WiFi sensing based on channel state information (CSI) collected from commodity WiFi devices has shown great potential across a wide range of applications, including vital sign monitoring and indoor localization. Existing WiFi sensing approaches typically estimate motion information directly from CSI. However, they often overlook the inherent advantages of channel impulse response (CIR), a delay-domain representation that enables more intuitive and principled motion sensing by naturally concentrating motion energy and separating multipath components. Motivated by this, we revisit WiFi sensing and introduce CIRSense, a new framework that enhances the performance and interpretability of WiFi sensing with CIR. CIRSense is built upon a new motion model that characterizes fractional delay effects, a fundamental challenge in CIR-based sensing. This theoretical model underpins technical advances for the three challenges in WiFi sensing: hardware distortion compensation, high-resolution distance estimation, and subcarrier aggregation for extended range sensing. CIRSense, operating with a 160 MHz channel bandwidth, demonstrates versatile sensing capabilities through its dual-mode design, achieving a mean error of approximately 0.25 bpm in respiration monitoring and 0.09 m in distance estimation. Comprehensive evaluations across residential spaces, far-range scenarios, and multi-target settings demonstrate CIRSense's superior performance over state-of-the-art CSI-based baselines. Notably, at a challenging sensing distance of 20 m, CIRSense achieves at least$3\times$higher average accuracy with more than$4.5\times$higher computational efficiency.

Embodied agents exhibit immense potential across a multitude of domains, making the assurance of their behavioral safety a fundamental prerequisite for their widespread deployment. However, existing research predominantly concentrates on the security of general large language models, lacking specialized methodologies for establishing safety benchmarks and input moderation tailored to embodied agents. To bridge this gap, this paper introduces a novel input moderation framework, meticulously designed to safeguard embodied agents. This framework encompasses the entire pipeline, including taxonomy definition, dataset curation, moderator architecture, model training, and rigorous evaluation. Notably, we introduce EAsafetyBench, a meticulously crafted safety benchmark engineered to facilitate both the training and stringent assessment of moderators specifically designed for embodied agents. Furthermore, we propose Pinpoint, an innovative prompt-decoupled input moderation scheme that harnesses a masked attention mechanism to effectively isolate and mitigate the influence of functional prompts on moderation tasks. Extensive experiments conducted on diverse benchmark datasets and models validate the feasibility and efficacy of the proposed approach. The results demonstrate that our methodologies achieve an impressive average detection accuracy of 94.58%, surpassing the performance of existing state-of-the-art techniques, alongside an exceptional moderation processing time of merely 0.002 seconds per instance. The source code and datasets can be found at https://github.com/ZihanYan-CQU/EAsafetyBench.

Robotic perception has been significantly advanced by integrating vision with additional sensing modalities such as acoustic and tactile sensors. However, existing methods largely emphasize external object properties, including appearance and geometry, while neglecting internal material attributes (e.g., composition) that are crucial for robust and reliable robotic manipulation. In this work, we propose augmenting robotic systems with radar sensing and introduce CRMaterial, a new camera-radar fusion framework powered by vision language models (VLMs) for accurate object material identification. Preliminary experiments demonstrate that our system improves material identification accuracy by 2.5× compared to a camera-only baseline.

The range image has emerged as a dominant representation of 3D LiDAR data, enabling the direct application of well-established image and video compression techniques. However, existing compression methods, primarily optimized for human visual perception, often compromise the fidelity of physical distance information embedded in range images, which is critical for downstream robotic tasks. Additionally, rate-distortion optimization (RDO)-based rate control remains largely unexplored in range image-based LPCC. To address these limitations, we introduce D-Compress, a new framework for detail-preserving, fast, and precise compression of LiDAR range images tailored for real-time streaming. D-Compress focuses on preserving fine-grained range image details while achieving both high compression speed and geometric accuracy. Compared to state-of-the-art (SOTA) codecs, our approach delivers superior geometric precision, high compression ratios, and robust rate control.

In this paper, we explore how to schedule multiple users to optimize information freshness in a pull-based wireless network, where the status updates from users are requested by randomly arriving queries at the destination. We use the age of information at query (QAoI) to characterize the performance of information freshness. Such a decision-making problem is naturally modeled as a Markov decision process (MDP), which, however, is prohibitively high to be solved optimally by the standard method due to the curse of dimensionality. To address this issue, we employ Whittle index approach, which allows us to decouple the original MDP into multiple sub-MDPs by relaxing the scheduling constraints. However, the binary Markovian query arrival process results in a bi-dimensional state and complex state transitions within each sub-MDP, making it challenging to verify Whittle indexability using conventional methods. After a thorough analysis of the sub-MDP’s structure, we show that it is unichain and its optimal policy follows a threshold-type structure. This facilitates the verification of Whittle indexability of the sub-MDP by employing an easy-to-verify condition. Subsequently, the steady-state probability distributions of the sub-MDP under different threshold-type policies are analyzed, constituting the analytical expressions of different Whittle indices in terms of the expected average QAoI and scheduling time of the sub-MDP. Building on these, we devise an efficient algorithm to calculate Whittle indices for the formulated sub-MDPs. The simulation results validate our analyses and show the proposed Whittle index policy outperforms baseline policies and achieves near-optimal performance.

This paper investigates a multiuser scheduling problem within an uplink multiple-input multi-output (MIMO) status update network, consisting of a multi-antenna base station (BS) and multiple single-antenna devices. The presence of multiple antennas at the BS introduces spatial degrees-of-freedom, enabling concurrent transmission of status updates from multiple devices in each time slot. Our objective is to optimize network-wide information freshness, quantified by the age of information (AoI) metric, by determining how the BS can best schedule device transmissions, while taking into account the random arrival of status updates at the device side. To address this decision-making problem, we model it as a partially observable Markov decision process (POMDP) and establish that the evolution of belief states for different devices is independent. We also prove that feasible belief states can be described by finite-dimensional vectors. Building on these observations, we develop a dynamic scheduling (DS) policy that minimizes a configurable drift in each time slot to solve the POMDP, and then derive an upper bound of its AoI performance, which is used to optimize the parameter configuration. To gain more design insights, we investigate a symmetric network, and put forth a fixed scheduling (FS) policy that minimizes the drift with an optimized fixed number scheduling, thereby yielding lower computational complexity. An action space reduction strategy is applied to further reduce the computational complexity of both DS and FS policies. Our numerical results validate our analyses and indicate that the DS policy with the reduced action space performs almost identically to the original DS policy, and both outperform the baseline policies.

The latest WiFi standard, IEEE 802.11ax (WiFi 6), introduces a novel uplink random access mechanism called uplink orthogonal frequency division multiple access-based random access (UORA). While existing work has evaluated the performance of UORA using conventional performance metrics, such as throughput and delay, its information freshness performance has not been thoroughly investigated in the literature. This is of practical significance as WiFi 6 and beyond are expected to support real-time applications. This paper presents the first attempt to fill this gap by investigating the information freshness, quantified by the Age of Information (AoI) metric, in UORA networks. We establish an analytical framework comprising two discrete-time Markov chains (DTMCs) to characterize the transmission states of stations (STAs) in UORA networks. Building on the formulated DTMCs, we derive an analytical expression for the long-term average AoI (AAoI), facilitating the optimization of UORA parameters for enhanced AoI performance through exhaustive search. To gain deeper design insights and improve the effectiveness of UORA parameter optimization, we derive a closed-form expression for the AAoI and its approximated lower bound for a simplified scenario characterized by a fixed backoff contention window and generate-at-will status updates. By analyzing the approximated lower bound of the AAoI, we propose efficient UORA parameter optimization algorithms that can be realized with only a few comparisons of different possible values of the parameters to be optimized. Simulation results validate our analysis and demonstrate that the AAoI achieved through our proposed parameter optimization algorithm closely approximates the optimal AoI performance obtained via exhaustive search, outperforming the round-robin and max-AoI policies in large and low-traffic networks.

This paper develops scheduling policies to optimize the information freshness, quantified by the age of information (AoI) metric, in an uplink multi-user SIMO status update system. The multi-user scheduling problem is formulated as a Markov decision process (MDP) to derive the optimal policy that minimizes the average AoI across devices. However, the optimal policy suffers from high complexity due to dimensionality in large networks. To address this, a low-complexity max-weight (MW) policy is developed for large-scale networks using the Lyapunov optimization framework. The MW policy dynamically determines the subset of devices to schedule in each time slot by maximizing the expected AoI drop of the subsequent time slot. Simulations are conducted to compare the performance of the optimal policy, the MW policy, and the baseline fixed scheduling (FS) policy that always schedules a fixed number of devices with the highest AoI. The results show that the MW policy achieves close-to-optimal performance. Moreover, for a given network setup, there exists an FS policy with a particular number of scheduled devices that can approach the MW policy. This observation inspired the development of another low-complexity scheduling policy, termed optimized FS (OFS). This policy further optimizes the number of devices scheduled under the FS policy based on specific network configurations. Closed-form expressions for the average peak AoI and the approximated average AoI of the FS policy with a given number of scheduled devices are derived to determine the optimal number of scheduled devices for the OFS policy under different network setups. Simulation results validate the theoretical analysis and show that the OFS policy achieves performance comparable to the MW policy while circumventing the need for per-slot optimization.

This paper presents DeepCRF, a new framework that harnesses deep learning to extract subtle micro-signals from channel state information (CSI) measurements, enabling robust and resilient radio-frequency fingerprinting (RFF) of commercial-off-the-shelf (COTS) WiFi devices across diverse channel conditions. Building on our previous research, which demonstrated that micro-signals in CSI, termed micro-CSI, most likely originate from RF circuitry imperfections and can serve as unique RF fingerprints, we develop a new approach to overcome the limitations of our prior signal space-based method. While the signal space-based method is effective in strong line-of-sight (LoS) conditions, we show that it struggles with the complexities of non-line-of-sight (NLoS) scenarios, compromising the robustness of CSI-based RFF. To address this challenge, DeepCRF incorporates a carefully trained convolutional neural network (CNN) with model-inspired data augmentation, supervised contrastive learning, and decision fusion techniques, enhancing its generalization capabilities across unseen channel conditions and resilience against noise. Our evaluations demonstrate that DeepCRF significantly improves device identification accuracy across diverse channels, outperforming both the signal space-based baseline and state-of-the-art neural network-based benchmarks. Notably, it achieves an average identification accuracy of 99.53% among 19 COTS WiFi network interface cards in real-world unseen scenarios using 4 CSI measurements per identification procedure.

Transparent objects are prevalent in everyday environments, but their distinct physical properties pose significant challenges for camera-guided robotic arms. Current research is mainly dependent on camera-only approaches, which often falter in suboptimal conditions, such as low-light environments. In response to this challenge, we present FuseGrasp, the first radar-camera fusion system tailored to enhance the transparent objects manipulation. FuseGrasp exploits the weak penetrating property of millimeter-wave (mmWave) signals, which causes transparent materials to appear opaque, and combines it with the precise motion control of a robotic arm to acquire high-quality mmWave radar images of transparent objects. The system employs a carefully designed deep neural network to fuse radar and camera imagery, thereby improving depth completion and elevating the success rate of object grasping. Nevertheless, training FuseGrasp effectively is non-trivial, due to limited radar image datasets for transparent objects. We address this issue utilizing large RGB-D dataset, and propose an effective two-stage training approach: we first pre-train FuseGrasp on a large public RGB-D dataset of transparent objects, then fine-tune it on a self-built small RGB-D-Radar dataset. Furthermore, as a byproduct, FuseGrasp can determine the composition of transparent objects, such as glass or plastic, leveraging the material identification capability of mmWave radar. This identification result facilitates the robotic arm in modulating its grip force appropriately. Extensive testing reveals that FuseGrasp significantly improves the accuracy of depth reconstruction and material identification for transparent objects. Moreover, real-world robotic trials have confirmed that FuseGrasp markedly enhances the handling of transparent items.

