., task uploading, task executing, and computation result downloading, and allows parallel transmissions and executions for different tasks. Then, we formulate the weighted sum energy consumption minimization problem to optimally allocate the task operation sequence, the uploading and downloading time durations as well as the starting times for uploading, executing and downloading, which is a challenging mixed discrete- continuous optimization problem and is NP-hard in general. We propose a method to obtain an optimal solution and develop a low-complexity algorithm to obtain a suboptimal solution, by connecting the optimization problem to a three-stage flow-shop scheduling problem and utilizing Johnson's algorithm as well as convex optimization techniques. Finally, numerical results show that the proposed sub-optimal solution outperforms existing comparison schemes.

Edge-caching, which only caches the contents near the users, has low implementation cost and performs comparably with the conventional on-path caching schemes in \textit{Information-Centric Networking} (ICN). However, the independent edge-caching can not capture the content popularity dynamics accurately since each cache node only has local content request information. In addition, the caching information of the cache nodes within the same neighborhood is not efficiently utilized. To solve these issues, we propose a coordinated edge-caching system, where the cache nodes within the same neighborhood can help each other to improve the caching performance. We design a caching decision method for each node, and the decision method takes caching information of edge nodes within the same neighborhood into consideration. We theoretically illustrate the effectiveness of the proposed scheme in typical network scenarios. Simulations are conducted on the real-world network topologies under both stationary and temporal popularity workloads, and the results show the performance of our proposed scheme is superior to the comparison schemes.

Joint pushing and caching is recognized as an efficient remedy to the problem of spectrum scarcity incurred by tremendous mobile data traffic. In this paper, by exploiting storage resources at end-users and predictability of user demand processes, we design the optimal joint pushing and caching to maximize bandwidth utilization, which is one of the most important concerns of network operators. In particular, we formulate the stochastic optimization problem as an infinite horizon average cost Markov Decision Process (MDP). By structural analysis, we show how the optimal policy achieves a balance between the current transmission cost and the future average transmission cost. In addition, we show that the optimal average transmission cost decreases with the cache size, revealing a tradeoff between the cache size and the bandwidth utilization. Due to the fact that obtaining a numerical optimal solution suffers the curse of dimensionality and implementing it requires a centralized controller and global system information, we develop a decentralized policy of polynomial complexity with the numbers of users and files as well as the cache size, by a linear approximation of the value function and optimization relaxation techniques. We also propose an online decentralized algorithm to implement the proposed low-complexity decentralized policy when priori knowledge of user demand processes is not available. Finally, using numerical results, we demonstrate the advantage of the proposed solutions over some existing designs.

Caching and multicasting are two promising methods to support massive content delivery in multi-tier wireless networks. In this paper, we consider a random caching and multicasting scheme with caching distributions in the two tiers as design parameters, to achieve efficient content dissemination in a two- tier large-scale cache-enabled wireless multicasting network. First, we derive tractable expressions for the successful transmission probabilities in the general region as well as the high SNR and high user density region, respectively, utilizing tools from stochastic geometry. Then, for the case of a single operator for the two tiers, we formulate the optimal joint caching design problem to maximize the successful transmission probability in the asymptotic region, which is nonconvex in general. By using the block successive approximate optimization technique, we develop an iterative algorithm, which is shown to coverage to a stationary point. Next, for the case of two different operators, one for each tier, we formulate the competitive caching design game where each tier maximizes its successful transmission probability in the asymptotic region. We show that the game has a unique Nash equilibrium (NE) and develop an iterative algorithm, which is shown to converge to the NE under a mild condition. Finally, by numerical simulations, we show that the proposed designs achieve significant gains over existing schemes.

Existing multicasting schemes for massive content delivery do not fully utilize multicasting opportunities in elastic content-oriented services. In this paper, we propose a novel temporal-spatial request aggregation-based multicasting scheme in a large-scale cache-enabled wireless network, which can effectively exploit multicasting opportunities in asynchronous file requests for elastic services to improve spectral efficiency. Utilizing tools from stochastic geometry, we derive tractable expressions for the successful transmission probability. The analytical results show that the successful transmission probability increases and the energy consumption decreases, at the cost of delay increase, in the large and small user density regions. Based on the analytical results, we further optimize the successful transmission probability with respect to the caching probability and BS on/off period. We obtain closed-form solutions in the two asymptotic regions. Finally, we characterize the temporal and spatial request aggregation gains in the two asymptotic regions, and reveal the impacts of the popularity profile on them.

Network coding-based caching at base stations (BSs) is a promising caching approach to support massive content delivery over wireless networks. However, existing network coding-based caching designs do not fully explore and exploit the potential advantages. In this paper, we consider the analysis and optimization of a random linear network coding-based caching design in large-scale successive interference cancellation (SIC)-enabled wireless networks. By utilizing tools from stochastic geometry, we derive a tractable expression for the successful transmission probability in the general file size regime. To further obtain design insights, we also derive closed-form expressions for the successful transmission probability in the small and large file size regimes, respectively. Then, we consider the successful transmission probability maximization by optimizing a design parameter, which is a complex discrete optimization problem. We propose a two-stage optimization framework and obtain a near optimal solution with superior performance and manageable complexity. The analysis and optimization results provide valuable design insights for practical cache and SIC enabled wireless networks. Finally, by numerical results, we show that the proposed near optimal caching design achieves a significant performance gain over some baseline caching designs.

