In the battlefield of cyberspace, sophisticated attackers often operate by following meticulously designed cyber kill chains, enabling them to maintain a persistent presence within victim systems while evading conventional detection mechanisms. Traditional honeypot-based deception defenses aim to uncover such threats by luring attackers into exposing their malicious activities through decoy systems. However, advanced attackers are frequently able to identify and avoid these traps, making it increasingly challenging to detect and engage them effectively. To overcome this challenge, this study proposes a novel defensive paradigm named as the safeguard mode, which emphasizes the covert identification of attackers rather than solely preventing initial breaches. By proactively recognizing potential threats in a hidden manner, victim systems can be better protected through early threat intelligence. Based on the propsoed safeguard mode concept, we propose$Honey^{4}$, abbreviated as$H^{4}$, a comprehensive framework designed to systematically entrap advanced threats.$H^{4}$comprises four core components: Honeypoint, Honeyproxy, Honeytrace, and Honeycenter, which work in concert to deceive, monitor, and analyze attacker behavior. Furthermore, we explore how Artificial Intelligence Generated Content (AIGC) techniques can enhance$H^{4}$'s capabilities, particularly as attackers themselves begin to leverage AI-driven tactics. The practical efficacy of the proposed safeguard mode and the$H^{4}$framework has been validated through being deployed in real scenarios including the 19th Asian Games and the Canton Fairs, and$H^{4}$has successfully captured a significant number of threatening IP addresses and malicious behavioral patterns, generating actionable cyber threat intelligence that fundamentally safeguards system defense.

Advanced Persistent Threats (APTs) have become one of the most prominent cybersecurity risks globally. The external network-facing (ENF) services (e.g., e-commerce platforms) within a system are particularly vulnerable, as they are directly exposed to the Internet and often serve as the primary targets for attackers. By deploying deception resources to protect these ENF services, defenders can detect threats early, block potential attacks, and enhance overall system resilience. However, most existing studies on cyber deception strategies assume simultaneous moves by both attacker and defender. Furthermore, few works have considered the evolution of the system state resulting from APT attacks on the ENF services. To address these limitations, this paper proposes a Cyber Deception Stackelberg Markov Game (CDSMG) for protecting ENF services, which dynamically captures state transitions and accurately characterizes the strategic interactions between defenders and APT attackers. In CDSMG, the defender acts as the leader, who proactively selects a subset of services to deploy the deception resources based on the current system state, while the APT attacker plays as the follower, making a best response which incorporates the defender’s policy into its own strategy. To overcome the challenge of the combinatorial optimization problem of selecting a subset of services, we propose a revised version of the PPO algorithm by using no-replacement sampling to select multiple services at once, thereby significantly reducing the action space size. Finally, experimental results demonstrate that our approach effectively defends against APT attacks. It not only outperforms several baseline methods but also exhibits better scalability and robustness under varied model parameter settings.

Wireless sensing enables contactless and accurate recognition of human activities and physiological states by using electromagnetic signals. As a promising enabler for sixth-generation (6 G) multi-functional networks, orthogonal time frequency space (OTFS) modulation exhibits strong resilience to high Doppler shifts in high-mobility environments, while also supporting precise human sensing in low-mobility scenarios. In this work, we propose a novel two-dimensional (2D) delay-Doppler motion profiling framework based on the OTFS waveform to extract distinctive features of human activities. To enhance recognition performance, a fractional-Doppler enhancement network is integrated with a convolutional neural network (CNN)-aided encoder-only Transformer architecture. Extensive experiments are conducted to assess the cross-domain generalization capability of the proposed OTFSensi system. Compared with existing classification models based on CNN, gated recurrent unit (GRU), and long short-term memory (LSTM) networks, OTFSensi demonstrates substantial improvements in adaptability across diverse environments and observation angles. Furthermore, a comparative analysis with various radio frequency (RF) sensing technologies confirms the superior classification performance achieved by OTFSensi.

In this paper, a cooperative passive sensing system in millimeter-wave (mmWave) band for simultaneous device localization and target tracking, namely mmAlert, is proposed. Specifically, in uplink communication with at least two transmitters, the receiver receives the line-of-sight (LoS) signals and the scattered signals off a moving target, respectively. Based on the received signals of the sensing time intervals, when a passive target moves along one or multiple unknown trajectories, mmAlert could measure the angles-of-arrival (AoAs) and bistatic Doppler frequencies of the echoes from the sensing target, and then jointly estimate the locations of the transmitters and the trajectories of the target. Specifically, the transmitters’ locations and the moving target’s trajectories can be searched by minimizing the weighted mean squared error of the AoA and Doppler measurements. The optimal solution of the minimization problem is prohibitive due to the large number of variables. Hence, a low-complexity algorithm based on the alternating optimization is proposed, where the extended Kalman filter (EKF) is introduced to quickly shape the trajectories. The mmAlert is implemented in a 60GHz communication testbed. The experiment shows with the received signal spanning a single trajectory, the average localization error of the transmitters and average trajectory reconstruction error are 0.76 m and 0.29 m, respectively. The average errors are suppressed to 0.07 m and 0.2 m respectively, if the received signal spanning 50 trajectories is used. This justifies the benefit of trajectory diversity in localization and tracking.

Fluid antenna system (FAS) revolutionizes wireless communications via utilizing position-flexible antennas that dynamically optimize channel conditions and mitigate multipath fading. This innovation is particularly valuable in indoor environments, in which signal propagation is severely degraded due to structural obstructions and complex multipath reflections. In this paper, we investigate the channel modeling and the joint optimization of antenna positioning, beamforming, and power allocation for indoor FAS. In particular, we propose a layout-specific channel model, and employ the novel group relative policy optimization (GRPO) algorithm for tackling the optimization problem. Compared to the state-of-the-art Sionna model, our model achieves an 83.3% reduction in computation time with an approximately 3 dB increase in root-mean-square error (RMSE). When simplified to a two-ray model, our model allows for a closed-form antenna position solution with near-optimal performance. For the joint optimization problem, our GRPO algorithm outperforms proximal policy optimization (PPO) and other baselines in sum-rate, while requiring only 50.8% computational resources of PPO, thanks to its group advantage estimation. Simulation results show that increasing either the group size or trajectory length in GRPO does not yield significant improvements in sum-rate, suggesting that these parameters can be selected conservatively without sacrificing performance.

The enhanced distributed channel access (EDCA) mechanism is used in current wireless fidelity (WiFi) networks to support priority requirements of heterogeneous applications. However, the EDCA mechanism can not adapt to particular quality-of-service (QoS) objective, network topology, and interference level. In this paper, a novel reinforcement-learningbased scheduling framework is proposed and implemented to optimize the application-layer quality-of-service (QoS) of a WiFi network with commercial adapters and unknown interference. Particularly, application-layer tasks of file delivery and delaysensitive communication are jointly scheduled by adjusting the contention window sizes and application-layer throughput limitation, such that the throughput of the former and the round trip time of the latter can be optimized. Due to the unknown interference and vendor-dependent implementation of the WiFi adapters, the relation between the scheduling policy and the system QoS is unknown. Hence, a reinforcement learning method is proposed, in which a novel Q-network is trained to map from the historical scheduling parameters and QoS observations to the current scheduling action. It is demonstrated on a testbed that the proposed framework can achieve a significantly better performance than the EDCA mechanism.

In this paper, the task offloading from vehicles with random velocities is optimized via a novel dynamic improvement framework. Particularly, in a vehicular network with multiple vehicles and base stations (BSs), computing tasks of vehicles are offloaded via BSs to an edge server. Due to the random velocities, the exact trajectories of vehicles cannot be predicted in advance. Hence, instead of deterministic optimization, the cell association, uplink time and throughput allocation of multiple vehicles in a period of task offloading are formulated as a finite-horizon Markov decision process. In the proposed solution framework, we first obtain a reference scheduling scheme of cell association, uplink time and throughput allocation via deterministic optimization at the very beginning. The reference scheduling scheme is then used to approximate the value functions of the Bellman's equations, and the actual scheduling action is determined in each time slot according to the current system state and approximate value functions. Thus, the intensive computation for value iteration in the conventional solution is eliminated. Moreover, a nontrivial average cost upper bound is provided for the proposed solution framework. In the simulation, the random trajectories of vehicles are generated from a high-fidelity traffic simulator. It is shown that the performance gain of the proposed scheduling framework over the baselines is significant.

In this paper, task offloading from vehicles with random velocities is optimized via a novel dynamic programming framework. Particularly, in a vehicular network with multiple vehicles and base stations (BSs), computing tasks of vehicles are offloaded via BSs to an edge server. Due to the random velocities, the exact locations of vehicles versus time, namely trajectories, cannot be determined in advance. Hence, instead of deterministic optimization, the cell association, uplink time, and throughput allocation of multiple vehicles during a period of task offloading are formulated as a finite-horizon Markov decision process. In order to derive a low-complexity solution algorithm, a two-time-scale framework is proposed. The scheduling period is divided into super slots, each super slot is further divided into a number of time slots. At the beginning of each super slot, we first obtain a reference scheduling scheme of cell association, uplink time and throughput allocation via deterministic optimization, yielding an approximation of the optimal value function. Within the super slot, the actual scheduling action of each time slot is determined by making improvement to the approximate value function according to the system state. Due to the successive improvement framework, a non-trivial average cost upper bound could be derived. In the simulation, the random trajectories of vehicles are generated from a high-fidelity traffic simulator. It is shown that the performance gain of the proposed scheduling framework over the baselines is significant.

This paper proposes an adaptive channel contention mechanism to optimize the queuing performance of a distributed millimeter wave (mmWave) uplink system with the capability of environment and mobility sensing. The mobile agents determine their back-off timer parameters according to their local knowledge of the uplink queue lengths, channel quality, and future channel statistics, where the channel prediction relies on the environment and mobility sensing. The optimization of queuing performance with this adaptive channel contention mechanism is formulated as a decentralized multi-agent Markov decision process (MDP). Although the channel contention actions are determined locally at the mobile agents, the optimization of local channel contention policies of all mobile agents is conducted in a centralized manner according to the system statistics before the scheduling. In the solution, the local policies are approximated by analytical models, and the optimization of their parameters becomes a stochastic optimization problem along an adaptive Markov chain. An unbiased gradient estimation is proposed so that the local policies can be optimized efficiently via the stochastic gradient descent method. It is demonstrated by simulation that the proposed gradient estimation is significantly more efficient in optimization than the existing methods, e.g., simultaneous perturbation stochastic approximation (SPSA).