This paper focuses on optimizing the long-term average age of information (AoI) in device-to-device (D2D) networks through age-aware link scheduling. The problem is naturally formulated as a Markov decision process (MDP). However, finding the optimal policy for the formulated MDP in its original form is challenging due to the intertwined AoI dynamics of all D2D links. To address this, we employ the Lyapunov optimization framework to develop a dynamic age-aware scheduling policy. Specifically, we explore two scenarios: known statistical channel state information (CSI) and known instantaneous CSI. For the statistical CSI case, we propose a message passing neural network (MPNN)-based policy for real-time scheduling. The MPNN is trained in an unsupervised manner with a loss function designed to minimize per-slot Lyapunov drift. For the instantaneous CSI case, we introduce a Gurobi-based policy, using the solver to minimize per-slot Lyapunov drift for scheduling decisions. To further reduce the computational complexity, we also propose a greedy heuristic policy that approximates drift minimization. Extensive simulation results show that our proposed age-aware scheduling policies have superior performance compared to the baselines, and can be applied to large-scale D2D networks.

This work investigates the design of an omnidirectional optical transceiver, to enable reliable inter-robot communication in mobile settings. The design addresses challenges stemming from the intrinsic directional nature of light, which can restrict signal coverage area and destabilize connections during robot movement. Our experiments demonstrate the system's capability to establish reliable high-speed optical communication links within a circular coverage zone of over 3 meters in radius. Our work advances the state-of-the-art in two significant ways: First, to our knowledge, this is the first demonstration of an omnidirectional optical communication system aligned with the IEEE 802.11bb standard. Second, the system leverages channel diversity to enhance signal quality by incorporating hardware-based omnidirectional signal combinations at the receiver, an analog signal-format agnostic method compatible with Wi-Fi and other wireless signals without modifying the underlying wireless system's digital signal processing chain.

In numerous WiFi sensing applications, such as passive human localization, the precision of sensing is often influenced by the estimation accuracy of multipath parameters. Several existing algorithms leverage pulse-shaping filter information to enhance multipath and channel estimation. However, WiFi chips do not disclose this filter information, and no current research has focused on measuring or estimating these pulse-shaping filters. In this paper, we introduce a new deep learning approach for the accurate estimation of pulse-shaping filters using channel state information (CSI), which incorporates both multipath channel information and pulse-shaping filter information. Specifically, we construct a convolutional neural network consisting of an encoder-regressor architecture, where the encoder translates the CSI into a latent representation, and the regressor subsequently estimates the pulse-shaping filter from this representation. Our proposed model's efficacy is demonstrated through its low normalized root mean squared error (NRMSE) in a variety of channel conditions, highlighting its ability to accurately estimate pulse-shaping filters.

This paper investigates the potential of large language models (LLMs) in accelerating the development of complex signal-processing algorithms on field-programmable gate arrays (FPGAs) for software-defined radio (SDR) systems. Using the Fast Fourier Transform (FFT) algorithm as a case study, we identify two common challenges in applying LLMs to realize intricate wireless communication algorithms on FPGA: 1) handling convoluted mathematical problems and 2) scheduling the execution of sub-modules within the hardware structure. To overcome the first problem, we adapt the chain-of-thought (CoT) prompting technique with a length-limit strategy to enhance the LLM’s Verilog writing performance. To handle the second problem, we develop a novel iterative in-context learning (IICL) prompting scheme that utilizes the iterative structure within the FFT module to perform in-context learning (ICL). These efforts significantly reduce the LLM’s error rate in completing the FFT implementation task and make possible the successful generation of a 64-point FFT module in Verilog, marking a significant milestone as the first LLM-written complex signal-processing algorithm for wireless communication on FPGA.

Due to the finite bandwidth of practical wireless systems, one multipath component can manifest itself as a discrete pulse consisting of multiple taps in the digital delay domain. This effect is called channel leakage, which complicates the multipath parameter estimation. In this study, we propose a new algorithm to estimate multipath parameters, including delay, angle of arrival (AOA), and angle of departure (AOD) of leaked channels. This is accomplished by leveraging the knowledge of pulse shaping functions, a technique that can be applied to enhance the precision of WiFi sensing. More specifically, we formulate the channel impulse response (CIR) between a transmit and a receive antenna as a linear combination of a set of overcomplete basis vectors, each corresponding to a different delay. Considering the limited number of paths in physical environments, we formulate the multipath parameter estimation as a group sparse recovery problem. We develop a two-stage approach based on variational expectation maximization (VEM) to solve the formulated problem. In the first stage, we estimate the sparse vectors and determine the number of physical paths and their associated delay parameters from the positions of the nonzero entries. In the second stage, we use Newton’s method to estimate the AOA and AOD of each path. The Cramér-Rao lower bound (CRLB) for multipath parameter estimation is derived for performance evaluation. Simulation results show that our algorithm can achieve superior estimation accuracy in multipath parameters compared to two benchmarking schemes and approach the CRLB.

This paper introduces CSI-RFF, a new framework that leverages micro-signals embedded within Channel State Information (CSI) curves to realize Radio-Frequency Fingerprinting of commodity off-the-shelf (COTS) WiFi devices for open-set authentication. The micro-signals that serve as RF fingerprints are termed “micro-CSI”. Through experimentation, we have found that the presence of micro-CSI can primarily be attributed to imperfections in the RF circuitry. Furthermore, this characteristic signal is detectable in WiFi 4/5/6 network interface cards (NICs). We have conducted further experiments to determine the most effective CSI collection configurations to stabilize micro-CSI. Yet, extracting micro-CSI for authentication purposes poses a significant challenge. This complexity arises from the fact that CSI measurements inherently include both micro-CSI and the distortions introduced by wireless channels. These two elements are intricately intertwined, making their separation non-trivial. To tackle this challenge, we have developed a signal space-based extraction technique for line-of-sight (LoS) scenarios, which can effectively separate the distortions caused by wireless channels and micro-CSI. Over the course of our comprehensive CSI data collection period extending beyond one year, we found that the extracted micro-CSI displays unique characteristics specific to each WiFi device and remains invariant over time. This establishes micro-CSI as a suitable candidate for device fingerprinting. Finally, we conduct a case study focusing on area access control for mobile robots. In particular, we applied our CSI-RFF framework to identify mobile robots operating in real-world indoor LoS environments based on their transmitted WiFi signals. To accomplish this, we have compared and employed anomaly detection algorithms for the authentication of 15 COTS WiFi 4/5/6 NICs that were carried by a mobile robot under both static and mobile conditions, maintaining an average signal-to-noise ratio (SNR) of 34 dB. Our experimental results demonstrate that the micro-CSI-based authentication algorithm can achieve an average attack detection rate close to 99% with a false alarm rate of 0% in both static and mobile conditions when using 20 CSI measurements to construct one fingerprint.

Enhancing information freshness in wireless networks has gained significant attention in recent years. To optimize or analyze information freshness, which is often characterized by the age of information (AoI) metric, extensive theoretical studies have been conducted on various wireless networks. Early research has demonstrated the significance of last-come-first-served (LCFS) packet scheduling and controlled status sampling (i.e., packet generation) in improving information freshness. These mechanisms have been widely adopted in subsequent studies. However, the effective implementation of these mechanisms in commercial off-the-shelf (COTS) wireless devices has not been thoroughly investigated, which could limit the practical application of information freshness-oriented protocols in real-world systems. Our work aims to address the gap by exploring the effective implementation of the information freshness-oriented mechanisms mentioned above in COTS WiFi devices that use the Linux operating systems. Our attempts reveal that the physical layer queue of WiFi devices operates on a first-come-first-served (FCFS) basis, and the packet generation process cannot be precisely controlled by default. To overcome these challenges, we develop Fresh-Fi, an information freshness-oriented protocol stack that involves careful customizations to the Linux networking protocol stack. Fresh-Fi mainly incorporates a mac80211 subsystem-based LCFS queue and a real-time kernel-based cross-layer tunnel between the mac80211 subsystem and the application layer for triggered packet generation. Our experiments show that implementing Fresh-Fi can significantly improve AoI performance. Specifically, we observed that Fresh-Fi improved AoI performance by over 13 times when compared to a baseline design that relies on an LCFS queue implemented in the application layer in standard Linux.

This paper investigates a multiuser scheduling problem within an uplink multiple-input multi-output (MIMO) status update network, consisting of a multi-antenna access point (AP) and multiple single-antenna devices. The presence of multiple an-tennas at the AP introduces spatial degrees-of-freedom, enabling concurrent transmission of status updates from multiple devices in each time slot. Our objective is to optimize network-wide information freshness, as measured by the age of information (AoI) metric, by determining how the AP can best schedule device transmissions, while taking into account the random arrival of status updates at the device side. It is worth noting that the AP has partial observations of the system time of the latest updates at each device when making scheduling decisions. To address this decision-making problem, we model it as a partially observable Markov decision process (POMDP) and establish that the evolution of belief states for different devices is independent. We also manage to characterize feasible belief states using three-dimensional vectors. Building on this foundation, we develop a dynamic scheduling (DS) policy to solve the POMDP and implement an action space reduction. Our numerical results indicate that the DS policy with the reduced action space performs almost identically to the original DS policy, and both outperform the baseline policy that schedules a fixed number of devices.

Integrated Sensing and Communication (ISAC) is an emerging technology that integrates wireless sensing and communication into a single system, transforming many applications, including cooperative mobile robotics. However, in scenarios where radio communications are unavailable, alternative approaches are needed. In this paper, we propose a new optical ISAC (OISAC) scheme for cooperative mobile robots by integrating camera sensing and screen-camera communication (SCC). Unlike previous throughput-oriented SCC designs that work with stationary SCC links, our OISAC scheme is designed for real-time control of mobile robots. It addresses new challenges such as image blur and long image display delay. As a case study, we consider the leader-follower formation control problem, an essential part of cooperative mobile robotics. The proposed OISAC scheme enables the follower robot to simultaneously acquire the information shared by the leader and sense the relative pose to the leader using only RGB images captured by its onboard camera. We then design a new control law that can leverage all the information acquired by the camera to achieve stable and accurate formations. We design and conduct real-world experiments involving uniform and nonuniform motions to evaluate the proposed system and demonstrate the advantages of applying OISAC over a benchmark approach that uses extended Kalman filtering (EKF) to estimate the leader's states. Our results show that the proposed OISAC-augmented leader-follower formation system achieves better performance in terms of accuracy, stability, and robustness.

This paper investigates user scheduling in a slotted downlink multi-user multiple-input multi-output (MU-MIMO) system, where a base station (BS) equipped with multiple antennas transmits status updates to multiple single-antenna devices. We assume perfect channel station information (CSI) is available at the BS to make scheduling decisions. The goal is to optimize the information freshness of the network, quantified by the network-wide average age of information (AoI), subject to the peak transmission power constraint in each time slot. To tackle this problem, we employ the Lyapunov optimization method to convert the constrained long-term optimization problem into a per-slot optimization problem. We propose two low-complexity algorithms to solve the per-slot optimization problem, namely the AoI-aware scheduling (AS) algorithm and the joint age- and channel-aware scheduling (JACS) algorithm. These algorithms aim to determine the optimal device or devices for delivering the latest status updates to them in each time slot. We evaluate the performance of the proposed algorithms through numerical simulations in both symmetric and asymmetric networks and compare them with two baseline algorithms: semi-orthogonal user group (SUG) and exhaustive search (ES) algorithms. Regardless of the network setup, our proposed algorithms consistently outperform SUG, and demonstrate performance comparable to that of the exhaustive search algorithm. Notably, when the peak transmission power is high enough, the proposed algorithms perform almost as well as the ES algorithm. Thanks to the joint consideration of CSI and AoI, JACS tends to have better performance than AS at the cost of slightly higher computational complexity.

This article puts forth a networking paradigm, referred to as just-in-time (JIT) communication, to support client-server applications with stringent request-response latency requirement. Of interest is not just the round-trip delay of the network, but the actual request-response latency experienced by the application. The JIT framework contains two salient features. At the client side, the communication layer will “pull” a request from the client just when there is an upcoming transmission opportunity from the network. This ensures that the request contains information that is as fresh as possible (e.g., a sensor reading obtained just before the transmission opportunity). At the server side, the network ascertains that the server, after receiving and processing the request to generate a response (e.g., a control command to be sent to the client), will have a transmission opportunity at just this time. We realize the JIT system, including the protocol stack, over a time-division-multiple-access (TDMA) network implemented on a System-on-Chip (SoC) platform. We prove that a TDMA network with a power-of-2 time slots per superframe is optimal for realizing the server-side JIT function. Our experimental results validate that JIT networks can yield significantly lower request-response latency than networks without JIT support can.

