Massive access or communication, emerging as one of six usage scenarios in 6G, has attracted considerable recent attention due to its potential to empower next-generation industrial cyber-physical systems such as smart grids, factory automation, industrial internet-of-things (IIoT), etc. However, to guarantee its QoS, the associated power control becomes computationally intractable with a huge number of users. In this paper, we present a tractable algorithm for power control in massive access, based on mean-field approximations. In particular, our aim is to maximize the overall throughput in each scheduling period, at the beginning of which each user has a finite number of backlogged bits. To achieve this goal and overcome the curse of dimensionality, a mean-field game (MFG) is formulated. Unfortunately, the formulated MFG is still a non-convex optimization problem. Enlightened by MAPEL, an efficient solver for non-convex power control problem, we leverage multiplicative linear fractional programming (MLFP) to tackle the non-convexity in our formulated MFG. Furthermore, the mean-field approximation assisted power control strategy requires low signaling overhead consumed for estimation and feedback of channel state information (CSI). Simulation results demonstrate that the proposed tractable power control attains substantial performance gains in both the overall throughput and computational complexity.

Integrating communication, sensing, and power transfer (ICSPT) is an emerging network paradigm for the sixth-generation (6G) systems, which is able to provide concurrent communication and sensing functions while simultaneously wirelessly powering low-power Internet of Things (IoT) devices with shared spectrum and hardware resources. To enhance the performance of ICSPT in fading channels, the outage probability (OP)constrained robust transmit beamforming design (OP-RTBD) is proposed, and a transmit power minimization problem is formulated with imperfect channel state information (CSI) by jointly optimizing information, sensing, and energy beam vectors at the base station (BS), subject to OP constraints on the communication rate, sensing Cramér-Rao bound, and energy transfer. To solve the non-convex problem, we propose a Bernstein-type inequality (BTI)-based method to conservatively approximate the probabilistic constraints to handle the CSI uncertainty. Then, a semi-positive definite relaxation-based method is proposed to solve the approximated problem. Simulation results show that the proposed OP-RTBD achieves near-optimal performance compared to the exhaustive search method with only less than 4% deviation, and it also significantly reduces the transmit power compared to baselines. Moreover, OP-RTBD exhibits strong robustness, achieving performance very close to that in perfect CSI scenarios, with a deviation of only less than 10%. Besides, the simulation results indicate that the BS’s transmit power should be allocated with priority to communication requirements over sensing and power transfer demands. Additionally, they further demonstrate that to simultaneously meet communication, sensing, and power transfer requirements, our proposed OP-RTBD in ICSPT is more energy-efficient, reducing energy consumption by approximately 10% and 20% compared to SWIPT and ISAC, respectively.

As an enhanced version of massive machine-type communication in 5G, massive communication has emerged as one of the six usage scenarios anticipated for 6G, owing to its potential in industrial internet-of-things and smart metering. Driven by the need for random multiple-access (RMA) in massive communication, as well as next-generation Wi-Fi, medium access control has attracted considerable recent attention. Holding the promise of attaining bandwidth-efficient collision resolution, multiaccess reservation no doubt plays a central role in RMA, e.g., the distributed coordination function (DCF) in IEEE 802.11. In this paper, we are interested in maximizing the bandwidth efficiency of reservation protocols for RMA under quality-of-service constraints. Particularly, we present a tree splitting based reservation scheme, in which the attempting probability is dynamically optimized by partially observable Markov decision process or reinforcement learning (RL). The RL-empowered tree-splitting algorithm guarantees that all these terminals with backlogged packets at the beginning of a contention cycle can be scheduled, thereby providing a first-in-first-out service. More importantly, it substantially reduces the reservation bandwidth determined by the communication complexity of DCF, through judiciously conceived coding and interaction for exchanging information required by distributed ordering. Simulations demonstrate that the proposed algorithm outperforms the CSMA/CA-based DCF in IEEE 802.11.

This paper investigates the active reconfigurable intelligent surface (RIS)-assisted integrated sensing, communication, and power transfer (ISCPT) networks, where rate-splitting multiple access (RSMA) scheme is employed to serve multiple downlink communication users. To promote the energy efficiency (EE) of such a system, we formulate an EE maximization problem by jointly optimizing the beamforming matrix, the sensing matrix, the active RIS matrix, the power splitting (PS) ratio vector, and the common rate allocation vector. Due to the non-convexity of the problem, we first design a successive convex approximation scheme with alternating optimization method (named SCA-AO) to solve it. As SCA-AO operates in an iterative manner, which is with relatively high computational complexity, we then design a mixture of experts (MoE)-based deep reinforcement learning (DRL) scheme with smooth clipping function (named MoE-SCF). In comparison, SCA-AO is able to achieve higher solution accuracy, while MOE-SCF has a shorter online execution response time. In order to integrate the advantages of both presented SCA-AO and MoE-SCF simultaneously, we further propose a hybrid MoE (H-MoE) scheme, where both the SCA-AO and the MoE-SCF are employed as expert strategies, and an opportunistic activator (OPA) is designed to dynamically select the best strategy generated by all expert combinations according to the performance evaluation function. Simulation results demonstrate that the proposed H-MoE promotes the system's EE by about 18.14% compared to traditional MoE, with similar response time. Additionally, compared to the SCA-AO, H-MoE significantly decreases the response time by approximately 56.17%, while only marginally compromising the EE performance by less than 3.1%.

Finite-blocklength coding (FBC) is a promising technology to achieve ultra-reliable and low-latency communications (URLLC) in emerging applications, e.g., autonomous driving and extended reality. The challenge lies in scheduling random arriving traffic in URLLC due to the blocklength constraint imposed by low latency. This paper designs a cross-layer mechanism, called the joint scheduling and FBC policy, to meet URLLC requirements for bursty traffic under AWGN and block fading channels. First, we model a single-user transmission system as a tandem queue model. In this model, a packet queue buffers randomly arriving packets. After these packets are encoded using FBC, the resulting encoded symbols are buffered in a symbol queue. To analyze the latency and reliability performance, we represent the system as a Markov chain and conduct steady-state and transition analyses. After that, we construct a non-convex problem to minimize delay subject to reliability and power constraints. Using a variable combination method, we convert this problem into a linear-fractional programming (LFP) problem. Notice that extremely high reliable requirement significantly increases the computational complexity of standard LFP method.We approximate the objective function and packet drop ratio constraint, and obtain a lower bound of the minimum average delay effectively with a marginal performance loss.

Smart grids are expected to play a vital role in the era of artificial intelligence (AI), because datacenters are now experiencing a dramatic increase in power consumption, especially with the vast deployments and applications of large language models (LLM). As a result, efficient and timely offloading of computational tasks such as model training in smart grid powered datacenters becomes a challenging but yet critical issue that has attracted considerable recent attention. In particular, our aim is to minimize the overall cost given the time-varying power supply along with its price induced by the dynamic nature of renewable energy, while satisfying a delay constraint that assures the quality-of-service (QoS). To achieve this goal, we present a mixed integer programming (MIP) for cost-efficient and latency-sensitive computation offloading in smart grid powered datacenters. A distributed algorithm for solving the MIP is judiciously conceived, which allows the datacenters and data owners to efficiently schedule the transmission of raw data and the computation for model training in a decentralized manner. Simulations demonstrate that the distributed protocol is capable of adapting to the dynamic energy price, time-varying supply of renewable energy, and even the line outage in smart grids with negligible latency and small signaling overhead.

Federated learning has achieved significant advancements in edge artificial intelligence (AI) by addressing issues related to data privacy and communication overload. Moreover, hierarchical federated learning over space-air-ground integrated networks (FedSAG), which consists of low-Earth orbit (LEO) satellites, unmanned aerial vehicles (UAVs), and edge devices, aims to provide AI services in sparsely populated regions lacking ground communication infrastructure. However, previous studies have overlooked the essential sensing process required for acquiring training data, potentially compromising training efficiency and model accuracy. In this paper, we propose an integrated sensing-communication-computation (ISCC) enabled FedSAG system, which allows remote edge devices to collect data via wireless sensing and collaboratively train a global model without sharing local data. We then analyse the convergence of the ISCC-enabled FedSAG and formulate two optimization problems. The first aims to minimize sensing variance under energy and time constraints, while the second seeks to reduce transmission energy through optimal route selection between UAVs and LEO satellite. We reformulate the problems to a minimum spanning tree and propose a Chu-Liu-Edmonds algorithm based a two-stage optimization. Simulation results demonstrate that our proposed algorithm significantly enhances convergence performance and reduces energy consumption.

Federated edge learning (FEEL) enables collaborative model training across distributed clients over wireless networks without exposing raw data. While most existing studies assume static datasets, in real-world scenarios, clients may continuously collect data with time-varying and non-independent and identically distributed (non-i.i.d.) characteristics. A critical challenge is how to adapt models in a timely yet efficient manner to such evolving data. In this paper, we propose FedTeddi, a temporal-drift-and-divergence-aware scheduling algorithm that facilitates fast convergence of FEEL under dynamic data evolution and communication resource limits. We first quantify the temporal dynamics and non-i.i.d. characteristics of data using temporal drift and collective divergence, respectively, and represent them as the Earth Mover's Distance (EMD) of class distributions for classification tasks. We then propose a novel optimization objective and develop a joint scheduling and bandwidth allocation algorithm, enabling the FEEL system to learn from new data quickly without forgetting previous knowledge. Experimental results show that our algorithm achieves higher test accuracy and faster convergence compared to benchmark methods, improving the rate of convergence by 58.4% on CIFAR10 and 49.2% on CIFAR-100 compared to random scheduling.

This paper addresses the challenges faced by air-ground vehicle networks (AGVN), with a focus on the rapid mobility of both unmanned ground vehicle (UGV) and unmanned aerial vehicles (UAVs) clusters. UAVs employ the non-orthogonal multiple access (NOMA) transmission technique to efficiently transfer data to UGV, aiming to enhance network performance by increasing spectral efficiency and supporting simultaneous transmissions. The system integrates UAV into the network perception environment, enabling cognitive activities and convenient transmission for UGV beyond base station coverage. We provide closed-form outage probability (OP) expressions for UAVs and UGV in two assist modes, i.e., amplify-and-forward UAV (A-UAV) and decode-and-forward UAV (D-UAV), using real-world situations with double Rayleigh fading (DRF) and outdated and imperfect channel state information (ipCSI). Additionally, in both relaying modes, the asymptotic expression for the OP is provided in the high SNR regime for UAV and UGV links. In addition, the system’s diversity order is derived, and the asymptotic analysis shows an intricate interplay of relative speed, channel outdatedness, and OP. Our research results show that when the relative speed decreases or the power increases, the OP decreases. Under the same conditions, the performance of A-UAV is more stable than that of D-UAV. Additionally, the study underscores the importance of assist mode and the number of UAVs in achieving optimal outage performance. Simulation results confirm our analytical results, emphasizing the trade-offs and performance indicators for AGVN system design. The study demonstrates potential avenues for maximizing system performance even within stringent constraints through strategic parameter adjustments.

Real-time computer vision (CV) is expected to play a vital role in virtual/augmented reality, factory automation, digital twin, and metaverse. To fully unlock its potential, latency- or freshness-constrained communications with a fidelity criterion has attracted considerable recent attention. One of its fundamental limits is the fidelity-timeliness tradeoff (FTT) that still remains open. In this paper, we investigate real-time CV oriented video streaming over AWGN and fading channels with fixed/variable-length lossy compression or even burst arrivals, in which reinforcement learning (RL) inspired intelligent cross-layer scheduling is adopted to minimize the distortion while satisfying a hard-delay or age-of-information (AoI) constraint. A low-complexity algorithm based on constrained Markov decision process (CMDP) and binary search is presented to compute the optimal FTT numerically. To shed more light on FTT, simple analytical upper and lower bounds are derived by leveraging the structural Markovian analysis of conceived bounding policies and saddle-point approximations. An asymptotic analysis will demonstrate that the gap between two bounds vanishes as the signal-to-noise-ratio (SNR) increases, thereby allowing the squeeze theorem to give an optimal but yet analytical FTT in the high-SNR regime. Finally, we find a distortion floor, below which the target AoI/latency becomes infinite, no matter how we increase the power supply.

Latency-aware scheduling has attracted considerable recent attention due to its potential wide-ranging applications in smart grids, automatic driving, telesurgery, and factory automation. Existing scheduling policies mainly exploit low-resolution adaptive modulation and coding (AMC), in which the number of legitimate transmission rate is quite limited or even binary owing to the stringently constrained hardware complexity. Recently, rate adaption with a huge number of selectable rates is made practical by cutting-edge AMC, e.g., deep neural network (DNN) empowered transceivers. However, the performance limit of high-resolution scheduling remains open. In this paper, its optimal delay-power tradeoff is presented by adopting infinitesimal analysis and stochastic order. Particularly, we conceive two quantized version of a high-resolution scheduling that reveal its upper and lower performance bounds. The two bounds are shown to converge to the performance limit of infinite-resolution scheduling with controllable variables as the resolution or the number of optional rates increases, thereby allowing us to leverage the squeeze theorem to attain its delay-power tradeoff. We show that the gap between two quantized cross-layer optimization under delay and power constraints respectively vanishes as the quantization error diminishes, thereby attaining the optimal scheduling through a fitting approach. Simulation results demonstrate the substantial gain of high-resolution scheduling.

Low-latency communication has recently attracted considerable attention owing to its potential of enabling delay-sensitive services in next-generation industrial cyber-physical systems. To achieve target average or maximum delay given random arrivals and time-varying channels, buffer-aware scheduling is expected to play a vital role. Evaluating and optimizing buffer-aware scheduling relies on its queueing analysis, while existing tools are not sufficiently tractable. Particularly, Markov chain and Monte-Carlo based approaches are computationally intensive, while large deviation theory (LDT) and extreme value theory (EVT) fail in providing satisfactory accuracy in the small-queue-length (SQL) regime. To tackle these challenges, a tractable yet accurate queueing analysis is presented by judiciously bridging Markovian analysis for the computationally manageable SQL regime and LDT/EVT for large-queue-length (LQL) regime where approximation error diminishes asymptotically. Specifically, we leverage censored Markov chain augmentation to approximate the original one in the SQL regime, while a piecewise approach is conceived to apply LDT/EVT across various queue-length intervals with different scheduling parameters. Furthermore, we derive closed-form bounds on approximation errors, validating the rigor and accuracy of our approach. As a case study, the approach is applied to analytically analyze a Lyapunov-drift-based cross-layer scheduling for wireless transmissions. Numerical results demonstrate its potential in balancing accuracy and complexity.

Event-triggered communication (ETC) has attracted considerable recent attention because it holds the promise of saving bandwidth and energy in real-time monitoring, networked control, distributed optimization, federated learning, etc. However, its performance limits remain open from a communication-theoretic perspective. This paper investigates event-triggered communications over AWGN channels, where events are modeled by a semi-Markov process. An ETC-oriented code is conceived by adding an all-zero codeword that represents a non-triggered state without any additional energy cost to the random codebook. We present the maximum achievable channel coding rates in both finite and infinite blocklength regimes. Even though the newly added codeword may incur a higher error probability, the rate of infinite blocklength coding is found to remain unchanged, thereby assuring the applicability of Shannon’s formula in ETC. Furthermore, we present two multi-user ETC protocols based on statistical multiplexing and joint coding respectively. Borrowing the idea of information freshness and queueing analysis, we reveal the distortion-power tradeoffs of single- and multi-user ETC. The ETC with Lebesgue sampling is shown to outperform the conventional periodic communication with Riemann sampling. The number of users in statistical multiplexing is also optimized. Numerical results validate our theoretical analysis and demonstrate the substantial power-saving gain of ETC.

Low-latency communication plays an increasingly important role in delay-sensitive applications by ensuring the real-time information exchange. However, due to the constraint on the maximum instantaneous power, guaranteeing bounded latency is challenging. In this paper, we investigate the reliability-latency-rate tradeoff in low-latency communication systems with finite-blocklength coding (FBC). Specifically, we are interested in the fundamental tradeoff between error probability, delay-violation probability (DVP), and service rate. Based on the effective capacity (EC), we present the gain-conservation equations to characterize the reliability-latency-rate tradeoffs in low-latency communication systems. In particular, we investigate the low-latency transmissions over an additive white Gaussian noise (AWGN) channel and a Nakagami-$m$fading channel. By defining the service rate gain, reliability gain, and real-time gain, we conduct an asymptotic analysis to reveal the fundamental reliability-latency-rate tradeoff of ultra-reliable and low-latency communications in the high signal-to-noise-ratio (SNR) regime. To analytically evaluate and optimize the quality-of-service-constrained throughput of low-latency communication systems adopting FBC, an EC-approximation method is conceived to derive the closed-form expression of that throughput. Our results may offer some insights into the efficient scheduling of low-latency wireless communications, in which statistical latency and reliability metrics are crucial.

Small-cell mobile edge computing (SE-MEC) networks amalgamate the virtues of MEC and small-cell networks, enhancing data processing capabilities of user devices (UDs). Nevertheless, time-varying wireless channels, dynamic UD requirements, and severe interference among UDs make it difficult to fully exploit the limited network resources and stably provide computing services for UDs. Therefore, efficient task offloading and resource allocation (TORA) is essential. Moreover, since multiple small cells are deployed, decentralized TORA schemes are preferred in practice. Thus, this paper aims to design distributed adaptive TORA schemes for SE-MEC networks. In pursuit of an eco-friendly design, an optimization problem is formulated to minimize the total energy consumption (TEC) of UDs subject to delay constraints. To effectively deal with network's dynamic characteristics, the reinforce learning framework is applied, where the TEC minimization problem is first modeled as a partially observable Markov decision process (POMDP), and then an efficient multi-agent proximal policy optimization (MAPPO)-based scheme is presented to solve it. In the presented scheme, each small-cell base station (SBS) serves as an agent and is capable of making TORA decisions only with its own local information. To promote collaboration among multiple agents, a global reward function is designed. A state normalization mechanism is also introduced into the presented scheme for enhancing learning performance. Simulation results show that although the proposed MAPPO-based scheme works in a distributed manner, it achieves very similar performance to the centralized one. In addition, it is demonstrated that the state normalization mechanism has a significant effect on reducing TEC.

Multiuser multicarrier communication, e.g. orthogonal frequency division multi-access (OFDMA), has been extensively investigated since the 4G era and applied in several mainstream mobile networking standards, because it holds the potential of high-throughput provision, low complexity, and flexible bandwidth allocation. In the upcoming 6G era, mobile networks are newly expected to provide deadline or hard-delay assurance for latency-sensitive traffics generated from factory automation, smart grids, telesurgery, and automatic driving, etc. However, whether the emerging hard-delay constraint can be effectively satisfied in multiuser multicarrier systems, where subcarriers are shared by users, remains open. As a result, a unified framework for realtime multiuser multicarrier communications is presented in this paper, based on the bipartite-graph model of OFDMA. In particular, we conceive a$\mathcal {H}$-matching empowered joint subcarrier allocation and power adaptation strategy, which is shown to meet the deadline requirements deterministically over frequency-selective channels with finite average transmission power. Furthermore, we leverage the theory of random bipartite graph matching to analyze the delay-constrained capacity as a function of the average transmission power, based on the approximate outage probability of the embedded matching diversity. To gain more insights, asymptotic analysis is adopted to obtain the deadline-constrained throughput when the number of independent subcarriers is huge.

Goal-oriented communication has become one of the focal concepts in sixth-generation communication systems owing to its potential to provide intelligent, immersive, and real-time mobile services. The emerging paradigms of goal-oriented communication constitute closed loops integrating communication, computation, and sensing. However, challenges arise for closed-loop timing analysis due to multiple random factors that affect the communication/computation latency, as well as the heterogeneity of feedback mechanisms across multi-modal sensing data. To tackle these problems, we aim to provide a unified timing analysis framework for closed-loop goal-oriented communication (CGC) systems over fading channels. The proposed framework is unified as it considers computation, compression, and communication latency in the loop with different configurations. To capture the heterogeneity across multi-modal feedback, we categorize the sensory data into the periodic-feedback and event-triggered, respectively. We formulate timing constraints based on average and tail performance, covering timeliness, jitter, and reliability of CGC systems. A method based on saddlepoint approximation is proposed to obtain the distribution of closed-loop latency. The results show that the modified saddlepoint approximation is capable of accurately characterizing the latency distribution of the loop with analytically tractable expressions. This sets the basis for low-complexity co-design of communication and computation.

In the typical radio frequency (RF)-based wireless power transfer (WPT) system, the wireless power station (WPS) connected to the grid transmits energy to charge low-power sensors via radio signals. Such a system may not be green and also difficult to deploy in some special areas including deserts and mountainous areas, because it depends on the grid. To achieve a green RF WPT system design, this paper considers that the WPS is powered by natural energy sources rather than the grid. To explore the maximal total amount of the energy that can be harvested by the sensors, we focus on the offline setting, so similar to many existing works on offline optimization, we assume that the WPS knows prior knowledge about energy arrivals and channel changes, and then formulate an optimization problem to maximize the total harvested energy via optimizing the WPS’s time-domain transmit power subject to multiple constraints, including the finite battery capacity at the WPS, the causal relationship between the natural energy harvesting and the WPT, and the transmit power budget of the WPS, where for practicality, the nonlinear energy harvesting (EH) model is also taken into account. To solve this non-convex problem, we first equivalently transform it by using the epigraph reformulation and the variable substitution, and then use the first-order Taylor expansion to get an approximate convex version. Then, we present a successive convex approximation (SCA)-based algorithm to improve the accuracy of the obtained solution for approaching the optimal one. For the special case with a single sensor, we further propose a branch and bound (BB)-based algorithm that is able to get a more accurate solution with lower complexity than the SCA-based one. Numerical results demonstrate that the proposed algorithms are able to achieve the near-global optimal solution. As the average recharge rate increases, compared with the other two baselines, i.e., the greedy power (GP) policy and the constant power (CP) policy, the total harvested energy achieved by the SCA-based algorithm is up to about 2.48 times and 1.37 times that of the baselines respectively. For the single-sensor case, the BB-based algorithm always outperforms the SCA-based one in terms of the total harvested energy while reducing the running time required for solving by about 90% on average.

Emergency Demand Response Programs (EDRP) have garnered significant attention for its potential to enhance the safety and reliability of smart grids. However, its effectiveness is often compromised by latency in detecting line outages due to noise in observed sources, limited sensor sampling rates, and constrained communication bandwidth. While advancements in quickest line outage detection have been made, the demand reduction actions initiated only after a detected change point may be too late as the EDRP is also time consuming, thereby potentially resulting in severe failures in smart grids. To address these challenges and attain high-assurance power systems, we present a joint framework that integrates quickest line outage detection with emergency demand response, allowing for simultaneous or parallel change point detection and demand reduction. In particular, a Constrained Markov Decision Process (CMDP) is formulated to maximize the average expected reward that characterizes both the revenue and risk of the smart grid, considering operational constraints related to its demand response capabilities. Both theoretical and numerical results demonstrate that our proposed integrated detection and control architecture outperforms the conventional layered successive implementations, achieving superior profit maximization and risk mitigation.

Massive access has attracted considerable recent attention due to its potential in 6G’s usage scenarios including massive communications and ubiquitous connectivity. To assure its quality-of-service (QoS), both channel and queue state information have to be exploited to efficiently schedule multiple devices to attain low latency or strike the optimal delay-power tradeoff. However, such a cross-layer scheduling will suffer from the high complexity when the number of users grows huge, thereby causing the curse of dimensionality. In this paper, we present joint channel- and queue-aware scheduling that minimizes the queueing delay of both orthogonal multiple access (OMA) and non-orthogonal multiple access (NOMA) in low complexity, by leveraging a mean-field approach originating from statistical mechanics. In particular, we conceive a unified mean-field approximation framework that substantially simplifies both the objective function and constraints of the constrained Markov decision process (CMDP) for optimizing the queueing delay in cross-layer scheduling of multiple users. The mean-field approximation allows us to find an asymptoticly optimal solution to the original CMDP or stochastic programming with very high dimension by solving a variational optimization with much lower dimension instead, thereby substantially reducing the computational complexity. Numerical results demonstrate the effectiveness of our mean-field optimization approach for massive access.

Event-triggered communication has attracted considerable recent attention because it holds the promise of saving bandwidth and energy in real-time monitoring, networked control, federated learning, etc. However, its performance limits remain unknown in theory. In this paper, we investigate from an information-theoretic perspective the fundamental tradeoff between the transmission power and the real-time reconstruction distortion of event-triggered communications. In particular, we focus on the real-time monitoring of a discrete event system, characterized by a jump or semi-Markov process, through AWGN channels. It is found that an all-zero codeword with the probability of the non-triggered event is forcibly added to the codebook, no matter how other codewords are selected. In this context, we derive the maximum achievable channel coding rates in both finite and infinite blocklength regimes, bridging the transmission power and the instantaneous throughput. By borrowing the idea of information freshness and queuing analysis, we further reveal the real-time reconstruction error of event-triggered communication given its instantaneous rate. It is shown that the event-triggered communication with Lebesgue sampling outperforms conventional periodic communication with Riemann sampling in terms of the power-distortion tradeoff. Numerical results validate our theoretical analysis and demonstrate the substantial power-saving gain given various target mean square errors (MSE).

Real-time wireless communication techniques are expected to play a vital role in emerging Time-Sensitive Networking, deterministic networking, and tactile internet. Although there has been much work on the optimal cross-layer scheduling with discrete-state fading models, how to minimize the average power given both throughput and deadline constraints over wireless channels with continuous fading states remains open. In this paper, the optimal joint channel and queue-aware scheduling policy for continuous fading channels is presented based on a joint quantization and fitting approach. In particular, by quantifying both channel and queue states of the fluid-model-based communications over continuous fading channels, we formulate its approximate Markov model, the optimal scheduling policy of which is then revealed by the Constrained Markov Decision Process (CMDP). As our previous work showed the above discretized policy has a threshold-based structure, it can be easily approximated by a deterministic scheduling policy. As a result, we leverage the fitting approach to find the asymptotically optimal deterministic scheduling. The deadline-constrained capacity, as a function of both the average power and maximum delay, is also determined. Numerical results validate the effectiveness of our analysis.

Distributed stochastic optimization has attracted considerable attention due to its potential of scaling the computational resources, reducing the training time, and helping protect user privacy in decentralized machine learning. However, the staggers and limited bandwidth may induce random computational and communication delays, thereby severely hindering the optimization or learning process. As a result, we are interested in the optimal policies and their performance analysis for latency-aware distributed Stochastic Gradient Descent (SGD). To understand the effect of staleness and error of gradients in distributed optimization, both of which may determine the convergence time, we present a unified framework based on the stochastic delay differential equation to characterize the random convergence time. It is interestingly found that the average convergence time is much more sensitive to the gradient staleness rather than its error. To provide further insights, we show that the time cost of fully asynchronous SGD is approximately determined by the product of the gradient staleness and the 2-norm of the Hessian matrix of the objective function. Moreover, small staleness may slightly accelerate the SGD, while large staleness will result in its divergence.

Hard-delay constrained wireless communication has attracted considerable recent attention because it holds the promise of playing a vital role in wireless Internet of Things (IoT) systems for supporting time-sensitive tasks. Hard-delay constrained communication refers to the case in which the delay-violation probability of packets is zero with a given deadline. How to strike the optimal power-latency-throughput tradeoff with a hard-delay constraint remains open. In this paper, we aim to characterize the hard-delay constrained capacity with a deadline which covers one or multiple coherence time of the fading channel. Specifically, we first conceive a low-complexity and yet sub-optimal cross-layer scheduling scheme to obtain an analytical bound of the hard-delay constrained capacity. Saddlepoint approximation is adopted to obtain its hard-delay constrained throughput as a function of the tolerated delay and average power, which provides us with properties of the achievable bound of the hard-delay constrained capacity. Further, we investigate the optimal joint channel and queue-aware scheduling to derive the hard-delay constrained capacity. In particular, by quantifying channel and queue states and recalling the optimality of threshold-based policies, we approximate the optimal hard-delay constrained scheduling by a deterministic policy obtained based on the quantized Constrained Markov Decision Process. More structures of this optimal scheduling policy are revealed through curve fitting by dividing system states into distinct regions. Finally, the hard-delay constrained capacity is presented as a function of both the average power and maximum delay, with the assistance of both theoretical analysis from the achievable bound and curve fitting.

This article investigates simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-assisted downlink multiuser multiple-input–single-output (MU-MISO) networks with the rate splitting multiple access (RSMA) scheme. A base station (BS) desires to simultaneously transmit messages to multiple users with the assistance of an STAR-RIS to enhance communication quality as well as extend the coverage of users. An optimization problem is formulated to maximize the achievable sum rate of the networks on the premise of satisfying the constraints on power budget at the BS, total common-stream rate of users, and individual users’ minimum rate requirements, via jointly optimizing the beamforming vectors, the common-stream rate allocation vector, and the transmission and reflection coefficients (TARCs) matrix. Due to the dynamic changes of communication links and the coupling of multiple variables, it is challenging to solve such a nonconvex optimization problem by utilizing traditional methods. Therefore, a proximal policy optimization (PPO)-based deep reinforcement learning (DRL) algorithm is proposed, where the reward function, the action space and the state space are designed properly. A constraint-satisfaction-processing (CSP) method is employed to further adjust the optimized transmit power to make sure that the obtained optimized results satisfy the power budget constraint. Simulation results show that the proposed PPO-based DRL algorithm converges well and achieves much better performance than several baselines, such as the soft actor–critic (SAC), the deep deterministic policy gradient (DDPG), the genetic algorithm (GA), the maximum ratio transmission (MRT), the zero-forcing (ZF), and the random methods. Moreover, it demonstrates that deploying STAR-RIS greatly enhances the system sum rate and user fairness compared to deploying traditional reflecting-only RIS (RO-RIS) and without RIS. Besides, it also shows that adopting the RSMA scheme achieves more notable performance gains than the nonorthogonal multiple access (NOMA) scheme in such a network.

With the growing popularity of electric vehicles (EVs), maintaining power grid stability has become a significant challenge. To address this issue, EV charging control strategies have been developed to manage the switch between vehicle-to-grid (V2G) and grid-to-vehicle (G2V) modes for EVs. In this context, multiagent deep reinforcement learning (MADRL) has proven its effectiveness in EV charging control. However, existing MADRL-based approaches fail to consider the natural power flow of EV charging/discharging in the distribution network and ignore driver privacy. To deal with these problems, this article proposes a novel approach that combines multi-EV charging/discharging with a radial distribution network (RDN) operating under optimal power flow (OPF) to distribute power flow in real time. A mathematical model is developed to describe the RDN load. The EV charging control problem is formulated as a Markov decision process (MDP) to find an optimal charging control strategy that balances V2G profits, RDN load, and driver anxiety. To effectively learn the optimal EV charging control strategy, a federated deep reinforcement learning algorithm named FedSAC is further proposed. Comprehensive simulation results demonstrate the effectiveness and superiority of our proposed algorithm in terms of the diversity of the charging control strategy, the power fluctuations on RDN, the convergence efficiency, and the generalization ability.

Federated learning (FL), as an emerging distributed machine learning paradigm, allows a mass of edge devices to collaboratively train a global model while preserving privacy. In this tutorial, we focus on FL via over-the-air computation (AirComp), which is proposed to reduce the communication overhead for FL over wireless networks at the cost of compromising in the learning performance due to model aggregation error arising from channel fading and noise. We first provide a comprehensive study on the convergence of AirComp-based FEDAVG (AIRFEDAVG) algorithms under both strongly convex and non-convex settings with constant and diminishing learning rates in the presence of data heterogeneity. Through convergence and asymptotic analysis, we characterize the impact of aggregation error on the convergence bound and provide insights for system design with convergence guarantees. Then we derive convergence rates for AIRFEDAVG algorithms for strongly convex and non-convex objectives. For different types of local updates that can be transmitted by edge devices (i.e., local model, gradient, and model difference), we reveal that transmitting local model in AIRFEDAVG may cause divergence in the training procedure. In addition, we consider more practical signal processing schemes to improve the communication efficiency and further extend the convergence analysis to different forms of model aggregation error caused by these signal processing schemes. Extensive simulation results under different settings of objective functions, transmitted local information, and communication schemes verify the theoretical conclusions.