The swift advancement of internet technologies has had profound effects across numerous sectors, placing cyber-security at the forefront of concerns for both businesses and governmental entities. Effective cyber risk assessment enables network administrators to reinforce their defenses against cyber attacks and mitigate potential losses. However, traditional approaches frequently concentrate solely on either the difficulty of exploiting vulnerabilities or the topological characteristics of attack graphs, and some require knowledge of the attacker’s strategies. This paper presents a novel network risk assessment methodology that combines cooperative game theory with host probabilistic Attack Graphs, called RACAG. Specifically, Shapley values is employed to provide an unbiased assessment the impact of each attack path on the overall system damage. The proposed approach does not require any prior knowledge of the attacker’s strategies but instead focuses on comprehensive analysis of the exploit difficulty of vulnerabilities in the system, asset information, network topology, and attacker traces detected by defense mechanisms. Experiments have confirmed the efficacy of RACAG in assessing the impact of attack paths on overall system damage. Additionally, they highlighted its advantages in the deployment of deceptive resources, enhancement of risk perception abilities, and its support for defenders in analyzing smokescreen tactic.

Characterizing the sensing and communication performance tradeoff in integrated sensing and communication (ISAC) systems is challenging in the applications of learning-based human motion recognition. This is because of the large experimental data sets and the black-box nature of deep neural networks. This article presents SDP3, a Simulation-Driven Performance Predictor and oPtimizer, which consists of SDP3 data simulator, SDP3 performance predictor and SDP3 performance optimizer. Specifically, the SDP3 data simulator generates vivid wireless sensing data sets in a virtual environment, the SDP3 performance predictor predicts the sensing performance based on the curve fitting method, and the SDP3 performance optimizer investigates the sensing and communication performance tradeoff analytically. It is shown that the simulated sensing data set matches the experimental data set very well in the motion recognition accuracy. By leveraging SDP3, it is found that the achievable region of recognition accuracy and communication throughput consists of a communication saturation zone, a sensing saturation zone, and a communication-sensing adversarial zone, of which the desired balanced performance for ISAC systems lies in the third one.

A two-time-scale approximate Markov decision process (MDP) is proposed to optimize the uplink queuing performance of a millimeter wave (mmWave) communication system. Exploiting the wireless sensing techniques, the locations of signal reflectors, blockers, and mobile agents, as well as the mobility pattern of agents, become available knowledge to facilitate predictive scheduling. Notice that the state variation of the wireless channel fading and transmission queue is much faster than that of mobile agents’ locations. The joint optimization of uplink power adaptation, time allocation, and analog beamforming of all the frames is formulated as a two-time-scale MDP with the average uplink energy, queuing length, and buffer overflow rate in the minimization objective. The scheduling in the larger time scale with an infinite horizon, i.e., the analog transceiver beamforming, adapts with the random motion of mobile agents; whereas the scheduling in the smaller time scale with a finite horizon, i.e., uplink power and time allocation, adapts with both the small-scale channel fading, queue dynamics and large-scale random motion. The optimal solution of two-time-scale MDP requests iterative optimization between Bellman’s equations of both time scales, whose computation complexity is prohibitive. A novel low-complexity solution framework is then proposed to obtain the optimal larger-time-scale and sub-optimal smaller-time-scale policies, where the performance of the proposed scheme is bounded analytically. Benefiting from the motion sensing and blockage prediction, the proposed scheduling scheme outperforms existing benchmarks in the numerical simulations.

In this paper, we characterize the generalized degrees-of-freedom (GDoF) region of the two-user$(M,N_{1},N_{2})$multiple-input multiple-output (MIMO) broadcast channel with delayed channel state information at the transmitter (CSIT), where there are one transmitter with$M$antennas and two receivers with$N_{1}$and$N_{2}$antennas, respectively. Under delayed CSIT, different from the existing converse approaches in the multiple-input single-output (MISO) GDoF and MIMO degrees-of-freedom (DoF) models, we incorporate new components into traditional approaches for this MIMO GDoF converse. For the achievability, we generalize the existing MISO achievable scheme. Our result reveals how the channel strength and antenna configuration impact the GDoF region of the two-user MIMO broadcast channel with delayed CSIT. Furthermore, the extension of our converse to a GDoF outer region of the$K$-user MIMO broadcast channel with delayed CSIT is also provided.

Spectrum sensing plays a crucial role in spectrum monitoring and management. However, due to the expensive cost of high-speed ADCs, wideband spectrum sensing is a long-standing challenge. In this paper, we present how to transform Ultra-wideband (UWB) devices into a spectrum sensor which can provide wideband spectrum monitoring at a low cost. Compared with the expensive high-speed ADCs which cost at least hundreds of dollars, a UWB device is only several dollars. As the low-cost UWB technology is not originally designed for spectrum sensing, we address the inherent limitations of low-cost devices such as limited memory, low SPI speed and low accuracy, and show how to obtain spectrum occupancy information from the noisy and spurious UWB channel impulse response. In this paper, we present, which not only can give accurate channel occupancy information, but also can precisely estimate the signal power and bandwidth. can also detect fleeting radar signals. We implement and perform extensive evaluations with both controlled experiments and field tests. Results show that can sense up to$900$MHz bandwidth with frequency range from 0.5GHz to 7GHz, and the power estimation error is less than$3$dB. can also accurately detect busy 5G channels and can classify the encrypted traffic from the channel usage patterns. We believe that provides a new paradigm for low-cost wideband spectrum sensing, which is critical for large-scale fine-grained spectrum monitoring.

Wireless sensor networks (WSNs) leveraging millimeter wave (mmWave) communication for bandwidth-demanding applications is considered in this article. Despite the large bandwidth, the delivery of delay-sensitive information collected by sensors may still face significant latency due to the vulnerability to intermittent link blockage. Hence, the guarantee of low age of information (AoI) in mmWave WSNs is not straightforward. In this article, the wireless sensing and dynamic programming techniques are jointly exploited to relieve the above issue. The former tracks the human blockers and predicts the chance of link blockage; the latter optimizes the transmission of multiple sensors based on the prediction. Particularly, the long-term optimization of sampling, uplink time and power allocation policies in a sensor network can be formulated as an infinite-horizon Markov decision process (MDP) with discounted cost, where the state transition probabilities can be predicted via wireless sensing. A novel low-complexity solution framework, namely COSMO, with a guaranteed performance in the worst case, is proposed. Simulations show that compared with heuristic benchmarks, benefiting from the prediction of the link blockage, COSMO can significantly suppress the average system cost, which consists of both AoI and energy consumption.

In this paper, the joint downlink delay-aware scheduling in a large time span, where the rotation of User Equipments (UEs) may lead to significant channel variation, is investigated via a novel approximate Markov Decision Process (MDP) method. Specifically, we consider the joint downlink power allocation and receiving UE selection of a number of successive frames in a millimeter Wave (mmWave) system with quasi-static scattering clusters in the channel and rotating UEs. The propagation statistics of scattering clusters can be tracked via a learning method. Since the rotation of UEs can be detected, future channel statistics can be forecast via embedded motion sensors. Hence, the overall scheduling is formulated as a finite-horizon MDP with non-stationary predictable state transition probabilities, where the average queuing delay and probability of transmission buffer overflow are considered in the objective of scheduling optimization. A novel low-complexity solution framework with an analytical performance bound is proposed to save the efforts of value iteration. Benefiting from the forecast of system statistics, superior performance to the benchmarks is shown by numerical simulations, particularly in the suppression of buffer overflow rate. Preliminary experiments via an mmWave testbed are conducted to demonstrate the feasibility of the sensor-assisted mmWave beam alignment.

Most of the existing research on degrees-of-freedom (DoF) with imperfect channel state information at the transmitter (CSIT) assume the messages are private, which may not reflect reality as the two receivers can request the same content. To overcome this limitation, we therefore consider the hybrid unicast and multicast messages. In particular, we characterize the optimal DoF region for the two-user multiple-input multiple-output (MIMO) broadcast channel (BC) with imperfect CSIT and hybrid messages. For the converse, we establish a three-step procedure to exploit the utmost possible relaxation. For the achievability, since the DoF region is with specific three-dimensional structure regarding antenna configurations and CSIT qualities, we verify the existence or non-existence of corner point candidates via the feature of antenna configurations and CSIT qualities categorization, and provide a hybrid message-aware rate-splitting scheme. Besides, we show that to achieve the strictly positive corner points, it is unnecessary to split the unicast messages into private and common parts. This implies adding a multicast message may mitigate the rate-splitting complexity.

Millimeter wave (MmWave) has been regarded as a promising technology to support high-capacity communications in 5G era. However, its high-layer performance such as latency and packet drop rate in the long term highly depends on resource allocation because mmWave channel suffers significant fluctuation with rotating users due to mmWave sparse channel property and limited field-of-view (FoV) of antenna arrays. In this paper, downlink transmission scheduling considering rotation of user equipments (UE) and limited antenna FoV in an mmWave system is optimized via a novel approximate Markov decision process (MDP) method. Specifically, we consider the joint downlink UE selection and power allocation in a number of frames where future orientations of rotating UEs can be predicted via embedded motion sensors. The problem is formulated as a finite-horizon MDP with non-stationary state transition probabilities. A novel low-complexity solution framework is proposed via one iteration step over a base policy whose average future cost can be predicted with analytical expressions. It is demonstrated by simulations that compared with existing benchmarks, the proposed scheme can schedule the downlink transmission and suppress the packet drop rate efficiently in non-stationary mmWave links.

The freshness of information, measured as Age of Information (AoI), is critical for many applications in next-generation wireless sensor networks (WSNs). Due to its high bandwidth, millimeter wave (mmWave) communication is seen to be frequently exploited in WSNs to facilitate the deployment of bandwidth-demanding applications. However, the vulnerability of mmWave to user mobility typically results in link blockage and thus postponed real-time communications. In this paper, joint sampling and uploading scheduling in an AoI-oriented WSN working in mmWave band is considered, where a single human blocker is moving randomly and signal propagation paths may be blocked. The locations of signal reflectors and the real-time position of the blocker can be detected via wireless sensing technologies. With the knowledge of blocker motion pattern, the statistics of future wireless channels can be predicted. As a result, the AoI degradation arising from link blockage can be forecast and mitigated. Specifically, we formulate the long-term sampling, uplink transmission time and power allocation as an infinite-horizon Markov decision process (MDP) with discounted cost. Due to the curse of dimensionality, the optimal solution is infeasible. A novel low-complexity solution framework with guaranteed performance in the worst case is proposed where the forecast of link blockage is exploited in a value function approximation. Simulations show that compared with several heuristic benchmarks, our proposed policy, benefiting from the awareness of link blockage, can reduce average cost up to 49.6%.