This paper considers a wireless uplink network consisting of multiple end devices and an access point (AP). Each device monitors a physical process with randomly generated status updates and sends these update packets to the AP in the uplink. The AP aims to schedule the transmissions of these devices to optimize the network-wide information freshness, quantified by the age of information (AoI) metric. Due to the stochastic arrival of the status updates at end devices, the AP only haspartial observationsof system times of the latest status update packets at end devices when making scheduling decisions. Such a decision-making problem can be naturally formulated as a partially observable Markov decision process (POMDP). We reformulate the POMDP into an equivalent belief Markov decision process (belief-MDP), by defining fully observable belief states of the POMDP as the states of the belief-MDP. The belief-MDP in its original form is difficult to solve as the dimension of its states can go to infinity and its belief space is uncountable. Fortunately, by carefully leveraging the properties of the status update arrival processes (i.e., Bernoulli processes), we manage to simplify the belief-MDP substantially, where every feasible state is characterized by a two-dimensional vector. Based on the simplified belief-MDP, we devise a low-complexity scheduling policy, termed Partially Observing Max-Weight (POMW) policy, for the formulated AoI-oriented scheduling problem. We derive upper bounds for the time-average AoI performance of the proposed POMW policy. We analyze the performance guarantee for the POMW policy by comparing its performance with a universal lower bound available in the literature. Numerical results validate our analyses and demonstrate that the performance gap between the POMW policy and its fully observable counterpart is proportional to the inverse of the lowest arrival rate of all end devices.

This paper analyzes and optimizes the average Age of Information (AAoI) of Frame Slotted ALOHA with Reservation and Data slots (FSA-RD) in a multi-access network, where multiple users transmit their randomly generated status updates to a common access point in a framed manner. Each frame consists of one reservation slot and several data slots. The reservation slot is further split into several mini-slots. In each reservation slot, users that want to transmit a status update will randomly send short reservation packets in one of the mini-slots to contend for data slots of the current frame. The reservation is successful only if one reservation packet is sent in a mini-slot. The data slots are then allocated to those users that succeed in the reservation slot. In the considered FSA-RD scheme, one user with a status update for transmission, termed active user, may need to perform multiple reservation attempts before successfully delivering it. As such, the number of active user(s) in different frames are dependent and thus the probability of making a successful reservation varies from frame to frame, making the AAoI analysis non-trivial. We manage to derive an analytical expression of AAoI for FSA-RD by characterizing the evolution of the number of active user(s) in each frame as a discrete-time Markov chain. We then consider the FSA-RD scheme with one reservation attempt per status update, termed FSA-RD-One. Thanks to the independent frame behaviors of FSA-RD-One, we attain a closed-form expression for its AAoI, which is further used to find the near-optimal reservation probability. Our analysis reveals the impact of key protocol parameters, such as frame size and reservation probability, on the AAoI. Simulation results validate our analysis and show that the optimized FSA-RD outperforms the optimized slotted ALOHA.

This paper investigates the Age of Information (AoI) performance of Frame Slotted ALOHA with Reservation and Data slots (FSA-RD). We consider a symmetric multi-access network where each user transmits its randomly generated status updates to an access point in a framed manner. Each frame consists of one reservation slot and several data slots. The reservation slot is made up of some mini-slots. In each reservation slot, users, with a status update packet to transmit, randomly send short reservation packets in one of the mini-slots to contend for data slots of the frame. The data slots are assigned to those users that succeed in reservation slot. To provide insights in optimizing the information freshness of FSA-RD, we manage to derive a closed-form expression of the average AoI under FSA-RD by applying a recursive method. Numerical results validate the analytical expression and demonstrate the influence of the frame size and reservation probability on the average AoI. We finally perform a comparison between the AoI performance of FSA-RD with optimized frame size and reservation probability, and that of slotted ALOHA with optimized transmission probability. The comparison results show that FSA-RD can effectively reduce the AoI performance of multi-access networks, especially when the status arrival rate of the network becomes large.

Time-sensitive wireless networks are an important enabling building block for many emerging industrial Internet-of-Things (IoT) applications. Quick prototyping and evaluation of time-sensitive wireless technologies are desirable for research and development efforts. Software-defined radio (SDR), by allowing wireless signal processing on a personal computer (PC), has been widely used for such quick prototyping efforts. Unfortunately, because of theuncontrollable delaybetween the PC and the radio board, SDR is generally deemed not suitable for time-sensitive wireless applications that demand communication with low and deterministic latency. For a rigorous evaluation of its suitability for industrial IoT applications, this article conducts a quantitative investigation of the synchronization accuracy and end-to-end latency achievable by an SDR wireless system. To this end, we designed and implemented a time-slotted wireless system on the universal software radio peripheral (USRP) SDR platform. We developed a time synchronization mechanism to maintain synchrony among nodes in the system. To reduce the delays and delay jitters between the USRP board and its PC, we devised aJust-in-timealgorithm to ensure that packets sent by the PC to the USRP can reach the USRP just before the time slots they are to be transmitted. Our experiments demonstrate that 90% (100%) of the time slots of different nodes can be synchronized and aligned to within ±0.5 samples or$\pm 0.05\mu \text{s}$(±1.5 samples or$\pm 0.15\mu \text{s}$), and that the end-to-end packet delivery latency can be down to 3.75 ms. This means that SDR-based solutions can be applied in a range of IIoT applications that require tight synchrony and moderately low latency, e.g., sensor data collection, automated guided vehicle (AGV) control, and human–machine interaction (HMI).

This paper studies the minimum-age scheduling problem in a wireless sensor network where an access point (AP) monitors the state of an object via a set of sensors. The freshness of the sensed state, measured by the age-of-information (AoI), varies at different sensors and is not directly observable to the AP. The AP has to decide which sensor to query/sample in order to get the most updated state information of the object (i.e., the state information with the minimum AoI). In this paper, we formulate the minimum-age scheduling problem as a multi-armed bandit problem with partially observable arms and explore the greedy policy to minimize the expected AoI sampled over an infinite horizon. To analyze the performance of the greedy policy, we 1) put forth a relaxed greedy policy that decouples the sampling processes of the arms, 2) formulate the sampling process of each arm as a partially observable Markov decision process (POMDP), and 3) derive the average sampled AoI under the relaxed greedy policy as a sum of the average AoI sampled from individual arms. Numerical and simulation results validate that the relaxed greedy policy is an excellent approximation to the greedy policy in terms of the expected AoI sampled over an infinite horizon.

This paper considers a wireless network with a base station (BS) conducting timely status updates to multiple clients via adaptive non-orthogonal multiple access (NOMA)/orthogonal multiple access (OMA). Specifically, the BS is able to adaptively switch between NOMA and OMA for the downlink transmission to optimize the information freshness of the network, characterized by the Age of Information (AoI) metric. For the simple two-client case, we formulate a Markov Decision Process (MDP) problem and develop the optimal policy for the BS to decide whether to use NOMA or OMA for each downlink transmission based on the instantaneous AoI of both clients. The optimal policy is shown to have a switching-type property with obvious decision switching boundaries. A suboptimal policy with lower computation complexity is also devised, which is shown to achieve near-optimal performance via numerical simulations. For the more general multi-client scenario, the optimal solution is the computationally intractable due to the large state and action spaces. As such, we devote to provide a feasible suboptimal policy with low computation complexity. Specifically, inspired by the proposed suboptimal policy of the two-client scenario, we formulate a nonlinear optimization problem to determine the optimal power allocated to each client by maximizing the expected AoI drop of the network in each time slot (i.e., minimizing the expected network-wide AoI of the next slot). The problem is shown to be non-convex, we manage to solve it by approximating it as a convex optimization problem. Simulation results validate the tightness of the adopted approximation. Specifically, the performance of the adaptive NOMA/OMA scheme by solving the convex optimization is shown to be close to that of the max-weight policy solved by exhaustive search. Besides, the adaptive NOMA/OMA scheme achieves significant performance improvement compared to the OMA scheme, especially when the number of clients in the network is large and the transmission SNR is high.

This article considers a cooperative Internet-of-Things (IoT) system with a source aiming to transmit randomly generated status updates to a designated destination as timely as possible under the help of a relay. We adopt a recently proposed concept, the Age of Information (AoI), to characterize the timeliness of the status updates. In the considered system, delivering the status updates via the one-hop direct link will have a shorter transmission time at the cost of incurring a higher error probability, while the delivery of status updates through the two-hop relay link could be more reliable at the cost of suffering longer transmission time. Thus, it is important to design the relaying protocol of the considered system for optimizing the information freshness. Considering the limited capabilities of IoT devices, we propose two low-complexity Age-oriented Relaying (AoR) protocols, i.e., the source-prioritized AoR (SP-AoR) protocol and the relay-prioritized AoR (RP-AoR) protocol, to reduce the AoI of the considered system. Specifically, in the SP-AoR protocol, the relay opportunistically replaces the source to retransmit the successfully received status updates that have not been correctly delivered to the destination, but the retransmission at the relay can be preempted by the arrival of a new status update at the source. Differently, in the RP-AoR protocol, once the relay replaces the source to retransmit the status updates that have not been successfully received by the destination, the retransmission at the relay will not be preempted by new status update arrivals at the source. By carefully analyzing the evolution of the instantaneous AoI, we derive closed-form expressions of the average AoI for both the proposed AoR protocols. We further optimize the generation probability of the status updates at the source in both protocols. Simulation results validate our theoretical analysis and demonstrate that the two proposed protocols outperform each other under various system parameters. Moreover, the protocol with better performance can achieve near-optimal performance compared with the optimal scheduling policy attained by applying the Markov decision process (MDP) tool.

In this paper, we investigate the age minimization problem for a two-hop relay system, under a resource constraint on the average number of forwarding operations at the relay. We first design an optimal policy by modelling the considered scheduling problem as a constrained Markov decision process (CMDP) problem. Based on the observed multi-threshold structure of the optimal policy, we then devise a low-complexity double threshold relaying (DTR) policy with only two thresholds, one for relay's AoI and the other one for the age gain between destination and relay. We derive approximate closed-form expressions of the average AoI at the destination, and the average number of forwarding operations at the relay for the DTR policy, by modelling the tangled evolution of age at relay and destination as a Markov chain (MC). Numerical results validate all the theoretical analysis, and show that the low-complexity DTR policy can achieve near optimal performance compared with the optimal CMDP-based policy. Moreover, the relay should always consider the threshold for its local age to maintain a low age at the destination. When the resource constraint is relatively tight, it further needs to consider the threshold on the age gain to ensure that only those packets that can decrease destination's age dramatically will be forwarded.

In this paper, we concern the uplink of a massive single-input multiple-output enabled ultra-reliable low-latency communication system, in which a single-antenna transmitter aims to timely and reliably send data to a receiver equipped with a large number of antennas over Rayleigh fading channels. For such a scenario, to eliminate the considerable overhead caused by channel estimation, we adopt a noncoherent maximum-likelihood (ML) receiver, which is known to be optimal in terms of average symbol-error rate for equiprobable discrete input signals. We propose a two-dimensional noncoherent constellation design framework to enhance the reliability of the considered system. Specifically, our design principle is to maximize the minimum Kullback-Leibler divergence between the conditional distributions induced by different transmitted signals under average power constraint for any given transmission rate. The resulting optimization problem is shown to be a challenging mixed discrete-continuous problem. We manage to solve the problem by deliberately designing optimal bit allocation and optimal constellation structure as a function of signal-to-noise ratios. We then unveil that the proposed constellation can facilitate efficient ML detection with low computational complexity. Finally, simulation results illustrate that the proposed scheme has a superior error performance than conventional training-based schemes and existing energy detection designs.