This paper studies the high-speed railway mobile networks (HSRMN), where multiple railway-side sensors (RSs) are deployed along the track to sense environmental data, and multiple train-mounted sensors (TSs) are deployed on the train to collect train data. Both RSs and TSs are scheduled to transmit their sensed data respectively to the ground base station (BS) in a time division multiple access (TDMA) mode. To keep the data received at the BS from the RSs as fresh as possible and also ensure that the TSs complete the given uploading tasks, an optimization problem is established to minimize the average age of information (AoI) of the data gathered from RSs by jointly optimizing sensors’ scheduling and transmission power control constrained by the maximum transmission power budget of RSs and TSs. Since the problem is non-convex and lacks an explicit expression of the objective function and the prior information about future channel state, we present a deep Q-learning network (DQN)-based method to solve it. Particularly, the BS is viewed as the agent, and the action space is constructed by scheduling policy and power control. To further accelerate the convergence speed of the presented DQN-based solution framework, an action space-reduced (ASR) version of the DQN-based method, i.e., the ASR-DQN-based method, is designed by deriving a closed-form solution to the optimal transmission power for a given sensors’ scheduling policy. Numerical simulations show that, compared to the DQN-based method, the ASR-DQN-based method decreases the number of episodes required for convergence by about 23% and reduces the running time by about 41%. Moreover, compared with three baselines, i.e., the random method, the round-robin method, and the deep-Sarsa method, our presented ASR-DQN-based method achieves the lowest average AoI and has the best robustness among these compared methods.

The emerging deterministic networking (DetNet) has stimulated an increasing enthusiasm for the investigation of supporting deterministic, ultra-reliable, and low-latency services. However, the time-varying channel characteristics and bursty data traffic bring uncertainties for transmission, thereby making the assurance of deterministic quality-of-service (QoS) a challenging issue in practice. In this paper, we are interested in supporting the deterministic QoS demand in a massive access scenario by reducing the bit dropping rate incurred by delay violation. To minimize the bit dropping rate, a cross-layer scheduling scheme with joint channel and buffer awareness is highly desired to efficiently adjust the resource allocation among users, whose complexity increases exponentially with the number of users. Fortunately, the complexity issue can be relieved by adopting the mean-field approximation approach, which can substantially simplify the design and analysis of the cross-layer scheduling scheme with massive users. Two threshold-based scheduling policies are proposed, which have low computational complexity. Besides, we also derive the deadline-constrained capacity for massive access, which is substantially superior to that of single user transmissions. Numerical results will demonstrate the effectiveness of the mean-field approximation based cross-layer scheduling scheme.

Recently, semantic communication (SC) has been regarded as one of the most potential paradigms of 6G. Current SC frameworks require the physical layer channel state information (CSI) in order to handle the severe signal distortion induced by channel fading. Since practical CSI cannot be obtained accurately and the overhead of channel estimation cannot be neglected, we therefore propose a generative adversarial network (GAN) based SC framework (Ti-GSC) that doesn’t require CSI. In Ti-GSC, there are two main modules, i.e., an autoencoder-based encoder-decoder module (AEDM) and a GAN-based non-CSI signal distortion suppression (SDS) module (GSDSM), where SDS only relies on learning the syntactic distribution and the semantics of the transmitted data, so no prior information such as CSI is needed by GSDSM. In order to measure signal distortion, a novel loss function is proposed where two terms, i.e., a syntactic distortion loss term and a semantic distortion loss term, are newly added, and a differentiable semantic measurement method is designed based on the intermediate layers of the AEDM decoder. To achieve better training results of Ti-GSC, two training schemes, i.e., the joint optimization based training (JOT) and the alternating optimization based training (AOT) are designed for the proposed Ti-GSC. Experimental results show that JOT is more efficient for Ti-GSC, and Ti-GSC outperforms conventional communication frameworks in terms of bilingual evaluation understudy (BLEU) score in both Rician and Rayleigh fading channels. Moreover, without CSI, the BLEU score achieved by Ti-GSC is about 40% and 62% higher than that achieved by existing SC frameworks in Rician and Rayleigh fading, respectively. Besides, each term of the presented loss function has a great impact on the BLEU performance of Ti-GSC, where in Rician fading syntactic learning has the greatest impact, and in Rayleigh fading, the adversarial learning becomes important.

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

Achieving high reliability and low latency is a critical challenge for a wide range of applications that demand strict performance guarantees, such as real-time systems, industrial automation, and autonomous vehicles. Our primary focus is on ultra-reliable low-latency communication (URLLC), which aims to ensure a real-time requirement. We propose a solution for hard delay-constrained communication, which meets strict latency requirements by incorporating variable-length coding in short-packet transmission systems. Our approach utilizes a cross-layer design using truncated channel inversion transmission across parallel channels. This system can be characterized as a two-dimensional Markov chain, which consists of both the packet queue buffering bits to be encoded and symbol queue buffering coded symbols to be transmitted. By leveraging embedded Markov chains, we formulate an optimization problem to minimize the average power consumption while converting the problem into a one-dimensional Markov chain. We present a heuristic algorithm to obtain hard delay-constrained policies and utilize gradient descent policy to refine the policies and explore the trade-offs between hard delay constraints and power consumption.

Real-time monitoring of Brownian motions, also known as Wiener processes, has received increasing attention due to its potential in autonomous driving, smart grids, and industrial automation, etc. However, how to minimize the average distortion in these monitoring systems under communication or bandwidth constraints remains open. In this paper, the optimal joint sampling, quantization, and reconstruction policies that minimize average Mean Square Error (MSE) given a rate-constrained communication link are presented and compared. In particular, we are interested in both Riemann and Lebesgue sampling schemes. It is shown that for Riemann sampling, a Kalman filtering aided one-bit quantization strategy with the highest possible sampling rate can achieve the minimum MSE, while the analogue transmission with periodic sampling will unfortunately suffer from an infinite monitoring error. We also present a timing side information (TSI) aided Lebesgue sampling strategy, which outperforms even the best Riemann sampling when the channel is efficiently shared. An M/D/I queuing model is formulated to reveal the asymptotic MSE of TSI -aided Lebesgue sampling. Our theoretical analysis and comparisons are validated by extensive numerical results.

Mobile cloud computing (MCC) is expected to play a vital role due to its capability of scaling the computational resources of mobile devices by offloading tasks to cloud data centers. However, how to jointly optimize the computation offloading in wireless networks and demand response (DR) in smart grids remains open. In this paper, we present a paradigm-shift architecture that operates data centers as virtual power plants, while assuring the Quality-of-Service (QoS) for mobile application. In particular, less tasks are offloaded to a data center when the power gird is heavily loaded, thereby being capable of leveraging batteries of massive mobile devices to compensate the instantaneous power shortage. We formulate a game-theoretic framework for DR-aware dynamic computation offloading that not only guarantees the device lifetime, but also avoids large latency. Mean-field approximation is also applied to tackle the computational complexity when the data center has to schedule a huge number of devices in a distributed manner. Simulation results demonstrate the potential of operating data centers as virtual power plants, where appropriate demand response, balanced device lifetime, and low service latency can be attained simultaneously.

Distributed machine learning including federated learning has attracted considerable attention due to its potential of scaling the computational resources, reducing the training time, and helping protect the user privacy. As one of key enablers of distributed learning, asynchronous optimization allows multiple workers to process data simultaneously without paying a cost of synchronization delay. However, given limited communication bandwidth, asynchronous optimization can be hampered by gradient staleness, which severely hinders the learning process. In this paper, we present a communication-constrained distributed learning scheme, in which asynchronous stochastic gradients generated by parallel workers are transmitted over a shared medium or link. Our aim is to minimize the average training time by striking the optimal tradeoff between the number of parallel workers and their gradient staleness. To this end, a queueing theoretic model is formulated, which allows us to find the optimal number of workers participating in the asynchronous optimization. Furthermore, we also leverage the packet arrival time at the parameter server, also referred to as Timing Side Information (TSI), to compress the staleness information for the stalenessaware Asynchronous Stochastic Gradients Descent (Asyn-SGD). Numerical results demonstrate the substantial reduction of training time owing to both the worker selection and TSI-aided compression of staleness information.

Hard delay-constrained wireless communication has attracted considerable attention recently, as it is expected to play an essential role in time-sensitive networks (TSN) and deterministic networks (DetNet), However, it has been a long challenge to develop techniques that satisfy the constraints on both bounded delay and average power. In this paper, we investigate the tradeoff between delay and throughput in a delay-bounded wireless system with random arrivals, which characterizes the fundamental performance limitations of delay-bounded communication over fading channels. In particular, for diversity-enabled wireless systems with the finite average power, we present the sufficient condition of the arrival process with which the hard delay constraint can be satisfied. Moreover, based on the proposed sufficient condition, a cross-layer policy is conceived to achieve the bounded delay. Through markovian queueing analysis, the average power consumption of this policy is derived as a function of latency threshold and throughput. By this means, the latency-throughput tradeoff is characterized given the average power.

Delay-constrained communications have attracted considerable recent attention because it holds the promise of playing a vital role in time sensitive networking (TSN) and deterministic networking (DetNet). In this paper, we investigate the tradeoff between power, latency, and throughput, which characterizes a fundamental performance limit of diversity enabled delay-bounded communications over fading channels. In particular, we are interested in a typical cross-layer scheduling policy, namely, the age-aware non-FIFO strategy, which is capable of satisfying a hard delay constraint by exploiting the physical-layer diversity methods. Saddle point approximation is adopted to obtain the analytical approximation of the average transmission power as a function of the throughput and the delay threshold normalized to the coherence time. This analytical expression characterizes the power-latency-throughput tradeoff of the diversity enabled delay-bounded communications over the fading channel. To validate our analysis, we present a discretization-based method to conduct the numerical calculation. The numerical results match well with the simulation results, which also shows that the approximation error will vanish with sufficiently small discretization granularity.

The straggler effect is the main bottleneck for Federated Learning (FL), where the performance of training is degraded by the slowest member. Another significant problem is unreliable communication, which somehow has been neglected in previous studies. That is, the transmission of local models is not successful every time. In this paper, we find that the problems of straggler effect and unreliable communication are implicitly caused by time divergence of User Equipments (UEs) in each training round. Based on this, we propose our solutions for these two problems and show that our solutions can be merged into a general one: the problem of the straggler effect and unreliable communication can be solved with a simple UE selection method. This method consists of two steps: First, we cluster UEs into several groups based on UEs' physical parameters or performance metrics; Second, in each training round, only UEs from the same group are chosen for FL operation. Full explanations are given why the time divergence is statistically reduced, and therefore it can mitigate the aforementioned two problems. Our solutions are further illustrated with some examples and validated by simulations.

The emerging next generation Ultra-Reliable and Low-Latency Communications (xURLLC) is expected to play a central role in supporting mission-critical mobile applications because it holds the promise of improving the Quality-of-Service (QoS) substantially. However, it is quite challenging to satisfy the hard delay constraint in harsh wireless environments due to sporadic deep fades, especially when the average power is strictly limited. In this paper, we aim at assuring hard delay constraints with the aid of frequency or spatial diversity techniques. To this end, we focus on both parallel and multiple-input-multiple-output (MIMO) fading channels, in which time domain power adaptation is exploited to provide just-in-time services (JITS). It is shown that the hard delay constraint can be satisfied with a finite average power when the frequency or spatial diversity gains are no less than two. By adopting the implicit function theorem, we reveal the relationship between the required average power, the delay constrained throughput, and the outage probability without power adaptation. Furthermore, by adopting Ferrari’s solution to fourth order algebraic equations, we show that hard delay constrained transmission is feasible even when the sub-channels in the frequency and spatial domains are highly but not fully correlated.

Extremely low-latency communication has attracted considerable recent attention because it holds the promise of supporting emerging real-time applications such as autonomous driving, smart grids, and Industrial Internet of Things (IIoT). Owing to the limited bandwidth in wireless environments, the sub-packets or even bits have to be transmitted successively, thereby inducing non-negligible delay-induced cost for real-time remote monitoring, estimation, decision making, and control. In this paper, we present a unified incremental decoding framework for real-time applications, the costs of which are extremely sensitive to the latency of each individual sub-packet or bit. In contrast to conventional methods, in which a decision is made after fully decoding the entire packet, the incremental decoding strategy allows monitors or actors to make their decisions in real time based on partially received packet. By this means, there is no need to wait for the whole packet to be decoded, thereby reducing the delay-induced costs substantially. To minimize cumulative cost during the real-time monitoring and control, we design source coding and decision making algorithms jointly, in which a backward induction property is found. Furthermore, we conceive a dynamic programming algorithm for a given source codebook to significantly reduce the cumulative decision costs while maintaining low computational complexity.

Real-time monitoring plays a pivotal role in the Industrial Internet of Things (IIoT) with potential applications in factory automation, automated driving, and telesurgery, thereby attracting considerable recent attention in anticipation of the development of the sixth-generation (6G) of wireless networks. In this paper, we present a paradigm-shift data compression method that makes use of timing side information (TSI) obtained by observing two synchronized clocks at a remote sensor and a monitor. In particular, the TSI is found to allow the transmitter to send fewer bits consumed in characterizing the changing or holding time of a piecewise-constant stochastic process. We borrow the idea of source coding with side information to reveal the performance limits of both TSI-based lossless and lossy compression, and to develop practical low-complexity source coding schemes. To further reduce the implementation complexity and the hardware cost, we also present a real-time monitoring scheme where the sensor does not necessarily measure the state transition time. A statistical signal processing algorithm is adopted to estimate the changing time accurately. Our theoretical and numerical results show that the compression gain owing to the TSI is quite substantial, especially when the communication latency and the delay jitter are limited.

Proactive pushing has emerged as a promising solution to scale the service capacity by utilizing idle spectrum resources when on-demand transmission cannot fully exploit the spectrum resources during off-peak times. How to schedule both pushing and on-demand transmission jointly to meet a higher spectrum efficiency becomes a critical issue. Moreover, efficient and fair spectrum sharing among multiple resource schedulers remains open. In this paper, we introduce virtual network operators (VNOs) as schedulers that pay for consumed bandwidth and jointly schedule pushing and on-demand services. We adopt nonlinear spectrum pricing schemes with a convex and increasing price function enabling each VNO to share spectrum resources appropriately and independently. Considering the revenue from users and the cost for spectrum, we formulate a Markov decision process (MDP) to maximize the profit of VNO. A modified value iteration algorithm is applied to solve the MDP with reduced computational complexity. Furthermore, we show the structure of the optimal policy and provide the upper and lower bounds for the optimal performance. We present a low-complexity heuristic policy that can scale in practice with large state spaces and action spaces.

The emerging Ultra-Reliable and Low-Latency Communication (URLLC) is expected to meet a hard or probabilistic delay constraint that plays a key role in Time Sensitive Networking (TSN) and Deterministic Networking (DetNet). In this paper, we are interested in simple bounds for delay-constrained wireless communications with deterministic or even random packet arrivals. More specifically, we present a sufficient condition and derive a legitimate data arrival rate, with which the bounded delay can be guaranteed deterministically with an average power constraint. Our derived results will show that the delay-bounded data rate increases with the average power and the tolerated latency normalized to the coherent time. Furthermore, we derive the upper and lower bounds of the delay-violation probability (DVP) when the bounded delay condition cannot be satisfied. It is revealed that in the log coordinate, the DVP as a function of the normalized tolerated latency may enjoy a non-linear decay, with a decay rate that increases with the latency bound. This is in contrast to the large deviation based performance analysis that focuses on the low-latency transmission policies achieving linearly decayed DVP only. Our result demonstrates that the joint channel-aware and queue-aware scheduling may significantly reduce the DVP, compared to the single-layer approaches.

Millimeter-wave (mmWave) communications have attracted substantial attention due to their potential to provide very large bandwidths. Unfortunately, the propagation of millimeter waves suffers from severe path loss and blocking, which limits the coverage of mmWave communication systems. To overcome this, mmWave hotspot empowered two-tier heterogeneous networks are expected to play an important role in the sixth generation (6G) systems. When the deployment of mmWave hotspots is not dense enough, or even sparse, assuring the quality of service (QoS) for mobile users becomes rather challenging. In this paper, we investigate pushing in two-tier heterogeneous networks with mmWave hotspots, in which popular content items are cached by a mobile user when they can be served by a mmWave hotspot. To this end, a unified framework is presented to analyze and optimize the effective throughput of pushing. Based on the effective throughput analysis, pushing policies with different mobility models and/or mmWave hotspot distributions are presented. Both theoretical and numerical results demonstrate the substantial caching gain due to user mobility in mmWave hotspot empowered two-tier networks.

Massive access has attracted considerable recent attention because it holds the promise of efficiently enabling connections of extensive devices in machine-type communications. A large portion of massive access protocols exploit the reservation-based mechanisms that can satisfy random access requests while avoiding much bandwidth waste due to packet collisions. In this paper, we present a unified framework based on a tandem queue model for analyzing and optimizing reservation-based random access protocols with arbitrary collision resolution methods. More specifically, the two queues characterize the channel reservation and packet transmission respectively. We present the high-dimensional Markov chain of the tandem queue, the transition matrix of which can be derived from arbitrary collision resolution algorithms. The transition matrix then allows us to calculate the average packet delay by solving a linear equation. To provide further insights, a typical reservation based access using tree splitting for collision resolution is analyzed from both theoretical and numerical perspectives.

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

Hard delay constrained communications have attracted considerable recent attention because of their potential applications in the emerging field of deterministic networking (DetNet). However, developing techniques to satisfy both hard delay constraints and average power constraints simultaneously has long been a challenge. In this paper, we consider hard delay constrained transmissions over frequency selective wireless channels or parallel fading channels, in which the instantaneous transmission power can be adapted. A time domain power allocation scheme, also referred to as the generalized channel inversion policy is proposed. We find that the hard delay constraint can be met when the number of parallel channels with independent channel gains is greater than or equal to two. Furthermore, given a target rate, the required average power can be obtained based on the explicit outage probability as a function of the instantaneous signal-to-noise ratio (SNR). To provide further insight, we present two approximate formulas of the average power based on our derived closed-form approximations for the outage probability in the high SNR regime, and also derive upper and lower bounds on the required average power.

Ultra-Reliable and Low-Latency Communication (URLLC) has attracted considerable attention because it has great potential in industrial automation and autonomous driving. As one of the promising techniques to solve the low-latency problem, finite blocklength coding has been a critical topic. How to reduce the delay with the finite blocklength coding in the URLLC system is a challenging issue. In this paper, a Lyapunov drift-based scheduling scheme is presented for short-packet transmission systems. Specifically, a scheduling scheme is designed to determine the transmission rate and maximize the system throughput with Lyapunov optimization. The average power constraint can be tackled with the virtual power queue method. So that the short-packet transmission problem can be formulated as a univariate optimization problem, which is non-convex. A sub-optimal solution to this optimization problem can be obtained by our presented algorithm, which ignores the higher order terms of the Taylor series. Through simulations, the performance of our proposed algorithm is shown to be very close to that of the optimal policy obtained by the exhaustive search algorithm.

Finite blocklength coding has attracted considerable recent attention because it holds the promise of ultra-reliable and low-latency communications (URLLC) in smart grids, autonomous driving, tele-surgery, and industrial internet of things (IIoT). However, as the instantaneous blocklength is constrained by the number of backlogged bits, short packet transmission with random packet arrival becomes a challenging issue. In this paper, we present a cross-layer mechanism referred to as the buffer-aware variable-blocklength coding to minimize the average delay of bursty traffics. To optimize and analyze the buffer-aware short packet transmission, we formulate a tandem queue model consisting of both the packet queue buffering bits to be encoded and the symbol queue buffering coded symbols to be sent. By deriving the transition probability matrix and the steady state probability of the two-dimensional Markov chain characterizing the tandem queue, we obtain the average latency as a function of the arrival rate and transmission power. Simulation results verify our theoretical analysis and demonstrate the potential of the buffer-aware variable-blocklength coding scheme.

Extremely low latency communications has attracted considerable attention because of its potential in emerging real-time applications such as autonomous driving, smart grids, and Industrial Internet of Things (IIoT). Incremental decoding, which allows a remote controller to make real-time decisions without waiting for the whole packet or codeword to be decoded, may significantly reduce the latency-induced cost, especially when the bandwidth is very limited. In this paper, we are interested in the incremental decoding for the real-time control of a Markovian system. In contrast to the conventional Markov decision process, in which the decision maker receives the state information immediately, we consider a remote control scenario in which the transmission delay of its remote state information is non-negligible. To maximize the average reward in such Markovian systems, we conceive a joint incremental decoding and Markov decision making policy. In particular, the instantaneous codebook is determined by the controller's local state, which is also known by the sensor, while the controller can make its decision in real time based on partially received packet. Furthermore, we demonstrate the policy's potential by applying it into automated highway driving scenario, the performance gain of which has been revealed by numerical results.

Real-time monitoring of the Brownian motion or Wiener process has received considerable attention because of its potential in autonomous driving, smart grids, and factory automation. However, conventional periodic sampling-based monitoring may induce error accumulation, which will lead to an infinite distortion as the monitoring time increases. To overcome this, we present a threshold-based sampling policy for the remote reconstruction of Brownian motions. With the aid of timing side information (TSI), the sampling time information can be efficiently compressed. To provide greater insight, we present the real-time and non-real-time reconstruction errors as functions of data rate and transmission delay. Finally, a multi-threshold sampling method is presented to further reduce the transmission rate in remote monitoring with reservation-based multiple access.

Queue length violation probability, i.e., the tail distribution of the queue length, is a widely used statistical quality-of-service (QoS) metric in wireless communications. Characterizing and optimizing the queue length violation probability have great significance in time sensitive networking (TSN) and ultra reliable and low-latency communications (URLLC). However, it still remains an open problem. In this paper, we put our focus on the analysis of the tail distribution of the queue length from the perspective of cross-layer design in wireless link transmission. We find that, under the finite average power consumption constraint, the queue length violation probability can achieve zero with diversity gains, while it can have a linear-decay-rate exponent according to large deviation theory (LDT) with limited receiver sensitivity. Besides, we find that the queue-length tail distribution with an arbitrary-decay-rate exponent under the finite average power constraint exists in the Rayleigh fading channel. Then, we generalize the sufficient conditions for the communication system belonging to these three scenarios, respectively. Moreover, we apply the above results to analyze the wireless link transmission in the Nakagami-m fading channel. Numerical results with approximation validate our analysis.

The Industrial Internet of Things (IIoT) has attracted considerable attention because of its capability in turning common objects into connective devices. In IIoT, Deterministic Networking (DetNet) is an important scenario that can provide the network layer ultra-low latency support. In this paper, we focus our attention on the asymptotic cross-layer analysis of delay-violation-probability and power tradeoff in DetNet. More specifically, we find that zero delay-violation-probability transmission cannot be achieved under causal channel status with finite average power consumption. To support the requirement of DetNet under casual channel status, we prove that zero delay-violation-probability transmission can be achieved through frequency diversity, the use of multiple antennas, and cooperative diversity. Under non-causal channel status, DetNet can be achieved when the hard delay constraint is more than one time slot. Moreover, we derive the optimal tradeoff between the delay-violation-probability and average power consumption under causal channel status, which is further verified through numerical simulations.

In the emerging Industrial Internet of Things (IIoT), real-time control is expected to play a key role. How to minimize the cost to be paid due to the transmission delay of digital signaling in real-time control system becomes a challenging problem. In this paper, we study incremental decoding based low latency communication for a real-time control system. The real-time control action will be updated, whenever a new bit is received instead of the entire codeword. In other words, when the controller obtains partial information of the digital signaling, it executes the control action immediately instead of waiting until the complete information is obtained, which is in contrast to the conventional real-time control. Our aim is to minimize the expected cumulative control cost over the control process by joint design of source coding and its corresponding real-time control scheme. To this end, we first show a recursive structure that reveals the relationship of minimal expected cumulative control cost among two adjacent decision epochs. Based on such structure, the optimal solution can be obtained by a recursive algorithm presented by us. Finally, our numerical results also demonstrate that the cumulative control cost over the control process can be significantly reduced by implementing the source codebook we proposed, compared with traditional source coding.

Ultra-Reliable and Low-Latency Communications (URLLC) has attracted significant attention in the envisioned sixth-generation mobile systems (6G). The scheduling of back-logged queues in URLLC is a challenging issue worthy of researchers and engineers to study. Lyapunov optimization theory is regarded as a promising tool to design low-complexity scheduling strategies. However, it is difficult to analyze the tradeoff between the transmission delay and the transmission power with Lyapunov-based scheduling strategies. To solve this problem, we present an approach based on the Markov analysis in this paper. Specifically, we first develop one low-complexity scheduling strategy with heavy traffic based on the Lyapunov optimization theory. Then, uniform quantization is used to quantize the scheduling strategies so that the upper bound and lower bound of transmission delay can be calculated based on the Markov analysis. After that, we further extend this Markov analysis approach to estimate the transmission delay of another Lyapunov-based scheduling strategy with the consideration of a virtual power queue. Finally, simulation results not only verify our theoretical derivation but also demonstrate the potential of the Lyapunov-based scheduling strategies.

Millimeter wave (mmWave) communications has attracted significant attention due to its potential for providing very large bandwidths. Unfortunately, the propagation of millimeter waves suffers from severe path loss and blocking, which limits the coverage of mmWave systems. To overcome this, mmWave hotspot empowered two-tier heterogeneous networks are expected to play an important role in the sixth generation (6G) of mobile communication systems. When the deployment of mmWave hotspots is not dense enough, or even sparse, assuring the quality of service (QoS) for mobile users becomes rather challenging. In this paper, we present a mobility-enabled pushing scheme, in which popular content items are cached by a mobile user when he/she can be served by an mmWave hotspot. Optimal pushing policies with statistical mobility models and predeter-mined trajectories are presented and analyzed respectively. Both theoretical and numerical results demonstrate the substantial caching gain due to user mobility in mmWave hotspot empowered two-tier networks.

The next generation massive access has attracted considerable recent attention due to its potential in smart meters, industrial internet of things (IIoT), and smart traffics, etc. However, how to achieve the minimum queuing delay in massive access still remains open, thereby making quality-of-service (QoS) assurance a challenging issue in practice. In this paper, we aim at minimizing the average queuing delay by applying cross-layer scheduling with joint channel and buffer awareness, the complexity of which increases exponentially with the number of users. Fortunately, with massive users or devices, mean-field approximation can be adopted to substantially simplify the design and analysis of the delay-optimal scheduling. More specifically, we present a cocktail filling policy and a queue-aware multiuser diversity protocol, in which all backlogged packets of a user will be served by either NOMA or TDMA mode respectively, if the user’s channel gain is beyond a certain threshold. The average queuing delay and queue-length-violation probability are derived based on a Markov model. Numerical results will also demonstrate that the mean-field approximation based joint physical and network layer scheduling is capable of improving the QoS in massive access.

Real-time monitoring has received considerable attention recently due to its potential in automatic driving, tele-surgery, and factory automation in the 6G era. In remote estimation or reconstruction of stochastic processes, the statistical properties of stochastic processes to be monitored play a central role. Among the stochastic processes interested by real-time applications, the Brownian motion, also known as the Wiener processes is a typical one. In this paper, we are interested in how to monitor Brownian motions efficiently, timely, and reliably. To achieve this goal, we reveal that the real-time estimation error is jointly determined by the quantization error and freshness of data samples. Based on this observation, we present an optimal joint sampling and quantization scheme that efficiently balances the quantization distortion and the age-of-information (AoI). Furthermore, we find that the error accumulation will lead to infinite distortion as monitoring time increases. To overcome this, a multi-layer error correction method is presented for infinite-time monitoring, in which bounded distortion can be achieved with limited data rate. Finally, to conquer the accumulation of transmission errors in unreliable channels, we present an error correction mechanism based on periodic feedback. Diffusion approximation is then adopted to determine the optimal feedback rate and interval.

Ultra-Reliable and Low-Latency Communications (URLLC) has attracted considerable attention because of its potential applications in factory automation, automated driving, and telesurgery anticipated for the era of the sixth-Generation (6G) networks. In URLLC with random channel gains and a hard delay constraint, the scheduling of backlogged queues and finite blocklength coding in the physical layer will make it very challenging to specify its performance limits. In this paper, we focus our attention on the asymptotic cross-layer analysis of URLLC when the Signal-to-Noise Ratio (SNR) is sufficiently high. More specifically, we find that a fundamental tradeoff exists among the service capability, latency, and error probability in the high SNR regime, which is characterized by a gain conservation equation. The main result of this work reveals that the sum of our defined service rate gain, real-time gain, and reliability gain is equal to one under the optimal policy. Numerical simulations are also exploited to validate that the derived gain conservation equation holds even with bounded random arrival.

Autonomous driving is considered one of the killer technologies in the intelligent era. The information transmission between autonomous vehicles and pedestrians (V2P) is very important to reduce the number of road accidents. Ambient backscatter communication (AmBC) technology can be used to increase the vehicle (or driver) awareness regarding the presence of pedestrians in a crosswalk to realize short-distance transmission of emergency messages. On the other hand, highly secure transmission in V2P networks is required to assure the broadcasting of emergency messages. However, the artificial noise scheme by an additional noise source to improve the physical layer security (PLS) is not suitable due to the dynamic vehicles. Therefore, in this paper, we propose a source-noise assisted AmBC transmission scheme in the V2P system, in which the noise is created by the ambient radio frequency (RF) source to improve the PLS performance. The closed-form expressions for the outage probability of the legitimate user and the intercept probability of eavesdropper are derived. Finally, the diversity gain performance of the system is studied by analyzing the asymptotic behaviors. The theoretical and simulation results show that the proposed scheme improves the system security performance at the expense of system reliability by utilizing the proposed source-noise aided scheme in the V2P network. Besides, the optimal reflection coefficient is related to the power allocation ratio for the signal of the reader and interference noise. The different reflection coefficient is required for achieving the best system performance under different power allocation ratios. Moreover, the results also show that there are error floors for the outage probability, depending on the reflection coefficient. Furthermore, the performance results show that the intercept probability will significantly decrease when the outage probability is fixed in the proposed scheme.

Different from the existing works that focus on transceiver design of over-the-air computation (AirComp) over static networks, we in this paper consider an unmanned aerial vehicle (UAV) aided AirComp system, where the UAV as a flying base station aggregates data from mobile sensors. The trajectory design of the UAV provides an additional degree of freedom to improve the performance of AirComp. We aim to minimize the time-averaged mean-squared error (MSE) of AirComp by jointly optimizing the UAV trajectory, receive normalizing factors, and sensors’ transmit power. To this end, we first propose a novel and equivalent problem transformation by introducing intermediate variables. This reformulation leads to a convex subproblem when fixing any other two blocks of variables, thereby enabling efficient algorithm design based on the principle of block coordinate descent and alternating direction method of multipliers (ADMM) techniques. In particular, we derive the optimal closed-form solutions for normalizing factors and intermediate variables optimization subproblems. We also recast the convex trajectory design subproblem into an ADMM form and obtain the closed-form expressions for each variable updating. Simulation results show that the proposed algorithm achieves a smaller time-averaged MSE while reducing the simulation time by orders of magnitude compared to state-of-the-art algorithms.

Antenna selection is capable of reducing the hardware complexity of massive multiple-input multiple-output (MIMO) networks at the cost of certain performance degradation. Reconfigurable intelligent surface (RIS) has emerged as a cost-effective technique that can enhance the spectrum-efficiency of wireless networks by reconfiguring the propagation environment. By employing RIS to compensate for the performance loss due to antenna selection, in this paper we propose a new network architecture, i.e., RIS-assisted massive MIMO system with antenna selection, to enhance the system performance while enjoying a low hardware cost. This is achieved by maximizing the channel capacity via joint antenna selection and passive beamforming while taking into account the cardinality constraint of active antennas and the unit-modulus constraints of all RIS elements. However, the formulated problem turns out to be highly intractable due to the non-convex constraints and coupled optimization variables, for which an alternating optimization framework is provided, yielding antenna selection and passive beamforming subproblems. The computationally efficient submodular optimization algorithms are developed to solve the antenna selection subproblem under different channel state information assumptions. The iterative algorithms based on block coordinate descent are further proposed for the passive beamforming design by exploiting the unique problem structures. Moreover, the proposed algorithms are feasible to any finite number of antennas, and thus can be applicable in both ordinary MIMO and massive MIMO settings. Experimental results will demonstrate the algorithmic advantages and desirable performance of the proposed algorithms for RIS-assisted massive MIMO systems with antenna selection.