Base station cooperation in heterogeneous wireless networks (HetNets) is a promising approach to improve the network performance, but it also imposes a significant challenge on backhaul. On the other hand, caching at small base stations (SBSs) is considered as an efficient way to reduce backhaul load in HetNets. In this paper, we jointly consider SBS caching and cooperation in a downlink large-scale HetNet. We propose an SBS cooperative transmission scheme under random caching at SBSs with the caching distribution as a design parameter. Using tools from stochastic geometry, we first derive a tractable expression for the successful transmission probability. Then, we consider the successful transmission probability maximization by optimizing the caching distribution, which is a challenging optimization problem with a non-convex objective function. By exploring optimality properties and using optimization techniques, we obtain a local optimal solution in the general case and the global optimal solution in a special case. Compared with some existing caching designs in the literature, e.g., the most popular caching, the i.i.d. caching and the uniform caching, the optimal random caching achieves better successful transmission probability performance.

In this paper, we jointly consider communication, caching and computation in a multi-user cache-assisted mobile edge computing (MEC) system, consisting of one base station (BS) of caching and computing capabilities and multiple users with computation-intensive and latency-sensitive applications. We propose a joint caching and offloading mechanism which involves task uploading and executing for tasks with uncached computation results as well as computation result downloading for all tasks at the BS, and efficiently utilizes multi-user diversity and multicasting opportunities. Then, we formulate the average total energy minimization problem subject to the caching and deadline constraints to optimally allocate the storage resource at the BS for caching computation results as well as the uploading and downloading time durations. The problem is a challenging mixed discrete-continuous optimization problem. We show that strong duality holds, and obtain an optimal solution using a dual method. To reduce the computational complexity, we further propose a low-complexity suboptimal solution. Finally, numerical results show that the proposed suboptimal solution outperforms existing comparison schemes.

Caching and multicasting at base stations are two promising approaches to support massive content delivery over wireless networks. However, existing scheduling designs do not fully exploit the advantages of the two approaches. In this paper, we consider the optimal dynamic multicast scheduling to jointly minimize the average delay, power, and fetching costs for cache-enabled content-centric wireless networks. We formulate this stochastic optimization problem as an infinite horizon average cost Markov decision process (MDP).By usingrelative value iterationand special structures of the request queue dynamics, we analyze the properties of the value function and the state-action cost function of the MDP for both the uniform and nonuniform channel cases. Based on these properties, we show that the optimal policy, which is adaptive to the request queue state, has a switch structure in the uniform case and a partial switch structure in the nonuniform case. Moreover, in the uniform case with two contents, we show that the switch curve is monotonically non-decreasing. Motivated by the switch structures of the optimal policy, we propose a low-complexity suboptimal policy, which exhibits similar switch structures to the optimal policy, and design a low-complexity algorithm to compute this policy.

The problem of finding network codes for general connections is inherently difficult in capacity constrained networks. Resource minimization for general connections with network coding is further complicated. Existing methods for identifying solutions mainly rely on highly restricted classes of network codes, and are almost all centralized. In this paper, we introduce linear network mixing coefficients for code constructions of general connections that generalize random linear network coding for multicast connections. For such code constructions, we pose the problem of cost minimization for the subgraph involved in the coding solution and relate this minimization to a path-based constraint satisfaction problem (CSP) and an edge-based CSP. While CSPs are NP-complete in general, we present a path-based probabilistic distributed algorithm and an edge-based probabilistic distributed algorithm with almost sure convergence in finite time by applying communication free learning. Our approach allows fairly general coding across flows, guarantees no greater cost than routing, and shows a possible distributed implementation. Numerical results illustrate the performance improvement of our approach over existing methods.

Recently, performance analysis for large-scale content-centric mobile ad hoc networks (MANETs) has received intense attention. In content-centric MANETs, content delivery consists of two operations, i.e., content placement and content retrieval, which may involve different network costs. However, the existing performance studies in content-centric MANETs mainly focus on content retrieval, and hence may not reflect the impact of content placement. In this paper, we investigate the asymptotic throughput and delay performance by considering the two operations of possibly different network costs. In particular, we introduce a general weighted sum delay cost of content placement and content retrieval as the delay performance metric. We consider an arbitrary content popularity distribution and study two mobility models in different time scales, i.e., fast and slow mobility. For each mobility model, we characterize the impacts of the network parameters on the network performance. By optimizing the content placement and retrieval for contents of different popularities, we design a general near-optimal scheme, the parameters of which reflect the delay weights of the two phases. We show that the network performance improves as the number of cached replicas increases until the number reaches a threshold. Finally, we show that our results are general and can incorporate some existing results as special cases.

Heterogeneous wireless networks (HetNets) provide a powerful approach to meeting the dramatic mobile traffic growth, but also impose a significant challenge on backhaul. Caching and multicasting at macro and pico base stations (BSs) are two promising methods to support massive content delivery and reduce backhaul load in HetNets. In this paper, we jointly consider caching and multicasting in a large-scale cache-enabled HetNet with backhaul constraints. We propose a hybrid caching design consisting of identical caching in the macro-tier and random caching in the pico-tier, and a corresponding multicasting design. By carefully handling different types of interferers and adopting appropriate approximations, we derive tractable expressions for the successful transmission probability in the general signal-to-noise ratio (SNR) and user density region as well as the high SNR and user density region, utilizing tools from stochastic geometry. Then, we consider the successful transmission probability maximization by optimizing design parameters, which is a very challenging mixed discrete-continuous optimization problem. By exploring structural properties, we obtain a near optimal solution with superior performance and manageable complexity. This solution achieves better performance in the general region than any asymptotically optimal solution, under a mild condition. The analysis and optimization results provide valuable design insights for practical cache-enabled HetNets.