In this paper, we propose a rate-splitting design and characterize the sum-degrees-of-freedom (DoF) for the K-user multiple-input-single-output (MISO) broadcast channel with mixed channel state information at the transmitter (CSIT) and order-(K − 1) messages, where mixed CSIT refers to the delayed and imperfect-current CSIT, and order-(K − 1) message refers to the message desired by K − 1 users simultaneously. In particular, for the sum-DoF lower bound, we propose a rate-splitting scheme embedding with retrospective interference alignment. In addition, we propose a matching sum-DoF upper bound via genie signalings and extremal inequality. Opposed to existing works for K = 2, our results show that the sum-DoF is saturated with CSIT quality when CSIT quality thresholds are satisfied for K > 2.

Federated edge learning (FEEL) is envisioned as a promising paradigm to achieve privacy-preserving distributed learning. However, it consumes excessive learning time due to the existence of straggler devices. In this article, a novel topology-optimized FEEL (TOFEL) scheme is proposed to tackle the heterogeneity issue in federated learning and to improve the communication-and-computation efficiency. Specifically, a problem of jointly optimizing the aggregation topology and computing speed is formulated to minimize the weighted summation of energy consumption and latency. To solve the mixed-integer nonlinear problem, we propose a novel solution method of penalty-based successive convex approximation (SCA), which converges to a stationary point of the primal problem under mild conditions. To facilitate real-time decision making, an imitation-learning-based method is developed, where deep neural networks (DNNs) are trained offline to mimic the penalty-based method, and the trained imitation DNNs are deployed at the edge devices for online inference. Thereby, an efficient imitation-learning-based approach is seamlessly integrated into the TOFEL framework. Simulation results demonstrate that the proposed TOFEL scheme accelerates the federated learning process and achieves a higher energy efficiency. Moreover, we apply the scheme to 3-D object detection with multivehicle point cloud data sets in the CARLA simulator. The results confirm the superior learning performance of the TOFEL scheme over conventional designs with the same resource and deadline constraints.

Direction-of-Arrival (DoA) estimation is essential in many Industrial Internet of Things (IIoT) applications where IoT users may require a high-resolution estimation algorithm for features limited to positioning and device localization. The conventional DoA estimation is challenging as the number of antenna arrays increases leading to an increase in design complexity and cost in RF circuitry. As a result, the conventional DoA estimation algorithms may not be suited for IIoT applications. In this work, a joint DOA and carrier frequency offset (CFO) schemes is introduced for typical uplink IIoT systems by equipping the receiver with a leaky-wave antenna (LWA). By considering that the LWA can simultaneously steer its beam direction toward the active user. As a result, each user is estimated independently. The joint algorithm is developed based on the high-resolution multiple signal classification scheme, which utilizes the LWA properties to compute the DoA spectrum. Furthermore, we develop an iterative CFO estimation approach as the desired solution to a multidimensional search. Subsequently, we further formulate the Cramér–Rao lower bounds (CRLB) to serve as a performance benchmark for the accuracy of the proposed scheme. The software define radio (SDR) platform is provided to validate the proposed algorithm in real-time. Finally, the simulation results and the SDR experiments justify that the proposed scheme offers more robustness than conventional counterparts with linear array antenna, which achieves a root-mean square error (RMSE) approach to the derived CLRB, especially at a medium signal-to-noise ratio (SNR) range which suited for IIoT applications.

This letter investigates joint beamforming and power optimization in a vehicle-to-infrastructure (V2I) system for integrated passive sensing and communications techniques. Specifically, the base station (BS) with multiple antennas delivers downlink data to multiple vehicles, respectively, and a sensing receiver simultaneously utilizes the downlink data signal to sense the motion of one vehicle. The sensing receiver is deployed separately. It collects the line-of-sight (LoS) signals from the BS and the scattered signals via the target vehicle, such that the distance and velocity of the target vehicle can be detected in a passive manner. As a result, the joint downlink beamforming and power optimization at the BS would be able to maximize the weighted summation of downlink data rates, subject to constraints on the signal-to-interference-plus-noise ratios (SINRs) of the two signals for passive sensing. In order to solve the above non-convex optimization problem, we first derive the optimal receiving beams for the data and sensing receivers given arbitrary transmission beam design, and then propose a low-complexity iterative algorithm to find a sub-optimal design of transmission beams based on the successive convex approximation (SCA) method. Simulations demonstrate the good performance, fast convergence and useful design insights.

Realizing edge intelligence consists of sensing, communication, training, and inference stages. Conventionally, the sensing and communication stages are executed sequentially, which results in excessive amount of dataset generation and uploading time. This paper proposes to accelerate edge intelligence via integrated sensing and communication (ISAC). As such, the sensing and communication stages are merged so as to make the best use of the wireless signals for the dual purpose of dataset generation and uploading. However, ISAC also introduces additional interference between sensing and communication functionalities. To address this challenge, this paper proposes a classification error minimization formulation to design the ISAC beamforming and time allocation. The globally optimal solution is derived via the rank-1 guaranteed semidefinite relaxation, and performance analysis is performed to quantify the ISAC gain over that of conventional edge intelligence. Simulation results are provided to verify the effectiveness of the proposed ISAC-assisted edge intelligence system. Interestingly, we find that ISAC is always beneficial, when the duration of generating a sample is more than the duration of uploading a sample. Otherwise, the ISAC gain can vanish or even be negative. Nevertheless, we still derive a sufficient condition, under which a positive ISAC gain is feasible.

Critical robotic tasks such as rescue and disaster response are more prevalently leveraging ML (Machine Learning) models deployed on a team of wireless robots, on which data parallel (DP) training over Internet of Things of these robots (robotic IoT) can harness the distributed hardware resources to adapt their models to changing environments as soon as possible. Unfortunately, due to the need for DP synchronization across all robots, the instability in wireless networks (i.e., fluctuating bandwidth due to occlusion and varying communication distance) often leads to severe stall of robots, which affects the training accuracy within a tight time budget and wastes energy stalling. Existing methods to cope with the instability of datacenter networks are incapable of handling such straggler effect. That is because they are conducting model-granulated transmission scheduling, which is much more coarse-grained than the granularity of transient network instability in real-world robotic IoT networks, making a previously reached schedule mismatch with the varying bandwidth during transmission. We present ROG, the first ROw-Granulated distributed training system optimized for ML training over unstable wireless networks. ROG confines the granularity of transmission and synchronization to each row of a layer’s parameters and schedules the transmission of each row adaptively to the fluctuating bandwidth. In this way the ML training process can update partial and the most important gradients of a stale robot to avoid triggering stalls, while provably guaranteeing convergence. The evaluation shows that, given the same training time, ROG achieved about 4.9%~6.5% training accuracy gain compared with the baselines and saved 20.4%~50.7% of the energy to achieve the same training accuracy.

Spectrum sensing plays a crucial role in spectrum monitoring and management. However, due to the expensive cost of high-speed ADCs, wideband spectrum sensing is a long-standing challenge. In this paper, we present how to transform Ultra-wideband (UWB) devices into a spectrum sensor which can provide wideband spectrum monitoring at a low cost. Compared with the expensive high-speed ADCs which cost at least hundreds of dollars, a UWB device is only several dollars. As the low-cost UWB technology is not originally designed for spectrum sensing, we address the inherent limitations of low-cost devices such as limited memory, low SPI speed and low accuracy, and show how to obtain spectrum occupancy information from the noisy and spurious UWB channel impulse response. In this paper, we present WISE, which not only can give accurate channel occupancy information, but also can precisely estimate the signal power and bandwidth. WISE can also detect fleeting radar signals. We implement WISE and perform extensive evaluations with both controlled experiments and field tests. Results show that WISE can sense up to 900MHz bandwidth and the power estimation error is less than 3dB. WISE can also accurately detect busy 5G channels. We believe that WISE provides a new paradigm for low-cost wideband spectrum sensing, which is critical for large-scale fine-grained spectrum monitoring.

Spectrum sensing plays a crucial role in spectrum monitoring and management. However, Due to the expensive cost of high-speed ADCs, wideband spectrum sensing is a long-standing challenge. In this demo, we present how to transform the low-cost Ultrawideband (UWB) devices into a spectrum sensor and showcase WISE, a low-cost wideband spectrum sensing system, which not only can give accurate channel occupancy information, but also can precisely estimate the signal power and bandwidth. Our demo will show that WISE can sense up to 900MHz bandwidth and the power estimation error is less than 3dB. WISE can also accurately detect busy 5G channels and fleeting radar signals. We believe that WISE provides a new paradigm for low-cost wideband spectrum sensing, which is critical for large-scale fine-grained spectrum monitoring.

Job dispatching is a fundamental problem in edge computing for load balancing among multiple edge servers. When implementing an edge computing system with distributed job dispatchers in a sizable network, such as a metropolitan area network (MAN), the highly dynamic transmission latency is nonnegligible, which could lead to outdated information being shared. Moreover, the fully observed system state is beyond reach as the reception of any broadcast is time consuming. In this article, we investigate the online distributed job dispatching problem in edge computing, where multiple access points (APs) collect jobs and then dispatch each job to an edge server. The distributed dispatcher on each AP would receive partially and outdated information exchanged via periodic broadcast. Hence, we formulate the distributed job dispatching problem by leveraging the partially observable Markov decision process (POMDP) and propose a novel approximate Markov decision process (MDP) solution framework, calledDecMDP, that bypasses the huge time complexity of conventional POMDP solutions. Both analytical and semi-analytical performance lower bounds are derived for the approximate MDP solution. Furthermore, we extendDecMDPto handle a more general scenario wherea prioriknowledge of the system is absent. Finally, extensive simulations based on the Google Cluster traces show that our policy can achieve the best performance when compared with heuristic baselines, e.g., achieving 20.67% reduction in average job response time, and consistently performs well under various parameter settings.

Edge federated learning (FL) is an emerging paradigm that trains a global parametric model from distributed datasets based on wireless communications. This paper proposes a unit-modulus over-the-air computation (UMAirComp) framework to facilitate efficient edge federated learning, which simultaneously uploads local model parameters and updates global model parameters via analog beamforming. The proposed framework avoids sophisticated baseband signal processing, leading to low communication delays and implementation costs. Training loss bounds of UMAirComp FL systems are derived and two low-complexity large-scale optimization algorithms, termed penalty alternating minimization (PAM) and accelerated gradient projection (AGP), are proposed to minimize the nonconvex nonsmooth loss bound. Simulation results show that the proposed UMAirComp framework with PAM algorithm achieves a smaller mean square error of model parameters’ estimation, training loss, and test error compared with other benchmark schemes. Moreover, the proposed UMAirComp framework with AGP algorithm achieves satisfactory performance while reduces the computational complexity by orders of magnitude compared with existing optimization algorithms. Finally, we demonstrate the implementation of UMAirComp in a vehicle-to-everything autonomous driving simulation platform. It is found that autonomous driving tasks are more sensitive to model parameter errors than other tasks since the neural networks for autonomous driving contain sparser model parameters.