Non-orthogonal multiple access (NOMA) has received considerable attention as a promising candidate for future mobile networks. How to reduce traffic delay through cross-layer scheduling in NOMA systems is a challenging issue. In this paper, a cross-layer approach for NOMA systems is designed to fulfill the delay requirements. In particular, scheduling decisions should adapt to the system dynamics. With the distribution information of the system dynamics, the constrained Markov decision process (CMDP) is employed to characterize the scheduling decision. The optimal scheduling policy can be obtained by converting the scheduling problem to linear programming. A computational approach based on the Kronecker product is conceived to formulate the linear programming when the dimension of the CMDP is high. Then the optimal delay-power tradeoff can be achieved. Moreover, the optimal decoding order is demonstrated that it can be obtained directly based on the channel states. The threshold-based structure of the optimal scheduling decisions is revealed. Without the distribution information, the Lyapunov approach is exploited to propose an online scheduling policy, where the virtual power queue is used to tackle the power constraint. In NOMA systems, our CMDP-based approach achieves a better performance over the Lyapunov approach by taking advantage of the distribution information.

Coordinated multipoint (CoMP) has received considerable attention as a promising technology to improve the transmission rates and spectral efficiency of cell edge users for future networks. How to design coordinated scheduling through a cross-layer approach is challenging in CoMP systems. In this paper, a pilot-efficient scheduling policy is presented in CoMP systems with large-scale antennas. Our policy selects users to access the spectrum and allocates the power to users based on the queue state information (QSI) and channel state information (CSI). Based on the Lyapunov optimization, the cross-layer scheduling problem can be modeled as a combinatorial optimization problem, which is nontrivial. To solve this problem, we decouple the combinatorial optimization problem as a user selection problem and a power allocation problem. Then a low-complexity iteration algorithm is proposed to solve the combinatorial optimization problem. Simulation results demonstrate that our presented policy has better performance over traditional methods. Moreover, by comparing to the exhaustive search algorithm, the performance of our proposed policy is similar to that of an optimal policy.

Over-the-air computation (AirComp) based federated learning (FL) is capable of achieving fast model aggregation by exploiting the waveform superposition property of multiple-access channels. However, the model aggregation performance is severely limited by the unfavorable wireless propagation channels. In this paper, we propose to leverage intelligent reflecting surface (IRS) to achieve fast yet reliable model aggregation for AirComp-based FL. To optimize the learning performance, we present the convergence analysis of our proposed IRS-assisted AirComp-based FL system, based on which we propose to maximize the number of scheduled devices of each communication round under certain mean-squared error (MSE) requirements. To tackle the formulated highly-intractable problem, we propose a two-step optimization framework. Specifically, we induce the sparsity of device selection in the first step, followed by solving a series of MSE minimization problems to find the maximum feasible device set in the second step. We then propose an alternating optimization framework, supported by the difference-of-convex programming for low-rank optimization, to efficiently design the aggregation beamformers at the BS and phase shifts at the IRS. Simulation results demonstrate that our proposed algorithm and the deployment of an IRS can achieve a higher FL prediction accuracy than the baseline schemes.

Energy harvesting (EH) aided communications hold a great potential in the design of green communication systems for their high energy efficiency. However, the random power supply due to EH may cause an intolerable delay in data transmission. To overcome this, a Reliable Energy Source (RES) is desired to provide transmission power when the large delay is induced. In this paper, we study the delay-optimal scheduling policy for EH aided communications with the constraint of average power provided by RES. More specifically, the delay-minimal scheduling is obtained through the two-dimensional Markov chain modeling and linear programming (LP) formulation. To further reduce the computational complexity, we present a value iteration algorithm, based on which we not only reveal a threshold-based structure of the delay-optimal scheduling policy for EH-aided communications with large-capacity batteries, but also conceive a low complexity policy that is asymptotically optimal. For EH-aided communications with finite-capacity batteries, we present a unified framework based on large deviation theory. The non-asymptotic framework demonstrates that the delay-power tradeoff curve of the low complexity scheduling policy is capable of converging to that of the delay-optimal policy exponentially as the capacity of the battery increases.

To provide reliable transmissions with low-latency requirements, we focus on Finite-Blocklength Coding (FBC) in Ultra-Reliable and Low-Latency Communications (URLLC). However, ensuring the reliability and latency of FBC has remained an open issue in URLLC. In this paper, we develop a queue-aware FBC scheme under random arrivals. With the awareness of queue length, we employ variable-length coding to jointly encode packets, through which we obtain a benefit on reliability. Meanwhile, we optimize latency under a cross-layer approach, in which two classes of variable-length codes are investigated with resources allocated in the frequency and time domains, respectively. To obtain an optimal reliability-latency tradeoff under variable-length FBC, we first present the reliability and latency performance for single links based on a Constrained Markov Decision Process (CMDP). Providing reliability with a power allocation, we then obtain the optimal tradeoff by a Linear Programming (LP) problem, in which the probability of violation of the constraints on queue length and the number of transmitted packets is minimized under average constraints on resources. Moreover, we show an optimal threshold-based policy under Bernoulli arrivals. We finally consider some extensions of the optimal tradeoff for multi-user downlinks as well as single links with retransmission.

Real-time monitoring and remote control of stochastic systems have attracted considerable attention due to their potential in task-oriented communications and industrial Internet of Things (IIoT). How to achieve ultra high-freshness in real-time monitoring and remote control becomes a challenging problem. In this paper, we are interested in the freshness oriented source coding with incremental decoding. This is contrast to con-ventional source encoding/decoding, in which a random sample is estimated after its entire codeword is received. Incremental decoding, however, allows the real-time estimation of a random sample once a new bit or channel coding block is decoded in the physical layer. Its source codebook is then optimized, based on which we further conceive a real-time decision policy. Our policies minimize the average mean square error (MSE) or decision cost by judiciously designed codebook for source encoding. Numerical results show that the incremental decoding substantially reduces the MSE and decision cost in real-time monitoring.

Proactive pushing has been recognized as a promising solution to support the dramatically increasing global data traffic, thereby gaining much attention recently. By proactively pushing popular files to users prior to their requests, the data traffic load over peak hours can be relieved, leading to substantially reduced content access latency. However, extra power consumption is incurred due to pushing. How to efficiently schedule proactive pushing and on-demand transmission becomes an important problem. In this paper, we present a unified framework for joint proactive pushing and on-demand transmission. Based on the joint proactive pushing and on-demand transmission scheme, we minimize the average content queueing latency through a Constrained Markov Decision Process (CMDP) approach while satisfying a power constraint. Through linear programming (LP) formulation, we obtain the optimal delay-power tradeoff of the traditional on-demand transmission only scheme and the joint pushing and on-demand transmission scheme, respectively. Finally, simulation results present that joint scheduling of pushing and on-demand transmission significantly reduces the content queueing delay.

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

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

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

Ultra-Reliable and Low-Latency Communications (URLLC) has attracted considerable attention because of its potential applications in factory automation, automated driving, and telesurgery anticipated for the era of the sixth-Generation (6G) networks. In URLLC with random channel gains and a hard delay constraint, the scheduling of backlogged queues and finite blocklength coding in the physical layer make it rather challenging to specify its performance limit. In this paper, we focus our attention on the asymptotic cross-layer analysis of URLLC when the Signal-to-Noise Ratio (SNR) is sufficiently high. More specifically, we find that a fundamental tradeoff exists between the latency and error probability in the high SNR regime, which is characterized by a gain conservation equation. The main result of this work reveals that the sum of our defined real-time gain and reliability gain is equal to one under the optimal scheduling policy. Numerical simulations are also exploited to validate that the derived gain conservation equation holds even with bounded random arrival.

The Industrial Internet of Things (IIoT) has attracted considerable attention recently due to its potential application in factory automation e.g., of manufacturing or production systems. As most manufacturing operations can be modeled by discrete event systems (DESs), how to monitor a DES remotely and in a timely manner through sensors and communication links needs investigation in IIoT. In this paper, we present a lossless data compression method for real-time monitoring of DESs. In particular, we find that timing side information (TSI) is available in delay-constrained communications. Based on the TSI, the data rate required to describe a DES can be substantially reduced. To this end, we derive the minimum data rate of a DES as a conditional entropy from an information-theoretic perspective. Low complexity compression algorithms are also developed. Both analytical and numerical results demonstrate the TSI-enabled compression gain in three typical scenarios.

Proactive pushing can exploit the spectrum underutilized during the off-peak time to push popular content files, thereby significantly improving the spectrum efficiency. Moreover, in a communication system that the virtual network operator (VNO) has to buy spectrum from the base station to conduct file transmission, proactive pushing has been recognized as a promising technology to improve the income of the VNO. However, the appropriate pushing schemes and the achievable income of the VNO are unclear yet. In this paper, joint pushing, pricing, and recommendation (JPPR) schemes are presented for cache-enabled radio access networks. We aim to investigate recommendation-based pushing policy to maximize the average income of the VNO. We establish a Markov chain model, which derives the average income of the VNO. Based on this, we formulate an optimization problem to achieve the maximum average income. We further convert the optimization problem into an equivalent linear programming problem. Moreover, a greedy algorithm is applied to solve the problem with lower computational complexity. Finally, simulation results show the significant income gains that can be achieved by JPPR schemes compared with the system without the JPPR schemes.

As mobile data traffic has explosively grown during the past decades, pushing popular contents to small cells has been proposed to deal with the growing data demands. To improve the cache hit ratio, the recommender system is employed to recommend cached contents when the requests are not hit by the cache. However, how to persuade users to accept recommended files remains an open problem. We conceive a method that the network operators can give a discount on the traffic cost of the recommended contents. However, some users may maliciously request unpopular contents to get the discount, which will reduce the profit of the virtual network operator (VNO). In order to punish the malicious behaviour, we the VNOcan reduce the recommendation probability to these users. Meanwhile, these malicious users will reduce the malicious probability to increase the revenue. To study the interactions between the profit of the VNO and the revenue of the users, we formulate a non-cooperative game to find the Nash equilibrium (NE) of the recommendation probability of the VNO and the malicious probability of the users. Simulation results indicate that the VNO's profit and the user's revenue can be significantly increased with the proposed system compared with the system without joint recommendation and pricing schemes.

With over-the-air latency on the order of 0.1ms an-ticipated in 6G systems, the practical design of Finite-Blocklength Coding (FBC) has the potential to achieve extremely low latency communications. For this purpose, we focus on a joint FBC scheme in multi-user downlink systems. With a requirement of extremely low latency, we jointly encode data bits of multiple users over their orthogonal channel resources. As a result, we obtain throughput gain of the downlink transmission by an enlarged blocklength of FBC. In particular, we first present the joint encoding design for multiple downlink users by a matrix-based method. Under the multi-user joint FBC scheme, we then formulate an Integer Programming (IP) problem to maximize the throughput of downlink users subject to an average constraint on transmission power. By converting the derived IP problem to a nonlinear bipartite matching problem, we finally present a unified algorithm to obtain the optimal power-constrained throughput within the low latency requirement.

Over-the-air computation (AirComp) provides a promising way to support ultrafast aggregation of distributed data. However, its performance cannot be guaranteed in long-distance transmission due to the distortion induced by the channel fading and noise. To unleash the full potential of AirComp, this paper proposes to use a low-cost unmanned aerial vehicle (UAV) acting as a mobile base station to assist AirComp systems. Specifically, due to its controllable high-mobility and high-altitude, the UAV can move sufficiently close to the sensors to enable line-of-sight transmission and adaptively adjust all the links' distances, thereby enhancing the signal magnitude alignment and noise suppression. Our goal is to minimize the time-averaging mean-square error for AirComp by jointly optimizing the UAV trajectory, the scaling factor at the UAV, and the transmit power at the sensors, under constraints on the UAV’s predetermined locations and flying speed, sensors’ average and peak power limits. However, due to the highly coupled optimization variables and time-dependent constraints, the resulting problem is non-convex and challenging. We thus propose an efficient iterative algorithm by applying the block coordinate descent and successive convex optimization techniques. Simulation results verify the convergence of the proposed algorithm and demonstrate the performance gains and robustness of the proposed design compared with benchmarks.

Non-orthogonal multiple access (NOMA) is recognized as one of the promising techniques in wireless communications. How to reduce the delay violation probability in NOMA systems is a challenging issue. In this paper, a cross-layer scheduling scheme is presented for NOMA systems. The scheduling scheme is designed to jointly determine the scheduling in the network layer and superposition coding process in the physical layer. In order to find the optimal scheduling scheme, we model the queue states of the users as a Markov chain, based on which the delay violation probability and the average power consumption can be analyzed. Then, we minimize the delay violation probability given constraint on average power consumption by formulating and solving a cross-layer optimization problem. We convert the optimization problem into an equivalent linear programming problem via variable substitution, which allows us to obtain the optimal delay-power tradeoff as well as the optimal scheduling policy. One of the optimal superposition coding policies can be determined directly, which can significantly reduce the computational complexity of the linear programming. Theoretical analyses and simulation results show that our approach achieves a better performance over the Longer Queue Highest Possible Rate (LQHPR) policy.

Age of Information (AoI) has attracted much attention recently due to its capability of characterizing the freshness of information in Industrial Internet of Things (IIoT). To improve the information freshness over a fading channel shared by multiple users, efficient scheduling methods are highly desired for IIoT. In this paper, we are interested in a cross-layer scheduling policy, in which power and rate-adaptive transmissions are enabled in the physical layer. More specifically, we adopt a probabilistic scheduling method to minimize AoI while satisfying an average power constraint. The optimization of the probabilistic scheduling policy is formulated as a Constrained Markov Decision Process (CMDP), the dimension of which grows exponentially with the number of users and the buffer size. To overcome the prohibitive computational cost, we reveal the threshold-based structure of the AoI-optimal scheduling. Then we conceive a low complexity iterative algorithm that gives us the optimal freshness-aware scheduling policy.

In this paper, we consider federated learning (FL) over a noisy fading multiple access channel (MAC), where an edge server aggregates the local models transmitted by multiple end devices through over-the-air computation (AirComp). To realize efficient analog federated learning over wireless channels, we propose an AirComp-based FedSplit algorithm, where a threshold-based device selection scheme is adopted to achieve reliable local model uploading. In particular, we analyze the performance of the proposed algorithm and prove that the proposed algorithm linearly converges to the optimal solutions under the assumption that the objective function is strongly convex and smooth. We also characterize the robustness of proposed algorithm to the ill-conditioned problems, thereby achieving fast convergence rates and reducing communication rounds. A finite error bound is further provided to reveal the relationship between the convergence behavior and the channel fading and noise. Our algorithm is theoretically and experimentally verified to be much more robust to the ill-conditioned problems with faster convergence compared with other benchmark FL algorithms.

Wireless federated learning (FL) holds the potential of preserving data privacy and reducing network traffic congestion, thereby attracting much recent attention. Due to the fading nature of wireless channels, wireless FL suffers from the random delay in each uplink and downlink transmission. As a result, how to analyze the overall random delay of a FL task over wireless fading channels remains open. To solve this challenging problem, we present a saddle point approximation based approach to obtain the distribution of the delay caused by communication in wireless FL systems. In particular, we obtain the uplink delay distribution and the downlink delay distribution by Lugannani-Rice formula. The overall delay distribution is then obtained through the convolution of those two distributions and the generating function. Simulation results demonstrate that the theoretical results provide accurate characterizations for the empirical results, which corroborates the validity of the analysis in this paper.

Computation offloading from multiple mobile devices (MDs) to multiple MEC servers (MEC-ss) in heterogeneous MEC systems with energy harvesting is investigated from a game theoretic perspective. The objective is to minimize the average response time of an MD that consists of data communication time, waiting time and processing time. M/G/1 queueing models are established for MDs and MEC-ss. The interference among MDs, the randomness in computation task generation, harvested energy arrival, wireless channel state, queueing at the MEC-s, and the power budget constraint of each MD are taken into consideration. A noncooperative computation offloading game is formulated. Furthermore, we reconstruct the optimization problem of an MD. A 2-step decomposition is presented and performed. Thereby, we arrive at a one-dimensional search problem and a greatly shrunken sub-problem. The sub-problem is nonconvex, but its Karush-Kuhn-Tucker (KKT) conditions have finite solutions. Therefore, we can obtain the optimal solution of the sub-problem by seeking the finite solutions. Thereafter, a distributive iterated best-response algorithm is designed. Simulations are carried out to illustrate the convergence performance and parameter effect of the proposed algorithm.

The cache-enabled multicast content pushing, which multicasts the content items to multiple users and caches them until requested, is a promising technique to alleviate the heavy network load by enhancing the traffic offloading. This, in turn, has called for the optimization of content pushing strategy while considering both the transmission and caching resources, which jointly result in the complicated coupling among pushing decisions and lead to high computational complexity. Unlike most existing approaches which simplify the pushing problem via bypassing the complicated coupling, in this paper, we propose a multicast content pushing strategy to maximize the offloaded traffic with the cost on content caching based on structured deep learning. Specifically, we design the convolution stage to extract the spatio-temporal correlations of one content item between different pushing decisions, and construct the fully-connected stage to capture the spatial coupling among the decisions of pushing different content items to different user devices. Moreover, to address the absence of the ground truth on multicast content pushing, we relax the transmission constraint to derive a performance upper bound for guiding the training direction. This relaxed problem is solved based on dynamic programming in a bottom-up manner. Compared to the state-of-the-art baselines including both the traditional model-based and the general neural network-based strategies, the proposed pushing strategy achieves significant performance gain in both the random-generated dataset and the real LastFM dataset. In addition, it is also shown that the proposed strategy is robust to the uncertainty of user request information.

Real-time status updating or monitoring plays a critical role in emerging applications including Industrial Internet of Things (IIoT) and Vehicular-to-Everything (V2X) systems. In these applications, the real-time status is mostly characterized by analog signal samples desiring lossy compression before digital transmissions. However, how to ensure the data freshness while reducing the distortion due to lossy compression still remains open. In this paper, we are interested in a cross-layer framework aiming at achieving low Age-of-Information (AoI) and compression distortion concurrently in real-time monitoring over fading channels. More specifically, a cross-layer optimization is formulated to jointly control the lossy compression in the application layer and data transmission in the physical layer. We present a hierarchical solution method by decomposing the cross-layer optimization into inner and outer problems. The objective of the inner problem, solved by convex optimization, is to minimize an age-weighted distortion function and strike the optimal tradeoff between the instantaneous AoI and compression loss. The objective of the outer problem, solved by dimension-reduced Constrained Markov Decision Process (CMDP), is to acquire the optimal scheduling policy reducing the average AoI and distortion. We demonstrate the structural results of the optimal cross-layer design, which substantially reduces the protocol complexity in practice.

Federated learning (FL) is a collaborative machine learning paradigm, which enables deep learning model training over a large volume of decentralized data residing in mobile devices without accessing clients’ private data. Driven by the ever increasing demand for model training of mobile applications or devices, a vast majority of FL tasks are implemented over wireless fading channels. Due to the time-varying nature of wireless channels, however, random delay occurs in both the uplink and downlink transmissions of FL. How to analyze the overall time consumption of a wireless FL task, or more specifically, a FL’s delay distribution, becomes a challenging but important open problem, especially for delay-sensitive model training. In this paper, we present a unified framework to calculate the approximate delay distributions of FL over arbitrary fading channels. Specifically, saddle point approximation, extreme value theory (EVT), and large deviation theory (LDT) are jointly exploited to find the approximate delay distribution along with its tail distribution, which characterizes the quality-of-service of a wireless FL system. Simulation results will demonstrate that our approximation method achieves a small approximation error, which vanishes with the increase of training accuracy.

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

Low-latency communication is expected to play a key role in the Industrial Internet of Things (IIoT). Although there has been considerable effort on reducing latency, few attention has been focused on the low-latency transmission of distortion-tolerant data, e.g. IIoT's analog samples, over fading channels. In this paper, we present a cross-layer approach to jointly adapt the transmission power, rate, and compression ratio based on the instantaneous buffer and channel states. In particular, to minimize the average delay under both average power and distortion constraints, we formulate a Constrained Markov Decision Process (CMDP) with multi-dimensional state and action spaces. A novel solution is then presented by judiciously decomposing the multi-dimensional CMDP into a deterministic hierarchical optimization consisting of a linear programming and a convex optimization problem. Furthermore, we show the optimality of a threshold-based scheduling policy that highly reduces the complexity and present an optimal delay-power-distortion tradeoff that characterizes a fundamental performance tradeoff between the physical, network, and application layers. The joint lossy compression and power allocation scheme realizes a deep cross-layer optimization of source coding, queuing control, and wireless transmission, which outperforms the traditional cross-layer design consisting of only two layers, not to mention layered protocols.

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

Proactive caching has emerged as a promising means to accommodate increased demands for wireless capacity. However, studies of proactive caching usually focus on minimizing the overall load of cache-aided networks. How to calculate each user's caching gain is still an open problem. In this paper, a two-phase cache-aided network is investigated, in which users with heterogeneous preferences are served by a base station through a shared link. Effective throughput is considered as a performance metric, which describes the reduction in each user's transmission cost. All possible values of effective throughputs achieved by legitimate caching policies form an achievable domain. It is proved that the achievable domain is a convex set and can be characterized by its boundary. A special type of caching policies, termed uncoded placement absolutely-fair (UPAF) caching, is studied. For the two-user case, games are formulated to allocate effective throughput gains for the two users. For the general multiuser case, a UPAF policy is proposed to organize user cooperation. It is shown that users with more concentrated preferences can obtain higher effective throughputs.

Low-latency and energy-efficient wireless communication holds the potential of enabling the industrial internet of things (IIoT), automatic driving, and telesurgery. Cross-layer scheduling, which is aware of both the channel and queue states, has attracted considerable attention recently because it is capable of reducing the average delay substantially while meeting a given average power constraint. As a result, there has been considerable work, in which joint channel and buffer aware scheduling is formulated as a constrained Markov decision process (CMDP). In general, the optimal solution to a CMDP problem is characterized by the stationary probability of actions, yielding probabilistic cross-layer scheduling with possibly high complexity in practice. In this paper, we are interested in the low-latency and energy-efficient stationary cross-layer scheduling policy for wireless channels with continuous channel states, e.g. Rayleigh fading. It is interestingly shown that a deterministic cross-layer scheduling policy can achieve the optimal tradeoff between the average delay and power. In other words, the signaling complexity of cross-layer scheduling can be significantly reduced without causing sub-optimality. Simulation results also demonstrate that our work provides a low-complexity solution for low-latency and energy-efficient wireless communications.

To enable a number of emerging applications, efforts from industry and academia have started to focus on defining 6G systems, in which more stringent requirements than those imposed on 5G systems are being considered. In particular, some 6G applications may require extremely low-latency on the order of 0.1ms, through which practical designs of channel coding can be investigated based on Finite-Blocklength Coding (FBC). In this paper, we focus on a joint FBC scheme over multi-user downlinks in the Industrial Internet of Things (IIoT), in which only several symbol durations are available for users within a requirement of extremely low-latency. Since a higher coding rate is obtained by enlarging the blocklength of FBC, we jointly encode users’ data bits over their allocated resources, through which an enlarged blocklength is attained. Specifically, we first formulate the multi-user joint encoding design with a matrix-based method. Then, we present the optimal power-constrained throughput within the extremely low-latency requirement by formulating a nonlinear bipartite matching problem. We finally demonstrate the benefit resulting from the joint FBC in terms of each user’s maximum obtainable distance. With the distance to each user varying, we also perform an analysis of the variation of the optimal power-constrained throughput.

Caching holds the promise of scaling the service capability of next-generation radio access networks (RANs), thereby attracting much recent attention in the era of 6G research. To enable caching, popular content items should be proactively pushed to the user ends (UEs) in the placement phase. In practice, most content placement is only allowed to exploit idle spectrum or timeslot that is not occupied by any on-demand transmissions. In this case, however, the performance analysis and optimizations of practical caching gains remain open. In this paper, we are interested in how pushing, as a secondary service, improves the overall performance in terms of energy efficiency and latency reduction. To this end, we formulate a Markovian queueing model, the caching gains of which are optimized via linear programming (LP) with acceptable computational complexity. Our work demonstrates that the peak traffic load due to burst on-demand transmissions, as primary services, can be effectively offloaded by pushing over idle timeslots, leading to substantially reduced power consumption and queueing delay.

Attracted by the advantages of multi-access edge computing (MEC) and non-orthogonal multiple access (NOMA), this article studies the resource allocation problem of a NOMA-MEC system in an ultra-dense network (UDN), where each user may opt for offloading tasks to the MEC server when it is computationally intensive. Our optimization goal is to minimize the system computation cost, concerning the energy consumption and task delay of users. In order to tackle the non-convexity issue of the objective function, we decouple this problem into two sub-problems: user clustering as well as jointly power and computation resource allocation. Firstly, we propose a user clustering matching (UCM) algorithm exploiting the differences in channel gains of users. Then, relying on the mean-field game (MFG) framework, we solve the resource allocation problem for intensive user deployment, using the novel deep deterministic policy gradient (DDPG) method, which is termed by a mean-field-deep deterministic policy gradient (MF-DDPG) algorithm. Finally, a jointly iterative optimization algorithm (JIOA) of UCM and MF-DDPG is proposed to minimize the computation cost of users. The simulation results demonstrate that the proposed algorithm exhibits rapid convergence, and is capable of efficiently reducing both the energy consumption and task delay of users.

Age of Information (AoI) has attracted much attention recently due to its capability of characterizing the freshness of information. To improve information freshness over fading channels, efficient scheduling methods are highly desired for wireless transmissions. However, due to the channel instability and arrival randomness, optimizing AoI is very challenging. In this paper, we are interested in the AoI-optimal transmissions with rate-adaptive transmission schemes in a buffer-aware system. More specifically, we utilize a probabilistic scheduling method to minimize the AoI while satisfying an average power constraint. By characterizing the probabilistic scheduling policy with a Constrained Markov Decision Process (CMDP), we formulate a Linear Programming (LP) problem. Further, a low complexity algorithm is presented to obtain the optimal scheduling policy, which is proved to belong to a set of semi-threshold-based policies. Numerical results verify the reduction in computational complexity and the optimality of semi-threshold-based policy, which indicates that we can achieve well real-time service with a low computational complexity.

Proactive caching is capable of reducing access latency and improving network throughput, thereby attracting attention from both industry and academia. However, the energy efficiency (EE) of proactive caching over fading channels has not been well studied yet. In this paper, we aim at presenting an energy-efficient content pushing policy by carefully adapting the transmit rate and power in pushing and on-demand delivery phases, while assuring delivery delay constraints. To this end, the average delay, delay-outage probability, and EE are analyzed for a general adaptive pushing policy based on the saddle point approximation. According to these results, we formulate EE maximization problems under average delay and delay-outage constraints, respectively, for two special types of adaptive pushing policies, namely, opportunistic pushing and water-filling-based pushing. Due to the high complexity of the grid search for solving the formulated problems, suboptimal algorithms are presented based on gradient descent and golden section search methods. Moreover, we mathematically derive the scaling property and numerically obtain request probability and delivery delay thresholds for content pushing. Simulations show that the presented pushing policies achieve higher EE than on-demand transmissions, especially when the content item is popular and the tolerable delivery delay is small.

Most recently, Ultra-Reliable Low-Latency Communication (URLLC) has attracted much attention due to its potential application in Factory Automation (FA), Vehicle-to-everything (V2X), telesurgery, etc. One of the key performance metrics for URLLC is the Queue-Length-Bound (QLB) violation probability. However, how to minimize the QLB violation probability remains open over time-varying channels because it relies on challenging cross-layer designs. In this paper, we formulate a Constrained Markov Decision Process (CMDP) framework to minimize this QLB violation probability while keeping the average transmission power low. To achieve a feasible solution, we then relax this CMDP problem into an unconstrained Markov Decision Process (MDP) and apply the value iteration algorithm. By this means, we acquire the optimal stationary deterministic policy. It is further shown that a threshold based policy can strike the optimal QLB violation probability and power tradeoff. Finally, a Linear Programming (LP) is adopted to solve this CMDP given a certain power constraint. We thus acquire the optimal probabilistic policy and its tradeoff between the QLB violation probability and the transmission power.

Proactive pushing holds the promise of significantly improving spectrum efficiency. However, we are confronted with challenges including increasingly scarce spectrum resources and extra costs of pushing. As a result, the spectrum should be carefully shared between proactive pushing and on-demand transmission to avoid waste of resources. In this paper, we take into account the constraints of spectrum resources. We consider a pricing-based resource scheduling so that we can make a profitably efficient use of the limited spectrum resources. To maximize the income of content providers, we formulate a non-linear optimization problem, which is linearized to be more tractable. Numerical results demonstrate that appropriate pushing can bring significant gain on the income with scarce spectrum resources.

Proactively pushing popular content files to users has been recognized as a promising technology to exploit the spectrum underutilized during the off-peak times, thereby attracting much attention recently. However, in a push-based system with the on-demand traffic, the appropriate pushing scheme and the achieved performance gains are unclear yet. In this paper, we aim to investigate the problem of joint scheduling of proactive pushing and on-demand transmission over shared spectrum. In particular, during the idle period of on-demand transmission, a probabilistic pushing scheme is presented based on the content request delay information (RDI). With pushing, the traffic load of on-demand transmission can be offloaded by extending transmission time, leading to the reduced power consumption. By establishing a Markov chain model, the energy efficiency gain of the pushing scheme proposed is derived. Based on this, we formulate an optimization problem to achieve maximum performance gains. The optimization problem is further converted into an equivalent linear programming (LP) problem through variable substitution. Finally, simulation results demonstrate the performance gains that can be achieved by proactively pushing.

Proactive caching is capable of offloading data traffic during on-peak hours and improving system capacity, thereby attracting much attention recently. However, the energy efficiency (EE) gain brought by proactive caching has not been fully exploited yet in fading channels. In this paper, we present energy-efficient content pushing methods in fading channels, while guaranteeing the average delay or delay-outage constraint. In particular, a general rate and power adaptation scheme is adopted to support content pushing and on-demand delivery, helping to improve the EE. To maximize the EE, we derive the average delay, delay-outage probability, and EE for any given adaptive policy. Based on these results, EE maximization problems are formulated for a specific adaptive scheme, namely, the water-filling scheme. Formulated problems are then converted into one-dimensional optimization problems to reduce computational complexity. Simulations indicate a request probability threshold for content pushing, above which the content pushing performs better than the traditional on-demand transmission.

In order to handle users' heterogeneous quality of service requirements at the network edge, user preference aware lossy data compression is presented for edge caching. The symbols generated by an information source are compressed and then are transmitted to a base station. Before being transmitted to users, the symbols can be re-compressed at the network edge. User preferences are described by weights on the symbols. All possible values of the rates over the transmission links and the distortions suffered by the users form an achievable domain. The achievable domain is a convex set and an optimization problem is formulated in order to obtain its lower boundary. For discrete sources, the optimization problem is nonconvex and a DCA-based iterative algorithm is proposed to provide suboptimal code designs.

Ultra-Reliable and Low-Latency Communication (URLLC), aiming at meeting the hard delay constraint, plays a central role in 5G or even 6G mobile communications, thereby attracting much recent attention. However, satisfying such constraint over fading channels means a critical or even impossible mission for the physical layer because channel inversion exhibits a large power penalty in extreme fading environments. In this paper, we present a cross-layer approach to minimize the delay violation probability, the probability that hard delay constraint can not be assured, in URLLC over fading channels. More specifically, a probabilistic scheduling scheme that jointly exploits the queue state and the channel state is conceived. We formulate a Constrained Markov Decision Process (CDMP) to minimize the delay violation probability with a given power constraint. Variable substitution and linear programming are adopted to obtain the optimal solution. The optimal policy is shown to be threshold-based, which also induces the piecewise linear delay violation probability-power tradeoff. The tradeoff curve characterizes the theoretical lower bound of the delay violation probability with a given power constraint.

Predicting the popularity of online contents especially videos has drawn a lot of attention recently, since successful prediction of popularity can benefit many practical applications such as recommender systems and proactive caching, and help optimize the advertisement strategies or balance the throughput in the network. In this paper, we formulate a popularity prediction problem and present an Attention-based Long Short Term Memory (LSTM) with Feature Embedding method (ALFE) to tackle the popularity prediction problem. Several features including publish time, follower count, and type of the video are considered and compared. Experiments on a real world dataset show that our method outperforms other competitive baselines from existing works in terms of prediction accuracy. The attention mechanism and feature embedding contribute to the improvement of accuracy. Among all the features, timestamp of popularity and video duration are shown to be the most informative ones, due to the regularity and periodicity of human daily activities.