Heterogenous wireless networks (Hetnets) provide a powerful approach to meet the massive growth in traffic demands, but also impose a significant challenge on backhaul. Caching at small base stations (BSs) and wireless small cell backhaul have been proposed as attractive solutions to address this new challenge. In this paper, we consider the optimal caching and user association to minimize the total time to satisfy the average demands in cached-enabled Hetnets with wireless backhaul. We formulate this problem as a mixed discrete- continuous optimization for given bandwidth and cache resources. First, we characterize the structure of the optimal solution. Specifically, we show that the optimal caching is to store the most popular files at each pico BS, and the optimal user association has a threshold form. We also obtain the closed-form optimal solution in the homogenous scenario of pico cells. Then, we analyze the impact of bandwidth and cache resources on the minimum total time to satisfy the average demands. Finally, using numerical simulations, we verify the analytical results.

Emerging Information-Centric Networking (ICN) architectures seek to optimally utilize both bandwidth and storage for efficient content distribution over the network. The Virtual Interest Packet (VIP) framework has been proposed to enable joint design of forwarding, caching, and congestion control strategies within the Named Data Networking (NDN) architecture. While the existing VIP algorithms exhibit good performance, they are primarily focused on maximizing network throughput and utility, and do not explicitly consider user delay. In this paper, we develop a new class of enhanced algorithms for joint dynamic forwarding, caching and congestion control within the VIP framework. These enhanced VIP algorithms adaptively stabilize the network and maximize network utility, while improving the delay performance by intelligently making use of VIP information beyond one hop. Generalizing Lyapunov drift techniques, we prove the throughput optimality and characterize the utility-delay tradeoff of the enhanced VIP algorithms. Numerical experiments demonstrate the superior performance of the resulting enhanced algorithms for handling Interest Packets and Data Packets within the actual plane, in terms of low network delay and high network utility.

Heterogeneous wireless networks (HetNets) provide a powerful approach to meet the dramatic mobile traffic growth, but also impose a significant challenge on backhaul. Caching and multicasting at macro and pico base stations (BSs) are two promising methods to support massive content delivery and reduce backhaul load in HetNets. In this paper, we jointly consider caching and multicasting in a large-scale cache-enabled HetNet with backhaul constraints. We propose a hybrid caching design to provide high spatial file diversity and a corresponding multicasting design for efficient content dissemination. By carefully handling different types of interferers, we derive tractable expressions for the successful transmission probability in the general signal-to-noise ratio (SNR) and user density region as well as the high SNR and user density region, utilizing tools from stochastic geometry. Then, we consider the successful transmission probability maximization by optimizing the design parameters, which is a very challenging mixed discrete-continuous optimization problem. By exploring the structural properties, we obtain a near optimal solution with superior performance and manageable (polynomial) complexity, based on a two-step optimization framework. The analysis and optimization results provide valuable design insights for practical cache-enabled HetNets.

Recently, a new class of decentralized random coded caching schemes have received increasing interest, as they can achieve order-optimal memory-load tradeoff through decentralized content placement when the file size goes to infinity. However, most of these existing decentralized schemes may not provide enough coded- multicasting opportunities in the practical operating regime where the file size is limited. In this paper, we focus on the finite file size regime and propose a decentralized random coded caching scheme and a partially decentralized sequential coded caching scheme. These two schemes have different requirements on coordination in the content placement phase and can be applied to different scenarios. The content placement of the proposed schemes aims at ensuring abundant coded-multicasting opportunities in the content delivery phase when the file size is finite. We analyze the worst-case (over all possible requests) loads of our schemes and show that the sequential coded caching scheme outperforms the random coded caching scheme in the finite file size regime. Analytical results indicate that, when the file size grows to infinity, the proposed schemes achieve the same memory- load tradeoff as Maddah-Ali-Niesen's decentralized scheme, and hence are also order optimal. Numerical results show that the two proposed schemes outperform Maddah-Ali-Niesen's decentralized scheme when the file size is not very large.

For general multi-hop queueing networks, delay optimal network control has unfortunately been an outstanding problem. The dynamic backpressure (BP) algorithm elegantly achieves throughput optimality, but does not yield good delay performance in general. In this paper, we obtain an asymptotically delay optimal control policy, which resembles the BP algorithm in basing resource allocation and routing on a backpressure calculation, but differs from the BP algorithm in the form of the backpressure calculation employed. The difference suggests a possible reason for the unsatisfactory delay performance of the BP algorithm, i.e., the myopic nature of the BP control. Motivated by this new connection, we introduce a new class of enhanced backpressure-based algorithms which incorporate a general queue-dependent bias function into the backpressure term of the traditional BP algorithm to improve delay performance. These enhanced algorithms exploit queue state information beyond one hop. We prove the throughput optimality and characterize the utility-delay tradeoff of the enhanced algorithms. We further focus on two specific distributed algorithms within this class, which have demonstrably improved delay performance as well as acceptable implementation complexity.