Heterogeneous cellular networks (HetNets), where low-power low-complexity base stations (Pico-BSs) are deployed inside the coverage of macro base stations (Macro-BSs), can significantly improve the spectrum efficiency by Pico- and Macro base station collaboration. Due to cross-tier interference, joint detection of uplink signals is widely adopted so that Pico-BS can either detect the uplink signals locally or forward them to Macro-BS for processing. The latter can achieve increased throughput at the cost of additional backhaul transmission. In this paper, we study the delay-optimal uplink scheduling problem in HetNets with limited backhaul capacity. Local signal detection or joint signal detection is scheduled in a unified delay-optimal framework. Specifically, we first prove that the problem is NP-hard and then formulate it as a Markov Decision Process. We propose an efficient algorithm, calledOLIUS, that can deal with the exponentially growing state and action space. Furthermore,OLIUSis online learning-based which does not require any prior knowledge on user behavior or channel characteristics. We prove the convergence ofOLIUSand derive an upper bound on its approximation error. Extensive experiments in various scenarios show our algorithm outperforms existing methods in reducing delay and power consumption.

The enormous data distributed at the network edge and ubiquitous connectivity have led to the emergence of the new paradigm of distributed machine learning and large-scale data analytics. Distributed principal component analysis (PCA) concerns finding a low-dimensional subspace that contains the most important information of high-dimensional data distributed over the network edge. The subspace is useful for distributed data compression and feature extraction. This work advocates the application of over-the-air federated learning to efficient implementation of distributed PCA in a wireless network under a data-privacy constraint, termed AirPCA. The design features the exploitation of the waveform-superposition property of a multi-access channel to realize over-the-air aggregation of local subspace updates computed and simultaneously transmitted by devices to a server, thereby reducing the multi-access latency. The original drawback of this class of techniques, namely channel-noise perturbation to uncoded analog modulated signals, is turned into a mechanism for escaping from saddle points during stochastic gradient descent (SGD) in the AirPCA algorithm. As a result, the convergence of the AirPCA algorithm is accelerated. To materialize the idea, descent speeds in different types of descent regions are analyzed mathematically using martingale theory by accounting for wireless propagation and techniques including broadband transmission, over-the-air aggregation, channel fading and noise. The results reveal the accelerating effect of noise in saddle regions and the opposite effect in other types of regions. The insight and results are applied to designing an online scheme for adapting receive signal power to the type of current descent region. Specifically, the scheme amplifies the noise effect in saddle regions by reducing signal power and applies the power savings to suppressing the effect in other regions. From experiments using real datasets, such power control is found to accelerate convergence while achieving the same convergence accuracy as in the ideal case of centralized PCA.

Wireless federated learning (FL) is an emerging machine learning paradigm that trains a global parametric model from distributed datasets via wireless communications. This paper proposes a unit-modulus wireless FL (UMWFL) framework, which simultaneously uploads local model parameters and computes global model parameters via optimized phase shifting. The proposed framework avoids sophisticated baseband signal processing, leading to both low communication delays and implementation costs. A training loss bound is derived and a penalty alternating minimization (PAM) algorithm is proposed to minimize the nonconvex nonsmooth loss bound. Experimental results in the Car Learning to Act (CARLA) platform show that the proposed UMWFL framework with PAM algorithm achieves smaller training losses and testing errors than those of the benchmark scheme.

The ever-growing popularity and rapid improving of artificial intelligence (AI) have raised rethinking on the evolution of wireless networks. Mobile edge computing (MEC) provides a natural platform for AI applications since it provides rich computation resources to train AI models, as well as low-latency access to the data generated by mobile and Internet of Things devices. In this paper, we present an infrastructure to perform machine learning tasks at an MEC server with the assistance of a reconfigurable intelligent surface (RIS). In contrast to conventional communication systems where the principal criteria are to maximize the throughput, we aim at optimizing the learning performance. Specifically, we minimize the maximum learning error of all users by jointly optimizing the beamforming vectors of the base station and the phase-shift matrix of the RIS. An alternating optimization-based framework is proposed to optimize the two terms iteratively, where closed-form expressions of the beamforming vectors are derived, and an alternating direction method of multipliers (ADMM)-based algorithm is designed together with an error level searching framework to effectively solve the nonconvex optimization problem of the phase-shift matrix. Simulation results demonstrate significant gains of deploying an RIS and validate the advantages of our proposed algorithms over various benchmarks.

Multi-view models for various multi-view applications (e.g., pose recognition, facial recognition) achieve higher accuracy when more sensing data (views) from different sensors are flexibly collected via wireless networks and combined into inference input. However, when the view number scales up, the application suffers a long latency to collect all the latest views before inference (vanilla workflow). We observed that collecting all the latest views before inference is unnecessary, because different views are often not equally important and important views have major contribution to the output. In this paper, we present a Multi-View Semantic-Aware Scheduling (MVSAS) system that automatically prioritizes views according to their importance and schedules the early transmission of the important views. We tackled the challenge to infer view importance by analyzing the inference intermediates and extracting the semantics (e.g., number of persons) of each view. Once important views are collected, needless to wait for other less important views, the important views are combined with stale version of less important views as inference input, so as to retain high accuracy while reducing the latency to collect views. Evaluation shows that MVSAS achieved at most 36.9% latency reduction while retaining at most 98.7% accuracy compared to the vanilla workflow.

This article studies the problem of Sum-secure Degrees of Freedom (SDoF) of the$(M,M,N,N)$multiple-input–multiple-output (MIMO) interference channel with local output feedback, so as to build an information-theoretic foundation and provide practical transmission schemes for 6G-enabled Vehicles-to-Vehicles (V2V). For this problem, we propose two novel transmission schemes, i.e., the interference decoding scheme and the interference alignment scheme, and thus establish a sum-SDoF lower bound. In particular, to optimize the phase duration, we analyze the security and decoding constraints and formulate a linear-fractional optimization problem. Furthermore, we show that the derived sum-SDoF lower bound is the sum-SDoF for$M \le N/2$,$N=M$, and$2N \le M$antenna configurations, and reveal that for a fixed$N$, the optimal$M$to maximize the sum-SDoF is not less than$2N$. Through simulations, we examine the secure sum-rate performance of proposed transmission schemes and reveal that using local output feedback can lead to a higher secure sum-rate than that by using delayed channel state information at the transmitter (CSIT).

For the two-user multiple-input multiple-output (MIMO) broadcast channel with delayed channel state information at the transmitter (CSIT) and arbitrary antenna configurations, all the degrees-of-freedom (DoF) regions are obtained. However, for the three-user MIMO broadcast channel with delayed CSIT and arbitrary antenna configurations, the DoF region of order-2 messages is still unclear and only a partial achievable DoF region of order-1 messages is obtained, where the order-2 messages and order-1 messages are desired by two receivers and one receiver, respectively. In this paper, for the three-user MIMO broadcast channel with delayed CSIT and arbitrary antenna configurations, we first design transmission schemes for order-2 messages and order-1 messages. Next, we propose to analyze the achievable DoF region of transmission scheme by transformation approach. In particular, we transform the decoding condition of transmission scheme w.r.t. phase duration into the achievable DoF region w.r.t. achievable DoF, through achievable DoF tuple expression connecting phase duration and achievable DoF. As a result, the DoF region of order-2 messages is characterized and an achievable DoF region of order-1 messages is completely expressed. Besides, for order-1 messages, we derive the sufficient condition, under which the proposed achievable DoF region is the DoF region.

Multi-point vehicular positioning is an essential operation for autonomous vehicles. However, the state-of-the-art positioning technologies, relying on reflected signals from a target (i.e., RADAR and LIDAR), cannot work without line-of-sight (LoS). Besides, it takes significant time for environment scanning and object recognition with potential detection inaccuracy, especially in complex urban situations. Some recent fatal accidents involving autonomous vehicles further expose such limitations. In this article, we aim at overcoming these limitations by proposing a novel relative positioning approach, called Cooperative Multi-point Positioning (COMPOP). The COMPOP establishes cooperation between a target vehicle (TV) and a sensing vehicle (SV) if a LoS path exists, where a TV explicitly lets an SV to know the TV's existence by transmitting positioning waveforms. This cooperation makes it possible to remove the time-consuming scanning and target recognizing processes, facilitating real-time positioning. One prerequisite for the cooperation is a clock synchronization between a pair of TV and SV. To this end, we use a phase-differential-of-arrival (PDoA) based approach to remove the TV-SV clock difference from the received signal. With clock difference correction, the TV's position can be obtained via peak detection over a 3D power spectrum constructed by a Fourier transform (FT) based algorithm. The COMPOP also incorporates nearby vehicles, without knowing their locations, into the above cooperation for the case without a LoS path. Specifically, several strong non-LoS (NLoS) links from the TV to the SV can be generated via mirror-like reflections over the neighboring vehicles' metal surfaces. Following the same procedures in the LoS case, virtual TVs mirrored by nearby vehicles can be detected. By exploiting the geometric relation between the virtual and actual TVs, COMPOP can be achieved by intelligently combining the virtual TVs to position the actual TV. The effectiveness of the COMPOP is verified by several simulations concerning practical channel parameters.

While machine-type communication (MTC) devices generate massive data, they often cannot process this data due to limited energy and computation power. To this end, edge intelligence has been proposed, which collects distributed data and performs machine learning at the edge. However, this paradigm needs to maximize the learning performance instead of the communication throughput, for which the celebrated water-filling and max-min fairness algorithms become inefficient since they allocate resources merely according to the quality of wireless channels. This paper proposes a learning centric power allocation (LCPA) method, which allocates radio resources based on an empirical classification error model. To get insights into LCPA, an asymptotic optimal solution is derived. The solution shows that the transmit powers are inversely proportional to the channel gain, and scale exponentially with the learning parameters. Experimental results show that the proposed LCPA algorithm significantly outperforms other power allocation algorithms.