Remote reconstruction of trajectories of dynamical systems is now emerging as a mission-critical application in Industry 4.0 and beyond. Unfortunately, the statistical properties of signals generated by dynamical systems usually remain unknown, which prohibits the use of classic source coding methods relying on a statistical source model. To overcome this difficulty, we present a paradigm shift data transmission scheme, assuring that the reconstruction error is limited with the aid of the computation unit. It is found that the bit rate for transmitting trajectories has a significant relationship with the predictability of dynamical systems. Together with the concept of the Lyapunov exponent, an error growth function is introduced in this paper to classify the dynamical systems according to their predictability. A general expression of the bit rate is obtained in this paper. Furthermore, it is shown that the asymptotic value of the bit rate to reconstruct trajectories of a chaotic system is given by its Lyapunov exponent. The bit rate to reconstruct trajectories of non-chaotic systems is also presented. Simulation results show that our scheme outperforms conventional information-theory-based coding schemes, and can significantly reduce bandwidth requirements.

The coronavirus disease 2019 (COVID-19) has recently attracted extensive attention due to its serious impact on public health worldwide. In this paper, we study and verify that the popularity of virus-related content has a negative correlation with the epidemic spread by means of statistical analysis. Inspired by this result, a practical solution of recommender system is proposed for pushing virus-related content, aiming to gain insight about the newly discovered virus for people and thus reduce the epidemic spread to the utmost extent. First, we formulate the optimization of recommendation policy subject to quality of experience (QoE) loss constraints as a finite-horizon Constrained Markov Decision Problem (CMDP). To solve this problem, then, we present both enumeration and heuristic methods, from perspectives of achieving optimal recommendation policy and reducing computational complexity, respectively. Finally, our simulations validate the benefit of our solution by showing that to recommend virus-related content following our strategy does help slow down the spread of the epidemic.

In a vehicular network environment, vehicles can download data through opportunistically-encountered Roadside Units (RSUs) with a lower cost, compared to that from Base Stations (BSs). However, the delay experienced by the vehicles might be undesirably prolonged if they only download data through RSUs. In this paper, we aim to minimize the average delay under the constraint of average cost by scheduling the download rates from RSUs and BSs. One challenge lying in the design of the downloading policy is the uncertainty of the download condition, i.e., whether at least an RSU is available or BS only, in the future slots. To overcome this challenge, we notice that vehicles from opposite directions can share their known download conditions to reduce this uncertainty. To make the most of this information, a Markov decision process (MDP) is used to model the system operations, based on which, average delay and cost can be analyzed to formulate the optimization problem. By solving this problem, the delay-minimal downloading policy can be obtained to achieve the optimal delay-cost tradeoff in the considered vehicular network. Finally, performance improvement with the help of information sharing among vehicles is validated by extensive simulations.

Delay-sensitive communication has attracted much recent attention in emerging 5G systems that are expected to provide Ultra-Reliable Low-Latency Communications (URLLC). However, it remains open on how to balance the queuing delay and the compression distortion in power-constrained transmission over time-varying channels. In this paper, we present a cross-layer approach to strike the optimal delay-power-distortion tradeoff. More specifically, the compression ratio in the application layer and the data rate in the physical layer jointly adapt to the buffer and channel state. To this end, we formulate a nonlinear optimization problem with a constrained Markov Decision Process (CMDP) to minimize the average queuing delay while keeping the distortion and power consumption low. Converting this problem into Linear Programming (LP) gives us both the optimal scheduling parameters and its delay-power-distortion tradeoff. We next show that the tradeoff between delay, power, and distortion could be further improved by introducing the weighted-average cost of power and distortion for each action. The weighted-average costs can be minimized and optimized to be a convex function of the transmission rate. With this convex cost-rate function, we find the optimal threshold-based policy.

Wireless power transfer (WPT) is an effective approach to enhance sustainability of mobile devices. In this context, we consider a system composed of a hybrid access point (HAP) that powers a wireless device (WD), provisioned with a finite energy storage and data butter. The WD uses the harvested energy to perform data transmission. We assume that both HAP and WD can perform power adaptation to save energy. Unlike most existing works, we focus on delay minimization in transmitting an arbitrary data arrival packets over wireless fading channels, which is extremely important for time-sensitive applications. Previous works mainly focused on slot-oriented optimization, where the harvested energy is consumed in the same slot, without considering the possibility of storing energy for future use. In contrast, we consider a long-term average delay minimization under average power consumption constraint at the HAP. This requires to consider the data arrival process, data and energy queue evolution, and the channel state statistics, thus, greatly increasing the optimization complexity. Our goal is to determine the optimal delay-power tradeoff and its corresponding scheduling strategy to decide the operations, e.g., data transmission by the WD and power transfer by the HAP, for arbitrary i.i.d arrival process and adaptive transmissions. Through two-dimensional Markov chain modeling and linear programming, we provide the optimal scheduling strategy. We corroborate the theoretical analysis by simulation results.

Being capable of characterizing the freshness of information, Age of Information (AoI) has attracted much attention recently. To provide better real-time service over fading channels, efficient scheduling methods are highly desired for wireless transmissions with freshness requirements. However, due to the channel instability and arrival randomness, it is challenging to achieve the optimal AoI. In this paper, we are interested in the AoI-optimal transmissions with truncated channel inversion, which has a low complexity transceiver architecture exploiting fixed coding and modulation. More specifically, we utilize a probabilistic scheduling method to minimize the AoI while satisfying an average power constraint. By characterizing the probabilistic scheduling policy with a Constrained Markov Decision Process (CMDP), we formulate a Linear Programming (LP) problem. Further, we present a low complexity algorithm to obtain the optimal scheduling policy, which is proved to belong to a set of semi-threshold-based policies. Numerical results verify the reduction in computational complexity and the optimality of semithreshold-based policy, which indicates that we can achieve well real-time service with a low calculating complexity.

To reduce content transmission power and network load pressure, content caching technology based on a large number of small base stations (SBSs) is considered to be an effective solution. However, due to the limited cache capacity and unknown content popularity, how to design an intelligent content caching policy has become a great challenge. In this paper, we propose a generative adversarial network (GAN) based on the distributional deep Q-Network (DDQN) algorithm, named QGAN, to learn the content caching policy. A content caching network that contains several cooperative SBSs is considered in the case of unknown content popularity, where each SBS fetches cached content from the adjacent SBS or cloud. Moreover, compared with three classical content caching policies and one reinforcement learning algorithm, the performance of the QGAN algorithm is verified. The simulation results show that the convergence rate is improved and the transmission cost is reduced with the proposed algorithm.

Energy harvesting (EH) aided millimeter Wave (mmWave) communications hold a great potential in the design of next generation wireless networks for their high data rate and energy efficiency. However, both the random power supply due to EH and the random blocking nature of mmWave channels induce severe queueing delay, thereby damaging the quality of service (QoS). To overcome this problem, in this paper, a cross-layer probabilistic scheduling is proposed for EH aided mmWave communications with random blocking channel. Our aim is to minimize the average delay while assuring that renewable energy is fully exploited. To achieve this goal, we formulate a two-dimensional Markov chain consisting of a data packet queue and a virtual queue of harvested energy. Based on the two-dimensional Markov chain, the average delay and the renewable power utilization of probabilistic scheduling policy are derived. On this basis, we obtain the delay optimal scheduling policy through a linear programming (LP) problem. More importantly, the structure of the delay optimal scheduling policy is shown to be threshold-based. Simulation results also validate that the threshold-based scheduling is capable of attaining significant QoS and energy efficiency gain.

Due to the stringent requirement of low-latency, Finite-Blocklength Coding (FBC) has been developed to guarantee reliability in Ultra-Reliable and Low-Latency Communications (URLLC). However, ensuring the reliability and latency of FBC for downlink transmissions has remained as an open issue for URLLC with random arrivals. In this paper, we focus on a multi-user downlink system with URLLC required. In the downlink transmission, we obtain a benefit on the reliability from a longer blocklength, generated by grouping and jointly encoding the multi-users' packets. By this means, a Joint Framing and Finite-Blocklength Coding (JF2BC) scheme is proposed to provide the requirements of reliability and latency. In particular, considering the queueing effects for latency under random arrivals, we employ a cross-layer approach to characterize the JF2BC policy. Under the queue-aware policy, we show an optimal tradeoff between the queueing delay and reliability by a Linear Programming (LP) problem, in which a matrix-based algorithm is developed to automatically generate the LP problem for the multi-user scenario. To optimize the tail distribution of queueing delay, we further extend the optimal tradeoff by using the violation probability of maximal queue length as the delay measure.

Internet of Things (IoT) technology suffers from the challenge that rare wireless network resources are difficult to meet the influx of a huge number of terminal devices. Cache-enabled device-to-device (D2D) communication technology is expected to relieve network pressure with the fact that the requesting contents can be easily obtained from nearby users. However, how to design an effective caching policy becomes very challenging due to the limited content storage capacity and the uncertainty of user mobility pattern. In this article, we study the jointly cache content placement and delivery policy for the cache-enabled D2D networks. Specifically, two potential recurrent neural network approaches [the echo state network (ESN) and the long short-term memory (LSTM) network] are employed to predict users' mobility and content popularity, so as to determine which content to cache and where to cache. When the local cache of the user cannot satisfy its own request, the user may consider establishing a D2D link with the neighboring user to implement the content delivery. In order to decide which user will be selected to establish the D2D link, we propose the novel schemes based on deep reinforcement learning to implement the dynamic decision making and optimization of the content delivery problems, aiming at improving the quality of experience of overall caching system. The simulation results suggest that the cache hit ratio of the system can be well improved by the proposed content placement strategy, and the proposed content delivery approaches can effectively reduce the request content delivery delay and energy consumption.

Data compression is an efficient technique for saving data storage and transmission costs in networks. Traditional data compression methods usually compress each content item according to its own statistical distribution of symbols and do not take into account user preferences on various content items. However, user preferences significantly impact the statistical distributions of symbols transmitted over communication links. This paper presents an edge source coding method to compress data at the network edge, in which codebooks are designed based on not only the statistical distributions of symbols in the content items but also the user preferences. In edge source coding, multiple content items might be compressed via the same codebook. For discrete user preferences, DCA (difference of convex functions programming algorithm) based and k-means++ based algorithms are proposed to derive codebook designs. For continuous user preferences, a sampling method is applied to yield codebook designs. In addition, edge source coding is extended to the two-user case and codebooks are designed to utilize multicasting opportunities. Simulation results demonstrate that edge source coding significantly reduces transmission costs for short content items.

Large-Scale Antenna System (LSAS) has played an important role in the emerging fifth-generation mobile systems (5G) due to its potential for excellent spectral efficiency. However, it may cause a mass of pilot overhead that is not conducive to the application of LSAS in massive Machine-Type Communications (mMTC), one of three typical traffic modes of 5G. In this paper, we present a pilot-efficient scheduling strategy for mMTC systems, in which the Base Stations (BS) are equipped with large-scale antennas, from a cross-layer perspective. Our scheme can not only schedule the massive devices to access the spectrum, but also allocate the BS' power without the need for much pilot overhead. More particularly, the users allowed to access the spectrum can be selected based on their queue state information without any channel estimations, while the power allocation only needs the channel estimations for scheduled users. We shall show the optimality of the presented policy based on the Lyapunov optimization theory. To solve the Lyapunov optimization problem, we present a low complexity two-layer iteration algorithm for more practical purposes. Simulation results demonstrate the substantial gain of our presented method over existing scheduling protocols of massive MIMO.

In this paper, delay-optimal and energy-efficient communication is studied for a single link under Markov random arrivals. We present the optimal tradeoff between delay and power over Additive White Gaussian Noise (AWGN) channels and extend the optimal tradeoff for block fading channels. Under time-correlated traffic arrivals, we develop a cross-layer solution that jointly considers the arrival rate, the queue length, and the channel state in order to minimize the average delay subject to a power constraint. For this purpose, we formulate the average delay and power problem as a Constrained Markov Decision Process (CMDP). Based on steady-state analysis for the CMDP, a Linear Programming (LP) problem is formulated to obtain the optimal delay-power tradeoff. We further show the optimal transmission strategy using a Lagrangian relaxation technique. Specifically, the optimal adaptive transmission is shown to have a threshold type of structure, where the thresholds on the queue length are presented for different transmission rates under the given arrival rates and channel states. By exploiting the result, we develop a threshold-based algorithm to efficiently obtain the optimal delay-power tradeoff. We show how a trajectory-sampling version of the proposed algorithm can be developed without the prior need of arrival statistics.

For the emerging Internet of Things (IoT), one of the most critical problems is the real-time reconstruction of signals from a set of aged measurements. During the reconstruction, distortion occurs between the observed signal and the reconstructed signal due to sampling and queuing delay. We focus on minimizing the average distortion defined as the 1-norm of the difference of the two signals under the scenario that a Poisson counting process is reconstructed in real-time on a remote monitor. We consider the reconstruction under three special sampling policies. For each of the policy, we derive the closed-form expression of the average distortion by dividing the overall distortion area into polygons and analyzing their structures. It turns out that the polygons are built up by sub-polygons that account for distortions caused by sampling and queuing delay. The closed-form expressions of the average distortion help us find the optimal sampling parameters that achieve the minimum distortion. In addition, we propose an interpolation algorithm to further decrease the average distortion and give its lower-bound on distortion for one of the three sampling policies. Simulation results are provided to validate our conclusion.

Mobile edge computing (MEC) is a promising technique to improve the quality of computation experience for mobile devices by providing computation resources in their close proximity. However, the design of scheduling policies for MEC systems inevitably encounters a challenging optimization problem that should take both transmissions and computations into consideration. In particular, how to jointly schedule transmissions and computations should adapt to the cross-layer system dynamics, i.e., random task arrivals and channel state variations. We formulate this scheduling problem as a joint optimization problem for both transmissions and computations in order to minimize the power consumption of mobile devices, while meeting the latency requirement. With given distributions of the system dynamics, Markov decision process (MDP) is used to model the system operations. Based on this model, the power-optimal scheduling policy can be obtained by converting the joint optimization problem to linear programming (LP) by using variable substitutions and thus the optimal power-latency tradeoff can be achieved. When the distribution information of the system dynamics is unknown, we exploit the Lyapunov optimization to present a low complexity scheduling policy. Our theoretical analysis and extensive simulation studies show that our approach can offer a good tradeoff between power consumption and latency.

Edge caching has become a promising technology in future wireless networks owing to its remarkable ability to reduce peak data traffic. However, the storage resource can be limited in practice hence only a small amount of files can be cached. How to improve the cache hit ratio in finite-buffer caching based on the prediction of user demands has become an important problem. In this paper, we study caching policies with finite buffer by exploiting the prediction of a user's request time, referred to as request delay information (RDI). Based on RDI, we maximize the average cache hit ratio through a Markov decision process (MDP) approach. Specifically, we formulate an MDP problem and apply a modified value iteration algorithm to find an optimal caching policy. Moreover, we provide an upper bound and a lower bound for the cache hit ratio, as well as an analytical cache hit ratio with small buffers. To address the issue that the state space can be prohibitively large in practice, we present a low-complexity heuristic caching policy that is shown to be asymptotically optimal. Simulation results show that introducing RDI may bring significant cache hit ratio gain when the buffer size is limited.

In unmanned aerial vehicle (UAV)-assisted massive multi-input multi-output (MIMO) millimeter-wave (mmWave) networks, beam-steering guarantees reliable and steady connection between flying base stations and ground users with the challenge of strict angular deviation. In this paper, we investigate a joint optimization problem of beamforming and beam-steering in the multi-UAV mmWave networks, considering line-of-sight (LoS) communication for UAVs. For the hybrid beamforming optimization of massive MIMO mmWave, we propose a hybrid beamforming scheme based on the cross-entropy estimation with the robustness algorithm inspired by machine learning, which aims to optimize the hybrid precoding matrix. For the beam-steering optimization, we propose a novel mean field game (MFG)-based massive MIMO angle control scheme to model the optimal mmWave channel optimization problem between UAVs and ground users. In addition, when dealing with the problem of initial sensitivity and difficulty to solve the partial differential equations in the MFG, we utilize reinforcement learning to achieve the mean field equilibrium, which is described as the mean field learning game algorithm. Finally, a joint beamforming and beam-steering optimization algorithm is proposed to maximize the system sum-rate. Simulation results show the significant improvements in sum-rate, energy efficiency, and spectral efficiency, which verify the effectiveness of the proposed algorithm.

Caching is a promising technology that holds the potential of substantially improving the bandwidth efficiency and reducing peak data traffic. As a result, the maximization of cache hit ratio in proactive caching has attracted much attention most recently. Unfortunately, the hit ratio can be very limited due to the constrained buffer size. In this paper, we are interested in maximizing the hit ratio for users with limited buffer size by exploiting the prediction of a user's request time for content files, also referred to as the request delay information (RDI). More specifically, the hit ratio is maximized by keeping the popular and storage-efficient content files in a receiver buffer. To achieve this goal, we formulate an infinite horizon Markov decision problem (MDP) that can be efficiently solved by a value iteration algorithm. For more practical applications, we present a heuristic caching policy that can greatly reduce the computational complexity when the buffer size is large and the content arrival rate is high, thereby holding a great potential in practice. Simulation results show that the RDI may bring significant hit ratio gain, especially in small buffer scenarios.

Proactive caching or pushing is capable of not only reducing the peak data rate and access latency, but also improving the overall spectral efficiency, thereby attracting much attention recently. However, there has not been much work on the energy efficiency (EE) of proactive caching over wireless links. In this paper, we present an energy-efficient pushing policy based on opportunistic transmissions with high EE in the physical layer. Our aim is to fully exploit the timeslots in which the channel gain is sufficiently high, while assuring that a user receives its desired content item within a certain average or maximal delay bound. To this end, we investigate the average delay, delay-outage probability, and average energy consumption of proactive caching with opportunistic transmissions. Based on these, we formulate two pushing-oriented EE maximization problems, the solutions of which imply optimal pushing methods. Simulations show that our pushing policies achieve a significant EE gain over on-demand opportunistic transmissions when the content item is popular.

Nowadays content caching at base stations (BSs) has attracted more and more attention in 5G networks with the ability of saving resources and reducing data traffic. However, in practice, it's a challenge to design a caching policy intelligently due to the limited storage capacity as well as time and space varying users' requests. In this paper, we propose an algorithm based on asynchronous advantage actor-critic (A3C) to solve the content caching problem. Considering some cooperative BSs, with each BS having a cache, every BS can fetch contents from either neighboring BSs or the backbone network, with different degrees of expenditure. In order to learn the optimal caching and sharing policy, the online A3C-based algorithm is designed to minimize the total transmission cost without knowing content popularity distribution. To evaluate the proposed algorithm, we compare the performance with the classical caching policies, including Least Recently Used (LRU), Least Frequently Used (LFU), Adaptive Replacement Cache (ARC) and one distributed algorithm in the literature. The simulation results show that the proposed A3C-based algorithm can achieve a low transmission cost and improve the convergence rate in the dynamic environment.

Ultra-Reliable and Low-Latency Communication (URLLC) has attracted significant attention due to its potential in factory automation, telesurgery, and automatic driving. However, little attention has been paid to URLLC when the traffic arrival is random. In this paper, a random arrival oriented URLLC policy is investigated for Additive White Gaussian Noise (AWGN) channels. More specifically, we develop a queue-aware variable-length channel coding scheme, in which the blocklength of channel coding is determined by the queue length. Further, from a cross-layer design perspective, we present the optimal tradeoff between latency and power consumption given the reliability constraint. To this end, we formulate a Markov chain to attain the delay and power consumption, based on which a Linear Programming (LP) problem is formulated to minimize the latency under a power constraint. By solving the derived LP problem, we obtain the optimal variable-length coding policies with a threshold-based structure imposed on the queue length.

Source coding is an efficient technique to save data storage and transmission costs. Traditional source coding methods are usually designed based on the statistical distribution of symbols generated by the information source. Notice that, in content-centric networks, the user preference has a significant impact on the statistical distribution of symbols transmitted in the links. This paper presents an edge source coding method to compress data in the network edge. An optimization problem is formulated to obtain the optimal codebook design. Based on the solution of the single codebook case, an optimality condition for the optimization problem is presented. For discrete user preferences, a variant of the k-means++ algorithm is used to give a suboptimal solution. For continuous user preferences, two algorithms are proposed to give codebook designs. Both theoretical analysis and simulations demonstrate the optimal codebook design should take into account the user preferences.

Proactive caching is a promising means to handle increasing wireless traffic. However, how heterogeneous user preferences impact the caching gain is still an open problem. In this paper, a two-phase cache-aided multicasting network is investigated, in which users with heterogeneous preferences are served by a base station through a shared link. It is shown that the achievable domain of effective throughput of the users is a convex set and can be characterized by its boundary in the positive orthant. A special type of caching schemes, named uncoded placement absolutely fair (UPAF) caching, is studied. For the two user case, the achievable domain of UPAF policies has a piecewise linear boundary. For the multiuser case, a feasible UPAF policy is proposed to obtain caching and multicasting gains. Simulation results demonstrate that users with more concentrated preferences can attain higher effective throughput.

Unmanned aerial vehicles (UAVs) can now be considered as aerial base stations (BSs) to support ultra-reliable and low-latency communications by establishing line-of-sight (LoS) connections to ground users. Moreover, combining UAVs with millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) will be a promissing solution. It can provide potentially high capacity wireless services due to their aerial positions and their ability to deploy on demand at specific locations. In this paper, we propose a low-cost and energy-efficient hybrid precoding architecture for UAVs, where the antenna part is realized by lens array. We investigate an efficient and energy-saving hybrid precoding scheme with robustness, which is inspired by the cross-entropy (CE) optimization in machine learning and the relative error estimation optimization. As for each selection of the hybrid precoders for obtaining the optimized precoder, we regarded it as a training process in machine learning, in which the training target is the CE-loss function between the predicted precoders and the target precoders. It aims to minimize the relative error between the predicted and actual values for optimizing the probability distributions of the elements in the analog hybrid precoder. Simulation results show that our proposed scheme can achieve higher sum rate and energy efficiency.

For the emerging Internet of Things (IoT), one of the most important basic problems is how to reconstruct signals in real-time from a set of under-sampled and delayed samples. The sampling omits the details of the signals of interest and the delayed samples against the requirement of real-time. As a result, distortion occurs between the interested signal and the reconstructed signal. In this paper, we focus on minimizing the average distortion defined as the 1-norm of the difference of the two signals under the scenario that a Poisson counting process is reconstructed in real-time on a remote monitor. We derive the average distortion-sampling rate function, with which the optimal sampling rate can be obtained as well as the minimum average distortion. To further decrease the average distortion, an algorithm is proposed by sacrificing the real-time requirement in a small degree.

Massive multiple input and multiple output (massive MIMO) has attracted wide attention since it holds the promise of providing excellent spectral efficiency. One of the basic issues of single-cell multi-user massive MIMO system is how to design efficient scheduling methods under the quality-of-service (QoS) requirements. In this paper, we present a joint channel and queue aware scheduling method based on Lyapunov optimization theory. Specifically, the channel gain can be estimated by default according to the characteristic of channel hardening so that we can jointly optimize the user selection and power allocation. What remains to be done is only to measure the direction between the base station and the active users, which is in contrast to the conventional process. To this end, we formulate a cross-layer control problem, which is a mixed integer nonlinear programming problem. To obtain the optimal solutions to this problem efficiently, we present a two-layer iteration algorithm. The inner layer is a dynamic programming algorithm based on water-filling in cellars and the outer layer is a low-complexity one-dimensional search algorithm.

Edge caching has attracted great attention recently due to its potential for reducing the service delays and the peak rate demand, especially when the quality-of-service (QoS) and data rate requirements of mobile users are ever increasing. One of the key issues in edge caching is the storage efficiency. To achieve high storage efficiency, we present an edge caching policy with time domain buffer sharing. More particularly, our scheme allows a Base Station (BS) to determine whether and how long a content item should be cached at the buffer of the BS. To this end, we formulate a queue-theoretic model, in which the storage cost can be determined by the maximum caching time of content items via Little's Law. Based on this model, we present a probabilistic caching policy with random maximum caching time to strike the optimal tradeoff between the storage cost and the overall hit ratio of content items. For content items having different popularity, we further investigate how the storage resources should be allocated among these content items. An efficient two-layer iteration algorithm is presented to solve the storage allocation problem, which is a nonconvex optimization problem.

The Internet of Things (IoT) technology has recently drawn more attention due to its ability to achieve the interconnections of massive physic devices. However, how to provide a reliable power supply to energy-constrained devices and improve the energy efficiency in the wireless powered IoT (WP-IoT) is a twofold challenge. In this paper, we develop a novel wireless power transmission (WPT) system, where an unmanned aerial vehicle (UAV) equipped with radio frequency energy transmitter charges the IoT devices. A machine learning framework of echo state networks together with an improved k -means clustering algorithm is used to predict the energy consumption and cluster all the sensor nodes at the next period, thus automatically determining the charging strategy. The energy obtained from the UAV by WPT supports the IoT devices to communicate with each other. In order to improve the energy efficiency of the WP-IoT system, the interference mitigation problem is modeled as a mean field game, where an optimal power control policy is presented to adapt and analyze the large number of sensor nodes randomly deployed in WP-IoT. The numerical results verify that our proposed dynamic charging policy effectively reduces the data packet loss rate, and that the optimal power control policy greatly mitigates the interference, and improve the energy efficiency of the whole network.

In this paper, storage efficient caching based on time domain buffer sharing is considered. The caching policy allows a user's device to determine whether and how long it should cache a content item according to the prediction of the user's random request time, also referred to as the request delay information (RDI). In particular, the aim is to maximize the caching gain for communications while limiting its storage cost. To achieve this goal, a queueing theoretic model for caching with infinite buffers is first formulated, in which Little's law is adopted to obtain the tradeoff between the hit ratio and the average buffer consumption. When there are multiple content classes with different RDIs, the storage efficiency is further optimized by carefully allocating the storage cost. For more practical finite-buffer caching, a G/GI/L/0 queue model is formulated, in which a diffusion approximation and the Erlang-B formula are adopted to determine the buffer overflow probability and the corresponding hit ratio. The optimal hit ratio is shown to be limited by the demand probability and buffer size for large and small buffers respectively. In practice, a user may exploit probabilistic caching with random maximum caching time and arithmetic caching without any need for content arrival statistics to efficiently harvest content files from the air.

Mobile cloud computing systems, or simply mobile clouds, have attracted tremendous attention because they allow mobile devices with limited computational resources to offload complex computations. However, due to the channel uncertainty and the complexity of a computation task, mobile computation offloading may suffer from poor outage performance that the offloaded task cannot be completed within the desired delay constraint. Thus, how to efficiently identify and overcome the outage bottleneck, which could be used to optimize resource allocation schemes and improve the system performance effectively is an open problem. In this paper, we shall develop a unified framework that minimizes the overall outage probability in various mobile computation offloading scenarios. More specifically, the outage bottleneck is defined and identified by adopting asymptotic analysis, without any need of the accurate outage probabilities in both transmissions and computations. To overcome the outage bottleneck, resource pairing, matching, and allocation policies are investigated. Both theoretical analysis and numerical results show that the outage bottleneck relies on not only the availability of spectrum and computation resources but also the probability distributions of computation complexities of the computation tasks.

Proactive caching is recognized as a promising approach to handle the rapid growth of data traffic, thereby attracting much attention recently. As a key performance metric of caching, the hit ratio is determined by demand probabilities of users for content items and caching decisions. Because the recommendation system is capable of shaping user demands, the joint caching and recommendation holds the potential of improving the hit ratio substantially. In this paper, joint pushing and recommendation (JPR) schemes are presented for multiuser multiple-input single-output (MISO) systems, in which content items are pushed over MISO downlinks with multicast beamforming. Aiming at maximizing the effective throughput, we formulate a multi-stage stochastic programming problem under the constraints of transmit power and quality of experience (QoE). Since the formulated problem is intractable, suboptimal online JPR policies are presented based on the convex-concave procedure and branch-and-bound methods. Simulations show that presented JPR policies are capable of attaining significant effective throughput gains.

This paper is motivated by the observation that the average queueing delay can be decreased by sacrificing power efficiency in wireless communications. In this sense, we naturally wonder what the minimum queueing delay is when the available power is limited and how to achieve the minimum queueing delay. To answer these two questions in the scenario where randomly arriving packets are transmitted over multi-state wireless fading channel, a probabilistic cross-layer scheduling policy is proposed in this paper, and characterized by a constrained Markov decision process. Using the steady-state probability of the underlying Markov chain, we are able to derive the mathematical expressions of the concerned metrics, namely, the average queueing delay and the average power consumption. To describe the delay-power tradeoff, we formulate a non-linear programming problem, which, however, is very challenging to solve. By analyzing its structure, this optimization problem can be converted into an equivalent linear programming problem via variable substitution, which allows us to derive the optimal delay-power tradeoff as well as the optimal scheduling policy. The optimal scheduling policy turns out to be dual-threshold-based, which means transmission decisions should be made based on the optimal thresholds imposed on the queue length and the channel state.

Non-orthogonal multiple access (NOMA) has attracted great attention in the fifth-generation (5G) system to meet the rapidly increasing demand on quality of service (QoS). Among various QoS requirements, the urgent latency requirement has been expected to be provided for the delay-sensitive applications. In this paper, the delay-optimal uplink transmission in NOMA is studied over a block fading channel based on a cross-layer design, by which average latency is minimized with reliability provided by power allocation. In particular, the superposition coding in the physical layer and the scheduling in the network layer are jointly determined by the joint probabilities on the decisions of coding orders and transmission rates with the aware channel and queue states. With a constrained Markov decision process (CMDP), the cross-layer optimization is formulated to minimize the average delay subject to the constraints on power and reliability. The optimal delay-power tradeoff is obtained by formulating an equivalent linear programming (LP), which can be presented for multiple users based on a unified algorithm. Moreover, the optimal joint scheduling and superposition coding (JSSC) policy is constructed by using the structural properties. Based on the optimal cross-layer design, the transmission latency is optimized in the practical NOMA system.

Proactively pushing content to users has emerged as a promising approach to improve the spectrum usage in off-peak times for fifth-generation mobile networks. However, owing to the uncertainty of future user demands, base stations (BSs) may not receive payments for the pushed files. To motivate content pushing, providing economic incentives to BSs becomes essential. Based on request delay information (RDI) that characterizes the users' request time for content files, this paper studies the profit maximization for a BS and a spectrum provider (SP) by developing a Stackelberg game. Specifically, the SP sets different selling prices of bandwidth for pushing and on-demand services, while the BS responds with the optimal quantity to purchase. In the game with non-causal RDI, a sub-gradient algorithm is presented to achieve a Stackelberg equilibrium (SE). For the game with statistical RDI, a closed-form expression is derived for an SE in the single-user scenario and a simulated annealing-based algorithm is designed to obtain an SE in the multi-user scenario. It is shown that the proposed games achieve greater profit for both the SP and the BS, compared with the on-demand scheme. Furthermore, pricing with statistical RDI attains performance closely approaching that with non-causal RDI, while being more practical.

A typical application of unmanned aerial vehicles (UAVs) is surveillance of distant targets, where data collected by its sensors need to be transmitted back to a ground terminal (GT) for further processing in a timely manner. Due to the limited battery capability of the UAV, the sensed data could be preprocessed in a UAV to reduce the amount of data transmitted, which could potentially reduce the average power consumption at the UAV, especially when the transmission link quality is poor. In this paper, a probabilistic approach is adopted to schedule the transmission and computing of the data tasks based on the UAV and GT's buffer states. The joint transmission and computing problem can be modeled as a four-dimensional Markov chain, based on which the average delay of each task and the average power consumption at the UAV can be obtained. Our design goal is to minimize the average power consumption under the delay constraints. To do that, a delay-constrained power minimization problem is solved by an proposed method to obtain the power-optimal joint transmission and computation scheduling (JTCS) policy efficiently. Finally, the optimization results are validated with extensive simulations.

Proactively pushing content to users has emerged as a promising approach to improve the spectrum usage in off-peak times and reduce the peak data rate for fifth-generation (5G) mobile networks. However, owing to uncertainty of future user demands, base stations (BSs) may not receive payments for the pushed files. To motivate content pushing, economic incentives and user demand predictions become essential. Based on random content request delays of users, this paper studies the profit maximization for the BS and the spectrum provider (SP). Specifically, the SP sets different selling prices of bandwidth for pushing and on-demand services to enhance the spectrum usage and maximize its own profit. The BS, on the other hand, aims to maximize its profit by determining the amount of bandwidth purchased. The tension between the SP and the BS is formulated as a Stackelberg game. The single-user case is considered first to show the existence and uniqueness of a Stackelberg equilibrium (SE), and derive a closed-form expression for the SE. For the multiuser case, a distributed price updating algorithm is presented to obtain the SE. Numerical results show that the proposed game can achieve greater profit for both the SP and the BS, compared with the traditional on-demand system.