Software-defined Internet-of-Things networking (SDIoT) greatly simplifies the network monitoring in large-scale IoT networks by per-flow sampling, wherein the controller keeps track of all the active flows in the network and samples the IoT devices on each flow path to collect real-time flow statistics. There is a tradeoff between the controller’s sampling preference and the balancing of loads among devices. On the one hand, the controller may prefer to sample some of the IoT devices on the flow path because they yield more accurate flow statistics. On the other hand, it is desirable to sample the devices uniformly so that their energy consumptions and lifespan are balanced. This paper formulates the flow sampling problem in large-scale SDIoT networks by means of a Markov decision process and devises policies that strike a good balance between these two goals. Three classes of policies are investigated: the optimal policy, the state-independent policies, and the index policies (including the Whittle index and a second-order index policies). The second-order index policy is the most desired policy among all: 1) in terms of performance, it is on an equal footing with the Whittle index policy, and outperforms the state-independent policies by much; 2) in terms of complexity, it is much simpler than the optimal policy, and is comparable to state-independent policies and the Whittle index policy; 3) in terms of realizability, it requires no prior information on the network dynamics, hence is much easier to implement in practice.

In this paper, we consider a multiuser uplink status update system, where a monitor aims to timely collect randomly generated status updates from multiple end nodes through a shared wireless channel. We adopt the recently proposed metric, termed age of information (AoI), to quantify the information timeliness and freshness. Due to the random generation of the status updates at the end node side, the monitor only grasps a partial knowledge of the status update arrivals. Under such a practical scenario, we aim to address a fundamental multiuser scheduling problem: how to schedule the end nodes to minimize the network-wide AoI? To solve this problem, we formulate it as a partially observable Markov decision process (POMDP), and develop a dynamic programming (DP) algorithm to obtain the optimal scheduling policy. By noting that the optimal policy is computationally prohibitive, we further design a low-complexity myopic policy that only minimizes the one-step expected reward. Simulation results show that the performance of the myopic policy approaches that of the optimal policy and is superior to that of the baseline policy.

This paper considers a cooperative status update system with a source aiming to send randomly generated status updates to a designated destination as timely as possible with the help of a relay. We adopt a recently proposed concept, the age of information (AoI), to characterize the timeliness of the status updates. We propose a new age-oriented opportunistic relaying (AoR) protocol to reduce the AoI of the considered system. Specifically, the relay opportunistically replaces the source to retransmit the successfully received status updates that have not been correctly delivered to the destination, but the retransmission at the relay can be preempted by the arrival of a new status update at the source. By carefully analyzing the evolution of the AoI, we derive a closed-form expression of the average AoI for the proposed AoR protocol. We further minimize the average AoI by optimizing the generation probability of the status updates at the source. Simulation results validate our theoretical analysis and demonstrate that the average AoI performance of the proposed AoR protocol is superior to that of the non-cooperative system.

Fast wireless data aggregation is a critical design challenge in Internet-of-Things (IoT) networks. In this paper, we consider a real-time status update IoT network, where an access point (AP) aims to aggregate data from multiple IoT devices using over-the-air computation (AirComp). To evaluate the freshness of the aggregated data at the AP, we propose the metric of age of aggregated information (AoAI), extended from the age of information (AoI), which is defined as the time elapsed since the generation of the latest valid aggregated data received at the AP. An aggregated status update is considered to be valid if the AirComp distortion, quantified by the mean-squarederror (MSE), is smaller than a pre-determined threshold. We formulate a constrained Markov decision process (MDP) problem for minimizing the average AoAI subject to the average transmit power constraint of each IoT device. The formulated constrained MDP problem is then reformulated as an unconstrained MDP problem by using the Lagrangian approach. By analyzing the structure of the MDP, we propose a state aggregation procedure to reduce the computational complexity. We further propose both offline and online scheduling algorithms to solve the problem. Simulation results show that the proposed algorithms significantly outperform the baseline algorithm with a fixed scheduling threshold in terms of the AoAI, and also strike a good balance between the AoAI and the total power consumption.

More and more emerging Internet of Things (IoT) applications involve status updates, where various IoT devices monitor certain physical processes and report their latest statuses to the relevant information fusion nodes. A new performance measure, termed the age of information (AoI), has recently been proposed to quantify the information freshness in time-critical IoT applications. Due to a large number of devices in future IoT networks, the decentralized channel access protocols (e.g. random access) are preferable thanks to their low network overhead. Built on the AoI concept, some recent efforts have developed several AoI-oriented ALOHA-like random access protocols for boosting the network-wide information freshness. However, all relevant works focused on theoretical designs and analysis. The development and implementation of a working prototype to evaluate and further improve these random access protocols in practice have been largely overlooked. Motivated as such, we build a software-defined radio (SDR) prototype for testing and comparing the performance of recently proposed AoI-oriented random access protocols. To this end, we implement a time-slotted wireless system by devising a simple yet effective over-the-air time synchronization scheme, in which beacons that serve as reference timing packets are broadcast by an access point (AP) from time to time. For a complete working prototype, we also design the frame structures of various packets exchanged within the system. Finally, we design a set of experiments, implement them on our prototype and test the considered algorithms in an office environment.

This paper considers a wireless network with a base station (BS) conducting timely transmission to two clients in a slotted manner via hybrid non-orthogonal multiple access (NOMA)/orthogonal multiple access (OMA). Specifically, the BS is able to adaptively switch between NOMA and OMA for the downlink transmission to minimize the information freshness, characterized by Age of Information (AoI), of the network. If the BS chooses OMA, it can only serve one client within a time slot and should decide which client to serve; if the BS chooses NOMA, it can serve both clients simultaneously and should decide the power allocated to each client. To minimize the weighted sum of expected AoI of the network, we formulate a Markov Decision Process (MDP) problem and develop an optimal policy for the BS to decide whether to use NOMA or OMA for each downlink transmission based on the instantaneous AoI of both clients. We prove the existence of optimal stationary and deterministic policy, and perform action elimination to reduce the action space for lower computation complexity. The optimal policy is shown to have a switching-type property with obvious decision switching boundaries. A suboptimal policy with lower computation complexity is also devised, which can achieve near-optimal performance according to our simulation results. The performance of different policies under different system settings is compared and analyzed in numerical results to provide useful insights for practical system designs.

Achieving ultra-reliable, low-latency and secure communications is essential for realizing the industrial Internet of Things (IIoT). Non-coherent massive multiple-input multiple-output (MIMO) has recently been proposed as a promising methodology to fulfill ultra-reliable and low-latency requirements. In addition, physical layer authentication (PLA) technology is particularly suitable for IIoT communications thanks to its low-latency attribute. A PLA method for non-coherent massive single-input multiple-output (SIMO) IIoT communication systems is proposed in this paper. Specifically, we first determine the optimal embedding of the authentication information (tag) in the message information. We then optimize the power allocation between message and tag signal to characterize the trade-off between message and tag error performance. Numerical results show that the proposed PLA is more accurate then traditional methods adopting the uniform tag when the communication reliability remains at the same level. The proposed PLA method can be effectively applied to the non-coherent system.

We consider the multicast transmission of a real-time Internet of Things (IoT) system, where a server transmits time-stamped status updates to multiple IoT devices. We apply a recently proposed metric, named age of information (AoI), to capture the timeliness of the information delivery. The AoI is defined as the time elapsed since the generation of the most recently received status update. Different from the existing studies that considered either multicast transmission without hard deadlines or unicast transmission with hard deadlines, we enforce a hard deadline for the service time of multicast transmission. This is important for many emerging multicast IoT applications, where the outdated status updates are useless for IoT devices. Specifically, the transmission of a status update is terminated when either the hard deadline expires or a sufficient number of IoT devices successfully receive the status update. We first calculate the distributions of the service time for all possible reception outcomes at IoT devices, and then derive a closed-form expression of the average AoI. Simulations validate the performance analysis, which reveals that: 1) the multicast transmission with hard deadlines achieves a lower average AoI than that without hard deadlines; and 2) there exists an optimal value of the hard deadline that minimizes the average AoI.

In this article, we consider the multicast transmission of a real-time Internet-of-Things (IoT) system, where an access point (AP) transmits timestamped status updates to multiple IoT devices. Different from the existing studies that only considered multicast transmission without deadlines, we enforce a deadline for the service time of each multicast status update, taking into account both the fixed and randomly distributed deadlines. In particular, a status update is dropped when either its deadline expires or it is successfully received by a certain number of IoT devices. Considering deadlines is important for many emerging IoT applications, where the outdated status updates are of no use to IoT devices. We evaluate the timeliness of the status update delivery by applying a recently proposed metric, named the Age of Information (AoI), which is defined as the time elapsed since the generation of the most recently received status update. After deriving the distributions of the service time for all possible reception outcomes at IoT devices, we manage to obtain the closed-form expressions of both the average AoI and the average peak AoI. Simulations validate the performance analysis, which reveals that the multicast transmission with deadlines achieves a lower average AoI than that without deadlines and there exists an optimal value of the deadline that can minimize the average (peak) AoI. Results also show that the fixed and random deadlines have respective advantages in different deadline regimes.

Achieving ultra-reliable, low-latency and secure communications is essential for realizing the industrial Internet of Things (IIoT). Non-coherent massive multiple-input multiple-output (MIMO) is one of promising techniques to fulfill ultra-reliable and low-latency requirements. In addition, physical layer authentication (PLA) technology is particularly suitable for secure IIoT communications thanks to its low-latency attribute. A PLA method for non-coherent massive single-input multiple-output (SIMO) IIoT communication systems is proposed in this paper. This method realizes PLA by embedding an authentication signal (tag) into a message signal, referred to as “message-based tag embedding”. It is different from traditional PLA methods utilizing uniform power tags. We design the optimal tag embedding and optimize the power allocation between the message and tag signals to characterize the trade-off between the message and tag error performance. Numerical results show that the proposed message-based tag embedding PLA method is more accurate than the traditional uniform tag embedding method which has an unavoidable tag error floor close to 10%.

This article considers a cognitive radio-based IoT monitoring system, consisting of an IoT device that aims to update its measurement to a destination using cognitive radio technique. Specifically, the IoT device as a secondary user (SIoT), seeks and exploits the spectrum opportunities of the licensed band vacated by its primary user (PU) to deliver status updates without causing visible effects to the licensed operation. In this context, the SIoT should carefully make use of the licensed band and schedule when to transmit to maintain the timeliness of the status update. The timeliness of the status update characterizes how the destination knows the latest information of the SIoT. We adopt a recent metric, Age of Information (AoI), to characterize the timeliness of the status update of the SIoT. We aim to minimize the long-term average AoI of the SIoT while satisfying the collision constraint imposed by the PU by formulating a constrained Markov decision process (CMDP) problem. We first prove the existence of optimal stationary policy of the CMDP problem. The optimal stationary policy (termed age-optimal policy) is shown to be a randomized simple policy that randomizes between two deterministic policies with a fixed probability. We prove that the two deterministic policies have a threshold structure and further derive the closed-form expression of average AoI and collision probability for the deterministic threshold-structured policy by conducting Markov Chain analysis. The analytical expression offers an efficient way to calculate the threshold and randomization probability to form the age-optimal policy. For comparison, we also consider the throughput maximization policy (termed throughput-optimal policy) and analyze the average AoI performance under the throughput-optimal policy in the considered system. Numerical simulations show the superiority of the derived age-optimal policy over the throughput-optimal policy. We also unveil the impacts of various system parameters on the corresponding optimal policy and the resultant average AoI.

This paper focuses on the signal design in a Gaussian amplify-and-forward (AF) relay system with superposition coding (SC) being applied at the relay, which allows the source and relay to transmit their own information within two time slots. Practical quadrature amplitude modulation (QAM) constellations are adopted at the source and relay. To improve the system error performance, we optimize the weight coefficients adopted at the source and relay to maximize the minimum Euclidean distance of the received composite constellation, subject to their individual average power constraints. The formulated optimization problem is shown to be a mixed continuous-discrete one that is non-trivial to resolve in general. By resorting to the punched Farey sequence, we manage to obtain the optimal solution to the formulated problem by first partitioning the entire feasible region into a finite number of sub-intervals and then taking the maximum over all the possible sub intervals. Simulation results are provided to demonstrate the superior performance of our proposed design based on SC over that using conventional time division multiple access (TDMA).