Caching and multicasting at base stations are two promising approaches to supporting massive content delivery over wireless networks. However, existing analysis and designs do not fully explore and exploit the potential advantages of the two approaches. In this paper, we consider the analysis and optimization of caching and multicasting in a large-scale cache-enabled wireless network. We propose a random caching and multicasting design. By carefully handling different types of interferers and adopting appropriate approximations, we derive a tractable expression for the successful transmission probability in the general region, utilizing tools from stochastic geometry. We also obtain a closed-form expression for the successful transmission probability in the high signal-to-noise ratio (SNR) and user density region. Then, we consider the successful transmission probability maximization, which is a very complex nonconvex problem in general. Using optimization techniques, we develop an iterative numerical algorithm to obtain a local optimal caching and multicasting design in the general region. To reduce complexity and maintain superior performance, we also derive an asymptotically optimal caching and multicasting design in the asymptotic region, based on a two-stage optimization framework. Finally, numerical simulations show that the asymptotically optimal design achieves a significant gain in successful transmission probability over some baseline schemes in the general region.

In heterogeneous networks (HetNets), strong interference due to spectrum reuse affects each user's signal-to-interference ratio (SIR), and hence is one limiting factor of network performance. In this paper, we propose a user-centric interference nulling (IN) scheme in a downlink large-scale HetNet to improve coverage/outage probability by improving each user's SIR. This IN scheme utilizes at most maximum IN degree of freedom (DoF) at each macro-base station to avoid interference to uniformly selected macro (pico) users with signal-to-individual-interference ratio below a macro (pico) IN threshold, where the maximum IN DoF and the two IN thresholds are three design parameters. Using tools from stochastic geometry, we first obtain a tractable expression of the coverage (equivalently outage) probability. Then, we obtain the asymptotic expressions of the coverage/outage probability in the low and high SIR threshold regimes. The analytical results indicate that the maximum IN DoF can affect the order gain of the outage probability in the low SIR threshold regime, but cannot affect the order gain of the coverage probability in the high SIR threshold regime. Moreover, we characterize the optimal maximum IN DoF, which optimizes the asymptotic coverage/outage probability. Finally, numerical results show that the proposed scheme can achieve good gains in coverage/outage probability over some baseline schemes.

Caching at small base stations (SBSs) has demonstrated significant benefits in alleviating the backhaul requirement in heterogeneous cellular networks (HetNets). While many existing works focus on what contents to cache at each SBS, an equally important problem is what contents to deliver so as to satisfy dynamic user demands given the cache status. In this paper, we study optimal content delivery in cache-enabled HetNets by considering the inherent multicast capability of wireless medium. We consider stochastic content multicast scheduling to jointly minimize the average network delay and power costs under a multiple access constraint. We establish a content-centric request queue model and formulate this stochastic optimization problem as an infinite horizon average cost Markov decision process (MDP). By using relative value iteration and special properties of the request queue dynamics, we characterize some properties of the value function of the MDP. Based on these properties, we show that the optimal multicast scheduling policy is of threshold type. Then, we propose a structure-aware optimal algorithm to obtain the optimal policy. We also propose a low-complexity suboptimal policy, which possesses similar structural properties to the optimal policy, and develop a low-complexity algorithm to obtain this policy.

Abstract Device‐to‐device (D2D) communication underlaying cellular networks is a promising technology to improve network resource utilization. In D2D‐enabled cellular networks, interference among spectrum‐sharing links is severer than that in traditional cellular networks, which motivates the adoption of interference cancelation (IC) techniques at the receivers. However, to date, how IC can affect the performance of D2D‐enabled cellular networks is still unknown. In this paper, we present an analytical framework for studying the performance of two IC methods, that is, unconditional IC and successive IC, in large‐scale D2D‐enabled cellular networks using the tools from stochastic geometry. To facilitate the interference analysis, we propose an approach of stochastic equivalence of the interference, which converts the two‐tier interference (interference from the cellular tier and D2D tier) to an equivalent single‐tier interference. Based on the proposed stochastic equivalence models, we derive the general expressions for the successful transmission probabilities of both cellular uplinks and D2D links in the networks where unconditional IC and successive IC are respectively applied. We demonstrate how these IC methods affect the network performance using both analytical and numerical results. Copyright © 2016 John Wiley & Sons, Ltd.

The problem of finding network codes for general connections is inherently difficult. Resource minimization for general connections with network coding is further complicated. Existing methods for identifying solutions mainly rely on very restricted classes of network codes, and are almost all centralized. In this paper, we introduce linear network mixing coefficients for code constructions of general connections that generalize random linear network coding (RLNC) for multicast connections. For such code constructions, we pose the problem of cost minimization for the subgraph involved in the coding solution and relate this minimization to a Constraint Satisfaction Problem (CSP) which we show can be simplified to have a moderate number of constraints. While CSPs are NP-complete in general, we present a probabilistic distributed algorithm with almost sure convergence in finite time by applying Communication Free Learning (CFL). Our approach allows fairly general coding across flows, guarantees no greater cost than routing, and shows a possible distributed implementation. Numerical results illustrate the performance improvement of our approach over existing methods.