Joint pushing and caching (JPC) is a promising approach to achieve high energy efficiency in content delivery systems. However, when content files are delivered in Gaussian broadcast channel, the optimal JPC policy that minimizes the transmission energy is still unknown. This paper focuses on the optimal JPC policy design in Gaussian broadcast channel, where the base station is capable of pushing private and common messages simultaneously. By pushing common messages to cache- enabled users, the transmission energy can be reduced, because the asynchronous requests for the same content file can be served by a single transmission. By analyzing the structure of the optimal policy under the constraints of buffer sizes and users' requests, we find that the pushing rate of the optimal policy remains unchanged between consecutive time instants corresponding to request times and request deadlines. Based on the structure of optimal pushing rate, we present a finite- dimension convex optimization problem, whose solution can be used to construct an optimal JPC policy.

Proactive caching holds the promise of increasing network throughput by caching popular contents at mobile users. The cache hit ratio heavily relies on the demand probability of a user for a content. As a powerful tool to shape user demand, the recommendation system has been introduced into caching networks to further improve the cache hit ratio. However, the joint design of pushing and recommendation for users with time-varying connectivity is still unknown. In this paper, we study joint pushing and recommendation (JPR) policies that maximize the effective throughput in wireless networks with time-varying user connectivity. Two offline and one online JPR policies are presented based on noncausal, statistical, and causal connectivity information, respectively. It is shown that the causal feedback brings significant throughput gain, especially when the recommendation weight and the connection probability are relatively small.

Pro-active pushing is a promising emerging communication technology to improve the resource efficiency and quality of service provisioning in mobile networks. This paper characterizes users' requests with request delay information (RDI), and proposes to use the cache-hit ratio (CHR) as the metric of the system performance in pushing strategy design. Different from conventional instant on-demand services, the pro-active pushing system can merge different users' requests for the same file at different time instants. The base station can therefore push files more efficiently by employing multicasting technologies. However, it is revealed that if one of the users' channel condition is significantly poor, it is a better choice to ignore the user in the pushing system from the overall CHR performance perspective. In the two-user one-file scenario, we derive analytical expressions of the CHR to help the BS determine the optimal pushing rate, which is shown to be a two-value-selection. In particular, for two users with uniformly distributed RDIs, the decision space is specifically characterized through analysis and calculation.

Caching is a technique that can efficiently reduce the peak traffic by storing content files locally during off-peak times. Recommendation systems help enhance the cache hit ratio by recommending content files to users. In this paper, we propose a grouped recommendation-aided coded caching scheme, also referred to as GRACE, which achieves a good tradeoff between the peak rate and the average hit ratio. In GRACE, the base station (BS) recommends a set of content files to each user, which are carefully selected to maximize the average hit ratio. To reduce the peak rate, the users and the content files are partitioned into groups, increasing coded multicasting opportunities among users. To find the optimal grouping policy, a separable bilinear programming method is adopted. Theoretical and numerical results show that GRACE outperforms the conventional coded caching scheme and can effectively boost the avPerage hit ratio and reduce the peak rate.

Content caching in wireless networks provides a substantial opportunity to trade off low cost memory storage with energy consumption, yet finding the optimal causal policy with low computational complexity remains a challenge. This paper models the Joint Pushing and Caching (JPC) problem as a Markov Decision Process (MDP) and provides a solution to determine the optimal randomized policy. A novel approach to decouple the influence from buffer occupancy and user requests is proposed to turn the high-dimensional optimization problem into three low-dimensional ones. Furthermore, a non-iterative algorithm to solve one of the sub-problems is presented, exploiting a structural property we found as generalized monotonicity, and hence significantly reduces the computational complexity. The result attains close performance in comparison with theoretical bounds from non-causal policies, while benefiting from higher time efficiency than the unadapted MDP solution.

In this paper, we aim to obtain the optimal tradeoff among average delay, and average transmission and computation power consumptions in a mobile computation offloading system. A probabilistic approach is developed to jointly determine the transmission and computation rate in each time-slot. We model the queue lengths in the mobile device and computation resource with a two- dimensional Markov chain. Based on this model, we obtain the average delay and power consumption. Then, we formulate a joint queues aware optimization problem to minimize the average power consumption of the mobile device given constraints on average delay of tasks and average power consumption of the computation resource. By converting the problem into a linear programming, we obtain the optimal power-delay tradeoff and power-optimal Joint Transmission and Computing Scheduling (JTCS) strategy. Finally, the optimization results are validated by extensive simulations.

Massive multiple-input multiple-output (massive MIMO) and the millimeter wave (mmWave) communication are known to be among the key technologies for the fifth generation (5G) mobile networks. The implementation of massive MIMO with the mmWave architecture requires the utilization of hybrid precoding technique with a low dimensional digital precoder and a high dimensional analog precoder. In this paper, we investigate the hybrid precoding design problem for the case of cooperative multicell multiuser massive MIMO system. This problem is formulated with the consideration of the user and base station (BS) association problem, and we propose an iterative algorithm to solve the formulated problem. The precoding problem is solved using the Eigen precoding algorithm whereas the Lagrangian based approach is proposed for the solution of the association problem. Simulation results show that our proposed solution can achieve higher energy efficiency and convergence rate.

The application of drone small cells (DSCs) which are unmanned aerial vehicles (UAVs) carrying communication payload to complete the construction of the high-altitude base stations, is playing an increasingly important role for providing emergent wireless services in different scenarios. In order to coordinate interference and reduce huge backhaul overhead among static ultra-dense DSCs on the low-altitude platform, the paper studies a DSC cluster precoding network with massive multiple-input multiple-output (mMIMO). Considering the disadvantage of the energy-constrained unmanned aerial base station (UABS), we investigate the problem of designing precoding at cluster DSCs to minimize the transmission power of UABSs. A modified cluster scheme based on the Euclidean distance is adopted to cluster the DSCs. We eliminate the intra-cluster interference via performing the modified zero-forcing method and coordinate the inter-cluster interference to achieve our target of reducing transmit power. A non-cooperative game among the DSC clusters is formulated, and the existence and uniqueness of the Nash equilibrium of the proposed game are proved. The non-convex optimization problem is solved via the iterative methods and the numerical results show the effectiveness of our proposed scheme.

Polar codes can provably achieve the capacity of a symmetric binary discrete memoryless channel and have low encoding and decoding complexity. However, the error rate performance of polar codes decoding in short and moderate length is not very well, moreover, the encoding and decoding of polar codes with the conventional serial mode will lead to poor throughput. In this paper, we propose a hardware architecture of parallel encoding and decoding scheme for polar codes concatenation with LDPC, and take advantage of the parallelism of belief propagation (BP) decoding algorithm of the two codes to reduce the decoding delay. We compare the performance of concatenated scheme with polar codes and investigate the throughput implemented on graphic processing unit (GPU) for Gaussian channel. Experiment results show that the performance of the concatenated scheme outperform only polar codes, and the throughput of the proposed parallel architecture is obviously faster than that of the serial.

Pushing and caching are recognized as promising techniques to improve network capacity and handle explosive growth in data traffic. Caching schemes for synchronous requests have been extensively investigated, but little attention has been paid to the asynchronous case. However, asynchronism is an inherent property of user requests. In this paper, request delay information (RDI) is introduced to characterize the asynchronous user requests. Based on RDI, a coded joint pushing and caching (C-JPC) method is proposed to minimize the network traffic by jointly determining when and which data packets are to be pushed and whether they should be cached. Optimal offline and online C-JPC policies for noncausal and causal RDI can be obtained by solving optimization problems, which however are intractable. Fountain coded caching (FCC) and generalized coded caching (GCC) methods are further proposed to give sub-optimal C-JPC policies with low complexity. In addition, lower and upper bounds on the optimal traffic volume are presented. It is shown that FCC and GCC achieve optimal or near-optimal volumes of traffic in some special cases. Simulation results demonstrate that C-JPC brings significant multicasting gains.

We characterize delay minimal power scheduling policies in energy harvesting communication systems. We consider a continuous-time system, where the delay experienced by each bit is given by the time spent by the bit in the queue waiting to be transmitted to its receiver. We first consider a single-user channel, where the transmitter has a finite-sized battery to save its harvested energy. Data arrives during the course of communication and are saved in a finite data buffer as well. We find the optimal power policy that minimizes the average delay experienced by the bits subject to energy and data causality constraints. We characterize the optimal solution in terms of Lagrange multipliers, and calculate their values in a recursive manner. We show that, different from the existing literature, the optimum transmission power is not constant between the energy and data arrival events; the transmission power starts high, decreases linearly, and potentially reaches zero between energy and data arrivals. Intuitively, untransmitted bits experience cumulative delay due to the bits to be transmitted ahead of them, and hence the reason for transmission power starting high and decreasing over time. Next, we study a multiuser version of this problem, namely, a two-user broadcast channel, and characterize the optimal transmission policies that minimize the sum delay. For this setting, we consider the case, where the transmitter has an infinite-sized battery, and that all data packets intended for the receivers are available at the beginning of the communication session. We characterize the optimal solution in terms of Lagrange multipliers, and present an iterative solution that calculates their values. Our results show that in the optimal policy, both users may not be served simultaneously all the time; there may be times, where only one of the two users is served alone. We also show that the optimal policy may have gaps in transmission in between energy arrivals, where none of the users is served, echoing the results of the single-user setting.

Proactively pushing content to users has emerged as a promising technology to cope with the explosively growing traffic demands of next-generation mobile networks. Yet, it is unclear whether content pushing can improve the energy efficiency (EE) of delay-constrained communications. With pushing, the energy consumption can be reduced by extending transmission time. However, if the user never needs the pushed content, pushing may incur wasted energy. Based on the random content request delay, this paper studies the maximization of EE subject to a hard delay constraint in an additive white Gaussian noise channel with pushing. In the scenarios of fixed and variable transmit powers, we propose transmission policies to allocate power based on difference of convex functions programming and dynamic programming. Moreover, the lower and upper bounds on EE are derived, and the user request probability thresholds are provided to determine whether or not to push a file for these two cases. It is shown that the EE of systems with pushing can be significantly improved with increasing content request probability and target transmission rate, compared with the on-demand scheme. Furthermore, pushing with variable power is found to bring extra EE gain at the expense of computational complexity.

Multicasting can reduce network traffic in multiuser systems by serving multiple users simultaneously. The benefits of multicasting basically come from the overlapping and synchronism of user requests. Pushing and caching are techniques that prestore content items in buffers closer to users based on the prediction of user requests, thereby providing a promising approach to eliminating the asynchronism of user requests and creating multicasting opportunities. This paper studies a multiuser wireless communication system, in which the buffers for caching are deployed in the user terminals. Based on the request delay information (RDI) which describes when the users request content items in a deterministic or statistical way, a joint pushing and caching (JPC) method is presented to schedule the content items pushed by the base station and cached in the user buffers. In this paper, multicasting JPC systems work in two modes, without or with the feedback of RDI. Furthermore, RDI is classified into three forms, namely, noncausal, statistical, and causal. Static and dynamic JPC policies are, respectively, proposed for the two work modes. In addition, the effective throughput resulting from JPC under the three forms of RDI is demonstrated via theoretical analysis and simulation.

Successive relaying holds the promise of recovering the multiplexing loss due to the half-duplex constraint, by allowing the source and relays to transmit messages simultaneously, but it may cause severe inter-relay interference (IRI). To overcome this, we apply an amplify-and-cancel method proposed in our previous work by Chen et al. into the successive amplify-and-forward (AF) relay model, thereby conceiving successive AF relaying with network interference cancellation. The proposed protocol benefits from low complexity because IRI is mitigated by linear analog processing without decoding any inter-relay signals. More specifically, a relay keeps receiving signals from the source and other relays before its own transmission, in order to obtain the prior knowledge of IRI that can be used to mitigate the IRI. We present an iterative expression of the power of the residual interference, based on which the achievable rate and the outage probability of the proposed scheme are derived. We further minimize its outage probability by optimizing the order of relays and present the optimal diversity and multiplexing tradeoff.

Group sparse beamforming is a general framework to minimize the network power consumption for cloud radio access networks, which, however, suffers high computational complexity. In particular, a complex optimization problem needs to be solved to obtain the remote radio head (RRH) ordering criterion in each transmission block, which will help to determine the active RRHs and the associated fronthaul links. In this paper, we propose innovative approaches to reduce the complexity of this key step in group sparse beamforming. Specifically, we first develop a smoothed ℓp-minimization approach with the iterative reweighted-ℓ2algorithm to return a Karush-Kuhn- Tucker (KKT) point solution, as well as enhance the capability of inducing group sparsity in the beamforming vectors. By leveraging the Lagrangian duality theory, we obtain closedform solutions at each iteration to reduce the computational complexity. The well-structured solutions provide opportunities to apply the large-dimensional random matrix theory to derive deterministic approximations for the RRH ordering criterion. Such an approach helps to guide the RRH selection only based on the statistical channel state information, which does not require frequent update, thereby significantly reducing the computation overhead. Simulation results shall demonstrate the performance gains of the proposed ℓp-minimization approach, as well as the effectiveness of the large system analysis-based framework for computing the RRH ordering criterion.

The communication-storage tradeoff, as a key performance metric of the fundamental limits of caching, has attracted considerable recent attention. In this paper, the issue of how much storage cost should be paid for a target effective throughput is investigated in a unified framework. This approach, from a queueing theoretic perspective, adopts Little's law to analyze the average buffer consumption, thereby giving a rate- cost function that relies only on the probability of content request delays. A time sharing policy along with its optimality criterion is further proposed to achieve the optimal storage efficiency. For pushing flows with heterogenous request delay information, a joint cost-rate allocation method is presented to maximize the overall storage efficiency in either a centralized or decentralized manner. Both analytical and numerical results reveal that the storage efficiency of caching is dominated by the demand probability and the maximum request delay.

In this paper, a novel resource allocation scheme based on discrete Cournot-Nash equilibria and optimal transport theory is proposed. The originality of this framework lies in the joint optimization of downlink bandwidth allocation and cooperation between base stations. A tractable formalization is given in the form of a quadratic optimization problem. A low complexity approximate solution is derived and theoretically characterized. Simulations highlight the existence of an optimal working point, that maximizes user satisfaction ratio and network load. The impact of the network deployment on the optimum is numerically investigated, thanks to the β-Ginibre model. Indeed, base stations are assumed to be drawn according to β-Ginibre point processes. Numerical analysis shows that the network performance increases with β going to one.

Wireless cloud computing system with computation offloading has attracted much attention due to the potential of alleviating the restrictions of limited resources in mobile devices. During the computation offloading, the steps of transmissions and computation need to be completed within the required time, otherwise the system will be in outage. Due to uncertainties of channel fading and computational complexities, such outage is the result of either transmission outage or computation outage. In this paper, a theoretical framework is proposed to analyze the outage of the wireless cloud computing system with multiple subcarriers and computation resources. Through theoretical analysis of the outage probability, the outage bottleneck can be identified. Numerical results have verified the theoretical analysis and concluded that the outage bottleneck depends not only on the distributions of complexities of tasks but also on the numbers of subcarriers and computation resources.

In this paper, we study a multi-pair bidirectional (or two-way) full-duplex (FD) massive MIMO relay (FDMMR) system, where the relay employs massive antennas and is operated in the amplify-and-forward (AF) mode. We analyze its spectral efficiency (SE) performance, when both imperfect channel estimation and channel aging effect are considered. We propose four power-scaling schemes based on the zero-forcing reception/zero-forcing transmission (ZFR/ZFT) relaying processing. Assuming that the number of relay antennas approaches infinity, the SE is analyzed in the context of the proposed power scaling schemes. Our analytical results show that the inter-pair interference caused by the other user pairs as well as the self-interference can be completely eliminated by the ZFR/ZFT processing at the relay. The self-loop interference and the inter- user interference can also be cancelled, if a power scaling scheme is carefully selected. Furthermore, our studies show that the channel aging may significantly degrade the SE of the system.

Caching is a promising technique to reduce traffic by storing popular content items in the buffers of users prior to user demand. In this paper, a two-phase physical layer multicasting system is investigated, in which the users are equipped with buffers and content request delay information (RDI) is available at the serving base station (BS). Based on RDI, transmissions are initiated to satisfy user requests within delay constraints. To minimize the expected traffic, a joint pushing and caching (JPC) method is presented to jointly determine the content items transmitted by the BS and cached in the user buffers. It is shown that minimizing the expected traffic can be transformed into a network coding problem. A framework is presented to formulate the JPC policies via optimization problems, which however are intractable. To find the optimal uncoded caching policy, the optimization problems are reduced to linear programs. In addition, a generalized coded caching algorithm is proposed to give a sub- optimal coded JPC policy. A lower bound on the expected traffic is also analyzed. Simulations show that the traffic resulting from the proposed algorithms approaches the lower bound if the buffer size is large.

In this paper, we study delay optimal scheduling of bursty data traffics over multi-state time-varying wireless channels, where bursty packet arrival in the network layer, queueing behavior in the data link layer, and rate adaptive transmission with flexible modulation in the physical layer are jointly considered from a cross-layer perspective. To achieve a minimum queueing delay under a power constraint, a probabilistic queue-aware and channel- aware cross-layer scheduling policy is proposed, and characterized by a Markov chain model, where the transmission rate, i.e., the number of packets delivered in each slot, is selected with probabilities based on the buffer and channel states in this slot. To reveal the optimal delay-power tradeoff, we formulate a non-linear optimization problem, which, however, is very challenging to solve. To make it tractable, we convert the optimization problem equivalently into a Linear Programming (LP) problem, which helps us achieve the optimal three-dimensional threshold-based scheduling policy analytically. It is found that the source should select one transmission rate jointly based on the channel state and the backlog in the queue.

As an emergency requirement of Quality of Service (QoS), low latency can be efficiently provided by a cross-layer scheduling. In particular, we consider a pair of Non-Orthogonal Multiple Access (NOMA) users with a random packet arrival over a block fading channel. A probabilistic cross-layer approach is considered to jointly determine the scheduling and superposition coding process, based on the buffer and channel state information. The joint scheduling and coding issue can be formulated as a two-dimension Markov chain, the state of which is a pair of queue lengths, based on which the average delay and power consumption can be obtained. A joint queue and channel aware optimization is formulated to minimize the average delay of packets given an average transmission power constraint. By converting the optimal problem into a linear programming, the optimal delay-power tradeoff can be obtained. We also discover that the optimal policy can be decomposed into a stationary superposition coding and a threshold-based scheduling policy.

In this paper, we aim to obtain the optimal delay-power tradeoff and the corresponding optimal scheduling policy for arbitrary i.i.d. arrival process and adaptive transmissions. The number of backlogged packets at the transmitter is known to a scheduler, who has to determine how many backlogged packets to transmit during each time slot. The power consumption is assumed to be convex in transmission rates. Hence, if the scheduler transmits faster, the delay will be reduced but with higher power consumption. To obtain the optimal delay-power tradeoff and the corresponding optimal policy, we model the problem as a Constrained Markov Decision Process (CMDP), where we minimize the average delay given an average power constraint. By steady-state analysis and Lagrangian relaxation, we can show that the optimal tradeoff curve is decreasing, convex, and piecewise linear, and the optimal policy is threshold-based. Based on the revealed properties of the optimal policy, we develop an algorithm to efficiently obtain the optimal tradeoff curve and the optimal policy. The complexity of our proposed algorithm is much lower than a general algorithm based on Linear Programming. We validate the derived results and the proposed algorithm through Linear Programming and simulations.

Proactive pushing and caching has recently emerged as a promising technology to improve the quality of service in mobile networks. Given the fact that users' demand for content is largely driven by social networking, this paper combines the analysis of social networking with proactive pushing and caching by constructing a human-in-the-loop system model with a physical multicasting transmission. By taking social network structure, prediction and joint pushing and caching (JPC) into account, a closed-loop system is obtained, in which the input consists of arrivals of new content items, the control procedure is based on prediction and JPC, and the output is the cache-hit ratio (CHR). A prediction and JPC based adjustment algorithm is proposed to maximize the CHR of this system. It is shown that the prediction window, which refers to how far in the future predictions are made, and the prediction error have a significant impact on the performance of the system.

In order to achieve high spectral efficiency, considerable attention has been paid to full-duplex (FD) and non-orthogonal multiple access (NOMA) technologies in the past decades. When FD users and half-duplex (HD) users access to the base station simultaneously, however, the rate region is still unknown. In this paper, we investigate hybrid NOMA (H-NOMA) systems, where a multi-antenna FD base station serves both a FD user and a HD uplink user over the same frequency band. The base station adopts successive interference cancellation (SIC) to decode the uplink signals, while the FD user is capable of adopting SIC to decode and cancel the inter-user interference. Given fixed transmission power, the achievable rate region of the H-NOMA system is the convex hull of the union of the rate regions of two alternative schemes with and without SIC at the FD user. Specifically, we derive the explicit expression of the rate region and demonstrate the potential of the proposed switching methods via numerical results.

In this paper, we propose a sparse and low-rank optimization approach for network topology control in the partially connected fog radio access network (Fog-RAN). In this model, the sparsity of the modeling matrix represents the number of non-connected interference links, while the rank of the matrix models the achievable symmetric degrees-of-freedom (DoF) allocations. This model helps find the network topologies with the maximum number of allowed connected interference links. However, the sparse and low-rank optimization problem turns out to be highly intractable due to the non-convex sparsity objective and non- convex fixed-rank rank constraint. To address the coupled challenges in the objective and constraint, we propose a smoothed Riemannian optimization framework by exploiting the quotient manifold geometry of fixed-rank matrices, followed by a smoothed sparsity inducing surrogate. The proposed Rie- mannian algorithm has much lower computational cost compared with state-of-art matrix factorization parameterized methods. Simulation results further demonstrate the appealing sparsity and low-rankness tradeoff in the proposed model, thereby guiding the network deployment in dense Fog-RAN.

Spectrum database plays an increasingly important role in spectrum measurements and monitoring, which lays foundations for accurate and real-time spectrum sensing in future cognitive radio networks. A successful identification of the channel propagation model is of great importance to construct spectrum database so as to give an accurate picture of spectrum use in real-world environments. In this paper, based on the principle behind the voting-based online sequential extreme learning machine (VOS-ELM) and the Spark cloud computing platform, a novel parallel VOS-ELM (PVOS-ELM) algorithm will be proposed and implemented for real-time channel model identification in real-world propagation environment. The power measurement, kurtosis and skewness etc. will be used as features which are extracted from the received signal. Furthermore, the novel data parallel and task parallel processing schemes will be proposed to improve the computation efficiency of the proposed algorithm on Spark cloud computing platform. Extensive simulations and experiments with real-world data samples will be carried out. The experimental results illustrate that the proposed Spark based PVOS-ELM algorithm enjoys a significant accuracy performance improvement in channel identification and a much higher computation efficiency.

Sparse code multiple access (SCMA) is being considered as a promising multiple access solution for 5G systems. A distinguishing feature of SCMA is that it combines the procedures of bit to constellation symbol mapping and subsequent spreading using multidimensional codebooks differentiated by users. Such codebooks dominate the system implementation as a main source of not only performance gain but also design complexity. This letter presents a joint constellation with mapping matrix design for SCMA codebooks, which formulates the constellations optimization as a nonconvex quadratically constrained quadratic programming problem based on a set of well-constructed mapping matrices. We elaborately solve the problem to achieve outperformance over existing SCMA design in terms of bit error rate (BER). For improving practicality, an approximate approach is further proposed to reduce the complexity significantly with a limited BER loss.

With the tremendous development of data science, using unstructured documents to analyze marketing dynamics is attracting a great deal of attention. In this letter, we propose an iterative scheme to extract the new words, which is often a bottleneck for Chinese natural language processing (NLP) in financial markets analysis. In contrast to existing static features, the key novelty is the proposed dynamic features that characterize the similarity of context patterns. Via iteration, distinguishable seed context patterns are extracted. Tested on a 203 MB corpus, 19 291 words representing emerging industries, entities, projects, and products were extracted with a precision of 89.8% and recall of 88.9%, which outperforms most competitor methods.

In massive network data analysis, especially online social network analysis, the complete network dataset is often difficult to obtain due to the huge cost in collecting and storing data. In order to recover missing data from sampled networks, we consider the network completion problem, which has attracted much attention from both academia and industry. In this letter, the network completion problem of sparse networks is solved by a proposed discrete-constrained nuclear-norm minimization (DNM) method. It is based on the sparsity of the number of nonzero elements and singular values, which leads to an optimization problem. Since the problem is NP-hard, relaxation and adjustment are applied to make it convex. The DNM method can be applied in many practical networks, as many real-world complex networks are sparse. The simulation results on real-world online social networks and artificially generated random networks indicate that the proposed DNM method outperforms many existing network completion methods.

In this paper, we aim to obtain the optimal tradeoff between the average delay and the average power consumption in a communication system. In our system, the arrivals occur at each timeslot according to a Bernoulli arrival process, and are buffered at the transmitter waiting to be scheduled. We consider a finite buffer and allow the scheduling decision to depend on the buffer occupancy. In order to capture the realism in communication systems, the transmission power is assumed to be an increasing and convex function of the number of packets transmitted in each timeslot. This problem is modeled as a constrained Markov decision process (CMDP). We first prove that the optimal policy of the Lagrangian relaxation of the CMDP is deterministic and threshold-based. We then show that the optimal delay-power tradeoff curve is convex and piecewise linear, and the optimal policies of the original problem are also threshold-based. Based on the results, we propose an algorithm to obtain the optimal policy and the optimal tradeoff curve. We also show that the proposed algorithm is much more efficient than using general methods. The theoretical results and the algorithm are validated by linear programming and simulations.

Pushing along with proactive caching, in which content items are transmitted prior to being requested by a user, holds the promise of trading scalable storage resources for substantial throughput increase in content-centric networks. In this paper, a joint pushing and caching (JPC) method is studied, which determines when and which content to be pushed to, and to be removed from, the receiver buffer based on content request delay information (RDI) that predicts a user's request time for certain content items. Both offline and online JPC policies with noncausal, statistical, and causal RDI are proposed based on a greedy algorithm and dynamic programming. The effective throughputs of the policies are evaluated, and they are seen to increase with both the receiver buffer size and the pushing limited channel capacity. The analysis also reveals the fundamental tradeoff between communication and storage resources. Furthermore, RDI feedback is shown to significantly enhance the performance of the online JPC without incurring much signaling overhead or complexity, especially in small buffer scenarios.

5G cellular network may have the features of smaller cell size, much denser resource deployment and almost random geometric pattern, resulted from diminishing spectrum resource and rapidly increasing diversified communication demands. The random small-cell network results in much more complicated interference scenarios, which cannot be formulated as the well-accepted hexagonal grid model. Therefore, how to model the interference pattern, and how to reuse the scarce spectrum resource to achieve the optimal performance for 5G cellular networks have attracted much attention from both academia and industry. In this paper, a brand new approach, referred to as the matrix graph, is proposed. This approach is robust to interference and random topology. Based on the derived properties of the matrix graph, an asymptotic optimal algorithm with low complexity is obtained to address the spectrum allocation problem with interference constraints, which is known as an NP-hard problem. The proposed algorithm yields a fundamental tradeoff between spectrum reuse ratio and computational complexity. Simulation results also support the theoretical performance gains. As a result, the proposed matrix graph approach is specifically useful for characterizing the next-generation cellular networks.

In this paper, we study the delay optimal scheduling policy for a multi-state wireless fading channel, by taking bursty packet arrivals and automatic repeat request-based packet transmission into account. In our system, the average delay each packet experiences includes the time it waits in the queue and the time it may take to retransmit due to packet delivery failure. To reduce the average delay, we propose a joint channel aware and queue-aware stochastic scheduling policy to determine whether and with which probability the source should transmit based on channel and buffer states, subject to an average power constraint at the transmitter. To find the optimal scheduling probabilities, we formulate a non-linear power-constrained delay minimization problem with the aid of controlled Markov decision processes. The optimization problem is then converted into an equivalent linear programming problem by introducing new variables from the steady-state probabilities of the underlying Markov chain and transmission probabilities. By analyzing its property, we derive the structure of the optimal solution, and exploit it to obtain the optimal probabilities analytically. It is found that the optimal scheduling policy has a double threshold structure, and can significantly reduce the average delay.

Topological interference management (TIM) provides a promising way to manage interference only based on the network connectivity information. Previous works on the TIM problem mainly focus on using the index coding approach and graph theory to establish conditions of network topologies to achieve the feasibility of topological interference management. In this paper, we propose a novel user admission control approach via sparse and low-rank optimization to maximize the number of admitted users for achieving the feasibility of topological interference management. However, the resulting sparse and low-rank optimization problem is non-convex and highly intractable, for which the conventional convex relaxation approaches are inapplicable, e.g., a simple ℓ1-norm relaxation approach yields the objective unbounded and non-convex. To assist efficient algorithms design for the formulated rank-constrained (i.e., degrees-of-freedom (DoFs) allocation) ℓ0-norm maximization (i.e., user capacity maximization) problem, we propose a novel non-convex but smoothed ℓ1-regularized minimization approach to induce sparsity pattern with bounded objective values. We further develop a Riemannian trust-region algorithm to solve the resulting rank-constrained smooth non-convex optimization problem via exploiting the quotient manifold of fixed-rank matrices. Simulation results demonstrate the effectiveness and optimality of the proposed Riemannian algorithm to maximize the number of admitted users for topological interference management.

For content-centric wireless networks, pushing and proactive caching hold the promise of greatly improving system throughput in the future. However, the throughput gains obtained from these techniques are limited by the energy of the base station and the buffer size of the receivers. To make full use of this energy and these buffers, this paper presents a Joint Pushing and Caching (JPC) method for physical layer multicasting systems, where the request delay information (RDI) is exploited in noncausal, statistical, and causal form. In JPC, the content items pushed by the base station and removed from the buffers of receivers in each timeslot are jointly determined. With noncausal or statistical RDI, an offline policy is presented by formulating and solving an optimization problem in order to maximize the effective throughput. With causal RDI, an online policy is presented to further enhance the effective throughput with little signalling overhead. Causal feedback of RDI is found to bring a substantial throughput gain.

Cross-layer design is a promising way to improve Quality of Services (QoS) by making use of the state information from different layers. In this paper, transmissions with random data arrival over fading channels are investigated. Probabilistic scheduling and adaptive modulation aware of both the queue state and the channel state are applied, hence we can formulate a Markov Decision Process. Based on its inherent Markov Reward Process, the average delay and power consumption are analysed and expressed by the steady-state probability distribution. We minimize the average delay given an average power constraint, so that by varying the power constraint, the optimal delay-power tradeoff curve is obtained. It is discovered that the optimization can be transformed into a linear programming. We further study the properties of the optimal scheduling policy and discover that it is threshold-based. These results are validated by numerical and Monte-Carlo simulations.

Pushing and caching hold the promise of significantly increasing the throughput of content-centric wireless networks. However, the throughput gain of these techniques is limited by the buffer size of the receiver. To overcome this, this paper presents a Joint Pushing and Caching (JPC) method that jointly determines the contents to be pushed to, and to be removed from, the receiver buffer in each timeslot. An offline and two online JPC policies are proposed respectively based on noncausal, statistical, and causal content Request Delay Information (RDI), which predicts a user's request time for certain content. It is shown that the effective throughput of JPC is increased with the receiver buffer size and the pushing channel capacity. Furthermore, the causal feedback of user requests is found to greatly enhance the performance of online JPC without inducing much signalling overhead in practice.

The cloud radio access network (Cloud-RAN) based mobile cloud computing (MCC) system offers a promising solution to offload computation-intensive tasks from battery and computation capability limited mobile devices to a cloud service provider via wireless transmission, thereby reducing the energy consumption and latency for mobile devices. However, the extra energy and latency caused by wireless transmission for offloading may offset the gains of computation offloading. In this paper, we focus on minimizing the network energy consumption while satisfying the delay requirements by jointly optimizing the communication resources (i.e., uplink and downlink beamforming design), computation resources (i.e., computation capability) and offloading decision (i.e., offload or not offload). Unfortunately, this problem turns out to be an NP-hard non-convex mixed integer non-linear programming (MINLP) problem. To resolve this challenge, the principle of uplink and downlink duality is exploited to aid efficient beamforming design. Furthermore, an effective iterative algorithm with polynomial time complexity is proposed in a holistic way to guide the offloading decision. Simulation results will demonstrate that the proposed iterative algorithm achieves near-optimal energy efficiency in Cloud-RAN based mobile cloud computing system.