In this paper, we consider a point-to-point wireless communication system. The source monitors a physical process and generates status update packets according to a Poisson process. The packets are transmitted to the destination by using finite blocklength coding to update the status (e.g., temperature, speed, position) of the monitored process. In some applications, such as real-time monitoring and tracking, the timeliness of the status updates is critical since the users are interested in the latest condition of the process. The timeliness of the status updates can be reflected by a recently proposed metric, termed the age of information (AoI). We focus on the packet management policies for the considered system. Specifically, the preemption and discard of status updates are important to decrease the AoI. For example, it is meaningless to transmit stale status updates when a new status update is generated. We propose three packet management schemes in the transmission between the source and the destination, namely non-preemption (NP), preemption (PR) and retransmission (RT) schemes. We derive closed-form expressions of the average AoI for the proposed three schemes. Based on the derived analytical expressions of the average AoI, we further minimize the average AoI by optimizing the packet blocklength for each status update. Simulation results are provided to validate our theoretical analysis, which further show that the proposed schemes can outperform each other for different system setups, and the proposed schemes considerably outperform the existing ones without packet management at medium to high generation rate.

This paper considers an Industrial Internet of Thing (IIoT) system with a source monitoring a dynamic process with randomly generated status updates. The status updates are sent to an designated destination in a real-time manner over an unreliable link. The source is subject to a practical constraint of limited average transmission power. Thus, the system should carefully schedule when to transmit a fresh status update or retransmit the stale one. To characterize the performance of timely status update, we adopt a recent concept, Age of Information (AoI), as the performance metric. We aim to minimize the long-term average AoI under the limited average transmission power at the source, by formulating a constrained Markov Decision Process (CMDP) problem. To address the formulated CMDP, we recast it into an unconstrained Markov Decision Process (MDP) through Lagrangian relaxation. We prove the existence of optimal stationary policy of the original CMDP, which is a randomized mixture of two deterministic stationary policies of the unconstrained MDP. We also explore the characteristics of the problem to reduce the action space of each state to significantly reduce the computation complexity. We further prove the threshold structure of the optimal deterministic policy for the unconstrained MDP. Simulation results show the proposed optimal policy achieves lower average AoI compared with random policy, especially when the system suffers from stricter resource constraint. Besides, the influence of status generation probability and transmission failure rate on optimal policy and the resultant average AoI as well as the impact of average transmission power on the minimal average AoI are unveiled.

To enable ultrareliable low-latency wireless communications required in the Industrial Internet of Things, in this paper we develop an energy-based modulation [i.e., non-negative pulse amplitude modulation (PAM)] constellation design framework for noncoherent detection in massive single-input multiple-output (SIMO) systems. We consider that one single-antenna transmitter communicates to a receiver with a large number of antennas over a Rayleigh fading channel, and the receiver decodes the transmitted information at the end of every symbol. For such an SIMO system with non-negative PAM modulation, we first propose a fast noncoherent maximum-likelihood decoding algorithm and derive a closed-form expression of its symbol error probability (SEP). We then enhance the system energy efficiency by finding the optimal PAM constellation that minimizes the exact SEP subject to a total signal power constraint for such a system with an arbitrary number of receiver antennas, signal-to-noise ratio (SNR), and constellation size. Furthermore, the closed-form upper and lower bounds on the optimal SEP are derived. Based on these bounds, the exact expression for coding gain of the dominant term of the SEP is presented for such an optimal massive SIMO system. We also present an asymptotic SEP expression at a high SNR regime and the approximate diversity gain of the system. Simulation results for the proposed optimal PAM constellation validate the theoretical analysis, and show that our presented optimal constellation attains significant performance gains over the currently available minimum-distance-based constellation systems.

We consider an Internet of Things (IoT) monitoring system, in which an IoT device monitors a physical process and transmits randomly generated status updates to its associated access point (AP) as timely as possible. The timeliness of the status updates is characterized by a recently introduced metric, termed the age of information (AoI), which is defined as the time elapsed since the generation of the last successfully received status update. The channel between the IoT device and the AP is considered to be error-prone and thus the status updates suffer from packet loss. Assuming that the AP provides no feedback to the IoT device, we adopt a practical truncated automatic repeat request (TARQ) scheme: the IoT device keeps transmitting the current status update repeatedly until the maximum allowable transmission times is reached or a new status update is generated. We characterize the inherent age-energy tradeoff for the considered IoT monitoring system. Specifically, a larger value of the maximum allowable transmission times reduces the average AoI, at the cost of incurring higher average energy consumption at the IoT device. Based on the evolution of AoI, we derive the closed-form expressions of the average AoI, the average peak AoI, and the average energy consumption. We then minimize the average AoI by optimizing the transmit power of the IoT device and the maximum allowable transmission times under an average transmit power constraint. Simulations validate the theoretical analysis and reveal that under the same average transmit power constraint, the adopted TARQ scheme achieves a lower average AoI than the classical ARQ scheme that allows an infinite number of retransmission times.

We consider a cognitive radio-based Internet-of-Things (CR-IoT) network consisting of one primary IoT (PIoT) system and one secondary IoT (SIoT) system. The IoT devices of both the PIoT and the SIoT, respectively, monitor one physical process and send randomly generated status updates to their associated access points (APs). The timeliness of the status updates is important as the systems are interested in the latest condition (e.g., temperature, speed, and position) of the IoT device. In this context, two natural questions arise: 1) how to characterize the timeliness of the status updates in CR-IoT systems? 2) which scheme, overlay or underlay, is better in terms of the timeliness of the status updates? To answer these two questions, we adopt a new performance metric, named the age of information (AoI). We analyze the average peak AoI of the PIoT and the SIoT for overlay and underlay schemes, respectively. Simple asymptotic expressions of the average peak AoI are also derived when the PIoT operates at high signal-to-noise ratio (SNR). Based on the asymptotic expressions, we characterize a critical generation rate of the PIoT system, which can determine the superiority of overlay and underlay schemes in terms of the average peak AoI of the SIoT. Numerical results validate the theoretical analysis and uncover that the overlay and underlay schemes can outperform each other in terms of the average peak AoI of the SIoT for different system setups.

This paper investigates the symbol error probability (SEP) of point-to-point massive multiple-input multiple-output (MIMO) systems using equally likely PAM, PSK, and square QAM signallings in the presence of transmitter correlation. The receiver has perfect knowledge of the channel coefficients, while the transmitter only knows first- and second-order channel statistics. With a zero-forcing (ZF) detector implemented at the receiver side, we design and derive closed-form expressions of the optimal precoders at the transmitter that minimizes the average SEP over channel statistics for various modulation schemes. We then unveil some nice structures on the resulting minimum average SEP expressions, which naturally motivate us to explore the use of two useful mathematical tools to systematically study their asymptotic behaviors. The first tool is the Szegö's theorem on large Hermitian Toeplitz matrices and the second tool is the well-known limit: limx→∞(1 + 1/x)x= e. The application of these two tools enables us to attain very simple expressions of the SEP limits as the number of the transmitter antennas goes to infinity. A major advantage of our asymptotic analysis is that the asymptotic SEP converges to the true SEP when the number of antennas is moderately large. As such, the obtained expressions can serve as an effective SEP approximations for massive MIMO systems even when the number of antennas is not very large. For the widely used exponential correlation model, we derive closed-form expressions for the SEP limits of both optimally precoded and uniformly precoded systems. Extensive simulations are provided to demonstrate the effectiveness of our asymptotic analysis and compare the performance limit of optimally precoded and uniformly precoded systems.

Integrating energy harvesting into cognitive radio networks can promisingly sustain the operations of energy-limited terminals and improve the spectral efficiency. Traditionally, secondary users (SUs) are normally assumed to harvest energy from the transmissions of primary users (PUs). In contrast, we consider an alternative scenario with a PU harvesting energy from its access point (AP) and SUs, and propose a cognitive relaying scheme. Each time block is divided into two periods. In the first period, AP transfers wireless energy to PU, and meanwhile, an SU and its relay cooperatively transmit secondary data using peak powers, where the resultant interference can help boost the amount of energy harvested by PU. In the second period, PU transmits data to AP using the harvested energy, and meanwhile, SU and its relay continue to cooperatively transmit secondary data using controlled powers under the interference constraint at AP. We analyze the throughput of both the two systems and reveal the impacts of key parameters. Numerical results show that our scheme can greatly improve the system spectral efficiency while satisfying the interference constraint of PU.

We consider the non-orthogonal multiple access (NOMA) design for a classical two-user multiple access channel (MAC) with finite-alphabet inputs. In contrast to the majority of existing NOMA schemes using continuous Gaussian distributed inputs, we consider practical quadrature amplitude modulation (QAM) constellations at both transmitters, whose sizes are not necessarily the same. By adjusting the scaling factors (i.e., instantaneous transmitting powers) of both users, we aim to maximize the minimum Euclidean distance of the received sum-constellation for a maximum likelihood (ML) receiver. The formulated problem is a mixed continuous-discrete optimization problem and in general it is nontrivial to resolve. By carefully examining the structure of the objective function, we discover that Farey sequence can be employed to tackle the formulated problem. However, the existing Farey sequence is not applicable when the constellation sizes of the two users are different. To address this challenge, we define a new type of Farey sequence, termed punched Farey sequence. Based on this new definition and its properties, we manage to attain a closed-form optimal solution to the original problem by first dividing the entire feasible region into a finite number of Farey intervals and then taking the maximum over all the subintervals. Finally, computer simulations are carried out to verify our theoretical analysis, and to demonstrate the advantages of the proposed NOMA over known orthogonal and non-orthogonal designs.

In this paper, we investigate a multiuser MIMO system consisting of one multiple-antenna access point (AP) and multiple single-antenna users under short-packet communication scenarios. Two fundamental schemes, i.e., time division multiple access (TDMA) and zero-forcing beamforming (ZFB), are considered for the users to receive individual information from the AP in the downlink. To analyze the performance of TDMA and ZFB in finite blocklength regime, we derive approximate closed-form expressions of the block error rate (BLER) for each user for both schemes. Simple asymptotic expressions at high signal-to- noise ratio (SNR) are also derived. Based on the derived expressions, we then formulate sum-BLER minimization problems in terms of blocklength allocation to each user in TDMA scheme and power allocation in ZFB scheme. We resolve the formulated problems by performing their convexity analysis. We finally validate our theoretical analysis and compare the performance of TDMA and ZFB schemes by simulation results, which show that TDMA and ZFB can outperform each other in different system setups.

In this paper, we consider the design of training beam sequence for sparse millimeter wave (mmWave) multiple-input multiple-output (MIMO) tracking systems. We use Markov random walks to model the temporal variations of the beam steering angle of arrival (AoA) and angle of departure (AoD), respectively. By exploiting the MIMO virtual channel representation, the AoA/AoD tracking problem is equivalent to choosing a set of directional training beams to find the nonzero elements in a two-dimensional virtual channel matrix. Furthermore, in contrast to existing work that used each transmitting-receiving beam pair once only, we consider a more general case such that each beam pair might be adopted more than once in the tracking procedure. As the number of repetitions of each transmitting-receiving beam pair can only be integer, the training beam sequence design problem is then formulated as an integer nonlinear programming (INLP) problem. To resolve the formulated INLP problem, we derive a tractable lower bound of the successful tracking probability and then decompose it into a set of convex INLP subproblems, which are solved by implementing an iterative branch-and-bound (BB) method. Numerical results show that our proposed iterative BB algorithm significantly outperforms the benchmark schemes and achieves near-optimal tracking performance.

In practical communication and computation systems, errors occur predominantly in adjacent positions rather than in a random manner. In this paper, we develop a stabilizer formalism for quantum burst error correction codes (QBECC) to combat such error patterns in the quantum regime. Our contributions are as follows. Firstly, we derive an upper bound for the correctable burst errors of QBECCs, the quantum Reiger bound (QRB). Secondly, we propose two constructions of QBECCs: one by heuristic computer search and the other by concatenating two quantum tensor product codes (QTPCs). We obtain several new QBECCs with better parameters than existing codes with the same coding length. Moreover, some of the constructed codes can saturate the quantum Reiger bounds. Finally, we perform numerical experiments for our constructed codes over Markovian correlated depolarizing quantum memory channels, and show that QBECCs indeed outperform standard QECCs in this scenario.