Caching and multicasting at base stations are two promising approaches to support massive content delivery over wireless networks. However, existing analysis and designs do not fully explore and exploit the potential advantages of the two approaches. In this paper, we jointly consider caching and multicasting to maximize the successful transmission probability in large-scale information-centric networks. We propose a random caching and multicasting scheme with a design parameter. Utilizing tools from stochastic geometry, we derive a tractable expression and a closed-form expression for the successful transmission probability in the general and high signal-to-noise ratio (SNR) regions, respectively. Then, using optimization techniques, we derive a simple asymptotically optimal design in the high SNR region, which provides important design insights. Finally, by numerical simulations, we show that the asymptotically optimal design also achieves a significant performance gain over some baseline schemes in the general SNR region, and hence is applicable and effective in practical cache-enabled information-centric networks.

For general connections, the problem of finding network codes and optimizing resources for those codes is intrinsically difficult and little is known about its complexity. Most of the existing solutions rely on very restricted classes of network codes in terms of the number of flows allowed to be coded together, and are not entirely distributed. In this paper, we consider a new method for constructing linear network codes for general connections of continuous flows to minimize the total cost of edge use based on mixing. We first formulate the minimum-cost network coding design problem. To solve the optimization problem, we propose two equivalent alternative formulations with discrete mixing and continuous mixing, respectively, and develop distributed algorithms to solve them. Our approach allows fairly general coding across flows and guarantees no greater cost than any solution without inter-flow network coding.

Heterogeneous networks (HetNets) with offloading is considered as an effective way to meet the high data rate demand of future wireless service. However, the offloaded users suffer from strong inter-tier interference, which reduces the benefits of offloading and is one of the main limiting factors of the system performance. In this paper, we investigate the use of an interference nulling (IN) beamforming scheme to improve the system performance by carefully managing the inter-tier interference to the offloaded users in downlink two-tier HetNets with multi-antenna base stations. Utilizing tools from stochastic geometry, we derive a tractable expression for the rate coverage probability of the IN scheme. Then, we optimize the design parameter, i.e., the degrees of freedom that can be used for IN, to maximize the rate coverage probability. Specifically, in the asymptotic scenario where the rate threshold is small, by studying the order behavior of the rate coverage probability, we characterize the optimal design parameter. For the general scenario, we show some properties of the optimal design parameter. Finally, by numerical simulations, we show the IN scheme can outperform both the simple offloading scheme without interference management and the almost blank subframes scheme in 3GPP LTE, especially in large antenna regime.

Device-to-device (D2D) communication underlaying cellular networks is a promising technology to improve network resource utilization. In D2D-enabled cellular networks, the interference among spectrum-sharing links is more severer than that in traditional cellular networks, which motivates the adoption of interference cancellation techniques such as successive interference cancellation (SIC) at the receivers. However, to date, how SIC can affect the performance of D2D-enabled cellular networks is still unknown. In this paper, we present an analytical framework for studying the performance of SIC in large-scale D2D-enabled cellular networks using the tools from stochastic geometry. To facilitate the interference analysis, we propose the approach of stochastic equivalence of the interference, which converts the two-tier interference (interference from both the cellular tier and D2D tier) to an equivalent single-tier interference. Based on the proposed stochastic equivalence models, we derive the general expressions for the successful transmission probabilities of cellular uplinks and D2D links with infinite and finite SIC capabilities respectively. We demonstrate how SIC affects the performance of large-scale D2D-enabled cellular networks by both analytical and numerical results.

Heterogeneous networks (HetNets) have strong interference due to spectrum reuse. This affects the signal-to-interference ratio (SIR) of each user, and hence is one of the limiting factors of network performance. However, in previous works, interference management approaches in HetNets are mainly based on interference level, and thus cannot effectively utilize the limited resource to improve network performance. In this paper, we propose a user-centric interference nulling (IN) scheme in downlink two-tier HetNets to improve network performance by improving each user's SIR. This scheme has three design parameters: the maximum degree of freedom for IN (i.e., maximum IN DoF), and the IN thresholds for the macro and pico users, respectively. Using tools from stochastic geometry, we first obtain a tractable expression of the coverage (equivalently outage) probability. Then, we characterize the asymptotic behavior of the outage probability in the high reliability regime. The asymptotic results show that the maximum IN DoF can affect the order gain of the asymptotic outage probability, while the IN thresholds only affect the coefficient of the asymptotic outage probability. Moreover, we show that the IN scheme can linearly improve the outage performance, and characterize the optimal maximum IN DoF which minimizes the asymptotic outage probability.

Caching and multicasting at base stations are two promising approaches to support massive content delivery over wireless networks. However, existing scheduling designs do not make full use of the advantages of the two approaches. In this paper, we consider the optimal dynamic multicast scheduling to jointly minimize the average delay, power and fetching costs for cache-enabled content-centric wireless networks. We formulate this stochastic optimization problem as an infinite horizon average cost Markov decision process (MDP). It is well-known to be a difficult problem and there generally only exist numerical solutions. By using relative value iteration algorithm and the special structures of the request queue dynamics, we analyze the properties of the value function and the state-action cost function of the MDP for both the uniform and nonuniform channel cases. Based on these properties, we show that the optimal policy, which is adaptive to the request queue state, has a switch structure in the uniform case and a partial switch structure in the nonuniform case. Moreover, in the uniform case with two contents, we show that the switch curve is monotonically non-decreasing. The optimality properties obtained in this paper can provide design insights for practical networks.