We consider a two-user energy harvesting broadcast channel, and characterize the delay minimal transmission policies that minimize the total delay experienced by the data packets in the system. We consider a continuous time system where the delay experienced by each bit is given by the time spent by the bit in the queue waiting to be transmitted to its receiver. We consider the case where all data packets are available at the transmitter at the beginning of the communication session. We characterize the optimal solution in terms of the Lagrange multipliers, and present an iterative algorithm that optimally calculates their values. Our results show that in the optimal policy, both users may not be served simultaneously all the time; there may be times where only the strong user or only the weak user is served alone. We also show that the optimal policy may have gaps in transmission where none of the users is served until the next energy arrival.

Cooperative communication is a promising technique, which can greatly improve the performance and extend the coverage of cellular networks. In this paper, the resource allocation problem is investigated for multi-carrier amplify-and-forward (AF) relay assisted multi-access networks with multiple users and subcarriers. The joint relay selection and subcarrier allocation problem is formulated as a combinatorial optimization problem so as to minimize the outage probability with fairness assurance. To solve this problem, we propose a constraint maximum H-matching approach, which is based on a correlated random bipartite graph (RBG) formulation. By analyzing the properties of H-matching method on correlated RBG, the tight approximation of outage probability will be obtained in closed-form formulas. By deriving the diversity-multiplexing tradeoff, it is shown that the proposed approach achieves the same frequency and cooperative diversity as if the network has only one user, while all of the users fairly share the multiplexing gain at the same time. The proposed H-matching method also enjoys a sublinear computation complexity for parallel implementations. Numerical results verified the theoretical derivations and optimality of the proposed approach.

In this paper polar codes are proposed for two receiver broadcast channels with receiver message side information (BCSI) and noncausal state available at the encoder, referred to as BCSI with noncausal state for short, where the two receivers know a priori the private messages intended for each other. An achievable rate region for BCSI with noncausal state is established and shown to strictly contain the straightforward extension of the Gelfand-Pinsker result. To achieve the established rate region, we present polar codes for the general Gelfand-Pinsker problem, which adopts chaining construction and utilizes causal information to pre-transmit the frozen bits. It is also shown that causal information is necessary to pre-transmit the frozen bits. Based on the result of Gelfand-Pinsker problem, we then propose polar codes for BCSI with noncausal state. The difficulty is that there are multiple chains sharing common information bit indices. To avoid value assignment conflicts, a nontrivial polarization alignment scheme is presented. It is shown that the proposed region is tight for degraded BCSI with noncausal state.

In this paper, we investigate power allocation for simultaneous wireless information-and-energy multicast in cognitive OFDM systems. Our objective is to maximize the energy efficiency (EE) subject to the maximum power constraint at cognitive base station (CBS), maximum receiver interference constraint at each primary user (PU) and minimum harvested energy constraint at each energy receiver (ER). Due to the non-convexity of objective function, fractional programming is adopted to transform the nonconvex problem to a convex one. However, the complexity of the traditional optimization method, i.e., interior point method, is still too high to solve the transformed problem. To this end, a bisection-search-based suboptimal algorithm is proposed. Simulation results show that the proposed algorithm can greatly reduce the complexity (up to 1/12 at most) at the cost of tiny performance loss (less than 2%) compared with traditional convex optimization algorithms.

As a promising technique, cooperative relaying has attracted more and more attention from academia and industry recently. In this paper, we investigate the scheme of Decode-and-Forward Two-way Relaying (DF-TWR) relying on a cross-layer design, which combines adaptive Network-coded Modulation (NCM) at the physical layer and truncated Automatic Repeat reQuest (ARQ) at the data link layer. The relay node utilizes Network-Coded Quadrature amplitude modulation (NC-QAM) where NCM imposes only a modest signal-to-noise ratio (SNR) degradation on the single-link QAM performance. Additionally, we derive the achievable spectral efficiency in closed-form for transmission over Rayleigh fading channels. It is shown that this combination of adaptive NC-QAM and truncated ARQ substantially improves the system's throughput compared to the schemes operating without ARQ.

Renewable energy powered cognitive radio (CR) network has gained much attention due to its combination of the CR's spectrum efficiency and the renewable energy's “green” nature. In the paper, we investigate the delay-optimal data transmission in the renewable energy aided CR networks. Specifically, a primary user (PU) and a secondary user (SU) share the same frequency in an area. The SU's interference to the PU is controlled by interference-signal-ratio (ISR) constraint, which means that the ISR at the PU receiver (Rx) should be less than a threshold. Under this constraint, the renewable energy powered SU aims to minimize the average data buffer delay by scheduling the renewable allocations in each slot. A constrained stochastic optimization problem is formulated when the randomness of the renewable arrival, the uncertainty of the SU's data generation, and the variability of the fading channel are taken into account. By analyzing the formulated problem, we propose two practical algorithms that is optimal for two special scenarios. And the two algorithms respectively give an upper and a lower bound for the general scenario. In addition, the availability of the PU's private information at the SU is discussed. Finally, numerical simulations verify the effectiveness of the proposed algorithm.

The cloud radio access network (Cloud-RAN) has recently been proposed as one of the cost-effective and energy-efficient techniques for 5G wireless networks. By moving the signal processing functionality to a single baseband unit (BBU) pool, centralized signal processing and resource allocation are enabled in cloud-RAN, thereby providing the promise of improving the energy efficiency via effective network adaptation and interference management. In this paper, we propose a holistic sparse optimization framework to design green cloud-RAN by taking into consideration the power consumption of the fronthaul links, multicast services, as well as user admission control. Specifically, we first identify the sparsity structures in the solutions of both the network power minimization and user admission control problems, which call for adaptive remote radio head (RRH) selection and user admission. However, finding the optimal sparsity structures turns out to be NP-hard, with the coupled challenges of the ℓ0-norm-based objective functions and the nonconvex quadratic QoS constraints due to multicast beamforming. In contrast to the previous works on convex but nonsmooth sparsity inducing approaches, e.g., the group sparse beamforming algorithm based on the mixed ℓ1/ℓ2-norm relaxation, we adopt the nonconvex but smoothed ℓp-minimization (02algorithm is developed, which will converge to a Karush-Kuhn-Tucker (KKT) point of the relaxed smoothed ℓp-minimization problem from the SDR technique. We illustrate the effectiveness of the proposed algorithms with extensive simulations for network power minimization and user admission control in multicast cloud-RAN.

Distributed storage that leverages cellular device-to-device (D2D) underlay has attracted rising research interest due to its potential to offload cellular traffic, improve spectral efficiency and energy efficiency, and reduce transmission delay. This paper investigates the overall transmission cost minimization problem based on a content encoding strategy to download a new content item or repair a lost content item in D2D-based distributed storage systems while guaranteeing users' quality of service. In addition to the optimization of the coding parameters, the cost minimization problem also considers the distribution of content items, the selection of content helpers for each content requester, and the spectrum reuse for establishing D2D links in between. Formulating a hypergraph-based three-dimensional matching problem among content helpers, requesters, and cellular user resources, we present a local search based algorithm with low complexity for optimization. Numerical results demonstrate the performance and the effectiveness of our proposed approach.

In this paper, energy-efficient secure communications via untrusted two-way relaying are investigated considering physical layer security to prevent the relays from intercepting the confidential information of users. The performance metric of secure energy efficiency (EE), defined as the ratio of the secrecy sum rate to the total power consumption, is maximized by jointly optimizing power allocation for all nodes, with the constraints of the maximum allowed power and the minimum target secrecy rate. To deal with this intractable nonconvex optimization problem, some optimization methods termed as fractional programming, alternate optimization, penalty function method, and difference of convex functions programming, are jointly applied to address a solution scheme with comparatively lower complexity. With these above-mentioned optimization methods, the primal problem is transformed into simple subproblems hierarchically so as to adopt the corresponding optimization algorithm. By simulations, the achievable secure EE, the secrecy sum rate and the total transmission power of the proposed scheme are compared with those of secrecy sum rate maximization. It is demonstrated that the proposed scheme can improve secure EE remarkably yet at the cost of secrecy sum rate loss. This fact also reveals the inherent tradeoff between energy and security.

Device-to-device (D2D) communication has great potential to improve spectrum efficiency and offload traffic for cellular networks. In this paper, we focus on a multiflow two-way D2D network with decode-and-forward (DF) relays, coexisting with OFDMA cellular network. The spectrum sharing and relay selection are considered to minimize the outage probability of the device in D2D networks. The induced problem is a complicated probabilistic integral programming. A novel weighted random bipartite graph (WRBG)-based minimum weight maximum matching (MWMM) approach will be proposed in this paper. To offload not only the traffic but also the signaling and computation overhead, the improved min-sum algorithm will be applied to find the MWMM in the distributed manner with only polynomial complexity. The proposed approach enjoys an advantage that the close-form approximation formulas for optimal outage probability and diversity-multiplexing tradeoff can be derived by analyzing the properties of MWMM on WRBG. Both the theoretical derivations and simulation results will illustrate that the proposed approach for multiflow two-way D2D networks achieves the same performance as single-flow two-way D2D systems. Therefore, the distributed WRBG matching approach yields not only a practical distributed algorithm, but also a simple and elegant theoretical framework for multiflow two-way D2D networks.

For transmitting data in a secret and energy-efficient manner in collaborative amplify-and-forward relay networks, the secure energy efficiency (EE) defined as the secret bits transferred with unit energy is maximized to satisfy each node power constraint and target secrecy rate requirement, based on physical security framework. The secure EE is maximized by joint source and relay power allocation, which is a nonconvex optimization problem. To cope with this difficulty, a solution scheme and corresponding algorithms are developed by jointly applying fractional programming, exact penalty, alternate search, and difference of convex functions programming. The key idea of the scheme is to convert the primal problem into simple subproblems step by step, such that related methods are adopted. It is verified that, compared with secrecy rate maximization, the proposed scheme improves the secure EE significantly yet with a certain loss of the secrecy rate due to the tradeoff between secure EE and secrecy rate. Furthermore, the proposed scheme achieves higher secure EE and secrecy rate than total transmission power minimization does, while with a certain increase of power consumption. These results indicate that a reasonable balance among secure EE, secrecy rate, and power consumption can be reached by the proposed scheme.

Communication by wireless portable devices usually has a low energy efficiency and suffers from channel fading. Because of these limitations, assuring Quality of Service(QoS) such as low average transmission delay and packet loss rate under a given energy constraint is becoming an important problem. One efficient solution is considered as cross-layer scheduling which aims to improve the overall performance by combining various layers. With the awareness of random packet arrival in the network layer and channel state in the physic layer, a probabilistic scheduling strategy is proposed in this paper. More specifically, we focus on the arbitrarily random packet arrival distribution when building the system model. Based on the Markov chain model, a linear programming (LP) problem is formulated to minimize the average transmission delay under a given power constraint. Based on the solution of the LP problem, a threshold-based optimal schedule policy can be derived.

The Cloud radio access network (Cloud-RAN) has great potentials to improve energy efficiency and increase capacity of wireless networks. In this paper, we investigate multicast beamforming design for network power minimization of Cloud-RAN, which is shown to be a highly intractable non-convex mixed integer non-linear programming problem. To provide an efficient solution to this highly complicated problem, we propose a three-stage algorithm based on the group-sparsity inducing norm, which minimizes network power by coordinated multicast beamforming and adaptively selecting active remote radio heads (RRHs). In particular, a novel quadratic variational weighted ℓ1=ℓ2-norm aided alternating algorithm is proposed to exploit the group-sparsity structure of the beamforming vector, thereby guiding the active RRH set selection. Given the selected RRH set, multicast beamforming is performed to minimize the network power consumption. Furthermore, to enhance the computation efficiency upon utilizing the shared computing resources in the cloud center, we employ the alternating direction method of multipliers (ADMM) algorithm to solve the resulting semidefinite programming problems in parallel. Extensive simulation results will demonstrate the effectiveness of the proposed multicast group sparse beamforming algorithm.

Network coding (NC) has been applied into two-way relay networks to achieve higher throughput and improve energy and spectrum efficiency. In this paper, we investigate the achievable rate region of two-way Decode-and-Forward (DF) MIMO relay downlink channels with multi-antenna relay node and users. To figure out the rate region of NC in multiuser MIMO system, a convex optimization solution is presented in this paper. The two schemes, Network Coding and Zero-Forcing BeamForming (ZFBF), are compared in terms of the achievable rate region with different channel states. When both of the two users know the messages destined for the other a priori, it is proved that NC is always the optimal compared with ZFBF. Meanwhile, ZFBF tends to achieve a larger rate region when the channel matrices of the two users approach to be orthogonal to each other. However, NC suffers from the maximum SNR loss in this special case. In particular, the extreme situation is studied to demonstrate the optimality of NC.

We consider an energy harvesting communication system, where both energy and data packets arrive at the transmitter during the course of communication. We determine the optimum packet scheduling scheme that minimizes the average delay experienced by all packets. We show that, different from the existing literature, the optimum transmission power is not constant between the energy harvesting and data arrival events; the transmission power starts high, decreases linearly, and potentially reaches zero between energy harvests and data arrivals. Intuitively, untransmitted bits experience cumulative delay due to the bits to be transmitted ahead of them, and hence the reason for transmission power starting high and decreasing over time between energy harvests and data arrivals.

The security requirements of data transmission over wireless networks are energy-limited in many situations. In this paper, the secure energy efficiency (EE), is defined as the ratio of the secrecy rate to the total power, and is investigated in a systematic way considering a decode-and-forward (DF) relay network with a potential eavesdropper. We maximize the secure EE subject to the individual power constraint and the minimum decoding rate constraint of the relay. To deal with the nonconvexity of the formulated problem, a fractional programming approach embedded with DC (difference of convex functions) programming is proposed to solve the problem by two-layer iterations. The key point of the proposed algorithm is to translate the primal problem into a series of convex subproblems, which can be solved by convex programming. It is verified by simulation that the proposed algorithm achieves much better secure EE than the conventional secrecy rate maximization yet with a minor performance loss measured by average secrecy rate or secrecy outage probability.

In this paper, the secure green communication over amplify-and-forward (AF) relaying channels is investigated by using the metric of secure energy efficiency (EE), defined as the number of bits securely delivered per unit energy consumption. We maximize the secure EE with the given maximum power and the minimum secrecy rate requirements. The difficulty of the problem comes from the nonconvexity of both the objective function and the secrecy rate constraint. Therefore, a suboptimal solution scheme, based on fractional programming, dual decomposition, and DC (difference of convex functions) programming, is addressed to solve this problem iteratively. The proposed solution scheme composed of three-layer iterations transforms the primal problem into easier subproblems at each iterative layer, such that the resulting subproblems can be solved by the corresponding optimization methods mentioned above. The simulation results demonstrate that the secure EE of the proposed scheme is superior than that obtained by the conventional schemes with a small decrease of the average secrecy rate.

In this paper, we study the performance of ultradensely distributed antenna system in multi-floor buildings with high user density. To reduce the pilot overhead, we consider multi-floor pilot reuse. We derive the closed-form approximations of the sum-rate for the system using linear receivers, including the linear minimum-mean-squared-error receiver and the maximal ratio combining (MRC) receiver. We demonstrate the spectral efficiency per unit volume of the system and show that the ultradensely distributed antenna system is a promising way to achieve the spectral efficiency target of 5G.

Advanced downlink (DL) decode-and-forward two-way relaying (DF-TWR) is developed for the sake of maximizing the attainable spectral efficiency. Network-coded phase-shift keying (NC-PSK) holds the potential of significantly increasing the data rate of the network, while adaptive modulation is a powerful technique of improving both the energy and spectral efficiency. Hence power-controlled adaptive NC-PSK has the potential of achieving performance enhancements over fading channels. Given this framework, based on the bit-error-ratio (BER) bounds developed, we derive the closed-form spectral efficiency for a continuous-rate, continuous-power adaptive NC-PSK scheme, where both the transmit power and the transmit rate are optimized subject to specific power- and BER- constraints. We then conceive and investigate a discrete-rate scheme relying on a pair of solutions proposed for maximizing the throughput of the network. Our simulation results reveal that the proposed schemes are capable of achieving a higher spectral efficiency than their fixed-power counterparts.

In this paper, we investigate the energy efficiency (EE) maximization in amplify-and-forward (AF) MIMO two-way relay channel (TWRC) combined with relay antenna selection (AS). An iterative energy efficient AS algorithm is proposed to jointly select the active receive and transmit antennas at the relay, as well as optimize the transmission power of the sources and relay. Specifically, the AS at each iteration is based on a derived closed-form iterative equation of EE, which guides us to select a pair of receive and transmit relay antennas that achieves the largest increment of EE under an initial transmission power. After the AS of each iteration, a power adaptation is immediately adopted where we calculate the optimal transmission power using fractional programming and set it as the initial one for the AS of the next iteration. Simulation results show that our proposed scheme achieves nearly the same performance of exhaustive search while with significantly reduced complexity. Moreover, it is capable of simultaneously improving EE and reducing the transmission power.

The problem of multicasting multi-messages over Gaussian broadcast channels with receiver message side information is investigated for K = 3 receivers. The problem generalizes various broadcasting scenarios where the receivers have some message side information, including broadcasting with common message. Based on rate splitting, network coding and Gelfand-Pinsker coding, a successive coding scheme is proposed, such that the network coded messages are successively decoded while the interference from previous decoded messages are simultaneously canceled at receivers. The optimal decoding order and power-rate allocation for maximizing the weighted sum-rate is derived. An outer bound of the capacity region is also established.

Green communications have been attracting increased research interest recently. Equipped with a rechargeable battery, a source node can harvest energy from ambient environments and rely on this free and regenerative energy supply to transmit packets. Due to the uncertainty of available energy from harvesting, however, intolerably large latency and packet loss could be induced, if the source always waits for harvested energy. To overcome this problem, one Reliable Energy Source (RES) can be resorted to for a prompt delivery of backlogged packets. Naturally, there exists a tradeoff between the packet delivery delay and power consumption from the RES. In this paper, we address the delay optimal scheduling problem for a bursty communication link powered by a capacity-limited battery storing harvested energy together with one RES. The proposed scheduling scheme gives priority to the usage of harvested energy, and resorts to the RES when necessary based on the data and energy queueing processes, with an average power constraint from the RES. Through two-dimensional Markov chain modeling and linear programming formulation, we derive the optimal threshold-based scheduling policy together with the corresponding transmission parameters. Our study includes three exemplary cases that capture some important relations between the data packet arrival process and energy harvesting capability. Our theoretical analysis is corroborated by simulation results.

This paper studies online power control policies for outage minimization in a fading wireless link with energy harvesting transmitter and receiver. The outage occurs when either the transmitter or the receiver does not have enough energy, or the channel is in outage, where the transmitter only has the channel distribution information. Under infinite battery capacity and without retransmission, we prove that threshold-based power control policies are optimal. We thus propose disjoint/joint threshold-based policies with and without battery state sharing between the transmitter and receiver, respectively. We also analyze the impact of practical receiver detection and processing on the outage performance. When retransmission is considered, policy with linear power levels is adopted to adapt the power thresholds per retransmission. With finite battery capacity, a three dimensional finite state Markov chain is formulated to calculate the optimal parameters and corresponding performance of proposed policies. The energy arrival correlation between the transmitter and receiver is addressed for both finite and infinite battery cases. Numerical results show the impact of battery capacity, energy arrival correlation and detection cost on the outage performance of the proposed policies, as well as the tradeoff between the outage probability and the average transmission times.

In this paper, we raise the concern on energy-efficient secure communication over a decode-and-forward relay network with potential eavesdroppers. Our objective is maximizing the secure energy efficiency (EE), which is defined as the ratio of secrecy rate to total power, subject to the given individual power, relay decoding rate, and target secrecy rate constraints. The problem is formulated as a unified mixed integer nonlinear optimization adapting to different assumptions of channel state information (CSI). To deal with this combinatorial optimization, a suboptimal solution scheme is developed upon some mathematical methods as decoupling of mixed integer programming, fractional programming, dual decomposition, and difference of convex functions programming. The core of the scheme is to transform the primal problem into simple subproblems step by step, and then convex programming can be exploited finally. The proposed scheme for the case with statistical eavesdropper's CSI, may be conditionally extended to the scenario with full CSI in which a local optimal solution may be obtained in certain cases. Finally, the performance and achievable secure EE of the proposed algorithm are demonstrated by simulations where the tradeoff between secure EE and secrecy rate is also revealed.

In this paper, we consider a multiple access channel, where multiple users equipped with energy harvesting batteries communicate to an access point. The users are supposed to share the channel via Time Division Multiple Access (TDMA). In many existing works, it is commonly assumed that the users' energy harvesting processes and storage status are known to all the users before transmissions. In practice, such knowledge may not be readily available. To avoid excessive overhead for realtime information exchange, we consider the scenario where the users schedule their individual transmissions according to the users' statistical energy harvesting profiles. We first show that in the case when each node has an infinite-capacity battery, equal-power TDMA is optimal for throughput maximization. Using Markov chain modeling, we then study the system performance for the finite-capacity battery case under the equal-power TDMA framework. We also consider an equal-time TDMA scheme, which assigns equal-length subslots to each user. It is found that equal-power TDMA always outperforms equal-time TDMA in the infinite-capacity battery case, while equal-time TDMA exhibits compatible or even slightly better performance in some scenarios when the batteries have finite capacities.

In this paper we study two-stage decode-and-forward coding schemes for relaying with multi-pair information exchange. A joint network and dirty-paper coding (JNDPC) scheme is proposed to serve as a basic element for coding in the broadcast stage. The JNDPC scheme embeds network coding into dirty-paper coding, which allows interference cancelation while fully utilizing the user side information. By using JNDPC, we propose a novel successive network coding (SNC) scheme where the interference to each pair of users are canceled simultaneously. The SNC scheme consists of two layers. The first layer consists of JNDPC to cancel interference at the encoder. In the second layer, JNDPC is sequentially organized to facilitate successive decoding at the decoders. The SNC scheme has a fixed decoding order and hence suffers a rate loss for users. With JNDPC, we then propose a rate-splitting SNC scheme which, together with SNC, outperform the state of the art schemes. For the multiple access stage, a full decode multiple access scheme and a functional decode multiple access scheme are presented. The achievable regions of all proposed broadcast and multiple stage coding schemes are established.

With tremendous power shortage and raising voice of greener energy usage, energy efficiency (EE) maximization in MIMO systems has received much attention in next generation wireless communications. Within recent years, there has been a lot of great work on power allocation and antenna selection in order to maximize EE under holistic power models. However, it is not a trivial work to derive a closed-form solution for energy efficiency optimization. In this paper, an asymptotic approach is adopted to obtain the analytical solutions of the EE optimal transmission schemes, as well as its performance limits. More specifically, we are interested in the capacity achieving dirty paper coding (DPC) and practical low-complexity zeroforcing beamforming (ZFBF), where EE is optimized with and without total power constraint. Closed-form formulas are given to determine the EE optimal number of antennas and transmit power. The proposed asymptotic analysis matches well with the numerical results when the number of users is moderately large, i.e., over 30.

The green communications, which focus on improving the energy efficiency, have attracted much attention from both academia and industry recently. In this paper, the tradeoff between outage probability and energy efficiency will be addressed for multi-flow cooperative communication systems with taking energy budget and devices energy consumption into consideration. The proposed approach will first formulate the multi-flow cooperative communication system as a weighted random bipartite graph (WRBG) model. The minimum weighted maximum matching (MWMM) method will then be proposed to select a relay for each source-destination (s-d) pair in order to minimize the outage probability and maximize the average energy efficiency simultaneously. By analyzing the properties of every sample of the WRBG model, the closed-form formulas for the outage probability and average energy efficiency will then be obtained. Therefore, based on the derived tradeoff, the outage probability and average energy efficiency can be balanced by adjusting the energy budgets and transmission rate according to the system requirement. Moreover, the proposed MWMM method also enjoys an advantage of the log-polynomial computation complexity for parallel implementations.

With the development of next generation mobile communications, the underlay coexistence problem of the OFDMA based Secondary System(SS) with LTE-A systems becomes more and more important, which yet has not been studied in a systematic way. In contrast to other Primary Systems(PS), the LTE-A system puts high demands on the low complexity of the coexistence strategies. This paper focuses on the resource allocation and interference mitigation issues in the aforementioned scenario, whose objective is to protect the spectrum utilization priority of PS as well as utilize secondary resource efficiently. The difficulty lies in the fact that even the subproblem, or power allocation with interference, is NP-Hard. Therefore, this paper will propose a two-phase resource allocation algorithm using maximum weighted Matching in the subcarrier allocation phase and interference Quantizing in the power allocation phase, referred to as the MQ algorithm. As presented in this paper, the MQ algorithm enjoys the advantage of polynomial complexity of O(KJ3+ LKJ), where K, J and L denote the number of SSs, subcarriers and quantizing steps, respectively. The simulation results will show that the proposed MQ algorithm is capable of achieving near optimal system and user throughputs, which are close to the exhaustive searching algorithm.

As the growth of wireless communications is accompanied by increased energy consumption, energy-efficient communication is becoming imperative. This paper will discuss the energy efficiency of MIMO systems with antenna selection. The optimal method for the maximization of energy efficiency is exhaustive search. To address the problem, an iterative algorithm which includes the transmit antenna selection and power adaptation is proposed. It is based on the iterative property of the energy efficiency, which is derived in this paper. This property guides us to select the antenna that achieves the largest energy efficiency increment at each step. There is also a power adaptation for each step where we calculate the optimal transmission power and then set it as the initial one for the next step. Moreover, one asymptotic property exists in the proposed algorithm, which states that the antenna selection and power adaptation can be decoupled in high and low SNR regimes. This fact reduces the complexity further and enables us to achieve the optimal performance with a greater probability. Simulation results show that the proposed algorithm achieves near-optimal performance in all the SNR regimes and has a remarkable gain over the no selection scheme in the energy efficiency and transmission power.

The problem of characterizing the capacity region for Gaussian broadcast channels with receiver message side information appears difficult and remains open for N ≥ 3 receivers. This paper proposes a joint network and Gelfand-Pinsker coding method for 3-receiver cases. Using the method, we establish a unified inner bound on the capacity region of 3-receiver Gaussian broadcast channels under general message side information configuration. The achievability proof of the inner bound uses an idea of joint interference cancelation, where interference is canceled by using both dirty-paper coding at the encoder and successive decoding at some of the decoders. We show that the inner bound is larger than that achieved by state of the art coding schemes. An outer bound is also established and shown to be tight in 46 out of all 64 possible cases.

This paper addresses the outage minimization problem for a wireless link where both the transmitter and the receiver are powered by harvested energy, and the energy arrival processes of both nodes are correlated. We propose three power control policies to minimize the outage probability, including threshold-based On-Off policy, joint scheduling policy, and linear power levels policy. With infinite battery capacity, we analyze the optimality of the thresholds with different correlations between energy arrivals at the transmitter and the receiver. With finite battery capacity, we use finite state Markov chain (FSMC) to obtain the optimality of our policies and also numerically evaluate their performance. The optimal thresholds for minimum outages are derived according to the average energy arrival rate and the system parameters. The numerical results show the performance gains using different policies, as well as the tradeoff between the minimum outage probabilities and the average transmission times.

Network coded modulation (NCM) holds the promise of significantly improving the efficiency of two-way wireless relaying. In this contribution, we propose near instantaneously adaptive variable-rate, variable-power QAM/PSK for NC-aided decode-and-forward two-way relaying (DF-TWR) to maximize the average throughput. The proposed scheme is optimized subject to both average-power and bit-error-ratio (BER) constraints. Based on the BER bounds, we investigate a discrete-rate adaptation scheme, relying on a pair of solutions proposed for maximizing the spectral efficiency of the network. We then derive a closed-form solution based power adaptation policy for a continuous-rate scheme and quantify the signal-to-noise ratio (SNR) loss imposed by NC-QAM. Our simulation results demonstrate that the proposed discrete adaptive NC-QAM/PSK schemes are capable of attaining a higher spectral efficiency than their fixed-power counterparts.

Cooperative communication suffers from multiplexing loss and low spectral efficiency due to the half duplex constraint of relays. To improve the multiplexing gain, successive relaying, which allows concurrent transmission of the source and relays, has been proposed. However, the severe inter-relay interference becomes a key challenge. In this paper, we propose a channel aware successive relaying protocol, also referred to as CAO-SIR, which is capable of thoroughly mitigating inter-relay interference by carefully adapting relays' transmission order and rate. In particular, a relay having a poorer link to the source is scheduled first to forward a message, the data rate of which is adapted to the link quality of the source-relay and relay-destination channels. By this means, each relay may decode the messages intended for the preceding relays, and then cancel these relays' interference in a low complexity which is equal to that of Decision Feedback Equalizer (DFE). To further optimize and analyze CAO-SIR, we present its equivalent parallel relay channel model, based upon which the adaptive relay selection and power allocation schemes are proposed. By employing M half duplex relays, CAO-SIR is capable of achieving an diversity-multiplexing tradeoff (DMT) given by d(r) = max (M + 1) 1- {(M+2/M+1r)} , (1 - r) , where d(r) and r denote the diversity and multiplexing gains, respectively. Its DMT asymptotically approaches the DMT upper bound achieved by (M + 1) × 1 MISO systems or M full duplex relays, when M is large.

Power allocation is an important issue for spectrum sharing of unlicensed bands, in which multiple unlicensed systems may coexist and operate. Some recent works have been reported on spectrum sharing in frequency-flat FF unlicensed bands. However, there has not been much work on power allocation for spectrum sharing in frequency-selective FS unlicensed bands. For multiple cooperative systems cooperating on FS interference channels ICs, we study an optimal power allocation strategy, which allows the transmission power density to vary within one subcarrier. By showing the duality of FS and parallel FF channels, we can therefore compute the achievable rate region of the proposed strategy when systems cooperate with each other. For non-cooperative scenarios, we construct a game-theoretical framework for multiple selfish systems on FS ICs. This framework enables us to utilize existing protocols designed for FF ICs to FS scenarios. By numerical results, in both cooperative and non-cooperative scenarios, we show that the proposed strategy achieves a larger rate region than a conventional strategy, where the transmission power density on each subcarrier is set equal. Our work can be regarded as an extension of previous works for FF scenarios. Copyright © 2012 John Wiley & Sons, Ltd.

The channel state information at the transmitter side (CSIT) is playing a more and more important role in the design of OFDM/OFDMA communication systems. Unfortunately, it is not trivial to conduct a performance analysis of OFDM systems with partial CSIT. Using the saddle-point approximation method, this paper will develop an analytical design and performance analysis framework for OFDM channels with 1 bit CSIT, which we shall refer to as the conditional outage exponent. The proposed framework will then allow us to present the fundamental relationship among the outage probability, transmission rate, SNR, outage capacity, delay-limited capacity, ergodic capacity, diversity-multiplexing tradeoff (DMT), finite-SNR DMT, and the number of diversity branches. It is surprising that the outage performance with 1 bit CSIT (conditional outage exponent) is worse than the corresponding one without CSIT (non-conditional outage exponent) in most cases. This counter-intuitive phenomenon occurs because the observation of the channel at the transmitter side will result in a state space collapse of the channel gains, i.e., from the prior probability space to the posterior probability space. As a result, the conditional outage exponent based framework can be easily used to design and evaluate the performance of existing and upcoming OFDM/OFDMA multichannel systems with 1 bit CSIT.

Cooperative transmission in vehicular networks is studied by using coalitional game and pricing in this paper. There are several vehicles and roadside units (RSUs) in the networks. Each vehicle has a desire to transmit with a certain probability, which represents its data burtiness. The RSUs can enhance the vehicles' transmissions by cooperatively relaying the vehicles' data. We consider two kinds of cooperations: cooperation among the vehicles and cooperation between the vehicle and RSU. First, vehicles cooperate to avoid interfering transmissions by scheduling the transmissions of the vehicles in each coalition. Second, a RSU can join some coalition to cooperate the transmissions of the vehicles in that coalition. Moreover, due to the mobility of the vehicles, we introduce the notion of encounter between the vehicle and RSU to indicate the availability of the relay in space. To stimulate the RSU's cooperative relaying for the vehicles, the pricing mechanism is applied. A non-transferable utility (NTU) game is developed to analyze the behaviors of the vehicles and RSUs. The stability of the formulated game is studied. Finally, we present and discuss the numerical results for the 2-vehicle and 2-RSU scenario, and the numerical results verify the theoretical analysis.