This paper studies the impact of the base station (BS) idle mode capability (IMC) on the network performance of multi-tier and dense heterogeneous cellular networks (HCNs). Different from most existing works that investigated network scenarios with an infinite number of user equipments (UEs), we consider a more practical setup with a finite number of UEs in our analysis. More specifically, we derive the probability of which BS tier a typical UE should associate to and the expression of the activated BS density in each tier. Based on such results, analytical expressions for the coverage probability and the area spectral efficiency (ASE) in each tier are also obtained. The impact of the IMC on the performance of all BS tiers is shown to be significant. In particular, there will be a surplus of BSs when the BS density in each tier exceeds the UE density, and the overall coverage probability as well as the ASE continuously increase when the BS IMC is applied. Such finding is distinctively different from that in existing work. Thus, our result sheds new light on the design and deployment of the future 5G HCNs.

Radio frequency energy harvesting is a promising technology to charge unattended Internet of Things (IoT) lowpower devices remotely. To enable this, in future IoT system, besides the traditional data access points (DAPs) for collecting data, energy access points (EAPs) should be deployed to charge IoT devices to maintain their sustainable operations. Practically, the DAPs and EAPs may be operated by different operators, and the DAPs thus need to provide effective incentives to motivate the surrounding EAPs to charge their associated IoT devices. Different from existing incentive schemes, we consider a practical scenario with asymmetric information, where the DAP is not aware of the channel conditions and energy costs of the EAPs. We first extend the existing Stackelberg game-based approach with complete information to the asymmetric information scenario, where the expected utility of the DAP is defined and maximized. To deal with asymmetric information more efficiently, we then develop a contract theory-based framework, where the optimal contract is derived to maximize the DAP's expected utility as well as the social welfare. Simulations show that information asymmetry leads to severe performance degradation for the Stackelberg game-based framework, while the proposed contract theory-based approach using asymmetric information outperforms the Stackelberg game-based method with complete information. This reveals that the performance of the considered system depends largely on the market structure (i.e., whether the EAPs are allowed to optimize their received power at the IoT devices with full freedom or not) than on the information availability (i.e., the complete or asymmetric information).

This paper develops and evaluates an accumulate-then-transmit framework for multiuser scheduling in a full-duplex (FD) wireless-powered Internet-of-Things (IoT) system, consisting of multiple energy harvesting (EH) IoT devices (IoDs) and one FD hybrid access point (HAP). All IoDs have no embedded energy supply and thus need to perform EH before transmitting their data to the HAP. Thanks to its FD capability, the HAP can simultaneously receive data uplink and broadcast energy-bearing signals downlink to charge IoDs. The instantaneous channel information is assumed unavailable throughout this paper. To maximize the system average throughput, we design a new throughput-oriented scheduling scheme, in which a single IoD with the maximum weighted residual energy is selected to transmit information to the HAP, while the other IoDs harvest and accumulate energy from the signals broadcast by the HAP. However, similar to most of the existing throughput-oriented schemes, the proposed throughout-oriented scheme also leads to unfair interuser throughput because IoDs with better channel performance will be granted more transmission opportunities. To strike a balance between the system throughput and user fairness, we then propose a fairness-oriented scheduling scheme based on the normalized accumulated energy. To evaluate the system performance, we model the dynamic charging/discharging processes of each IoD as a finite-state Markov chain. Analytical expressions of the system outage probability and average throughput are derived over Rician fading channels for both proposed schemes. Simulation results validate the performance analysis and demonstrate the performance superiority of both proposed schemes over the existing schemes.

This letter investigates an amplify-and-forward two-way relay network (TWRN) for short-packet communications. We consider a classical three-node TWRN consisting of two sources and one relay. Both two time slots (2TS) scheme and three time slots (3TS) scheme are studied under the finite blocklength regime. We derive approximate closed-form expressions of sum-block error rate (BLER) for both schemes. Simple asymptotic expressions for sum-BLER at high signal-to-noise ratio (SNR) are also derived. Based on the asymptotic expressions, we analytically compare the sum-BLER performance of 2TS and 3TS schemes, and attain an expression of critical blocklength, which can determine the performance superiority of 2TS and 3TS in terms of sum-BLER. Extensive simulations are provided to validate our theoretical analysis. Our results discover that 3TS scheme is more suitable for a system with higher differences between the average SNR of both links, and relatively lower requirements on data rate and latency.

This letter investigates the proactive eavesdropping for multiple suspicious links either through interfering or assisting the links. Considering the power constraint at eavesdropper, our objective is to maximize weighted sum eavesdropping rate of multiple suspicious links via jointly optimizing their intervention strategies (jamming or relaying) and the corresponding transmit power at eavesdropper. The formulated problem is shown to be a mixed-integer nonlinear programming (MINLP) problem, which is NP-hard in general. By identifying the separable structure of the formulated problem, we decouple the complex MINLP problem into two subproblems: 1) a jamming subproblem; and 2) a relaying subproblem. These two subproblems are then solved by further recasting them into a combinational problem and a typical concave optimization problem, respectively. Numerical simulations show that our proposed approach can achieve higher eavesdropping rate than conventional eavesdropping approaches.

This paper is concerned with the channel estimation problem in multi-user millimeter wave wireless systems with large antenna arrays. We develop a novel simultaneous-estimation with iterative fountain training (SWIFT) framework, in which multiple users estimate their channels at the same time and the required number of channel measurements is adapted to various channel conditions of different users. To achieve this, we represent the beam direction estimation process by a graph, referred to as the beam-on-graph, and associate the channel estimation process with a code-on-graph decoding problem. Specifically, the base station (BS) and each user measure the channel with a series of random combinations of transmit/receive beamforming vectors until the channel estimate converges. As the proposed SWIFT does not adapt the BS's beams to any single user, we are able to estimate all user channels, simultaneously. Simulation results show that SWIFT can significantly outperform the existing random beamforming-based approaches, which use a predetermined number of measurements, over a wide range of signal-to-noise ratios and channel coherence time. Furthermore, by utilizing the users' order in terms of completing their channel estimation, our SWIFT framework can infer the sequence of users' channel quality and perform effective user scheduling to achieve superior performance.

We consider underlay spectrum sharing with wireless energy transfer in a large-scale cognitive radio network, where each primary user (PU) can harvest radio frequency energy from its associated access point (AP). An energy cooperation zone is applied around each PU, wherein the secondary user (SU) with the best channel quality toward PU is selected to cooperatively transfer wireless energy. With SUs' assistance, each PU can harvest a predefined amount of energy in a shorter time, so there will be more concurrent primary links in the network. We analyze the transmission probability of PUs by properly modeling their energy statuses using Markov chain. Furthermore, a guard zone is applied around each active AP to prohibit the nearby SU transmissions to avoid strong interference, and SUs outside the guard zones of all the active APs can access the spectrum opportunistically. Under the constraint that the area throughput of primary system should be improved by at least a certain degree, the area throughput of the secondary system is maximized by jointly determining the SUs' density and the guard zone radius. Numerical results show that our scheme can well accommodate SUs' transmissions while guaranteeing PUs' performance requirement.

This paper focuses on the non-orthogonal multiple access (NOMA) design for a classical two-user multiple access channel (MAC) with finite-alphabet inputs. In contrast to most of the existing NOMA designs using continuous Gaussian input distributions, we consider practical quadrature amplitude modulation (QAM) constellations at both transmitters, the sizes of which are assumed to be not necessarily identical. We propose maximizing the minimum Euclidean distance of the received sum constellation with a maximum likelihood (ML) detector by adjusting the scaling factors (i.e., instantaneous transmitted powers and phases) of both users. The formulated problem is a mixed continuous-discrete optimization problem, which is nontrivial to resolve in general. By carefully observing the structure of the objective function, we define a new type of Farey sequence, termed punched Farey sequence to tackle the formulated problem. Based on this, we manage to achieve a closed-form optimal solution to the original problem by first dividing the entire feasible region into a finite number of Farey intervals and then taking the maximum over all possible intervals. The resulting sum constellation is proved to be a regular QAM constellation of a larger size, and hence, a simple quantization receiver can be implemented as the ML detector for the demodulation. Moreover, the superiority of NOMA over time-division multiple access in terms of minimum Euclidean distance is rigorously proved. We subsequently address how to extend our design framework intended for the two-user MAC to systems with multiple users and multiple antennas. Finally, simulation results are provided to verify our theoretical analysis and demonstrate the merits of the proposed NOMA over existing orthogonal and non-orthogonal designs.

By enabling wireless devices to be charged wirelessly and remotely, radio frequency energy harvesting (RFEH) has become a promising technology to power the unattended Internet of Things (IoT) low-power devices. To enable this, in future IoT networks, besides the conventional data access points (DAPs) responsible for collecting data from IoT devices, energy access points (EAPs) should be deployed to transfer radio frequency (RF) energy to IoT devices to maintain their sustainable operations. In practice, the DAPs and EAPs may be operated by different operators and a DAP should provide certain incentives to motivate the surrounding EAPs to charge its associated IoT device(s) to assist its data collection. Motivated by this, in this paper we develop a contract theory-based incentive mechanism for the energy trading in RFEH assisted IoT systems. The necessary and sufficient condition for the feasibility of the formulated contract is analyzed. The optimal contract is derived to maximize the DAP's expected utility as well as the social welfare. Simulation results demonstrate the feasibility and effectiveness of the proposed incentive mechanism.

This paper develops a novel channel estimation approach for multi-user millimeter wave (mmWave) wireless systems with large antenna arrays. By exploiting the inherent mmWave channel sparsity, we propose a novel simultaneous-estimation with iterative fountain training (SWIFT) framework, in which the average number of channel measurements is adapted to various channel conditions. To this end, the base station (BS) and each user continue to measure the channel with a random subset of transmit/receive beamforming directions until the channel estimate converges. We formulate the channel estimation process as a compressed sensing problem and apply a sparse estimation approach to recover the virtual channel information. As SWIFT does not adapt the BS's transmitting beams to any single user, we are able to estimate all user channels simultaneously. Simulation results show that SWIFT can significantly outperform existing random-beamforming based approaches that use a fixed number of measurements, over a range of signal-to-noise ratios.

Conventional microwave bands are expected to encounter bandwidth shortage due to the ever-increasing demand for high speed wireless services. The use of millimeter wave (MMW) spectrum has been regarded as one of the promising solutions to support data traffic demands in future cellular networks. MMW cellular networks are envisioned to be densely deployed to attain acceptable coverage and rate. In dense networks, intercell interference emerges as the main factor of degrading the coverage and capacity. As such, coordination of base stations (BSs) should be implemented to improve the system performance. In this paper, we aim to quantify the performance of BS coordination via disjoint clustering in dense MMW cellular networks. Using tools from stochastic geometry, the coverage probability and area spectral efficiency are derived by incorporating the key features of MMW systems, i.e., blockage and directional antenna. Simulation results are provided to validate the accuracy of the analytical results under various system parameters and demonstrate the performance superiority of BS coordination via disjoint clustering over the non-coordinated case. The results suggest that the optimal cluster size to achieve the maximum area spectral efficiency (ASE) increases as the blockage factor, which is determined by the density and the average size of the buildings, decreases.

This paper considers the joint antenna selection (AS) problem for a classical two-user non-orthogonal multiple access (NOMA) network where both the base station and users are equipped with multiple antennas. Since the exhaustive-search-based optimal AS scheme is computationally prohibitive when the number of antennas is large, two computationally efficient joint AS algorithms, namely max-min-max AS (AIA-AS) and max-max-max AS (A3-AS), are proposed to maximize the system sum-rate. The asymptotic closed-form expressions for the average sum-rates for both AIA-AS and A3-AS are derived in the high signal-to-noise ratio (SNR) regime, respectively. Numerical results demonstrate that both AIA-AS and A3-AS can yield significant performance gains over comparable schemes. Furthermore, AIA-AS can provide better user fairness, while the A3-AS scheme can achieve the near-optimal sum-rate performance.