In this paper, we study the grid power-delay tradeoff in a point-to-point energy harvesting wireless communication system with finite energy storage capacity serving delay-sensitive applications. This communication system is powered by both grid and renewable power sources. First, we consider the average grid power consumption minimization subject to the data queue stability and renewable energy availability constraints. By exploring the optimality property and using the theory of random walks, we transform the grid power minimization problem to an asymptotically equivalent problem. Using the Lyapunov drift approach, we obtain an online dynamic power control policy to solve the asymptotically equivalent problem. Then, we introduce a novel analysis framework to study the grid power-delay tradeoff relationship of the online power control in the small delay regime. Specifically, using continuous-time approximation, dynamic programming and sample-path approach, we obtain bounds on the average delay and grid power consumption, which are asymptotically tight in the small delay regime. Based upon the derived closed-form expressions, we quantify the impacts of energy storage capacity and some other system parameters on the grid power-delay tradeoff.

In this paper, we study the impact of human mobility on throughput and delay for people-centric applications in mobile ad hoc networks (MANETs). We consider a general human mobility model for MANETs, which can capture important features of human mobility, such as time correlation, node correlation, location heterogeneity, and node heterogeneity. Multiple unicasts with general arrival processes are delivered, and nodes are equipped with infinite buffers. Under our system model, we first characterize the network stability region in terms of the probability of each node set visiting each location and the amount of transmission resources at each location. We show that the node correlation and heterogeneity of locations' popularity usually decrease the size of the network stability region, whereas the diversity of locations visited by a node usually increases the size of the network stability region. Then, by solving a stability-related optimization problem, we develop a throughput-optimal policy based on the obtained optimal solution. We obtain the upper and lower bounds of the delay performance under the proposed policy. Finally, using simulations based on a theoretical model and some real traces, we verify the analytical results and compare the performance of the proposed policy with some existing policies.

Device-to-device (D2D) communication underlaying cellular networks is a promising technology to improve network resource utilization. In D2D-enabled cellular networks, interference generated by D2D communications is usually viewed as an obstacle to cellular communications. However, in this paper, we present a new perspective on the role of D2D interference by taking security issues into consideration. We consider a large-scale D2D-enabled cellular network with eavesdroppers overhearing cellular communications. Using stochastic geometry, we model such a network and analyze the signal-to-interference-plus-noise ratio (SINR) distributions, connection probabilities and secrecy probabilities of both the cellular and D2D links. We propose two criteria for guaranteeing performances of secure cellular communications, namely the strong and weak performance guarantee criteria. Based on the obtained analytical results of link characteristics, we design optimal D2D link scheduling schemes under these two criteria respectively. Both analytical and numerical results show that the interference from D2D communications can enhance physical layer security of cellular communications and at the same time create extra transmission opportunities for D2D users.

Delay optimal control of multi-hop networks remains a challenging problem even in the simplest scenarios. In this paper, we consider delay optimal control of a two-hop half-duplex network with independent identically distributed ON-OFF fading. Both the source node and the relay node are equipped with infinite buffers and have exogenous bit arrivals. We focus on delay optimal link selection to minimize the average sum queue length over a finite horizon subject to a half-duplex constraint. To solve the problem, we introduce a new approach, whereby an actual discrete time system (ADTS) is approximated using a virtual continuous time system (VCTS). We obtain an asymptotically delay optimal policy in the VCTS. Using the relationship between the VCTS and the ADTS, we obtain an asymptotically delay optimal policy in the ADTS. The obtained policy has both a priority feature and a safety stock feature. It offers good design insights for wireless relay networks. In addition, the obtained policy has a closed-form expression, does not require knowledge of arrival statistics, and can be implemented online. Finally, using renewal theory and the theory of random walks, we analyze the average delay resulting from the asymptotically delay optimal policy.

This paper investigates random walk mobility models with location heterogeneity, where different locations may have different neighboring regions. We consider$n$locations in a one-dimension network and investigate two cases, i.e.,full-range locationswhere nodes situated have the capability to shuffle throughout the network andlong-range locationswhere nodes are allowed to move to positions nearby within a certain range. In the former situation, with the exact expressions derived, we find location heterogeneity has a critical impact on the first hitting time of random walk, varying from$\Theta (n)$to$\Theta \left(n^3\right)$according to different extent of heterogeneity. The result covers, as two special cases, both the classic independent and identically distributed (i.i.d) mobility and traditional random walk when varying the number of full-range locations. In the latter one, our asymptotic results on both the first crossing time and cover time suggest that they are inversely proportional to the range of neighboring region$r$($\propto r^{-2}$and$\propto r^{-1}$, respectively). Furthermore, with multiple concurrent random walks introduced, the first hitting time can be drastically decreased and the effect is strengthened if combined with location heterogeneity. In addition, our investigation into the stationary distribution of nodes indicates that the uniformity no longer holds due to different transition probabilities, as a result of location heterogeneity. We also conduct extensive simulation results to verify our observations and enhance the understanding on the impact of network parameters. Based on the insights obtained, we move forward to investigate the impact of location heterogeneity in two-dimension networks.