In this paper, we consider the power allocation of the physical layer and the buffer delay of the upper application layer in energy harvesting green networks. We analyze the delay-optimal power allocation problem over fading channels. The total power required for reliable transmission includes the transmission power as well as the circuit power. The harvested power (which is stored in a battery) and the grid power constitute the power resource. The objective is to find a policy to minimize the buffer delay under the constraint on the average grid power. The policy is a two-dimensional vector with the transmission rate and the power allocation of the battery as its elements. In each transmission, the transmitter decides the transmission rate and the allocated power from the battery (the rest of the required power will be supplied by the power grid). A constrained Markov decision process (MDP) problem is formulated when the data arrival process, the harvested energy arrival process, and the channel process are Markov processes. We prove that the optimal policy can be obtained as follows. First, we solve the optimal rate through a reduced MDP problem that is only related to the average harvested energy but not the harvested energy arrival process. Second, the battery's power allocation can be given based on the optimal rate. By analyzing the reduced MDP problem through the transformations to the average cost MDP and discount optimal MDP, we derive some structural properties of the optimal policy. Moreover, the closed-form expression is obtained for the independent and identically distributed (i.i.d.) cases.

Owing to the capability of accumulating mutual information from the transmission of previous nodes, rateless codes can boost the network performance considerably, and hence have sparked much interest recently. However, how to efficiently schedule multi-hop multi-relay transmissions with the aid of rateless codes remains a challenging work. Particularly, it requires high complexity to find an optimal route due to its inherent combinatorial nature. In this paper, we formulate the optimal transmission scheduling as a dynamic programming (DP) problem by defining a novel state and constructing a tree-structured state transition diagram. It is from a point of view of DP that we further propose two low-complexity algorithms, namely S-DIRAC and Fano-DIRAC, with negligible performance loss based on the idea of sequential decoding of convolutional codes. Simulation results indicate that the low-complexity algorithms almost always find the optimal solution and show the superiority of routing with mutual information accumulation compared to conventional shortest path routing.

Network coding (NC) compresses two traffic flows with the aid of low‐complexity algebraic operations, hence holds the potential of significantly improving both the efficiency of wireless two‐way relaying, where each receiver is collocated with a transmitter and hence has prior knowledge of the message intended for the distant receiver. In this contribution, network coded modulation (NCM) is proposed for jointly performing NC and modulation. As in classic coded modulation, the Euclidean distance between the symbols is maximised, hence the symbol error probability is minimised. Specifically, the authors first propose set‐partitioning‐based NCM as an universal concept which can be combined with arbitrary constellations. Then the authors conceive practical phase‐shift keying/quadrature amplitude modulation (PSK/QAM) NCM schemes, referred to as network coded PSK/QAM, based on modulo addition of the normalised phase/amplitude. To achieve a spatial diversity gain at a low complexity, a NC oriented maximum ratio combining scheme is proposed for combining the network coded signal and the original signal of the source. An adaptive NCM is also proposed to maximise the throughput while guaranteeing a target bit error probability (BEP). Both theoretical performance analysis and simulations demonstrate that the proposed NCM can achieve at least 3 dB signal‐to‐noise ratio gain and two times diversity gain.

The parallel fading channel, which consists of finite number of subchannels, is very important, because it can be used to formulate many practical communication systems. The outage probability, on the other hand, is widely used to analyze the relationship among the communication efficiency, reliability, signal-to-noise ratio (SNR), and channel fading. To the best of our knowledge, the previous works only studied the asymptotic outage performance of the parallel fading channels which are only valid for a large number of subchannels or high SNRs. In this paper, a unified performance metric, which we shall refer to as the outage exponent, will be proposed. Our approach is mainly based on the large deviations theory and Meijer'sG-function. It is shown that the proposed outage exponent is not only an accurate estimation of the outage probability for any number of subchannels, any SNR, and any target transmission rate, but also provides an easy way to compute the outage capacity, finite-SNR diversity-multiplexing tradeoff, and SNR gain. The asymptotic performance metrics, such as the delay-limited capacity, ergodic capacity, and diversity-multiplexing tradeoff can be directly obtained by letting the number of subchannels or SNR tend to infinity. Similar to Gallager's error exponent, a reliable function for parallel fading channels, which illustrates a fundamental relationship between the transmission reliability and efficiency, can also be defined from the outage exponent. Therefore, the proposed outage exponent provides a complete and comprehensive performance measure for parallel fading channels.

Multicast/broadcast is regarded as an efficient technique for wireless cellular networks to transmit a large volume of common data to multiple mobile users simultaneously. To guarantee the quality of service for each mobile user in such single-hop multicasting, the base-station transmitter usually adapts its data rate to the worst channel condition among all users in a multicast group. On one hand, increasing the number of users in a multicast group leads to a more efficient utilization of spectrum bandwidth, as users in the same group can be served together. On the other hand, too many users in a group may lead to unacceptably low data rate at which the base station can transmit. Hence, a natural question that arises is how to efficiently and fairly transmit to a large number of users requiring the same message. This paper endeavors to answer this question by studying the problem of multicasting over multicarriers in wireless orthogonal frequency division multiplexing (OFDM) cellular systems. Using a unified utility maximization framework, we investigate this problem in two typical scenarios: namely, when users experience roughly equal path losses and when they experience different path losses, respectively. Through theoretical analysis, we obtain optimal multicast schemes satisfying various throughput-fairness requirements in these two cases. In particular, we show that the conventional multicast scheme is optimal in the equal-path-loss case regardless of the utility function adopted. When users experience different path losses, the group multicast scheme, which divides the users almost equally into many multicast groups and multicasts to different groups of users over nonoverlapping subcarriers, is optimal .

In wireless cellular systems, it is an important and challenging task to reliably multicast to numerous users that require the same contents at one transmission. In this paper, we propose a network coding based multicast scheme for wireless cellular OFDM systems. The base station encodes source packets with linear network coding and multicasts the coded packets to the target users over multicarriers. Thus, the users can correctly recover the source message as long as they successfully receive a certain number of packets. The reliability of coded wireless multicast is characterized by the user outage probability, which can be greatly reduced by efficiently exploiting frequency diversity gain via network coding. We show that the BS shall adjust the data transmission rate per carrier to strike a good balance between the reliability at each subcarrier and the redundancy among coded packets. It is also found that full diversity gain and no diversity gain should be exploited in the high and low SNR regimes, respectively. Simulation results also reveal that network coding generally provides great advantage for reliable wireless multicasting to a large number of users.

Cooperative spectrum sensing has been exploited to improve the detection reliability in cognitive radio networks. Likelihood ratio test (LRT) is optimal for distributed detection in cooperative sensing. However, it is not applicable in practice due to the lack of the local signal-to-noise ratio (SNR) and the fading effect of the control channels between the secondary users and fusion center. In this paper, an adaptive fusion in air (FAIR) cooperative spectrum sensing scheme based on distributed beamforming is proposed to solve this problem. First, a sequential estimation method by exploiting the global decision feedback is adopted at cognitive users to estimate the local SNR. Then a distributed beamforming scheme is introduced among cognitive users to transmit the local LRTs to maximize the received signal at fusion center. The transmission of the weighted sensing data is conducted in an analog way at the same time slot so that data fusion can be automatically carried out in air. Simulation results show that the performance of our proposed practical method is close to the theoretically optimal cooperative spectrum sensing. Meanwhile, higher spectrum efficiency is achieved due to data fusion in air.

In this paper, a random combinatorial optimization approach, which we shall refer to as random bipartite graph (RBG) based maximum matching, will be proposed to investigate and solve the joint relay selection and subchannel allocation problem in cooperative networks. By studying the properties of the maximum matching on RBG, the outage probability and diversity-multiplexing tradeoff of the proposed RBG matching method will be obtained. It will then be demonstrated that the outage probability and diversity-multiplexing tradeoff of the RBG matching method for cooperative communication systems with multiple source-destination pairs is the same as that of relay systems with only one source and one destination, i.e., d(r) = N (K + 1)(1 - 2r), where N is the number of subchannels, and K is the number of relay nodes. In addition, it will be shown that the proposed algorithm for maximum matching enjoys a sublinear computation complexity O(N2/3). Simulation results will illustrate the potential of the proposed RBG matching method as well as verify the theoretical derivations.

Automatic Repeat reQuest (ARQ) and Rateless Coding (RC) are two major feedback-based error control schemes. However, due to the lack of common metrics for measuring the performance, ARQ and RC have not been systematically compared yet. In this paper, we establish a unified analytical framework based on two novel metrics, namely forward redundancy and feedback redundancy, and present a comparative study on ARQ and RC from the point of view of redundancy. In particular, we conduct a fair comparison of both schemes over point-to-point fading channels and broadcast fading channels. The comparison indicates that RC is capable of beating ARQ completely at low signal-to-noise ratios in broadcast communications. In other cases, neither of them could dominate the other. Therefore, we propose a selection method to determine which scheme to employ with given system parameters.

Coordinated Multi-Point (CoMP) transmission is a promising scheme which is capable of suppressing the co-channel interference (CCI) and further improves spectral efficiency. In CoMP, the system performance highly depends on the accuracy of Channel State Information (CSI) feedback. Previous works are mainly under an ideal assumption that perfect CSI is available at BSs. However, this consideration is impractical especially in multi-user and multi-antenna circumstance. In this paper, Coordinated Scheduling/Coordinated Beamforming (CS/CB) is considered with limited CSI feedback in homogeneous COMP cellular network. From the perspective of cross-layer design, a layered limited feedback with optimized codebook based CS/CB algorithm is also proposed. Simulation results demonstrated that CS/CB with the proposed layered feedback scheme is capable of achieving higher throughput over traditional single-cell signal processing (SCP) approach with perfect CSIT. Furthermore, the feedback load can be reduced by 88.56% with proposed feedback scheme compare with the traditional feedback approach.

The authors propose a cross-layer approach, which exploits distributed beamforming in the physical layer and token passing in the media access control (MAC) layer, to improve quality of service (QoS) for secondary users (SUs) with bursty traffics in cognitive relay systems. In this scheme, source-to-destination transmissions are relayed by some SU nodes, which can form a distributed beamformer to forward messages in busy timeslots while completely eliminating interference to primary users (PUs). In contrast with previous cognitive relaying protocols, this scheme can utilise more spectrum resources, namely idle timeslots (or temporal spectrum holes) as well as busy timeslots (or spatial spectrum holes). Based on a token passing mechanism, an opportunistic scheduling protocol is then developed to dynamically balance available spectrum holes between the source and the relays, and hence adapts to bursty arrival of secondary traffics and random presence of PUs. By formulating a tandem queueing analytical framework, the performance of the proposed scheme is then analysed using a multi-dimensional Markov chain model. Numerical results demonstrate that the proposed scheme can achieve significant QoS gains over conventional cognitive relaying protocols that utilise only idle timeslots.

Cognitive radio (CR) provides a promising solution to the spectrum scarcity problem by implementing opportunistic spectrum access over the licensed spectrum. However, spectrum holes are discontinuous in frequency and time, resulting in a challenge to CR transmissions. Fortunately, rateless codes can be utilised to exploit these distributed spectrum opportunities in an aggregate way. In such system, how to conduct the sensing and transmission is a key problem that affects the system performance. Therefore in this study, the authors propose a rateless-coded transmission protocol in a multi-channel CR system, addressing the media access control (MAC) layer sensing issues. Specifically, how many channels and which ones should be sensed in each time slot. Owing to the dynamics of channel availability, stochastic control is a necessity. Therefore the authors analyse the average throughput and formulate an optimisation problem to find the optimal sensing policy based on the theory of partially observable Markov decision process (POMDP). The myopic sensing policy is also studied owing to intractable computation complexity of a general POMPD. Moreover, the authors propose a heuristic policy with comparable performance and low complexity. Simulation results will show that the heuristic policy is superior to the static policy and has almost the same performance as the myopic policy.

Recent works have shown that cooperative diversity can be achieved by using relay selection (RS), distributed space-time coding (DSTC), and distributed beam-forming (DBF) in narrow-band decode-and-forward (DF) cooperative networks with one source-and-destination (s-d) pair. However, the joint resource allocation for broadband DF cooperative networks with multiple s-d flows has not received much attention yet. In this paper, a random hypergraph based unified matching framework is proposed, under which five feasible types of multi-flow DF cooperative networks will be considered. In each type, the maximum matching method will be applied to RS, DSTC, and DBF schemes so as to achieve the optimal channel allocation and relay selection with fairness assurance. By analyzing the properties of maximum matching, the outage probability of each s-d pair after resource allocation will be obtained. The results of diversity-multiplexing tradeoff will show that the proposed framework is capable of achieving the full frequency diversity and cooperative diversity for each s-d pair simultaneously, while the frequency multiplexing is equally shared. Based on the unified framework, the random rotation based parallel Hopcroft-Karp (R2PHK) algorithm will then be designed, which can work in each destination node independently, and shall enjoy a poly-logarithmic complexity O(log2loN), where N is the number of channels and lois a constant.

Cognitive Radio (CR) can significantly improve the utilization of the precious radio spectrum by allowing Secondary Users (SUs) to borrow the licensed spectrum if they do not cause harmful interference to Primary Users (PUs). As a wireless technology, CR confronts the challenges of wireless channels inevitably and thus wishes to employ node cooperation to achieve spatial diversity gain. However, conventional cooperative diversity technologies require two idle timeslots for each transmission. This implies two temporal spectrum holes are needed for each transmission when the technologies are applied to CR Networks (CRNs). This can cause severe delay, as temporal spectrum holes are only available from time to time in CRNs. In this paper, we present a cross-layer approach, where cooperative beamforming is adopted to forward messages in busy timeslots without causing interference to PUs, so as to achieve cooperative diversity gain and improve Quality of Service (QoS) for SUs without consuming additional idle timeslots or temporal spectrum holes. In the physical layer, the beamforming weight vector and the cooperative diversity gain are obtained using a geometric approach. The MAC layer of the cooperative communication in CRNs can be modeled by a tandem queue, where the source queue is the bottleneck. Therefore, we propose an optimal opportunistic priority scheduling scheme in the MAC layer, the timeout probability of which is obtained using an absorbing Markov chain. A cross-layer optimization of the transmission rate is then carried out to jointly reduce the timeout and outage probabilities. Its significant QoS gain is demonstrated by simulations.

There have been increasing interests in integrating cooperative diversity into Cognitive Radios (CRs). However, conventional cooperative diversity protocols require at least two randomly available idle timeslots or temporal spectrum holes for one transmission, thus leading to limited throughput and/or large latency. In this paper, we propose a novel cross-layer approach for efficient scheduling in CR systems with bursty secondary traffics. Specifically, cooperative beamforming is exploited for Secondary Users (SUs) to access busy timeslots or spatial spectrum holes without causing interference to primary users. We first propose a basic cooperative beaMforming and Automatic repeat request aided oppoRtunistic speCtrum scHeduling (MARCH) scheme to balance available spectrum resources, namely temporal and spatial spectrum holes, between the source and the relays. To analyze the proposed scheme, we develop a tandem queuing framework, which captures bursty traffic arrival, dynamic availability of spectrum holes, and time-varying channel fading. The stable throughput region and the average delay are characterized using a structured matrix-analytical method. We then obtain delay optimal scheduling schemes for various scenarios by jointly optimizing the scheduling parameters. Finally, we propose a modified scheme, MARCH-IR, which combines MARCH with Incremental Relay selection to further improve the system performance. Simulation results reveal that the proposed schemes provide significant Quality of Service (QoS) gains over conventional scheduling schemes that access only temporal spectrum holes.

Network coding, which can combine various traffic flows or packets via algebraic operations, has the potential of achieving substantial throughput and power efficiency gains in wireless networks. As such, it is considered as a powerful solution to meet the stringent demands and requirements of next-generation wireless systems. However, because of the random and asynchronous packet arrivals, network coding may result in severe delay and packet loss because packets need to wait to be network-coded with each others. To overcome this and guarantee quality of service (QoS), we present a novel cross-layer approach, which we shall refer to as Buffer-Aware Network Coding, or BANC, which allows transmission of some packets without network coding to reduce the packet delay. We shall derive the average delay and power consumption of BANC by presenting a random mapping description of BANC and Markov models of buffer states. A cross-layer optimization problem that minimizes the average delay under a given power constraint is then proposed and analyzed. Its solution will not only demonstrate the fundamental performance limits of BANC in terms of the achievable delay region and delay-power tradeoff, but also obtains the delay-optimal BANC schemes. Simulation results will show that the proposed approach can strike the optimal tradeoff between power efficiency and QoS.

Analog network coding is a promising technique which can greatly improve the transmission efficiency of wireless communications. In two-way relay systems with multiple subchannels, multiple user pairs and multiple relays, however, the optimal joint relay selection and subchannel allocation problem has not been studied in a systematic way. In this paper, a random combinatorial optimization approach, referred to as the weighted random bipartite graph (WRBG) based minimum weighted matching (MWM) method, will be proposed to solve this problem. By analyzing the properties of the MWM on WRBG, we shall derive the outage probability and diversity-multiplexing tradeoff of each user after relay selection and channel allocation. Theoretical results will demonstrate that the outage probability, cooperative diversity, and frequency diversity of the proposed WRBG based MWM method for multi-user two-way relay systems is the same as that of two-way relay systems with only one user pair. The proposed algorithm for MWM also enjoys a low computation complexity of O(log2N) for parallel implementations, where N is the number of subchannels. Simulation results will illustrate the potential of the proposed method and also verify the theoretical derivations.

Due to the broadcast nature of wireless transmissions, overheard signals can be exploited in multi- hop wireless networks so as to improve the network performance. It has been shown that energy accumulation and information accumulation are two main approaches to utilize multiple overheard signals. However, how to jointly accumulate energy and information in multi-hop wireless networks is still unknown. In this paper, we propose a new rateless coded relaying scheme in linear multi-hop wireless networks, where energy and information can be jointly accumulated by superimposing two rateless-coded packets generated from the same information bits using different codebooks. The average end-to-end throughput is analyzed. Furthermore, we formulate an optimization problem to find the optimal power allocation ratio and obtain the least transmission time for relay nodes. Simulation results will show that compared to pure energy accumulation and pure information accumulation, our proposed scheme can achieve much higher throughput.

Relay selection has been shown to be a practical and effective way to achieve cooperative diversity. In wide-band OFDM cooperative communication systems with multiple source and destination nodes, however, the best relay selection and subchannel allocation has not been studied in a systematic way. In this paper, a random combinatorial optimization approach, referred to as the random bipartite graph based maximum matching (RBG matching), will be proposed to solve this problem. By applying the method of Euler beta function and generalized hypergeometric function, we will first derive a new closed-form outage probability for the best relay selection in the decode-and-forward (DF) scheme. Based on this result and the properties of the maximum matching on RBG, the outage probability and diversity-multiplexing tradeoff of the proposed RBG matching method will also be derived. As a result, we will demonstrate that the outage probability and the cooperative and frequency diversity-multiplexing tradeoff of the RBG matching method for cooperative communication systems with multiple source-destination pairs is the same as that of relay systems with only one source and one destination. Besides, the proposed algorithm for maximum matching also enjoys a sublinear computation complexity O(N2/3), where N is the number of subchannels. Simulation results will illustrate the potential of the proposed RBG matching method, and also verify the theoretical derivations.

Cooperative diversity is an emerging and powerful solution that can significantly improve the diversity order and link reliability over the harsh wireless fading channels. Although there has been a lot of work on achieving full diversity by using advanced space-time coding or signal processing schemes, how to increase the spectral efficiency or multiplexing gain with half-duplex relays has not received much attention so far. In this paper, we will present a spectral efficient cooperative diversity method, which adopts our recently proposed Network Interference CancEllation (NICE) to thoroughly mitigate the inter-relay interference in successive relaying, where a source and a relay may send different messages simultaneously. We shall also investigate the optimal diversity multiplexing tradeoff of the proposed method, and show that its diversity gain is strictly greater than that of the two-timeslot relaying protocols for large multiplexing gains. Moreover, the decoding algorithm for the relays and destination has a similar complexity as that of the decision feedback equalizer.

In this paper, we propose an opportunistic scheduling scheme to serve bursty traffics in cognitive radios, where cooperative beamforming is exploited to access busy timeslots or spatial spectrum holes to forward messages without causing interference to primary users. Specifically, based on cooperative beamforming in the physical layer and automatic repeat request for error recovery in the link layer, our proposed scheme strives to balance available spectrum resources, namely temporal and spatial spectrum holes, between the source and the relays so as to greatly reduce the average delay. To analyze the proposed scheme, we then develop a tandem queueing analytical framework, which captures bursty traffic arrival, dynamic availability of spectrum holes, and time-varying channel fading. By modelling it with a multi-dimensional Markov chain, the average delay is derived using a structured matrix-analytical method. Finally, we obtain delay optimal scheduling schemes by jointly optimizing the scheduling parameters. Simulation results reveal that the proposed scheme provides significant quality of service gains over conventional scheduling schemes that access only temporal spectrum holes.

We propose a cooperative beamforming aided incremental relaying scheme to improve spectrum efficiency of cognitive radio systems. In this scheme, the source and relays can utilize cooperative beamforming to activate packet retransmission in busy timeslots or spatial spectrum holes, if the destination fails to receive the packets transmitted from the source. Therefore, cooperative diversity gain is obtained without consuming extra idle timeslots. Given a packet loss constraint, our proposed scheme endeavors to further improve the system throughput by dynamically adjusting the maximum number of retransmissions. We derive the average throughput of the proposed scheme and obtain the maximum throughput by optimizing the scheduling parameters. Theoretical and simulation results reveal that the proposed scheme obtains a significant throughput gain compared to direct transmission as well as conventional incremental relaying schemes utilizing idle timeslots only.

Cooperative communication with rateless codes has attracted much attention recently because it combines spatial diversity of multiple nodes and high bandwidth efficiency of rateless codes. However, relay selection for asynchronous relaying, which shows great potential for practical applications, has not been carefully studied yet. In this paper, based on rateless codes, we propose a simple probabilistic relay selection protocol for asynchronous multi-relay networks, where no symbol-level inter-node synchronization or multiuser detection is needed. In such asynchronous networks, a decoding relay can help the other relays which have not decoded the message yet. Moreover, the tradeoff between the first hop and the second hop can be controlled by setting the decoding relay threshold. We analyze the average end-to-end throughput and find an optimal decoding relay threshold to maximize the throughput. Simulation results show the superiority of the proposed protocol at low Signal-to-Noise Ratios (SNRs) when the relays are close to the source.

In cognitive relay systems, spectrum holes exist in 4 dimensions (4-D), namely, time, frequency, location, and direction. How to efficiently utilize these different kinds of spectrum holes to provide quality-of-service (QoS) guarantees for secondary users (SU) is a challenging task. In this paper, we first identify the benefits of separately applying cooperative beamforming and rateless coding aided relaying technologies. Specifically, cooperative beamforming has the particular advantage of exploiting spatial or directional spectrum holes without causing interference to PUs. On the other hand, rateless coding is capable of utilizing different kinds of spectrum opportunities in an aggregate way with low complexity. Both cooperative beamforming and rateless coding aided cognitive relaying schemes have been proposed to support either elastic or real-time traffics for SUs. Furthermore, we combine cooperative beamforming and rateless coding together in order to provide an efficient and robust way to utilize 4-D spectrum holes in cognitive relay systems, where spectrum sensing and channel estimation may be imperfect. A substantial performance gain can be obtained by the combination, compared to using these two techniques separately.

An interesting distributed throughput-optimal CSMA MAC protocol, called adaptive CSMA, was proposed recently to schedule any strictly feasible rates inside the capacity region. Of particular interest is the fact that the adaptive CSMA can achieve a system utility arbitrarily close to that is achievable under a central scheduler. However, a specially designed transport-layer rate controller is needed for this result. An outstanding question is whether TCP Reno (one of the most mature versions of TCP) is compatible with adaptive CSMA and can achieve the same result. The answer to this question will determine how close to practical deployment adaptive CSMA is. Our answer is yes and no. First, we observe that running TCP Reno directly over adaptive CSMA results in severe starvation problems. Effectively, its performance is no better than that of TCP Reno over legacy CSMA (IEEE 802.11), and the potentials of adaptive CSMA cannot be realized. We then propose a multi connection TCP solution with active queue management and prove that it can work with adaptive CSMA to achieve optimal utility. NS-2 simulations demonstrate that our solution can alleviate starvation and achieve fair and efficient rate allocation. We remark that multi-connection TCP can be implemented at either application or transport layer. Application-layer implementation requires no kernel modification, making the solution readily deployable in networks running adaptive CSMA. Our results show that adaptive CSMA can work well with only light-weight TCP modifications, bringing it a step closer to practicaiity.

Spectrum sensing is the first key functionality to realize Cognitive Radios which also meet harsh performance demands under various severe conditions, e.g. the reliability and sensitivity demands under the low sensing overhead constraint. Cooperative sensing exploiting SPRT is an efficient sensing method which can significantly decrease sensing delay and sensing overhead, thereby, has been extensively studied. However, how to implement sequential detection with low signaling complexity and less sensing delay has not been considered yet. In this paper, we proposed an efficient and reliable cooperative sensing scheme where each SU reports their local decisions multiple times before the global decision is made. We will demonstrate that our proposed scheme is capable of not only achieving the high sensing performance but also the low sensing delay and overhead.

Network coding compresses multiple traffic flows with the aid low-complexity algebraic operations, hence holds the potential of significantly improving both the power and bandwidth efficiency of wireless networks. In this contribution, the novel concept of Network Coded Modulation (NCM) is proposed for jointly performing network coding and modulation in bi-directional/duplex relaying. Each receiver is colocated with a transmitter and hence has prior knowledge of the message intended for the distant receiver. As in classic coded modulation, the Euclidian distance between the symbols is maximized, hence the Symbol Error Ratio (SER) is minimized. Specifically, we conceive NCM methods for PSK, PAM and QAM based on modulo addition of the normalized phase or amplitude. Furthermore, we propose low complexity decoding algorithms based on the corresponding conditional minimum distance criteria. Our performance analysis and simulations demonstrate that NCM relying on PSK is capable of achieving a SER at both receivers of the NCM scheme as if the relay transmitted exclusively to a single receiver only. By contrast, when our NCM concept is combined with PAM/QAM, an SNR loss (<;1.25 dB) is imposed at one of the receivers, usually at the one having a lower data rate in a realistic different rate scenario. Finally, we will demonstrate that the proposed NCM is compatible with existing physical layer designs.

Geographic routing is a scalable routing scheme for wireless networks, where nodes make local routing decisions using position information. Cooperative transmissions utilize spatial diversity to combat fading in wireless channels. In this paper, we incorporate cooperative transmissions into geographic routing, and propose the Location-Aware Cooperative Routing (LACR). In LACR, a node that receives Route Request (RREQ) makes an individual decision on whether and how to rebroadcast RREQ based on its position and capability to cooperate. A theoretical analysis of the impact of cooperative transmissions on the transmission range extension is presented to guide the measurement of the potential performance of each node. Through simulation, we show that LACR performs better in terms of higher probability to find a route and higher throughput in comparison to Single-Input-Single-Output (SISO) based routing protocol.

Due to the potential of enhancing traffic safety, protecting environment, and enabling new applications, vehicular communications, especially vehicle-to-vehicle (V2V) communications, has recently been receiving much attention. Because of both safety and non-safety real-time applications, V2V communications has QoS requirements on rate, latency, and reliability. How to appropriately design channel allocation is therefore a key MAC/PHY layer issue in vehicular communications. The QoS requirements of real-time V2V communications can be met by achieving a low outage probability and high outage capacity. In this paper, we first formulate the subchannel allocation in V2V communications into a maximum matching problem on random bipartite graphs. A distributed shuffling based Hopcroft-Karp (DSHK) algorithm will then be proposed to solve this problem with a sub-linear complexity of O(N^{2/3}), where N is the number of subchannels. By studying the maximum matching generated by the DSHK algorithm on random bipartite graphs, the outage probabilities are derived in the high (two near vehicles) and low (two far away vehicles) SNR regimes, respectively. It is then demonstrated that the proposed method has a similar outage performance as the scenario of two communicating vehicles occupying N subchannels. By solving high degree algebraic equations, the outage capacity can be obtained to determine the maximum traffic rate given an outage probability constraint. It is also shown that the proposed scheme can take an advantage of small signaling overhead with only one-bit channel state information broadcasting for each subchannel.

OFDMA is a promising technique because it is capable of improving the transmission reliability and efficiency of multi-user wireless communications. However, previous works on the performance of OFDMA did not properly consider the fundamental relationship between multiplexing and diversity in OFDMA systems. As a comprehensive performance metric, the diversity-multiplexing tradeoff will be applied in this paper to evaluate the subcarrier allocation scheme. The OFDMA system will be formulated into a correlated random bipartite graph model, in which, whether the edges occur or not depends on the distribution of the channel fading. The \mathcal{H}-matching method, which is used to determine the maximum collection of vertex-disjoint copies of a fixed sub-graph \mathcal{H} contained in a given graph, will then be developed to address the optimal subcarrier allocation problem. Theoretical analysis will show that the proposed \mathcal{H}-matching method achieves the optimal outage performance at a given target multiplexing gain, which implies that the optimal diversity-multiplexing tradeoff can be achieved by only allocating subcarriers. Although the \mathcal{H}-matching problem is NP-complete, the proposed Random Rotation and Expansion based Hopcroft-Karp (R^2EHK) algorithm can still achieve the asymptotically optimal outage performance (i.e., optimal diversity-multiplexing tradeoff) with a sub-linear complexity. Furthermore, the channel state information needed is only one bit per subcarrier. Simulation results will verify the theoretical analysis and will show that the performance loss of the R^2EHK algorithm is negligible compared to the exhaustive search method. In addition, it is also shown that the R^2EHK algorithm has at least a 2 dB SNR gain compared to the interleaved subcarrier allocation with water-filling power allocation in IEEE 802.16 standards.

OFDM is playing a more and more important role in wireless communication systems. Unfortunately, it is not trivial to conduct a performance analysis of OFDM systems. Therefore, it is highly desired to develop an analytical design and performance analysis framework for OFDM channels. In this paper, we consider a unified performance metric for OFDM channels, which we shall refer to as outage exponent. The outage exponent, which is a special exponentially tight upper bound on outage probabilities, presents the fundamental relationship among the outage probability, target transmission rate, capacity of AWGN channel, SNR, and the number of diversity branches. The SNR gains of different coding schemes and the (finite-SNR and asymptotic) diversity-multiplexing tradeoff can be obtained from the outage exponent directly. In order to calculate the outage exponent for OFDM channels, we shall apply the large deviations theory, which will not only obtain an accurate estimation of the rate function, but also the coefficient of the exponential function. It is shown that the obtained outage exponent can allow the accurate estimation of the additional power required to decrease the outage probability by a specified value. Therefore, the outage exponent can be easily used to design and evaluate the performance of existing and upcoming OFDM systems.

Spectrum sensing is a fundamental technology to detect the presence of primary user in cognitive radio networks. Usually, the requirements for spectrum sensing are very stringent. It requires that the spectrum sensing scheme has a good performance even in extremely low signal-to-noise ratio (SNR) environments. In this paper, we propose a novel spectrum sensing method based on stochastic resonance (SR) noise enhance detection (NED) to meet the requirement. For the proposed SR NED based spectrum sensing scheme, a specific signal-independent SR noise is added to the received signal so that the probability distribution of the detection statistics is modified to match the applied nonlinear suboptimal detector better. The performance of the applied detector can then be significantly improved according to the basic principle of SR NED. Under the constraint of the false alarm probability, the method to find the optimal SR noise is provided for both single node sensing and multiple nodes cooperative sensing by maximizing the probability of detection. For single node sensing, the popular energy detector is considered. For cooperative sensing, both maximal ratio combination (MRC) and equal gain combination (EGC) based energy detectors are investigated. Theoretical analysis and simulations results show that the proposed SR NED based spectrum sensing schemes can achieve much better performance than that of the applied original detectors.

Recent work has shown that distributed (or cooperative) beamforming can achieve cooperative gain, such as throughput gain and diversity gain, with no need for extra spectral holes in Cognitive Wireless Networks (CWNs). However, how to efficiently schedule cooperative beamforming to improve the quality of service of unlicensed secondary users has not been well addressed. In this paper, a simple opportunistic scheduling scheme is proposed to serve delay-sensitive traffics in CWNs with cooperative beamforming. After the probabilities of outages due to channel fading and random appearance of primary users are analyzed, respectively, the overall outage probability of our scheme is minimized by optimizing the scheduling parameter. The optimal scheduling scheme is then derived for the high-SNR regime. Simulation results show that compared to conventional schemes without cooperative beamforming, our scheduling scheme can significantly lower down the probability of message transmission failure within a given time period.