This paper proposes and analyzes a new full-duplex (FD) cooperative cognitive radio network with wireless energy harvesting (EH). We consider that the secondary receiver is equipped with a FD radio and acts as a FD hybrid access point (HAP), which aims to collect information from its associated EH secondary transmitter (ST) and relay the signals. The ST is assumed to be equipped with an EH unit and a rechargeable battery such that it can harvest and accumulate energy from radio frequency (RF) signals transmitted by the primary transmitter (PT) and the HAP. We develop a novel cooperative spectrum sharing (CSS) protocol for the considered system. In the proposed protocol, thanks to its FD capability, the HAP can receive the PT's signals and transmit energy-bearing signals to charge the ST simultaneously, or forward the PT's signals and receive the ST's signals at the same time. We derive analytical expressions for the achievable throughput of both primary and secondary links by characterizing the dynamic charging/discharging behaviors of the ST battery as a finite-state Markov chain. We present numerical results to validate our theoretical analysis and demonstrate the merits of the proposed protocol over its non-cooperative counterpart.

In this paper, we study a wireless-powered two-way relay system, in which both wireless-powered sources exchange information through a multi-antenna relay. Both sources are assumed to have no embedded energy supply and thus first need to harvest energy from the radio frequency signals broadcasted by the relay before exchanging their information via the relay. We aim to maximize the sum throughput of both sources by jointly optimizing the time switching duration, the energy beamforming vector and the precoding matrix at the relay. The formulated problem is non-convex and hard to solve in its original form. Motivated by this, we simplify the problem by reducing the number of variables and by decomposing the precoding matrix into a transmit vector and a receive vector. We then propose bisection search, 1-D search and iterative algorithms to optimize each variable. Numerical results show that our proposed scheme can achieve higher throughput than the conventional scheme without optimization on beamforming vector and precoding matrix at the relay.

This paper investigates the optimal resource allocation of a downlink non-orthogonal multiple access (NOMA) system consisting of one base station and multiple users. Unlike existing short-term NOMA designs that focused on the resource allocation for only the current transmission timeslot, we aim to maximize a long-term network utility by jointly optimizing the data rate control at the network layer and the power allocation among multiple users at the physical layer, subject to practical constraints on both the short-term and long-term power consumptions. To solve this problem, we leverage the recently developed Lyapunov optimization framework to convert the original long-term optimization problem into a series of online rate control and power allocation problems in each timeslot. The power allocation problem, however, is shown to be non-convex in nature and thus cannot be solved with a standard method. However, we explore two structures of the optimal solution and develop a dynamic programming-based power allocation algorithm, which can derive a globally optimal solution, with a polynomial computational complexity. Extensive simulation results are provided to evaluate the performance of the proposed joint rate control and power allocation framework for NOMA systems, which demonstrate that the proposed NOMA design can significantly outperform multiple benchmark schemes, including orthogonal multiple access schemes with optimal power allocation and NOMA schemes with non-optimal power allocation, in terms of average throughput and data delay.

This paper focuses on the design of non-orthogonal multiple access in a classical two-transmitter two-receiver Z-channel, wherein one transmitter sends information to its intended receiver from the direct link while the other transmitter sends information to both receivers from the direct and cross links. Unlike most existing designs using (continuous) Gaussian input distribution, we consider the practical finite-alphabet (i.e., discrete) inputs by assuming that the widely used quadrature amplitude modulation constellations are adopted by both transmitters. To balance the error performance of two receivers, we apply the max-min fairness design criterion in this paper. More specifically, we propose to jointly optimize the scaling factors at both transmitters, which control the minimum Euclidean distance of transmitting constellations, to maximize the smaller minimum Euclidean distance of two resulting constellations at the receivers, subject to an individual average power constraint at each transmitter. The formulated problem is a mixed continuous-discrete optimization problem and is thus intractable in general. By resorting to the Farey sequence, we manage to attain the closed-form expression for the optimal solution to the formulated problem. This is achieved by dividing the overall feasible region of the original optimization problem into a finite number of sub-intervals and deriving the optimal solution in each sub-interval. Through carefully observing the structure of the optimal solutions in all sub-intervals, we obtain compact and closed-form expressions for the optimal solutions to the original problem in three possible scenarios defined by the relative strength of the cross link. Simulation studies are provided to validate our analysis and demonstrate the merits of the proposed design over existing orthogonal or non-orthogonal schemes.

This paper investigates a wireless-powered two-way relay network (WP-TWRN), in which two sources exchange information with the aid of one amplify-and-forward (AF) relay. Contrary to the conventional two-way relay networks, we consider the scenario that the AF relay has no embedded energy supply, and it is equipped with an energy harvesting unit and rechargeable battery. As such, it can accumulate the energy harvested from both sources' signals before helping forwarding their information. In this paper, we develop a power splitting-based energy accumulation (PS-EA) scheme for the considered WP-TWRN. To determine whether the relay has accumulated sufficient energy, we set a predefined energy threshold for the relay. When the accumulated energy reaches the threshold, relay splits the received signal power into two parts, one for energy harvesting and the other for information forwarding. If the stored energy at the relay is below the threshold, all the received signal power will be accumulated at the relay's battery. By modeling the finite-capacity battery of relay as a finite-state Markov Chain (MC), we derive a closed-form expression for the system throughput of the proposed PS-EA scheme over Nakagami-m fading channels. Numerical results validate our theoretical analysis and show that the proposed PS-EA scheme outperforms the conventional time switching- based energy accumulation (TS-EA) scheme and the existing power splitting schemes without energy accumulation.

In this paper, we consider the channel estimation problem in millimeter wave (mmWave) wireless systems with large antenna arrays. By exploiting the inherent sparse nature of the mmWave channel, we develop a novel rate-adaptive channel estimation (RACE) algorithm, which can adaptively adjust the number of required channel measurements based on an expected probability of estimation error (PEE). To this end, we design a maximum likelihood (ML) estimator to optimally extract the path information and the associated probability of error from the increasing number of channel measurements. Based on the ML estimator, the algorithm is able to measure the channel using a variable number of beam patterns until the receiver believes that the estimated direction is correct. This is in contrast to the existing mmWave channel estimation algorithms, in which the number of measurements is typically fixed. Simulation results show that the proposed algorithm can significantly reduce the number of channel estimation measurements while still retaining a high level of accuracy, compared to existing multi-stage channel estimation algorithms.

This paper investigates a wireless energy harvesting cooperative network (WEHCN) consisting of a source, a decode-and-forward (DF) relay and a destination. We consider the relay as an energy harvesting (EH) node equipped with EH circuit and a rechargeable battery. Moreover, the direct link between source and destination is assumed to exist. The relay can thus harvest and accumulate energy from radio-frequency signals ejected by the source and assist its information transmission opportunistically. We develop an incremental accumulate-then-forward (IATF) relaying protocol for the considered WEHCN. In the IATF protocol, the source sends its information to destination via the direct link and requests the relay to cooperate only when it is necessary such that the relay has more chances to accumulate the harvested energy. By modeling the charging/discharging behaviors of the relay battery as a finite-state Markov chain, we derive a closed-form expression for the outage probability of the proposed IATF. Numerical results validate our theoretical analysis and show that the IATF scheme can significantly outperform the direct transmission scheme without cooperation.

This paper investigates a wireless-powered cooperative network (WPCN) consisting of one source-destination pair and multiple decode-and-forward (DF) relays. We consider that the DF relays are wireless-powered such that they rely only on the harvested energy from the source to perform information forwarding. Furthermore, these relays are equipped with separate energy and information receivers as well as energy storage to accumulate the harvested energy. In this paper, we develop an energy threshold based multi-relay selection (ETMRS) scheme for the considered WPCN, in which each relay determines to switch between energy harvesting and information forwarding modes in a fully distributed manner. By modeling the charging/discharging of the discrete-level battery at each relay as a finite state Markov Chain (MC), we derive a closed-form expression for the system outage probability of the proposed ETMRS scheme over independent but not necessarily identical Rayleigh fading channels. Numerical results validate our theoretical analysis and show that the proposed ETMRS scheme can outperform the existing single-relay selection scheme, especially when the source's transmission rate is relatively high.

Evidence indicates that downloading on-demand videos accounts for a dramatic increase in data traffic over cellular networks. Caching popular videos in the storage of small-cell base stations (SBS), namely, small-cell caching, is an efficient technology for reducing the transmission latency while mitigating the redundant transmissions of popular videos over back-haul channels. In this paper, we consider a commercialized small-cell caching system consisting of a network service provider (NSP), several video retailers (VRs), and mobile users (MUs). The NSP leases its SBSs to the VRs for the purpose of making profits, and the VRs, after storing popular videos in the rented SBSs, can provide faster local video transmissions to the MUs, thereby gaining more profits. We conceive this system within the framework of Stackelberg game by treating the SBSs as specific types of resources. We first model the MUs and SBSs as two independent Poisson point processes, and develop, via stochastic geometry theory, the probability of the specific event that an MU obtains the video of its choice directly from the memory of an SBS. Then, based on the probability derived, we formulate a Stackelberg game to jointly maximize the average profit of both the NSP and the VRs. In addition, we investigate the Stackelberg equilibrium by solving a non-convex optimization problem. With the aid of this game theoretic framework, we shed light on the relationship between four important factors: the optimal pricing of leasing an SBS, the SBSs allocation among the VRs, the storage size of the SBSs, and the popularity distribution of the VRs. Monte Carlo simulations show that our stochastic geometry-based analytical results closely match the empirical ones. Numerical results are also provided for quantifying the proposed game-theoretic framework by showing its efficiency on pricing and resource allocation.

In this letter, we develop a low-complexity transceiver design, referred to as semirandom beam pairing, for sparse multipath massive multiple-input-multiple-output (MIMO) channels. By exploring a sparse representation of the MIMO channel in the virtual angular domain, we generate a set of transmit-receive beam pairs in a semirandom way to support the simultaneous transmission of multiple data streams. These data streams can be easily separated at the receiver via a successive interference cancelation technique, and the power allocation among them are optimized based on the classical waterfilling principle. The achieved degree of freedom (DoF) and capacity of the proposed approach are analyzed. Simulation results show that, compared to the conventional singular value decomposition-based method, the proposed transceiver design can achieve near-optimal DoF and capacity with a significantly lower computational complexity.

This paper investigates a wireless-powered communication network (WPCN) setup with one multi- antenna access point (AP) and one single-antenna source. It is assumed that the AP is connected to an external power supply, while the source does not have an embedded energy supply. But the source could harvest energy from radio frequency (RF) signals sent by the AP and store it for future information transmission. We develop a discrete time-switching (DTS) protocol for the considered WPCN. In the proposed protocol, either energy harvesting (EH) or information transmission (IT) operation is performed during each transmission block. Specifically, based on the channel state information (CSI) between source and AP, the source can determine the minimum energy required for an outage-free IT operation. If the residual energy of the source is sufficient, the source will start the IT phase. Otherwise, EH phase is invoked and the source accumulates the harvested energy. To characterize the performance of the proposed protocol, we adopt a discrete Markov chain (MC) to model the energy accumulation process at the source battery. A closed-form expression for the average throughput of the DTS protocol is derived. Numerical results validate our theoretical analysis and show that the proposed DTS protocol considerably outperforms the existing harvest-then-transmit protocol when the battery capacity at the source is large.

This paper studies a power beacon-assisted wireless-powered communication network, consisting of one hybrid access point (AP), one information source, and multiple power beacons (PBs). The source has no embedded power supply, and thus, has to harvest RF energy from the AP in the downlink before transmitting its information to the AP in the uplink. The PBs are deployed to help the AP charge the source in the downlink. However, in practice, the AP and PBs may belong to different operators. Thus, incentives are needed for the PBs to assist the AP during DL energy transfer phase, which is referred to as “energy trading”. We formulate this energy trading process as a Stackelberg game, in which the AP is a leader and the PBs are the followers. We then derive the Stackelberg equilibrium of the formulated game. Numerical results show that the proposed scheme can achieve better performance as either the number of the PBs or the value of the gain per unit throughput increase, and as the distance between source and PBs decreases.