In this paper, we investigate connectivity and transmission delay of secondary users in large-scale wireless cognitive radio (CR) ad hoc networks from a percolation-based perspective. Using the random connection model, we study the unreliability of wireless secondary links in CR ad hoc networks, which has not been well studied in the existing literature. By introducing two auxiliary random graphs and using continuum percolation theory, we study the impacts of key system parameters on connectivity and transmission delay in a CR network. We first characterize three behavioral regions of connectivity for a secondary network, i.e., disconnectivity, long-term connectivity and instantaneous connectivity regions. We show that the unreliability of secondary links does not affect the disconnectivity region, but affects the long-term connectivity and instantaneous connectivity regions. Using the ergodic theorem, we then study the scaling behavior of transmission delay with respect to the distance between two randomly chosen secondary users in a connected secondary network for two cases. Specifically, when propagation delay is negligible, we show that transmission delay scales linearly and sub-linearly with distance in the long-term connectivity and instantaneous connectivity regions, respectively. When propagation delay is considered, we show that transmission delay scales linearly with respect to distance in both the long-term connectivity and instantaneous connectivity regions.

Heterogeneous networks (HetNets) with offloading is considered as an effective way to meet the high data rate demand of future wireless service. However, offloaded users suffer from strong inter-tier interference, which reduces the benefits of offloading and is one of the main limiting factors of system performance. In this paper, we investigate an interference nulling (IN) scheme in improving system performance by carefully managing the inter-tier interference to the offloaded users in downlink two-tier HetNets with multi-antenna base stations. Utilizing tools from stochastic geometry, we first derive a tractable expression for the rate coverage probability of the IN scheme. Then, by studying its order, we obtain the optimal design parameter, i.e., the degree of freedom that can be used for IN, to maximize the rate coverage probability. Finally, we analyze the rate coverage probabilities of the simple offloading scheme without interference management and the multi-antenna version of the almost blank subframes (ABS) scheme in 3GPP LTE, and compare the performance of the IN scheme with these two schemes. Both analytical and numerical results show that the IN scheme can achieve good performance gains over both of these two schemes, especially in the large antenna regime.

Optimal queueing control of multi-hop networks remains a challenging problem even in the simplest scenarios. In this paper, we consider a two-hop half-duplex relaying system with random channel connectivity. The relay is equipped with a finite buffer. We focus on stochastic link selection and transmission rate control to maximize the average system throughput subject to a half-duplex constraint. We formulate this stochastic optimization problem as an infinite horizon average cost Markov decision process (MDP), which is well-known to be a difficult problem. By using sample-path analysis and exploiting the specific problem structure, we first obtain an equivalent Bellman equation with reduced state and action spaces. By using relative value iteration algorithm, we analyze the properties of the value function of the MDP. Then, we show that the optimal policy has a threshold-based structure by characterizing the supermodularity in the optimal control. Based the threshold-based structure and Markov chain theory, we further simplify the original complex stochastic optimization problem to a static optimization problem over a small discrete feasible set and propose a simple algorithm to solve the static optimization problem. Furthermore, we obtain the closed-form optimal threshold for the symmetric case. The analytical results obtained in this paper also provide design insights for two-hop relaying systems with multiple relays equipped with finite relay buffers.

For general multi-hop queueing networks, delay optimal network control has unfortunately been an outstanding problem for some time. The dynamic backpressure (DBP) algorithm is an elegant network control algorithm achieving throughput optimality. However, it does not yield good delay performance in general. In this paper, we formulate the delay optimal network control problem for general multi-hop queueing networks. We obtain an asymptotically delay optimal control policy. Surprisingly, we show that the asymptotically delay optimal control resembles the DBP algorithm in basing resource allocation and routing on a backpressure calculation, but differs from the DBP algorithm in the form of the backpressure calculation employed. This difference suggests a possible reason for the poor delay performance of the DBP algorithm. To the best of our knowledge, this is the first work which provides an analytical connection between delay optimal control and the throughput optimal DBP algorithm. The connection provides a theoretical basis for designing enhanced DBP algorithms with improved delay performance via the use of QSI beyond one hop.

Energy efficiency and quality-of-service (QoS) have been two key considerations in the design of modern multi-user communication systems. In this paper, we study optimal rate control over the multiple-access channel to minimize the sum transmission energy under general QoS constraints. We model the data flows and QoS constraints using a cumulative curves methodology and formulate the optimization problem as a continuous-time control problem. We analyze the optimality properties and show that the optimization problem has a dynamic programming (DP) structure induced by successive interference cancellation (SIC). Based on the DP structure, we propose a low-complexity solution, which is amenable to an appealing graphical visualization and has the same order of complexity as the single user energy minimization problem. We bound the energy gap between the low-complexity solution and the optimal solution, and show that the energy gap diminishes to zero in the symmetric high SNR regime.

Considering backhaul consumption, it may not be the best choice to engage all the time in full cooperative MIMO for interference mitigation. In this paper, we propose a novel downlink partial cooperative MIMO (Pco-MIMO) physical layer (PHY) scheme, which allows flexible tradeoff between the partial data cooperation level and the backhaul consumption. Based on this Pco-MIMO scheme, we consider dynamic transmit power and rate allocation according to the imperfect channel state information at transmitters (CSIT) and the queue state information (QSI) to minimize the average delay cost subject to average backhaul consumption constraints and average power constraints. The delay-optimal control problem is formulated as an infinite horizon average cost constrained partially observed Markov decision process (CPOMDP). By exploiting the special structure in our problem, we derive an equivalent Bellman Equation to solve the CPOMDP. To reduce computational complexity and facilitate distributed implementation, we propose a distributed online learning algorithm to estimate the per-flow potential functions and Lagrange multipliers (LMs) and a distributed online stochastic partial gradient algorithm to obtain the power and rate control policy. We prove the convergence and asymptotic optimality of the proposed solution.