Rateless coding ensures reliability for time-varying channels by providing ever-increasing redundancy at the packet level. However, the optimal packet length for rateless codes has not been carefully studied from the application layer and the physical layer. In this paper, we present a practical wireless communication system consisting of an LT coding module, a channel coding module, and error detection modules. By analyzing the system performance, we find the impacts of packet length on reliability and efficiency, and formulate the optimization problem that maximizes the throughput efficiency over time- varying channels. We also compare the performance of the rateless coding system with the conventional one which does not utilize LT codes. Simulation results show that the rateless coding system are superior to the conventional system in a large SNR range for slow channel variations and a relative small SNR range for fast channel variations.

The diversity-multiplexing tradeoff, which relates the transmission reliability and efficiency, is an important performance metric in wireless communications. So far only an asymptotic tradeoff result has been obtained for OFDM channels, and such result is only valid for high SNRs. To characterize the outage performance of OFDM systems in realistic SNRs, a finite-SNR framework that analyzes and describes the diversity-multiplexing tradeoff will be proposed in this paper. New upper and lower bounds on outage probabilities will be derived by using the method of integral round a contour, Laurent series, and the properties of Meijer's G-function and Gamma function. The finite-SNR diversity gain, as a function of the multiplexing gain and SNR, will also be computed by Meijer's G-function. We will then show that the finite-SNR diversity-multiplexing tradeoff will converge to the corresponding asymptotic results as SNR tends to infinity. As a result, the finite-SNR diversity-multiplexing tradeoff can be used to estimate the additional SNR required to decrease the outage probability by a specified amount for a given multiplexing gain.

Cognitive radio (CR) is a powerful solution that can significantly improve the utilization of the precious limited radio spectrum. It allows secondary users (SUs) to opportunistically access spectral holes of the licensed spectrum without causing harmful interference to primary users (PUs). However, waiting for idle timeslots may induce very poor quality of service (QoS) for SUs. To alleviate this, an opportunistic relaying protocol exploiting distributed beamforming and token passing is proposed in this paper. We consider a cognitive radio network (CRN), where SUs constitute a two-hop relaying network. Specifically, a distributed beamforming method is applied to enable concurrent transmissions of PUs and SUs, thereby improving the opportunistic spectrum access. Our protocol applies a token passing mechanism in the MAC layer to dynamically balance transmission opportunities between two hops, and hence adapts to the random packet arrival and PUs' presence. We shall formulate a Markov chain to analyze the performance of this protocol. Numerical results show that our proposed protocol can significantly improve QoS of SUs in terms of the packet-loss rate and average delay, compared to conventional relaying protocols that utilize only silent timeslots.

Cognitive radios are efficient techniques to improve the utilization of the spectrum. Spectrum sensing is the key functionality to improve the spectrum efficiency and avoid harmful interference to the licensed users. By exploiting the spatial diversity of the different secondary users, cooperative sensing can achieve better sensing performance compared to the local sensing, thereby, receiving much attention recently. However, in large scale cognitive radio networks, such as Wireless Regional Area Network (WRAN) defined by IEEE 802.22, the secondary users may have quite different sensing SNRs due to the different pass loss and other environment parameters. How to efficiently transmit and fuse the sensing data of cooperative sensing in large scale cognitive radios have not been fully studied yet. This paper presents a sequential sensing data transmission and fusion approach for large scale cognitive radios to minimize the average sensing time by dividing the users into different sets according to their SNR parameters. The analytical and simulation results both show that the proposed approach not only decreases the sensing time but also certifies the accuracy of detection.

In cognitive OFDMA networks, spectrum sensing is highly needed to accurately observe the spectrum environment, so as to avoid harmful interference to licensed users. However, to save energy, selfish users may not be willing to perform sensing. In order to encourage cognitive users to sense the multiple channels as well as guarantee fairness among the sensing users, this paper presents a joint PHY-MAC framework for cognitive OFDMA systems. In this framework, a higher access priority in the MAC layer will be rewarded to sensing users and the scheduling decision is made based on both the channel quality and sensing contribution of each user. The throughput for the cognitive system and the fairness for each cognitive user are analyzed under the joint framework. Simulation results will then show that the proposed protocol can achieve fairness and efficiency for cognitive users.

Rateless codes can be used for mutual information accumulation in multi-hop wireless networks. However, the impact of spatial reuse and/or node cooperation on performance of the linear multi-hop network employing rateless codes has not been carefully studied yet. In this paper, we present three rateless coded forwarding schemes in linear multi-hop networks, namely, multi-hop forwarding with no spatial reuse, multi-hop forwarding with spatial reuse, and cooperative forwarding with spatial reuse. By analyzing and comparing their performance, we conclude that mutual information accumulation with spatial reuse improves the average throughput but induces a larger latency, while node cooperation, based on rateless codes and spatial reuse, reduces the average delay but suffers a throughput loss.

OFDMA performs a fundamental role in wired/wireless communications. One of the key techniques in OFDMA is the resource allocation, which has been attaching much attention from both academia and industry. In this paper, we describe an innovative combinatorial method to study this problem. An OFDMA system will first be formulated into a random bipartite graph (RBG). To meet various system configurations and requirements, different matching methods will be proposed to perform subcarrier allocation. By studying the properties of RBG matching, we will obtain close-form formulas for outage probabilities so as to evaluate the performance of subcarrier allocation algorithms. It is then demonstrated that by exploiting the frequency diversity and multi-user diversity, the proposed matching method can minimize the outage probability with fairness assurance, and achieve the same diversity-multiplexing tradeoff as point-to-point OFDM systems. The induced subcarrier allocation algorithms also enjoy a sub-linear computation complexity of O(N2/3) for parallel implementations, where N is the number of subcarriers. Besides, the proposed RBG matching method only needs one-bit CSI feedback.

Spectrum sensing is one of the key functionalities in cognitive radios which enables opportunistic spectrum access. In a cognitive radio system, secondary users need to detect the emergence of primary users as soon as possible to avoid harmful interference. In particular, sensing performance can be evaluated by detection delay and sensing overhead. Sequential detection techniques such as quickest detection can achieve minimum detection delay, while MAC layer sensing scheduling of periodic energy detection has demonstrated its high sensing efficiency. These motivate us to propose a joint PHY-MAC spectrum sensing algorithm in this letter, which employs sequential probability ratio test in the PHY layer and a probability-based sensing scheduling mechanism in the MAC layer. This algorithm can minimize detection delay with limited sensing overhead. Simulation results reveal that it has remarkable performance improvement compared with periodic energy detection.

This paper considers the problem of optimal subcarrier allocation in OFDMA systems to achieve the minimum outage probability while guaranteeing fairness. The optimal subcarrier allocation algorithm and the maximum frequency diversity gain are both analyzed through the maximum matching method based on the random bipartite graph theory. Accordingly, a surprising result is found, which shows that the maximum frequency diversity gain in subcarrier-sharing OFDMA systems is the same as that in point-to-point OFDM systems that serve only one user by using N subcarriers. It is then demonstrated that this maximum frequency diversity gain can be achieved by a proposed Random Vertex Rotation based Hopcroft-Karp (RVRHK) algorithm with the time complexity of O(N2.5), where N is the number of subcarriers. Because the theoretical analysis and the RVRHK algorithm are both based on the maximum matching method, the maximum frequency diversity in OFDMA systems is referred to as the max-matching diversity in this paper.

OFDMA technology can significantly improve the transmission reliability and efficiency because of its inherent frequency diversity and frequency multiplexing. In our recent work [B.Bai,W.Chen, Z.Cao and K. B. Letaief (2009) ], we have derived the optimal diversity-multiplexing tradeoff for OFDMA systems under the assumption that each subcarrier occupies the entire coherence bandwidth. However in practical OFDMA systems, such as IEEE 802.16, there are many subcarriers in one coherence bandwidth, i.e., each coherence bandwidth is split into multiple subcarriers which brings the correlation of channel gains among these subcarriers. In this paper, we focus on the diversity-multiplexing tradeoff in this kind of OFDMA systems. First, a correlated random bipartite graph is adopted to formulate this problem. To resolve the user conflicts in subcarrier allocation, the maximum proper /-matching method is introduced to minimize the user outage probability with fairness assurance at given multiplexing gains. Based on this model, the optimal diversity-multiplexing tradeoff curve is obtained. Two extreme points are considered: (1) the full diversity gain is the number of coherence bands, i.e., the same as that in point-to-point OFDM systems; and (2) given a coherence bandwidth, the maximum multiplexing gain is equal to the frequency band equally allocated to each user. The random vertices rotation and extension based Hopcroft-Karp algorithm is then proposed as an optimal subcarrier allocation scheme, which can achieve the optimal tradeoff curve with the time complexity of O(S2.5), where S is the total number of subcarriers.

OFDMA is a potential technology that can flexibly allocate subcarriers while providing diversity gain to multiple users. In our recent work, we showed a surprising result that the maximum frequency diversity gain in OFDMA systems is equal to the number of independent subcarriers, i.e., the same as that in point-to-point OFDM systems despite of the fact that multiple users will share a common set of subcarriers. However, the diversity gain only characterizes the first-order outage performance in the high SNR regime, and the outage performance in the low SNR regime, which is very important in practice, is still an open problem. In this paper, we first formulate the subcarrier allocation problem in OFDMA systems as a random bipartite graph model. Then, a more precise outage probability is derived in the high SNR regime using a high-order analysis of the maximum matching on a random bipartite graph. An approximate outage probability in the low SNR regime is also obtained by studying the complement of a random bipartite graph. It is then demonstrated that the coefficient of the second-order term in the outage probability expression is zero except for the scenario of two users with two or three subcarriers.

Cognitive radio is a powerful solution that can significantly improve the utilization of the precious limited radio spectrum. It allows secondary users (SUs) to opportunistically access spectral holes in the licensed spectrum without causing harmful interference to primary users (PUs). However, the secondary communication opportunity becomes extremely poor when primary systems are heavily loaded. In this paper, a distributed beamforming method is proposed to allow concurrent transmissions of PUs and SUs, thereby improving the opportunistic spectrum access. Specifically, a SU source broadcasts a message to a set of cognitive users, which can serve as a set of relays, when PUs are absent. The relays that correctly decode the message will create a distributed beamformer to forward the message to the SU destination without causing any interference irrespective of whether PUs are silent or not. To achieve this, we use the method of orthogonal projection to obtain the beamforming weight vector. In addition, we derive the distribution of the received signal power at the SU destination, based on which the average outage probability of our proposed scheme is analyzed when PUs' occupation changes fast. Theoretical and numerical results reveal that the spatial diversity order of this scheme equals the number of SU relays minus that of primary receivers. Furthermore, numerical results show that the outage probability of this scheme outperforms other schemes that access the spectrum only when PUs are absent.

Existing spectrum management policies have led to significant over-allocation and under-utilization of the licensed spectrum. To overcome this, cognitive radio is proposed for secondary users to share the licensed spectrum without causing harmful interference to primary users. As such, the transmit power of a secondary user is limited even when it accesses the spectrum hole. Therefore, multihop transmission is a potential method to deliver the data of secondary users over large distance. In such relay systems, the utilization of rateless codes is suitable for the opportunistic spectrum access of cognitive radio. There has been some work in this area. However, the multi-relay cognitive communication with rateless codes has not been carefully investigated. In this paper, we propose a rateless coding based cooperative transmission scheme for cognitive radio networks, where the average end-to-end throughput is analyzed and optimized. We also propose a block search algorithm to find the optimal number of decoding relays with low complexity. Simulation results show that the optimized relay cognitive cooperative transmission can achieve the maximal throughput.

OFDMA technology can significant improve the transmission reliability in multi-user communication systems because of its inherent frequency diversity. In a recent work, we have derived a surprising result which demonstrates that OFDMA systems can achieve a frequency diversity gain which is equal to the total number of independent subcarriers. In this paper, we shall show that the frequency diversity and the frequency multiplexing can be simultaneously achieved in OFDMA systems with a fundamental tradeoff between them. The random bipartite graph theory is used to model and analyze this diversity-multiplexing tradeoff problem. In particular, the maximum proper f-matching is introduced as a subcarrier allocation method which can minimize the user outage probability with fairness assurance given some multiplexing requirements. Similar to the Zheng-Tse tradeoff in MIMO systems, the optimal diversity-multiplexing tradeoff in multi-user OFDMA systems and it will be shown that its curve can be characterized by a piecewise linear function, despite of the user conflicts in the subcarrier allocation.

With the proliferation of wireless multimedia applications, multicast/broadcast has been recognized as an efficient technique to transmit a large volume of data to multiple mobile stations at the same time. In most multicast systems, the transmitter (e.g. base station) adapts its data rate to the furthest located users, so as to guarantee service quality to as many users as possible. Predictably, the more users in a multicast group, the lower data rate the base station can transmit. On the other hand, grouping more users together leads to a more efficient utilization of spectrum bandwidth, as these users are served simultaneously. This bring the interesting problem that presses for solution: how to group users in a cell into multicast groups and how to allocate a fixed amount of bandwidth resource to the groups, to achieve a good balance between throughput and fairness in multicast systems. In this paper, we formulate the united user grouping and bandwidth allocation strategy into a utility-based optimization problem. One method of signomial programming is used to solve the non-convex optimization problem. Numerical results will show that this suboptimal algorithm performs well even compared to the optimal one. Moreover, through theoretical analysis, we prove that the best user grouping and bandwidth allocation scheme of throughput maximization is to allocate the entire bandwidth to the unique group containing the users located within a ring-shaped region with an optimal outer radius r*.

In cognitive radio networks, knowledge of the position of the primary users is very important as it can be used to avoid harmful interference to the primary users, while at the same time be exploited to improve the spectrum utilization. In this paper, a semi range-based localization algorithm is proposed for the secondary users in cognitive radio networks to estimate the positions of the primary users. The basic idea of the proposed algorithm is to take advantage of the estimated detection probabilities, which can be obtained from the binary detection indictors of the secondary users, in order to estimate the distances between themselves and the primary users. The accuracy of the proposed localization algorithm is further improved by introducing an iterative least squares algorithm. The Cramer-Rao lower bound of the mean square error of the proposed localization estimator is also derived. Extensive simulations will then show that the actual mean square error achieved by the proposed localization algorithm is reasonably close to the lower bound, which demonstrates that the proposed method is near optimal.

In cognitive radio systems, cooperative spectrum sensing in the physical layer is highly desired to detect the primary user accurately and to guarantee the quality of service (QoS) of the primary user. Due to the energy consumption in sensing the channels, the selfish users may not be willing to contribute to the cooperative sensing while they want to occupy more idle channels observed. To deal with this problem, we propose in this paper a game theoretic approach which will advocate users to spend power to sense the channel by using the access opportunity in the MAC layer as a payoff. In this protocol, the users who sense the channel are given higher priority to access the idle channel and meanwhile, the multiuser diversity in the MAC layer is exploited to increase the throughput for cognitive systems. The expressions for the average throughput and consumed power for a single user will be derived and then Nash equilibrium will be studied for the game model. It will be shown that the game will be characterized by the prisoner's dilemma. To guarantee the fairness and achieve higher throughput, we will design an evolutionary game protocol in which the Nash equilibrium can be dynamically changed based on the behaviors of cognitive users. Simulation results will show that the proposed protocol can achieve fairness and efficiency for cognitive users.

We first consider a topology consisting of one source, two destinations and one relay. For such a topology, it is shown that a network coding based cooperative (NCBC) multicast scheme can achieve a diversity order of two. In this paper, we discuss and analyze NCBC in a systematic way as well as compare its performance with two other multicast protocols. The throughput, delay and queue length for each protocol are evaluated. In addition, we present an optimal scheme to maximize throughput subject to delay and queue length constraints. Numerical results will demonstrate that network coding can bring significant gains in terms of throughput.

Due to the broadcasting nature of wireless transmission, concurrently active links can cause mutual interference to each other. This greatly limits the throughput, as well as, results in poor communication reliability especially for wireless systems with multiple links or hops. To overcome this limitation, many interference cancellation techniques, which have mainly focused on the interference among single-hop links, have been designed. In this paper and in contrast to most previous work, we present an efficient method, which we refer to as network interference cancellation or NICE, for effectively mitigating the interference from multi-hop transmissions. This method will make use of the prior knowledge about the interference, which an interfered node can obtain by receiving and processing the signals from the source node of a multi-hop transmission. Two NICE protocols, namely, decode-and-cancel, and amplify-and-cancel are proposed and analyzed. The two proposed protocols will be considered in relay-assisted wireless access networks as well as wireless ad hoc networks without fixed infrastructure to demonstrate the potential of NICE. It will be shown that by using NICE, more links are able to transmit simultaneously in the same frequency band, thereby, highly improving the spatial reuse of spectrum along with the throughput. Numerical results will also show that both of the two NICE protocols can achieve more than 30% throughput gain over conventional interference free scheduling methods.

In ad-hoc networks, cooperative diversity is especially beneficial where the use of multiple antennas may be impractical. There has been a lot of work on improving the peer-to-peer link quality by using advanced coding or power and rate allocation between a single source node and its relays. However, how to fairly and efficiently allocate resources among multiple users and their relays is still unknown. In this paper, a multiuser cooperative protocol is proposed, where a power reward is adopted by each node to evaluate the power contributed to and by others. It will be shown that the proposed fair cooperative protocol (FAP) can significantly improve the fairness performance compared to full cooperation. It is further demonstrated that in energy-constrained cooperative ad-hoc networks, fairness can actually bring significant throughput gains. The tradeoff between fairness and throughput is analyzed and two price-aware protocols, FAP-R and FAP-S, will be further proposed to improve fairness. Simulation results will validate our analysis and show that compared to the direct transmission (i.e., without cooperation) and the full cooperation, our proposed FAP, FAP-R and FAP-S can achieve much better fairness performance along with substantial throughput gains.

In this paper, we describe an efficient and energy conservative unicast routing technique for multi-hop wireless sensor networks over Rayleigh fading channels, which we shall refer to as assistant opportunistic routing (AsOR) protocol. In contrast to previous works, this protocol is presented from a systematic energy conservation perspective. During the source-destination transmission, the AsOR protocol forwards the data stream through a sequence of nodes, which are classified as three different node sets, namely, the frame node, the assistant node and the unselected node. The frame nodes are indispensable to decode-and-forward while the assistant nodes provide protections for unsuccessful opportunistic transmissions. Based on the AsOR protocol, each predetermined route can be divided into several disjoint segments, and we establish a mathematical model to characterize the energy consumptions for each node in one segment. Furthermore, we provide a method for selecting the optimal value N*, the number of nodes in one transmission segment, which can lead to the minimum average energy consumption. Numerical results will confirm that the proposed protocol is energy conservative compared with other two traditional routing protocols both in slow and fast Raleigh fading channels and that the method for searching the optimal value N* is efficient. Finally, robustness analysis for the theoretical results are presented.

OFDM can provide frequency diversity gain for point-to-point communications over frequency-selective slow fading channel. Recent works show that OFDM may also form a flexible and efficient multiple access method, which is often referred to OFDMA. However, the user outage probability and the optimal frequency diversity gain in OFDMA systems are not known. In this paper, random bipartite graph is used to model and analyse the multi-user subcarrier allocation problem over frequency-selective slow fading channels. Our aim is to minimize the user outage probability as well as guarantee fairness by dynamic allocating various subcarrier to each user. An optimal subcarrier allocation algorithm, which we shall refer to as the Hungarian method with random vertices rotation, is introduced to achieve these objectives. A simple but effective approximation equation for user outage probability is then derived. It is shown that the optimal frequency diversity gain in OFDMA system is the same as the point-to-point OFDM system. In particular, the frequency diversity gain does not decay as the number of users increases.

With the proliferation of wireless multimedia applications, multicast/broadcast has been recognized as an efficient technique to transmit a large volume of data to multiple mobile stations at the same time. In most multicast systems, the transmitter (e.g., base station) adapts its data rate to the worst channel among all users in the multicast group, so as to guarantee service quality to each user. Predictably, the more users in a multicast group, the lower data rate the base station can transmit. On the other hand, grouping more users together leads to a more efficient utilization of spectrum bandwidth, as these users are served simultaneously. A natural question that arises is how to group users to maximize the throughput of multicast systems, given a fixed amount of bandwidth resource. In this paper, we attempt to answer this important question that has not been addressed before. Through theoretical analysis, we prove that (1) the average throughput increases with the number of users in a multicast group, when the number of subcarriers allocated to a group is proportional to the number of users therein. Moreover, the throughput approaches infinite-bandwidth Gaussian channel capacity when the number of users gets large; (2) the number of users, and hence the number of subcarriers, that is needed for throughput to be arbitrarily close to its asymptotic value increases almost linearly with the transmit SNR. Our analysis is validated through simulations.

Power allocation is an important issue for spectrum sharing of unlicensed bands, in which multiple unlicensed systems may coexist and operate. Recently some works have been reported on game theoretical analysis for multiple systems cooperating in frequency-flat unlicensed bands. However, there has not been much work on the cooperative and competitive strategic behavior of multiple mutually interfering systems in frequency-selective unlicensed bands. In this paper, we construct a game theoretical framework for multiple selfish systems on frequency-selective Interference Channels (IC). This framework enables us to utilize existing protocols designed for frequency-flat ICs in frequency-selective scenarios and can be regarded as an extension of previous results for frequency-flat scenarios.

In Mobile Ad hoc NETworks (MANETs), random access and dynamic routing are two critical techniques for mobile nodes to convey information without centralized scheduling. Conventionally, random access and dynamic routing are implemented at the Medium Access Control (MAC) and the network layer, respectively. However, the current MAC protocol Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) cannot support dynamic routing efficiently. To overcome this limitation, we shall propose a cross-layer protocol which takes dynamic routing into consideration when the mobile nodes contend to access the channel. In the proposed distributed protocol, the routing packets of the network layer are transmitted within the contention period. Since the transmission of routing packets is separated from data transmission in the time-domain, our cross- layer protocol eliminates the collision caused by the transmission of short routing packets. Simulation results will show that our design could significantly improve the system performance at both the MAC and network layers.

In future wireless communication area, a key issue is the resource allocation and scheduling over wireless channel. Various aspects of this issue have been studied. However, few studies are on the QoS guaranteed uplink multiple traffic scheduling in multi-user wireless network. The scheduling problem is addressed in this paper. We consider the TDM-OFDMA uplink multi-access queuing system with four types of traffic. Each type has specific QoS requirements, such as minimum rate, maximum latency and maximum jitter. This QoS guaranteed cross-layer scheduling issue is modeled as a convex optimization problem. We also prove our scheduling method can guarantee the minimum rate, maximum latency and maximum jitter asymptotically, meanwhile it also minimizes the residual integrated workload. According to the solvability of this optimization problem, we define the scheduling algorithm stability region, and design a heuristic algorithm for connection admission control. The numerical results show the substantial performance of the proposed algorithm.

Cognitive radio (CR) is an emerging and powerful solution that can significantly improve the utilization of limited radio spectrum resources by allowing secondary users to borrow unused spectrum from primary licensed networks. In conventional CR protocols, a secondary user (SU) is allowed to transmit only when the primary users (PU) are not active. However, waiting for idle timeslots may induce large packet delay and loss and result in poor quality of service (QoS) for the secondary user. To overcome this, a joint coding and scheduling method for cognitive multiple access is proposed in this paper. In the physical layer, a successive interference decoder is utilized to thoroughly mitigate the SU's interference to PU. A joint channel- aware and queue-aware scheduling protocol is then proposed, at the MAC layer, to minimize the average packet delay of SU given an average transmit power constraint. We shall formulate Markov models to derive the analytical results of delay, packet- loss rate, and power consumption of the proposed scheme. The optimal scheduling parameter and the minimal average delay are also obtained by solving a cross-layer optimization problem.

Dynamic resource allocation is a key technique that can significantly improve the performance of next generation wireless systems under guaranteed QoS to users. Most of the current resource allocation algorithms are, however, limited to unicast traffics. In practice, how to efficiently allocate various resources in multicast wireless systems is not known. In this paper, we shall study dynamic resource allocation for OFDMA-based single-cell multicast systems. Specifically, we shall formulate an optimization problem to maximize the system throughput given a set of available resources (power and sub-carriers). The optimal resource allocation solution is proposed along with a low- complexity algorithm. In two extreme cases, namely, low and high SNR regimes, the low-complexity allocation algorithm is further simplified. Numerical results will show that the system throughput is significantly improved by using our proposed algorithms.

Cognitive Radios allow secondary users to share the spectrum with primary users. The fairness between secondary users in Cognitive Radio networks is an important issue. This paper analyzes the fairness based on the distributed opportunistic spectrum access scheme. A fair multiple access scheme using fast catch-up strategy is proposed. The theoretical result of the first passage time to achieve fairness is given. Simulation results demonstrate that the Fair OSA scheme can achieve fairness much faster when a new secondary user accesses the spectrum. A complex practical scenario is simulated to validate the fairness in more general situations.

Node cooperation is an emerging and powerful solution that can overcome the limitation of wireless systems as well as improve the capacity of the next generation wireless networks. By forming a virtual antenna array, node cooperation can achieve high antenna and diversity gains by using several partners to relay the transmitted signals. There has been a lot of work on improving the link performance in cooperative networks by using advanced signal processing or power allocation methods among a single source node and its relays. However, the resource allocation among multiple nodes has not received much attention yet. In this paper, we present a unified cross- layer framework for resource allocation in cooperative networks, which considers the physical and network layers jointly and can be applied for any cooperative transmission scheme. It is found that the fairness and energy constraint cannot be satisfied simultaneously if each node uses a fixed set of relays. To solve this problem, amulti-statecooperationmethodology is proposed, where the energy is allocated among the nodes state-by-state via a geometric and network decomposition approach. Given the energy allocation, the duration of each state is then optimized so as to maximize the nodes utility. Numerical results will compare the performance of cooperative networks with and without resource allocation for cooperative beamforming and selection relaying. It is shown that without resource allocation, cooperation will result in a poor lifetime of the heavily-used nodes. In contrast, the proposed framework will not only guarantee fairness, but will also provide significant throughput and diversity gain over conventional cooperation schemes.

Time-slotted ALOHA packetized multiple-access has been extensively used in satellite communications, and has recently been attracting considerable attention in wireless Ad- hoc networks. In this paper, we consider a time-slotted ALOHA system which combines multiple-access, broadcasting channels and rate splitting. This system allows some transmission bits to be reliably received even when collisions occur and more bits to be reliably received in the absence of collisions. In contrast to previous work, our work focuses on the case in which the transmission channels obeyi.i.d(independent and identically distributed) Rayleigh fading. Two fundamental problems are considered. The first one is the calculation of the system capacity, and the second one is how to select an appropriate channel coding scheme with which the system achieves its capacity. We shall review the single time slotted capacity, and present the total capacity expression with the knowledge of channel side information. We will then derive a threshold for the transmission probability in a single time slot provided that all the users have the same transmission probability. It will be shown that when the transmission probability is greater than the derived threshold, low-resolution codes can help the system achieve its capacity. We shall also present an explicit estimation method for computing the threshold, and prove that it can be extended to the more general case when the transmission probabilities are approximately equal.

Network coding is an emerging and powerful solution that can significantly improve the throughput and power efficiency of wireless networks by allowing mixing of various traffic flows via algebraic operations. With network coding, however, a packet has to wait to be network-coded with others given the stochastic nature of the packet arrival process of the various flows. This may result in large delay and packet-loss rate. To overcome this limitation, a novel network coding approach, which we shall refer to as opportunistic network coding (ONQ, is presented in this paper. In this proposed approach, whether a packet is transmitted with or without network coding is determined by the buffer's queue state at a given node. We shall derive ONC's performance in terms of delay, packet-loss, and power consumption by formulating a Markov Chain and a Hidden Markov Model for the delay and power analysis. More importantly, we will develop an optimal ONC strategy with minimal average delay and zero packet-loss rate. In particular, we will show that there exists a fundamental tradeoff between average delay and power, which characterizes the performance limit of ONC.

In this paper, we consider the time-slotted ALOHA packetized multiple-access system where the transmission channels obeyi.i.d(independent identically distributed) Rayleigh fading. Two fundamental problems are considered. The first one is the calculation of system capacity, and the second one is how to select a channel coding for the system achieving its capacity. Here we firstly review the single time slotted capacity, and present the total capacity expression with the knowledge of channel side information. Then we deduce a thresholdthetasfor transmission probability in a single time slot provided that all the users have the same transmission probability. It will be proved that when transmission probability is greater than the thresholdthetas, low-resolution code can help the system achieve its capacity. Moreover, we present an explicit threshold estimation, and prove that it can be extended to a more general case when transmission probabilities are approximately equal. In the end, simulation and numerical results show the validity and robustness of theoretical results.

User cooperation is a powerful solution that can significantly improve the reliability of wireless networks by using several relays to achieve diversity gains. There has been a lot of work on improving the peer-to-peer link quality of a single source-destination pair. However, how to fairly and efficiently allocate resources among multiple nodes has not received much attention yet. In this paper, we propose a novel cooperative diversity method that can achieve fair and efficient resource allocation. We shall show that fairness cannot be achieved by using fixed sets of relays in general. A multi-state cooperation method, where the relay set of each node can be changed, is then proposed to solve this problem. In this proposed approach, the energy is allocated among the nodes via a finite step iterative algorithm. In each step, the relay sets of nodes are changed so that each step will generate a cooperation state, which characterizes the cooperation relationship among the nodes. Based on the energy allocation result, the duration of each state is then optimized so as to minimize the outage probability. We shall show that the proposed method can not only guarantee fairness, but also provide significant diversity gain over conventional cooperation schemes.

Multi-hop wireless networks are expected to play a key role in the next generation wireless systems. In such networks, the presence of multiple links may create severe interference during signal reception and this will greatly limit the network capacity. There has been a lot of work on interference cancellation in single-hop wireless networks. However, how to effectively mitigate interference caused by multi-hop transmission has not received much attention. This paper presents a cross-layer approach for interference cancellation in multi-hop networks. Specifically, two cooperative interference cancellation strategies, which we shall refer to as decode-and-cancel protocol and amplify-and-cancel protocol, are proposed. These schemes take advantage of the presence of a common packet being transmitted through the multiple nodes or hops. It is shown that at a given interfered node, the interference can be estimated and cancelled based upon the received signal of this common packet during the preceding hops. We shall derive the capacity regions of the proposed two protocols and show that they can significantly increase the link capacity.

Multi-hop wireless networks are expected to play an important role in the next-generation wireless systems. One of the central problems in such networks is the network capacity. This paper presents a novel cross layer method for interference cancellation and network coding, which significantly increases the capacity of multi-hop wireless networks. We decompose the multi-hop network into a cell-like sub-network, which we refer to as a wireless switching network. In the proposed approach, multiple nodes, each with its self-information, can communicate via relay nodes. The nodes' self information can then be utilized to cancel the multiuser interference and enable network coding. We shall derive the capacity regions of two cross layer strategies, and show that they are larger than that of the traditional broadcast channel.

In ad-hoc networks, cooperative diversity is especially desired where the use of multiple antennas may be impractical due to the size of nodes. There has been a lot of work on improving the peer-to-peer link quality by using advanced coding or power and rate allocation between a single source node and its relays. However, how to efficiently and fairly allocate resources among multiple users and their relays is still unknown. In this paper, a novel multiuser cooperation protocol is proposed, where multiuser diversity scheme is adopted to schedule different source/destination pairs and each pair computes its required rate based on a power reward. Power reward is adopted by each node to evaluate the power contributed to and by others so as to guarantee fairness. It will be shown that in energy-constrained cooperative ad-hoc networks, fairness can actually bring significant throughput gains. Simulation results will validate our analysis and show that compared to direct transmission and full cooperation protocols, much higher aggregate throughput can be achieved by the proposed Fair Cooperation Protocol thanks to improved fairness.

In multiuser wireless communication systems, dynamic allocation of transmit power is an important means to deal with the time-varying nature both at physical layer and at network layer. Optimal power allocation with perfect channel and buffer state information is studied in this paper. We first build up the cross-layer model by integrating that of physical layer and network layer and then formulate the optimization problem on power allocation. We prove that the optimal solution to power allocation problem can be regarded as an extension of the traditional water-filling (TWF) technique, which is called "water-filling in cellar" (WFIC) policy. The corresponding dynamic programming algorithm is presented. Finally, numerical experiments are employed to illustrate the advantage of our proposed policy.