The scheduling of downlink video streaming in a massive multiple-input-multiple-output (MIMO) network is considered in this paper, where active users arrive randomly to request video contents of a finite playback duration via their service base stations. Each video consists of a sequence of segments, which can be transmitted to the requesting users with variable video bitrates. To facilitate adaptive video streaming, a number of physical-layer frames are grouped as a super frame. We formulate the adaptation of transmitted segment number, frame allocation and segment bitrate in all the super frames as an infinite-horizon Markov decision process (MDP), whose objective is a discounted measurement of the average Quality-of-Experience (QoE). A novel approximate MDP method is proposed to obtain a low-complexity scheduling policy. Specifically, a baseline policy is introduced and its asymptotic value function is derived analytically. The low-complexity scheduling policy will be obtained from one-step iteration based on the analytical expression, which becomes a performance lower bound on the derived policy. It is shown by simulations that the proposed low-complexity scheduling policy has significant performance gain over the baseline policy.

In this paper, we investigate online distributed job dispatching in an edge computing system residing in a Metropolitan Area Network (MAN). Specifically, job dispatchers are implemented on access points (APs) which collect jobs from mobile users and distribute each job to a server at the edge or the cloud. A signaling mechanism with periodic broadcast is introduced to facilitate cooperation among APs. The transmission latency is non-negligible in MAN, which leads to outdated information sharing among APs. Moreover, the fully-observed system state is discouraged as reception of all broadcast is time consuming. Therefore, we formulate the distributed optimization of job dispatching strategies among the APs as a Markov decision process with partial and outdated system state, i.e., partially observable Markov Decision Process (POMDP). The conventional solution for POMDP is impractical due to huge time complexity. We propose a novel low-complexity solution framework for distributed job dispatching, based on which the optimization of job dispatching policy can be decoupled via an alternative policy iteration algorithm, so that the distributed policy iteration of each AP can be made according to partial and outdated observation. A theoretical performance lower bound is proved for our approximate MDP solution. Furthermore, we conduct extensive simulations based on the Google Cluster trace. The evaluation results show that our policy can achieve as high as 20.67% reduction in average job response time compared with heuristic baselines, and our algorithm consistently performs well under various parameter settings.

Massive multiple-input multiple-output communications can achieve high-level security by concentrating radio frequency signals towards the legitimate users. However, this system is vulnerable in a Rician fading environment if the eavesdropper positions itself such that its channel is highly “similar” to the channel of a legitimate user. To address this problem, this paper proposes an angle aware user cooperation (AAUC) scheme, which avoids direct transmission to the attacked user and relies on other users for cooperative relaying. The proposed scheme only requires the eavesdropper’s angle information, and adopts an angular secrecy model to represent the average secrecy rate of the attacked system. With this angular model, the AAUC problem turns out to be nonconvex, and a successive convex optimization algorithm, which converges to a Karush-Kuhn-Tucker solution, is proposed. Furthermore, a closed-form solution and a Bregman first-order method are derived for the cases of large-scale antennas and large-scale users, respectively. Extension to the intelligent reflecting surfaces based scheme is also discussed. Simulation results demonstrate the effectiveness of the proposed successive convex optimization based AAUC scheme, and also validate the low-complexity nature of the proposed large-scale optimization algorithms.

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

The scheduling of downlink video streaming in a massive multiple-input multiple-output (MIMO) network is considered in this paper, where active users arrive randomly to request video contents of a finite playback duration via their service base stations (BSs). Each video content consisting of a sequence of segments can be transmitted to the requesting users with variable video bitrates. We formulate the joint control of transmitted segment number, frame allocation and segment bitrate in all the super frames (each comprising multiple frames) as an infinite-horizon Markov decision process (MDP). The maximization objective is a discounted measurement of the average Quality of Experience (QoE). Since there is no efficient method for scheduling design with random user arrivals and departures in the existing literature, a novel approximate MDP method is proposed to obtain a low-complexity scheduling policy, where a lower bound on its performance is derived. Specifically, we first introduce a baseline policy and derive its asymptotic value function. One-step policy iteration is then applied to improve this value function, yielding the mentioned low-complexity policy. Finally, we propose a novel and efficient reinforcement learning (RL) algorithm to evaluate the value function when the prior knowledge on user arrival intensity is absent.

While machine-type communication (MTC) devices generate considerable amounts of data, they often cannot process the data due to limited energy and computational power. To empower MTC with intelligence, edge machine learning has been proposed. However, power allocation in this paradigm requires maximizing the learning performance instead of the communication throughput, for which the celebrated water-filling and max-min fairness algorithms become inefficient. To this end, this paper proposes learning centric power allocation (LCPA), which provides a new perspective on radio resource allocation in learning driven scenarios. By employing 1) an empirical classification error model that is supported by learning theory and 2) an uncertainty sampling method that accounts for different distributions at users, LCPA is formulated as a nonconvex nonsmooth optimization problem, and is solved using a majorization minimization (MM) framework. To get deeper insights into LCPA, asymptotic analysis shows that the transmit powers are inversely proportional to the channel gains, and scale exponentially with the learning parameters. This is in contrast to traditional power allocations where quality of wireless channels is the only consideration. Last but not least, a large-scale optimization algorithm termed mirror-prox LCPA is further proposed to enable LCPA in large-scale settings. Extensive numerical results demonstrate that the proposed LCPA algorithms outperform traditional power allocation algorithms, and the large-scale optimization algorithm reduces the computation time by orders of magnitude compared with MM-based LCPA but still achieves competing learning performance.

In this paper, we investigate the scheduling design of a mobile-edge computing (MEC) system, where the random arrival of mobile devices with computation tasks in both spatial and temporal domains is considered. The binary computation offloading model is adopted. Every task is indivisible and can be computed at either the mobile device or the MEC server. We formulate the optimization of task offloading decision, uplink transmission device selection and power allocation in all the frames as an infinite-horizon Markov decision process (MDP). Due to the uncertainty in device number and location, conventional approximate MDP approaches to addressing the curse of dimensionality cannot be applied. A novel low- complexity sub-optimal solution framework is then proposed. We first introduce a baseline scheduling policy, whose value function can be derived analytically. Then, one-step policy iteration is adopted to obtain a sub-optimal scheduling policy whose performance can be bounded analytically. Simulation results show that the gain of the sub-optimal policy over various benchmarks is significant.

In this paper, the uplink adaptation for massive multiple-input-multiple-output (MIMO) networks without the knowledge of user density is considered. Specifically, a novel cooperative uplink transmission and detection scheme is first proposed for massive MIMO networks, where each uplink frame is divided into a number of data blocks with independent coding schemes and the following blocks are decoded based on previously detected data blocks in both service and neighboring cells. The asymptotic signal-to- interference-plus-noise ratio (SINR) of the proposed scheme is then derived, and the distribution of interference power considering the randomness of the users' locations is proved to be Gaussian. By tracking the mean and variance of interference power, an online robust rate adaptation algorithm ensuring a target packet outage probability is proposed for the scenario where the interfering channel and the user density are unknown.

Multi-point detection of the full-scale environment is an important issue in autonomous driving. The state-of- the-art positioning technologies (such as RADAR and LIDAR) are incapable of real-time detection without \emph{line-of-sight} (LoS). To address this issue, this paper presents a novel multi-point vehicular positioning technology via \emph{millimeter-wave} (mmWave) transmission that exploits multi-path reflection from a \emph{target vehicle} (TV) to a \emph{sensing vehicle} (SV), which enables the SV to fast capture both the shape and location information of the TV in \emph{non-LoS} (NLoS) under the assistance of multi-path reflections. A \emph{phase-difference-of- arrival} (PDoA) based hyperbolic positioning algorithm is designed to achieve the synchronization between the TV and SV. The \emph{stepped-frequency-continuous-wave} (SFCW) is utilized as signals for multi-point detection of the TVs. Transceiver separation enables our approach to work in NLoS conditions and achieve much lower latency compared with conventional positioning techniques.

In this paper, downlink transmission scheduling of popular files is optimized with the assistance of wireless cache nodes. Specifically, the requests of each file, which is further divided into a number of segments, are modeled as a Poisson point process within its finite lifetime. Two downlink transmission modes are considered: 1) the base station reactively multicasts the file segments to the requesting users and selected cache nodes and (2) the base station proactively multicasts some file segments to the selected cache nodes without requests. The cache nodes with decoded file segments can help to offload the traffic via other spectrum. Without the proactive multicast, we formulate the downlink transmission resource minimization as a dynamic programming problem with random stage number, which can be approximated via a finite-horizon Markov decision process (MDP) with fixed stage number. To address the prohibitively huge state space, we propose a low-complexity scheduling policy by linearly approximating the value functions of the MDP, where the bound on the approximation error is derived. Moreover, we propose a learning-based algorithm to evaluate the approximated value functions for unknown geographical distribution of requesting users. Finally, given the above reactive multicast policy, a proactive multicast policy is introduced to exploit the temporal diversity of shadowing effect. It is shown by simulation that the proposed low-complexity reactive multicast policy can significantly reduce the resource consumption at the base station, and the proactive multicast policy can further improve the performance.

We consider a low-complexity transceiver design for secure communications of a multiuser multi-antenna system with an external multi-antenna eavesdropper. We propose to employ Tomlinson–Harashima precoder (THP) to simultaneously transmit message-bearing signals and artificial noise (AN) according to the quantized channel state information (CSI). Based on random vector quantization of the channel vectors, we obtain analytical approximations of the ergodic secrecy rate (ESR) of each legitimate receiver (LR) and the ergodic secrecy sum rate of the system with arbitrary system parameters. Based on the obtained analytical results, the near-optimal power allocation to the information signals and AN can be obtained using a numerical method. We show using an information-theoretical method that, besides the advantage in the ESR over the linear precoding scheme, THP can reduce the supported rate of the eavesdropper’s channel by preventing the eavesdropper from obtaining the legitimate channels’ (quantized) CSI. The ESR loss of each LR compared with the perfect CSI case will increase without bound for a fixed number of feedback bits. We also derive a feedback bit scaling law to solve this problem. Finally, numerical results are provided to verify our analytical results and also the advantage of the proposed secure nonlinear transceiver over the corresponding linear scheme.

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

Efficient virtual machine (VM) management can dramatically reduce energy consumption in data centers. Existing VM management algorithms fall into two categories based on whether the VMs’ resource demands are assumed to be static or dynamic. The former category fails to maximize the resource utilization as they cannot adapt to the dynamic nature of VMs’ resource demands. Most approaches in the latter category are heuristic and lack theoretical performance guarantees. In this paper, we formulate the dynamic VM management as a large-scale Markov decision process (MDP) problem and derive an optimal solution. Our analysis of real-world data traces supports our choice of the modeling approach. However, solving the large-scale MDP problem suffers from the curse of dimensionality. Therefore, we further exploit the special structure of the problem and propose an approximate MDP-based dynamic VM management method, called MadVM. We prove the convergence of MadVM and analyze the bound of its approximation error. Moreover, we show that MadVM can be implemented in a distributed system with at most two times of the optimal migration cost. Extensive simulations based on two real-world workload traces show that MadVM achieves significant performance gains over two existing baseline approaches in power consumption, resource shortage, and the number of VM migrations. Specifically, the more intensely the resource demands fluctuate, the more MadVM outperforms.