Delay-optimal control of multi-hop networks remains a challenging problem even in the simplest scenarios. In this paper, we consider delay-optimal control of a two-hop half-duplex network with i.i.d. on-off fading. Both the source node and the relay node are equipped with infinite buffers and have exogenous bit arrivals. We focus on delay-optimal link selection to minimize the average bit delay subject to a half-duplex constraint. To solve the problem, we introduce a new approach whereby an actual discrete time system (ADTS) is approximated using a virtual continuous time system (VCTS). Using dynamic programming, we recursively solve the delay minimization problem in the VCTS in terms of a simpler prototype problem, which can be addressed using continuous-time optimal control techniques. We show that the obtained solution in the VCTS is asymptotically optimal in the ADTS. Our solution has a closed-form expression and does not require knowledge of the arrival statistics. Finally, using renewal theory and the theory of random walks, we analyze the average delay resulting from the asymptotically optimal solution.

In this paper, a comprehensive survey is given on several major systematic approaches in dealing with delay-aware control problems, namely the equivalentrate constraint approach, the Lyapunov stability drift approach, and the approximate Markov decision process approach using stochastic learning. These approaches essentially embrace most of the existing literature regarding delay-aware resource control in wireless systems. They have their relative pros and cons in terms of performance, complexity, and implementation issues. For each of the approaches, the problem setup, the general solution, and the design methodology are discussed. Applications of these approaches to delay-aware resource allocation are illustrated with examples in single-hop wireless networks. Furthermore, recent results regarding delay-aware multihop routing designs in general multihop networks are elaborated. Finally, the delay performances of various approaches are compared through simulations using an example of the uplink OFDMA systems.

We consider delay-optimal link selection for a two-hop three-node cooperative network with bursty packet arrivals, where both the source node and the half-duplex cooperative node have exogenous arrivals. We consider the problem of minimizing the random sum queue length process subject to link selection constraints under a general bursty bit flow model and obtain a simple closed-form delay-optimal link selection policy, requiring only 1 bit of state information for each queue. Furthermore, using the structure of the delay-optimal link selection policy, we obtain the closed-form average bit delay performance for deterministic and Poisson packet arrival processes, Finally, we derive a new lower bound for the delay penalty incurred by the (throughput-optimal) dynamic backpressure (DBP) link selection algorithm, as compared with the delay-optimal link selection policy.

In this paper, we consider delay-optimal power and subcarrier allocation design for OFDMA systems with NFsubcarriers, K mobiles and one base station. There are K queues at the base station for the downlink traffic to the K mobiles with heterogeneous packet arrivals and delay requirements. We shall model the problem as a K-dimensional infinite horizon average reward Markov Decision Problem (MDP) where the control actions are assumed to be a function of the instantaneous Channel State Information (CSI) as well as the joint Queue State Information (QSI). We propose an online stochastic value iteration solution using stochastic approximation. The proposed power control algorithm, which is a function of both the CSI and the QSI, takes the form of multi-level water-filling. We prove that under two mild conditions in Theorem 1, the proposed solution converges to the optimal solution almost surely (with probability 1) and the proposed framework offers a possible solution to the general stochastic NUM problem. By exploiting the birth-death structure of the queue dynamics, we obtain a reduced complexity decomposed solution with linear O(KNF) complexity and O(K) memory requirement.

In this paper, we consider delay-optimal power and subcarrier allocation design for OFDMA systems with K mobiles and one base station. There are K queues at the base station for the downlink traffic to the K mobiles with heterogeneous packet arrivals and delay requirements. We shall model the problem as a K-dimensional infinite horizon average reward Markov decision problem (MDP) where the control actions are assumed to be a function of the instantaneous channel state information (CSI) as well as the joint queue state information (QSI). This problem is challenging because it corresponds to a stochastic network utility maximization (NUM) problem where general solution is still unknown. We propose an online stochastic value iteration solution using stochastic approximation. The proposed power control algorithm, which is a function of both the CSI and the QSI, takes the form of multi-level water-filling. We prove that under some mild conditions, the proposed solutions converge to the optimal solution almost surely and the proposed framework offers a possible solution to the general stochastic NUM problem. By exploiting the birth-death structure of the queue dynamics in the Poisson arrivals, we obtain a reduced complexity decomposed solution with linear O(KNF) complexity and memory requirement.

In this paper, we consider distributive subband, power and rate allocation for a two-hop downlink transmission in an orthogonal frequency-division multiple-access (OFDMA) cellular system with fixed relays which operate in decode-andforward strategy. We take into account of the penalty of packet errors due to imperfect CSIT and system fairness by considering weighted sum goodput as our optimization objective. Based on the cluster-based architecture, we obtain a fast-converging distributive solution with only local imperfect CSIT by using decomposition of the optimization problem. To further reduce the signaling overhead and computational complexity, we propose a reduced feedback distributive solution, which can achieve asymptotically optimal performance for large number of users with arbitrarily small feedback overhead per user.We also derive asymptotic average system throughput so as to obtain useful design insights.