In this paper, a novel learning-based rate adaptation mechanism is proposed for a downlink massive multiple-input-multiple-output (MIMO) network with underlaid device-to-device (D2D) links, where the link signal-to-interference-plus-noise ratio (SINR) cannot be accurately predicted before transmission, even its distribution statistics are unknown at the very beginning. Specifically, two coexistence schemes are considered: (1) the D2D links only reuse the downlink subframes; and (2) the D2D receivers also join the uplink channel estimation of the associated cells. For the second scheme, the downlink interference to the D2D receivers is suppressed at the cost of channel training overhead. The geographic distributions of the selected downlink and D2D users in each frame are modeled as two independent stochastic processes with unknown statistics. As a result, the distribution of interference power is unknown to the transmitters. In order to facilitate robust rate allocation, we first derive the asymptotic expressions of downlink and D2D signal-to-interference-plus-noise ratios (SINRs) for sufficiently large antenna number, and show that their distributions can be approximated by Gaussian or exponential random variables. Subsequently, distributive learning algorithms are proposed to evaluate the means and variances of these random variables. This enables the BSs and D2D transmitters to determine the transmission rates under a constraint on packet outage probability.

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

In this paper, a novel learning-based rate adaptation mechanism is proposed for downlink massive multiple- input-multiple-output (MIMO) networks with underlaid device-to-device (D2D) links, where neither the instantaneous channel state information of interfering channels nor their statistics is available at the downlink or D2D transmitters. Specifically, a coordinated scheme, where the D2D receivers join the uplink channel estimation of the associated cell, is investigated. The geographic distributions of selected downlink and D2D users are modeled as stationary and ergodic stochastic processes with unknown statistics. In order to facilitate robust rate allocation, we first derive the asymptotic signal-to-interference-and-noise ratio (SINR) for both downlink and D2D links, and show that their distributions due to unknown interfering channel can be approximated by Gaussian random variables. Then distributive reinforcement learning algorithms are proposed to evaluate the means and variances of these random variables. As a result, the base stations (BSs) and D2D transmitters could determine the transmission rates with a tolerable target packet outage probability.

In this paper, the downlink file transmission within a finite lifetime is optimized with the assistance of wireless cache nodes. Specifically, the number of requests within the lifetime of one file is modeled as a Poisson point process. The base station multicasts files to downlink users and the selected the cache nodes, so that the cache nodes can help to forward the files in the next file request. Thus we formulate the downlink transmission as a Markov decision process (MDP) with random number of stages, where transmission power and time on each transmission are the control policy. Due to random number of file transmissions, we first proposed a revised Bellman's equation, where the optimal control policy can be derived. In order to address the prohibitively huge state space, we also introduce a low-complexity sub-optimal solution based on a linear approximation of the value function. The approximated value function can be calculated analytically, so that conventional numerical value iteration can be eliminated. Moreover, the gap between the approximated value function and the real value function is bounded analytically. It is shown by simulation that, with the approximated MDP approach, the proposed algorithm can significantly reduce the resource consumption at the base station.

Heterogeneous cellular networks (HetNets) can significantly improve the spectrum efficiency, where low-power low-complexity base stations (Pico-BSs) are deployed inside the coverage of macro base stations (Macro-BSs). Due to cross-tier interference, joint detection of the uplink signals is widely adopted so that a Pico-BS can either detect the uplink signals locally or forward them to the Macro-BS for processing. The latter can achieve increased throughput at the cost of additional backhaul transmission. However, in existing literature the delay of the backhaul links was often neglected. In this paper, we study the delay-optimal uplink scheduling problem in HetNets with limited backhaul capacity. Local signal detection or joint signal detection is scheduled in a unified delay-optimal framework. Specifically, we first prove that the problem is NP-hard and then formulate it as a Markov Decision Process problem. We propose an efficient and effective algorithm, called OLIUS, that can deal with the exponentially growing state and action spaces. Furthermore, OLIUS is online learning based which does not require any prior statistical knowledge on user behavior or channel characteristics. We prove the convergence of OLIUS and derive an upper bound on its approximation error. Extensive experiments in various scenarios show that our algorithm outperforms existing methods in reducing delay and power consumption.

The information-theoretic techniques can ensure security in communication regardless of the computational power of the attackers. The requirements for applying such techniques require: 1) an advantage over the eavesdroppers' quality of reception and 2) the location information on the eavesdropper. Traditionally, the performance of a secure communication link is measured using the metrics of secrecy capacity or outage probability, which are both related to the relative quality of the legitimate link compared with that of the eavesdropper link. In this paper, we present a new metric, called secrecy pressure, which measures the security level of the surface/environment where the legitimate link is embedded but is independent of the position of the eavesdropping node. The metric can be also visualized as a secrecy map. The analytical results show how the optimization of the secrecy pressure measure can lead to decide the optimum transmit antenna orientation and/or the position and power of an additional interfering node (friendly jammer).

The performance of downlink massive multiple-input- multiple-output networks with co-channel device-to- device communications is investigated in this paper. Specifically, we consider a cellular network with sufficient number of antennas at the base station and typical cell coverage, where the cell users and device-to-device transmitters are randomly distributed. In the analysis, the asymptotic signal- to-interference ratios for both downlink and device- to-device links are first obtained, which depend on the pathloss and small-scale fading of the interference channels. Since these information may not be available at the service base station or device-to-device transmitter, there exists a chance of packet outage. Therefore, we continue to derive the closed-form approximation of the average goodput, which measures the average number of information bits successfully delivered to the receiver. As a result, the system design trade-off between downlink and co-channel device-to-device communications can be investigated analytically. Moreover, the performance region in which the co- channel device-to-device communications could lead to better overall spectral efficiency can be obtained. Finally, it is shown by simulations that the analytical results matches the actual performance very well.

We consider nonlinear transceiver design for downlink multiuser multi-antenna secure communications with an external multi-antenna eavesdropper. The multi-antenna transmitter simultaneously transmits confidential-message-bearing signals and artificial noise (AN) using nonlinear Tomlinson Harashima precoding based on the limited channel state information feedback. For the proposed nonlinear secure transceiver, we reveal the mechanism behind which makes this nonlinear precoding superior than the linear precoding methods in guaranteeing secrecy of wireless multiuser multi-antenna systems. We also obtain analytical bounds of the ergodic secrecy rate of each legitimate receiver and the ergodic secrecy sum rate of the system. Based on the analytical result, the near optimal power allocation to the information signals and AN can be obtained using numerical method. Numerical results are shown to verify the advantage of the proposed nonlinear method over the linear zero-forcing precoding.

In this paper we propose a novel evolutionary detection algorithm for large-scale multiple-input multiple-output (MIMO) systems, utilizing the concepts of quantum bit and quantum rotation gate in quantum computing. Specifically, we consider the detection of BPSK and 4-QAM signals, and the uncertainty on the information bits at the receiver is modeled as a sequence of quantum bits, which is referred to as a quantum particle. The proposed algorithm begins with a population of such particles, each initialized randomly. Then by the aid of quantum rotation gate along with a fitness function, a simple mechanism is proposed to allow all quantum particles to evolve in a guided manner towards a potentially optimal area and finally converges. It is shown by simulations that the proposed algorithm can achieve near-optimal performance.

Motivated by practical scenarios, we study congestion games with failures. We investigate two models. The first model is congestion games with both resource and agent failures, where each agent chooses the same number of resources with the minimum expected cost. We prove that the game is potential and hence admits at least one pure-strategy Nash equilibrium (pure-NE). We also show that the Price of Anarchy and the Price of Stability are bounded (equal to 1 in some cases). The second model is congestion games with only resource failures (CG-CRF), where resources are provided in packages, and their failures can be correlated with each other. Each agent can choose multiple packages for reliability’s sake and utilize the survived one having the minimum cost. CG-CRF is shown to be not potential. We prove that it admits at least one pure-NE by constructing one efficiently. Finally, we discuss various applications of these two games in the networking field. To the best of our knowledge, this is the first paper studying congestion games with the coexistence of resource and agent failures, and we give also the first proof of the existence of a pure-NE in congestion games with correlated package failures.

Information-theoretical approaches can ensure security, regardless of the computational power of the attackers. Requirements for the application of this theory are: 1) assuring an advantage over the eavesdropper quality of reception and 2) knowing where the eavesdropper is. The traditional metrics are the secrecy capacity or outage, which are both related to the quality of the legitimate link against the eavesdropper link. Our goal is to define a new metric, which is the characteristic of the security of the surface/environment where the legitimate link is immersed, regardless of the position of the eavesdropping node. The contribution of this paper is twofold: 1) a general framework for the derivation of the secrecy capacity of a surface, which considers all the parameters that influence the secrecy capacity and 2) the definition of a new metric to measure the secrecy of a surface: the secrecy pressure. The metric can be also visualized as a secrecy map, analogously to weather forecast. Different application scenarios are shown: from “forbidden zone” to Gaussian mobility model for the eavesdropper. Moreover, the secrecy outage probability of a surface is derived. This additional metric can measure, which is the secrecy rate supportable by the specific environment.

In this paper, a multiuser wireless powered communication network is considered where all users harvest energy from power beacons by wireless power transfer to support their uplink information transmission. A frequency-division duplex transmission scheme is adopted, where downlink power transfer and uplink information transmission are separated in different frequency bands. Compared with the time-division duplex scheme considered in most of existing literature, the frequency-division duplex scheme has more freedom to optimize the charging time of different cells respectively, and more suitable for distributed deployment of power beacons. The transmission slots and power allocation problem can be formulated as an optimization problem, which is proved to be convex. We derive the optimal slots allocation algorithm with the purpose of fair sum-throughput maximization. Moreover, the asymptotic expression of throughput is obtained, which provides useful insight on the deployment of power beacons. Simulation results demonstrate the proposed scheme can achieve significant performance gains compared to existing scheme from the literature.

We study transceiver design for secure communication of multiuser multi-antenna systems with assistant of a cooperative helper. There is an external multiple-antenna eavesdropper trying to obtain the confidential messages. Due to the finite-rate constraint of feedback channels, only quantized channel state information of the legitimate users is available at the transmitter and the helper. A nonlinear precoding strategy using Tomlinson Harashima precoding at the legitimate transmitter and a null-space beamforming scheme at the helper are proposed based on the quantized channel state information. Assuming a genie-aided perfect successive interference cancelation at Eve, we obtain closed-form expressions of the performance bounds for the achievable ergodic rate of each legitimate user and the ergodic secrecy sum rate. Numerical results illustrate that our proposed nonlinear-precoded strategy outperforms linear precoding scheme.

Efficient virtual machine (VM) management can dramatically reduce energy consumption in data centers. Existing VM management algorithms fall into two categories based on whether the VMs' resource demands are assumed to be static or dynamic. The former category fails to maximize the resource utilization as they cannot adapt to the dynamic nature of VMs' resource demands. Most approaches in the latter category are heuristical and lack theoretical performance guarantees. In this work, we formulate dynamic VM management as a large-scale Markov Decision Process (MDP) problem and derive an optimal solution. Our analysis of real-world data traces supports our choice of the modeling approach. However, solving the large-scale MDP problem suffers from the curse of dimensionality. Therefore, we further exploit the special structure of the problem and propose an approximate MDP-based dynamic VM management method, called MadVM. We prove the convergence of MadVM and analyze the bound of its approximation error. Moreover, MadVM can be implemented in a distributed system, which should suit the needs of real data centers. Extensive simulations based on two real-world workload traces show that MadVM achieves significant performance gains over two existing baseline approaches in power consumption, resource shortage and the number of VM migrations. Specifically, the more intensely the resource demands fluctuate, the more MadVM outperforms.

Current large-scale computing services, such as online social networking and web searching, make the wired links in the data centers with an Ethernet infrastructure oversubscribed. Therefore, researchers consider to augment the data centers with wireless communication, called a hybrid data center network (HDCN), to improve the communication flexibility and network capacity. In this paper, we investigate how to use the wireless communication in hybrid DCNs from a cross-layer view. In the network layer, we propose a routing protocol to minimize the number of hops for data flows, and a congestion control protocol to reduce the congestion and deal with sporadic link failure. In the physical layer, we study the channel and power allocation problem with the SINR and QoS constraints in hybrid DCNs. In single channel scenarios, we prove the problem to be a geometric programming problem. In multi-channel scenarios, we prove the problem to be NP-hard and propose a Greedy based Online Channel and Power Allocation (GOCPA) algorithm. Our proposed protocols in network and physical layers collaborate to manage the wireless communication in hybrid DCNs. Extensive simulations show that our protocols can significantly increase the network throughput, decrease the latency, and moreover increase the robustness of the networks.

Numerous methods for detecting audio splicing have been proposed. Environmental-signature-based methods are considered to be the most effective forgery detection methods. The performance of existing audio forensic analysis methods is generally measured in the absence of any anti-forensic attack. Effectiveness of these methods in the presence of anti-forensic attacks is therefore unknown. In this paper, we propose an effective anti-forensic attack for environmental-signature-based splicing detection method and countermeasures to detect the presence of the anti-forensic attack. For anti-forensic attack, dereverberation-based processing is proposed. Three dereverberation methods are considered to tamper with the acoustic environment signature. Experimental results indicate that the proposed dereverberation-based anti-forensic attack significantly degrades the performance of the selected splicing detection method. The proposed countermeasures exploit artifacts introduced by the anti-forensic processing. To detect the presence of potential anti-forensic processing, a machine learning-based framework is proposed. In particular, the proposed anti-forensic detection method uses a rich-feature model consisting of Fourier coefficients, spectral properties, high-order statistics of musical noise residuals, and modulation spectral coefficients to capture traces of dereverberation attacks. The performance of the proposed framework is evaluated on both synthetic data and real-world speech recordings. The experimental results show that the proposed rich-feature model can detect the presence of anti-forensic processing with an average accuracy of 95%.

It has been shown in the existing literature that data symbols could help to relieve the pilot contamination issue of massive multiple-input multiple-output systems. In this paper, we show that pilot information of interference users could be exploited jointly with the data-assisted transmission mechanism. Specifically, we consider the uplink transmission of a massive multiple-input multiple-output network where there are backhauls with significant delay between base stations. Although real-time inter-base station cooperation is infeasible; pilot information, whose update frequency is very low, of closest interference users can be notified to the serving base station. Conventionally, estimating the interference channel will lead to larger pilot overhead. In our proposed scheme, however, the detected uplink data could provide sufficient degree- of-freedom to estimate the channels of the closest interference users without increasing pilot overhead. Hence, the inter-cell interference can be suppressed efficiently. In order to obtain useful insights on system-level performance, a stochastic geometry based framework is established to analyze the distribution of the signal-to-interference ratio of the proposed scheme. The closed-from expression of the asymptotic bound is thereby derived. It is shown that the analytical expression fits the numerical simulations very well, and the proposed scheme has significant gain over the data-assisted uplink scheme without backhaul.

In Cognitive Radio Networks (CRNs), a fundamental operation for the secondary users (SUs) is to establish communication through choosing a common available channel at the same time slot, which is referred to as rendezvous. In this paper, we study fast rendezvous for two SUs.

A novel data-assisted channel estimation scheme is proposed for uplink massive multiple-input multiple-output (MIMO) systems to alleviate the performance bottleneck due to pilot contamination. Specifically, the uplink resource within a fading block is first divided into a number of data blocks, which are encoded respectively; then the detected data blocks are utilized iteratively to refine the channel estimation, improving the signal-to-interference-plus-noise ratio (SINR) of the following data blocks. In the existing literature, the system performance can not scale up with the number of base station's antenna due to the effect of pilot contamination. The proposed scheme provides a promising approach to improve the uplink SINR by the order θ(LM/L+M) without increasing the length of pilot sequence, where L and M are the number of transmission symbols within a fading block and the number of base station's antennas respectively. It is shown by simulations that the uplink performance of massive MIMO systems is significantly increased by the proposed scheme.

Audio splicing is one of the most common manipulation techniques in the audio forensic world. In this paper, the magnitudes of acoustic channel impulse response and ambient noise are considered as the environmental signature and used to authenticate the integrity of query audio and identify the spliced audio segments. The proposed scheme firstly extracts the magnitudes of channel impulse response and ambient noise by applying the spectrum classification technique to each suspected frame. Then, correlation between the magnitudes of query frame and reference frame is calculated. An optimal threshold determined according to the statistical distribution of similarities is used to identify the spliced frames. Furthermore, a refining step using the relationship between adjacent frames is adopted to reduce the false positive rate and false negative rate. Effectiveness of the proposed method is tested on two data sets consisting of speech recordings of human speakers. Performance of the proposed method is evaluated for various experimental settings. Experimental results show that the proposed method not only detects the presence of spliced frames, but also localizes the forgery segments. Comparison results with previous work illustrate the superiority of the proposed scheme.

The performance of wireless networks is fundamentally limited by interference [or, equivalently, the signal-to-interference ratio (SIR)]. In an attempt to characterize the interference as a direction-selective quantity and motivated by the useful analogy between classical propagation channels and wireless networks, we propose a novel network description framework, namely the interference azimuth spectrum (IAS). The IAS represents the distribution of interference in the angular domain and is parallel to the conventional power azimuth spectrum (PAS) used in propagation channels. We also extend this concept to the directional characterization of average achievable rate, assuming that interference is treated as noise. Provided with this analytical framework, we present the notion of local area outage, defined as the probability that a receiver is in the state of outage within a local area where both interference and desired signal are assumed to be wide-sense stationary (WSS). We further propose the geometry-based stochastic models (GBSMs) as a part of the IAS framework, where the interfering terminals are randomly distributed according to a specific probability density function (pdf) of their positions. The GBSMs are applicable to a wide variety of wireless network environments without and with interferer clustering. The proposed methodology would provide useful insight on the design and performance assessment of future networks, featured by opportunistic, randomized, and dense placement of nodes.

In order to evaluate the performance of emerging wireless systems such as relay, sensor, and mobile ad hoc networks with multihop coverage extensions, spatial-temporal network channel models are required. These models should include the directional characteristics of network information flows in order to exploit the spatial domain due to the deployment of advanced antenna systems. Furthermore, the models should be capable of handling non-stationary scenarios with dynamic evolution of network nodes. In an attempt to solve these two problems which have not been fully addressed in the existing literature, and motivated by the useful analogy between classical propagation channels and wireless networks, we propose a novel spatial-temporal network modeling framework, where the relevant figure-of-merit (FOM) may be received signal power, channel capacity, event detection error exponent, etc., depending on the network's type. Our methodology would be most useful for the design and analysis of future networks featured by decentralized, random, and dense placement of nodes.

This paper proposes a novel constellation alignment and interference cancellation scheme for wireless systems. Compared with the existing literatures on interference channel capacity analysis with lattice codes, a general signal processing algorithm is proposed for the systems using mutual baseband signal processing components, i.e., channel coding and QAM modulation. Specifically, multiple interference transmitters are proposed to align their QAM constellations at the signal receiver; the receiver first demodulates the aligned interference symbols by a novel constellation, then detects the aligned interference message which is the bitwise exclusive or of each individual interference message, and finally, cancels the aligned interference and detects the desired signal. Since the complicated joint detection in the existing lattice-based interference alignment literatures is avoided, the overall complexity is dramatically reduced. With the proposed scheme, the signal receiver can exploit both modulation and channel coding structure of interferences to improve the quality of interference cancellation. Hence, it is shown that compared with the existing wireless systems without constellation alignment, the proposed scheme could significantly improve the receiver's performance in the scenario with multiple interference sources.

In this paper, a low-complexity delay-aware cross-layer scheduling algorithm for two-hop relay communication systems is proposed. The complex interactions of the queues at the source node and the$M$relay nodes (RSs) are modeled as an infinite horizon average reward Markov decision process (MDP), whose state space involves the joint queue state information (QSI) of the queues at the source node and the$M$RSs as well as the joint channel state information (CSI) of all S-R and R-D links. To address the curse of dimensionality, an equivalent MDP formulation is first proposed, where the system state depends only on global QSI. Furthermore, using approximate MDP and stochastic learning, an auction-based distributed online learning algorithm is derived, where each node iteratively estimates a per-node value function based on real-time observations of the local CSI and local QSI as well as signaling between relays. The combined distributed learning converges almost surely to a global optimal solution for large arrivals. Finally, it is showed by simulations that the proposed scheme achieves significant gain compared with various baselines such as the conventional CSIT-only control and the throughput optimal control (in stability sense).

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

In this paper, we propose an opportunistic buffered decode-wait-and-forward (OBDWF) protocol to exploit both relay buffering and relay mobility to enhance the system throughput and the end-to-end packet delay under bursty arrivals. We consider a point-to-point communication link assisted by K mobile relays. We illustrate that the OBDWF protocol could achieve a better throughput and delay performance compared with existing baseline systems such as the conventional dynamic decode-and-forward (DDF) and amplified-and-forward (AF) protocol. In addition to simulation performance, we also derived closed-form asymptotic throughput and delay expressions of the OBDWF protocol. Specifically, the proposed OBDWF protocol achieves an asymptotic throughput ΘL(\log_2 K) with ΘL(1) total transmit power in the relay network. This is a significant gain compared with the best known performance in conventional protocols (ΘL(\log_2 K) throughput with ΘL(K) total transmit power). With bursty arrivals, we show that both the stability region and average delay of the proposed OBDWF protocol can achieve order-wise performance gain ΘL(K) compared with conventional DDF protocol.

In this paper, we propose a distributive delay-optimal power and relay selection algorithm for two-hop cooperative OFDM systems. The complex interactions of the queues at the source node and the M relays (RSs) are modeled as an infinite horizon average reward Markov Decision Process (MDP), whose state space involves the joint queue state (QSI) of the queue at the source node and the queues at the M RSs as well as the joint channel state (CSI) of all S-R links and R-D links. As a first step to address the curse of dimensionality, we propose a reduced state MDP formulation. From the associated Bellman's equation, we show that the delay-optimal power control (and link selection algorithm), which are functions of both the CSI and QSI, has a multi-level water-filling structure. Furthermore, using stochastic learning, we derive a distributive online learning algorithm in which each node recursively estimates a per-node potential function based on real-time observations of the local CSI and local QSI only. We show that the combined distributive learning converges almost surely to a global optimal solution for large arrivals. Finally, we show by simulation that the delay performance of the proposed scheme is significantly better than various baselines such as the conventional CSIT-only control and the throughput optimal control (in stability sense).

In this paper, we consider a relay-assisted cognitive cellular downlink system dynamically accessing a spectrum licensed to a primary network, thereby improving the efficiency of spectrum usage. A cluster-based relay-assisted architecture is proposed, where relay stations are employed for minimizing the interference to users in the primary network and achieving fairness for cell-edge users. Based on this architecture, an optimal solution is derived for jointly controlling data rate, transmission power, and subband allocation to optimize the weighted sum goodput where proportional fair scheduling (PFS) is included as a special case. As shown by simulations, the proposed solution achieves significant throughput gains and better user fairness compared with existing designs.

In this paper, utilizing the relay buffers, we propose an opportunistic decode-wait-and-forward relay scheme for a point-to-point communication system with a half-duplexing mobile relay network. The proposed scheme achieves the maximum throughput of Θ(log K) at a cost of O(1) total transmission power and O(K=q) average end-to-end packet delay, where 0 ≪ q ≤ ½ measures the speed of relays' mobility. It can be proved that this system throughput is unattainable for the existing designs with low relay mobility. Therefore, the proposed relay scheme can exploit the time diversity and relays' mobility more efficiently.

In most of the existing works on cognitive radio (CR) systems, the spectrum sensing and the cross-layer scheduling are designed separately, where the cross-layer scheduling is performed based on the hard sensing information (HSI) generated from the sensing modular. In this paper, we shall propose a joint cross-layer and sensing design and study its performance advantages over the aforementioned traditional decoupled approaches. We shall also propose a joint design framework to optimize a system utility by adapting the power allocation and the subcarrier assignment across the secondary users (under a average interference constraint to the primary users) based on both the channel state information (CSI) and the raw sensing information (RSI). Simulation results reveals its substantial performance gain over the conventional CR systems.

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

In this paper, we propose a novel closed-loop approach for robust downlink multi-antenna cross-layer design with outdated channel state information at the transmitter (CSIT). Based on the ACK/NAK feedbacks from the mobiles, the proposed cross-layer design does not require the knowledge of CSIT error statistics. We formulate the cross-layer design as a mixed combinatorial search and Markov decision process (MDP). While it is well-known that general solutions for MDP are very complex, one important contribution of this paper is that we obtain simple closed-form power, rate and user allocation policies which are asymptotically optimal for small target frame error rate (FER). Simulation results illustrate that the performance of the proposed closed-loop cross-layer design is very robust with respect to the outdated CSIT, CSIT error model mismatch as well as channel variations due to Doppler.

In most of the existing works on cognitive radio (CR) systems, the spectrum sensing and the cross-layer scheduling are designed separately. Specifically, the sensing module first determines whether or not a channel resource is available for the CR system based on the sensing information. The scheduling module then schedules the data transmission of different users on the available channels based on the hard-decision sensing information (HSI). In this paper, we shall propose a joint crosslayer and sensing design and study its performance advantages over the aforementioned traditional decoupled approaches. We shall consider the downlink transmission of an OFDMA-based secondary system sharing the spectrum with primary users using cognitive radio technology. We shall rely on the joint design framework to optimize a system utility, which adapts the power allocation and the subcarrier assignment across the secondary users (under a average interference constraint to the primary users) based on both the channel state information (CSI) and the raw sensing information (RSI). In addition, we shall also propose a distributed implementation for the cross-layer sensing and scheduling design using primal-dual decomposition approach. Simulation results reveals the substantial performance gain of the proposed joint design over the conventional CR systems.

Cross-layer designs for OFDMA systems have been shown to offer significant gains of spectral efficiency by exploiting the multiuser diversity over the temporal and frequency domains. In this paper, we shall propose a robust optimal cross-layer design for downlink TDD-OFDMA systems with imperfect channel state information at the base station (CSIT) and unknown interference in slow fading channels. Exploiting the ACK/NAK (1-bit) feedbacks from the mobiles, the proposed cross-layer design does not require knowledge of the CSIT error statistics or interference statistics. To take into account of the potential packet error due to the imperfect CSIT and unknown interference, we define average system goodput (which measures the average b/s/Hz successfully delivered to the mobile) as our optimization objective. We formulate the cross-layer design as a state-space control problem. The optimal power, optimal rate and optimal user allocations are determined as the output equations from the system states based on dynamic programming approach. Simulation results illustrate that the performance of the proposed closed-loop cross-layer design is very robust with respect to imperfect CSIT, unknown interference, model mismatch as well as channel variations due to Doppler.

Cross-layer scheduling and hybrid ARQ (HARQ) are effective means to improve the spectral efficiency of wireless systems. However, most of the existing works handle HARQ and cross-layer scheduling in a decoupled manner based on heuristic approaches. In this paper, we propose an integrated design framework for cross-layer scheduling and HARQ design in multiuser systems with slow fading channel and outdated knowledge of channel state information at the base station (CSIT). We consider both the chase combining and incremental redundancy in HARQ. We define the average system goodput (which measures the average bits successfully delivered to the receiver) as the measure of system performance. Based on information theoretical approach, we derive the asymptotically optimal cross-layer policy (power allocation, rate allocation and user selection policies) to optimize the average system goodput with HARQ. In addition, we derive analytically the closed-form expression of the average system goodput, from which we can obtain useful design insights such as the role of HARQ in the overall cross-layer gain, the tradeoff between the system goodput and the HARQ delay as well as the sensitivity of the average system goodput with respect to the CSIT quality.

Cross-layer scheduling and hybrid ARQ (HARQ) are effective means to improve the spectral efficiency of wireless systems and have been widely adopted in next generation wireless systems. However, most of the existing works handle HARQ and cross-layer scheduling in a decoupled manner based on heuristic approach. In this paper, we propose an integrated design framework for cross-layer scheduling and HARQ design in TDD multiuser systems with outdated knowledge of channel state information at the base station (CSIT). We focus on incremental redundancy in HARQ and derive the asymptotically optimal cross-layer policy (power allocation, rate allocation and user selection policies) to optimize the system goodput. In addition, we derive analytically closed-form expressions for the average system goodput where we can obtain useful design insights. For instance, the average system goodput scales in the order of O(In L) where L is the maximum transmission number of a packet, illustrating how the HARQ can enhance the cross-layer system goodput gain.

In most existing works, perfect knowledge of channel state information at the transmitter (CSIT) is assumed to realize the potential benefit of cross-layer scheduling and spatial multiplexing gains of MIMO/OFDMA systems. However, perfect knowledge of CSIT is not easy to achieve in practice due to estimation noise or delay in feedback. In this paper, we shall focus on the cross-layer design of downlink multi-antenna OFDMA systems with imperfect CSIT for slow fading channels. We shall show that our proposed cross-layer scheduler can exploit the multiuser diversity and spatial multiplexing gain even in the presence of moderate CSIT error.

Cross layer designs for OFDMA systems have been shown to offer significant gains of spectral efficiency by exploiting multiuser diversity over the temporal and frequency domains. However, in most existing designs, perfect knowledge of channel state information at the transmitter (CSIT) is assumed. When we have imperfect CSIT and unknown interference at the receiver, there may be packet transmission outage (error) and it is a tricky problem for cross layer design with imperfect CSIT and unknown interference. In this paper, we shall propose a robust optimal cross layer design for downlink TDD-OFDMA systems with imperfect channel state information (CSIT) and unknown interference in slow fading channels. Exploiting the ACK/NAK (1-bit) feedbacks from the mobiles, the proposed cross layer design does not require knowledge of the CSIT error nor interference statistics. Furthermore, for sufficiently large number of feedbacks, the system will converge to steady state (as if perfect CSIT were available). Simulation results illustrate that the performance of the proposed closed-loop cross layer design is very robust with respect to imperfect CSIT, unknown interference, model mismatch as well as channel variations due to Doppler.

MIMO-OFDMA is a promising technology to accommodate multi-user transmission over frequency selective fading channels with high spectral efficiency. In multi-user MIMO-OFDMA systems, cross layer scheduling is very important to exploit the multi-user selection diversity over the spatial and frequency domains. In this paper, we focus to investigate the design and performance of downlink multi-user MISO-OFDMA cross layer scheduler for time-division duplex (TDD) systems. We propose a systematic framework for the optimal design of the cross layer scheduling with imperfect knowledge of CSIT. We first exam the optimal solution for the scheduling problem. Because of its huge complexity, some sub-optimal algorithms are proposed to achieve reasonable performance-complexity tradeoff. We found that while the ideal schedulers (designed for perfect CSIT) fails to exploit the spatial multiplexing and cross-layer gains, the proposed cross-layer scheduling algorithms designed for imperfect CSIT achieves significant spectral efficiency even at large CSIT errors