In this paper, we consider a wireless-powered cooperative communication network, which consists of one hybrid access point (AP), one source and one relay to assist information transmission. Unlike conventional cooperative networks, the source and relay are assumed to have no embedded energy supplies in the considered system. Hence, they need to first harvest energy from the radio-frequency (RF) signals radiated by the AP in the downlink (DL) before information transmission in the uplink (UL). Inspired by the recently proposed harvest-then-transmit (HTT) and harvest-then-cooperate (HTC) protocols, we develop a new adaptive transmission (AT) protocol. In the proposed protocol, at the beginning of each transmission block, the AP charges the source. AP and source then perform channel estimation to acquire the channel state information (CSI) between them. Based on the CSI estimate, the AP adaptively chooses the source to perform UL information transmission either directly or cooperatively with the relay. We derive an approximate closed-form expression for the average throughput of the proposed AT protocol over Nakagami-m fading channels. The analysis is then verified by Monte Carlo simulations. Results show that the proposed AT protocol considerably outperforms both the HTT and HTC protocols.

In this paper, we investigate the optimal resource allocation in a power beacon-assisted wireless-powered communication network (PB-WPCN), which consists of a set of hybrid access point (AP)-source pairs and a power beacon (PB). We assume that all sources have no embedded power supply. Thus, each source first harvests energy from the signals broadcast by its associated AP and/or the PB in the downlink (DL) and then uses the harvested energy to transmit its information to the AP in the uplink (UL). The PB is deployed to assist the APs during the DL wireless energy transfer (WET) phase. We formulate an optimization problem for the considered network, in which the DL WET time of each AP-source pair and the energy allocation of the PB are jointly optimized to maximize the weighted sum-throughput of all AP-source pairs in the UL. We also propose a waterfilling-based algorithm to solve the formulated problem in a distributed manner. Numerical results are performed to validate the convergence of the proposed algorithm and demonstrate the impacts of various system parameters.

We investigate the spectrum sharing problem of a radio frequency (RF)-powered cognitive radio network, where a multi-antenna secondary user (SU) harvests energy from RF signals radiated by a primary user (PU) to boost its available energy before information transmission. In this paper, we consider that both the PU and SU are rational and self-interested. Based on whether the SU helps forward the PU's information, we develop two different operation modes for the considered network, termed as non-cooperative and cooperative modes. In the non-cooperative mode, the SU harvests energy from the PU and then use its available energy to transmit its own information without generating any interference to the primary link. In the cooperative mode, the PU employs the SU to relay its information by providing monetary incentives and the SU splits its energy for forwarding the PU's information as well as transmitting its own information. Optimization problems are respectively formulated for both operation modes, which constitute a Stackelberg game with the PU as a leader and the SU as a follower. We analyze the Stackelberg game by deriving solutions to the optimization problems and the Stackelberg Equilibrium (SE) is subsequently obtained. Simulation results show that the performance of the Stackelberg game can approach that of the centralized optimization scheme when the distance between the SU and its receiver is large enough.

In this paper, we study a full-duplex wireless-powered communication network (FD-WPCN), which consists of one full-duplex (FD) hybrid access-point (H-AP) and one FD user. The H-AP and user are both equipped with two antennas, one for downlink wireless energy transfer (WET) from the H-AP to user and the other for uplink wireless information transfer (WIT) from the user to H-AP, where WET and WIT are performed simultaneously through the same frequency band. We consider the scenario that the role of each antenna (i.e., transmission or reception) is not predefined and propose an antenna pair selection (APS) scheme to improve the performance by optimally configuring the transmit and receive antennas at each node. The closed-form expressions for outage probability and probability density function (PDF) of the received signal-to-noise ratio (SNR) at the H-AP are derived. Based on the PDF, we then calculate the closed-form expressions of ergodic capacity, SNR moments and symbol error rate (SER). Finally, we verify the analytical results through Monte Carlo simulations.

In this paper, we investigate optimal resource allocation in a power beacon-assisted wireless-powered communication network (PB-WPCN), which consists of a set of hybrid access point (AP)-source pairs and a power beacon (PB). Each source, which has no embedded power supply, first harvests energy from its associated AP and/or the PB in the downlink (DL) and then uses the harvested energy to transmit information to its AP in the uplink (UL). We consider both cooperative and non-cooperative scenarios based on whether the PB is cooperative with the APs or not. For the cooperative scenario, we formulate a social welfare maximization problem to maximize the weighted sum-throughput of all AP-source pairs, which is subsequently solved by a water-filling based distributed algorithm. In the non-cooperative scenario, all the APs and the PB are assumed to be rational and self-interested such that incentives from each AP are needed for the PB to provide wireless charging service. We then formulate an auction game and propose an auction based distributed algorithm by considering the PB as the auctioneer and the APs as the bidders. Finally, numerical results are performed to validate the convergence of both the proposed algorithms and demonstrate the impacts of various system parameters.

In this paper, we consider simultaneous wireless information and power transfer (SWIPT) in relay interference channels, where multiple source-destination pairs communicate through their dedicated energy harvesting relays. Each relay needs to split its received signal from sources into two streams: one for information forwarding and the other for energy harvesting. We develop a distributed power splitting framework using game theory to derive a profile of power splitting ratios for all relays that can achieve a good network-wide performance. Specifically, non-cooperative games are respectively formulated for pure amplify-and-forward (AF) and decode-and-forward (DF) networks, in which each link is modeled as a strategic player who aims to maximize its own achievable rate. The existence and uniqueness for the Nash equilibriums (NEs) of the formulated games are analyzed and a distributed algorithm with provable convergence to achieve the NEs is also developed. Subsequently, the developed framework is extended to the more general network setting with mixed AF and DF relays. All the theoretical analyses are validated by extensive numerical results. Simulation results show that the proposed game-theoretical approach can achieve a near-optimal network-wide performance on average, especially for the scenarios with relatively low and moderate interference.

In this paper, we consider simultaneous wireless information and power transfer (SWIPT) in relay interference channels, where multiple source-destination pairs communicate through their dedicated energy harvesting relays. Game theory is applied to design a distributed power splitting scheme for the considered system. Particularly, a non-cooperative game is formulated, in which each link is modeled as a strategic player who aims to maximize its own achievable rate. The existence and uniqueness of the Nash equilibrium (NE) for the formulated game is analyzed. A distributed algorithm is proposed based on the best response functions to achieve the NE. Numerical results show that the proposed game-theoretical approach can achieve a near-optimal network-wide performance.

In this paper, we consider a wireless-powered cooperative communication network, which consists of a hybrid access-point (AP), a hybrid relay, and an information source. In contrast to the conventional cooperative networks, the source in the considered network is assumed to have no embedded energy supply. Thus, it first needs to harvest energy from the signals broadcast by the AP and/or relay, which have constant power supply, in the downlink (DL) before transmitting the information to the AP in the uplink (UL). The hybrid relay can not only help to forward information in the UL but also charge the source with wireless energy transfer in the DL. Considering different possible operations of the hybrid relay, we propose two cooperative protocols for the considered network. We jointly optimize the time and power allocation for DL energy transfer and UL information transmission to maximize the system throughput of the proposed protocols. Numerical results are presented to compare the performance of the proposed protocols and illustrate the impacts of system parameters.

In heterogeneous networks (HetNets), the load between macro-cell base stations (MBSs) and small-cell BSs (SBSs) is imbalanced due to transmit power disparities and ad-hoc deployment of SBSs. This significantly impacts the system performance and user experience. Associating more users to the SBSs is an effective way to solve this problem. In this paper, we formulate the user-BS association problem as a distributed optimization problem with proportional fairness as the objective. Specifically, we propose a novel distribute algorithm based on the belief propagation (BP) method to solve the user-BS association problem via iteratively message passing between the users and BSs. Also, we develop an approximation calculation in the BP method to reduce the computational complexity and transmission overhead of message passing. Simulation results show that the proposed algorithm well approaches the optimal system performance (by exhausting search) with low complexity and fast convergence.

In this paper, we investigate a new polynomial pricing function based demand side management scheme for future smart grid, where the consumers are charged based on their instantaneous load. A non-cooperative game is formulated, where each consumer aims to minimize their total energy cost based on the given pricing function. By casting this game in the variational inequality framework, we present sufficient conditions for the uniqueness of the optimal solution. We then propose a distributed and simultaneous algorithm to achieve the optimal solution. Sufficient conditions for the geometrical convergence of our algorithm are also provided and proved. Numerical results reveal that our proposed algorithm converges very quickly, and is effective in encouraging consumers to shift their energy usage from peak to non-peak times.

Multi-source multi-destination wireless relay network coded systems are investigated in this paper. To achieve multiple interpretations at different receivers, we employ nested codes in our proposed system. Besides, an opportunistic scheduling (OS) technique is adopted at the relay to maximize the system capacity. The proposed system model combines the merits of both nested codes and OS. First, we present the detailed coding process of the proposed scheme. Then, we derive the upper bounds on the bit error probability of the schemes with and without OS. Finally, we investigate good codes for our system and carry out simulations to validate the theoretical analysis.

In this paper, we design an outage probability constrained robust collaborative beamforming approach for the distributed multi-relay network in the downlink, where the channel state information (CSI) available is imperfect. We aim to minimize the total transmit power of the relay nodes whilst keeping the outage probability at the destination node below the predefined threshold. Assuming that the CSI mismatches follow Gaussian distribution, the equivalent counterpart for the outage probability constraint on the required signal-to-noise-ratio (SNR) is given explicitly. We show that though the original optimization problem is non-convex thus very intractable, it could be optimally solved by using the well-known interior-point method together with an efficient one-dimension search. Simulation results reveal that our proposed approaches can guarantee the quality-of-service (QoS) in term of outage probability in statistical sense while the non-robust scheme fails to do so.

In this study, the performances of selection cooperation are investigated in a scenario based on decode-and-forward and where equal gain combining (EGC) technique is adopted at the destination. Assuming that the channels suffer from independent non-identical Rayleigh fading, we first derive the cumulative distribution function, probability density function and moment generating function for the total instantaneous signal-to-noise ratio (SNR) at the destination after EGC. Then, these statistical functions are used to derive closed-form expressions for average SNR output, outage probability and average symbol error rate (SER) of selection cooperation. The results hold for arbitrary number of relays and refer to multiple-phase shift keying (M-PSK) modulations. Finally, simulations are carried out to verify the correctness of our theoretical analysis. A random network model is introduced to investigate the effect of relay number and path loss exponent on outage probability and average SER. In addition, based on this random network, the comparison between the performance of selection cooperation with EGC and that of selection cooperation with maximal ratio combining are performed.

Based on the selection decode-and-forward cooperative protocol, a novel distributed quality of service (QoS) aware routing algorithm is proposed to minimise the total energy consumption of the wireless links from the cross-layer design perspective. For the non-cooperative and cooperative truncated automatic repeat request schemes, performance of packet error rate with M-PSK and M-QAM symbols over Nakagami-m fading channels is first analysed, and then system throughput is derived. Subsequently, transmission power is optimally allocated while satisfying the end-to-end throughput requirement. With polynomial complexity, using the traditional distributed shortest path algorithm, the route that includes a cascade of non-cooperative and cooperative building blocks and has the minimal total link cost (defined as the average total consumed power) is constructed to bear the information flow. In contrast to the previous non-cooperative routing schemes, this cooperative routing algorithm can significantly reduce the total energy consumption.

In this paper, we propose a novel link-utility-based cooperative MAC (LC-MAC) protocol for wireless multi-hop networks. By fully utilizing the broadcast nature of wireless multi-hop networks, the node that has overheard data packets in the previous hop may become a partner of the transmitter in the current hop. As diversity gain can be achieved by virtual antenna array formed by transmitter and partner, one-phase cooperative transmission is introduced to improve the throughput. In LC-MAC, based on the instantaneous channel measurements, each node tries to maximize its own link-utility (indicator of a node's ability to cooperate) by jointly adjusting transmission rate and power. Subsequently, distributed backoff procedure is activated to select the best node that has the maximum link-utility. The optimal transmission type, rate and power are uniquely determined by the best node. Since only local information is required, LC-MAC is a completely distributed protocol. Finally, extensive simulations are performed to investigate the impact of scenario and protocol parameters on the performance of LC-MAC. Numerical results show that LC-MAC significantly outperforms the cooperative relay-based rate adaptation (CRBAR) scheme and the receiver-based rate adaptation (RBAR) scheme in terms of throughput and energy efficiency.

Cooperative diversity has recently been proposed as a promising technology to achieve spatial diversity in wireless networks. In this paper, we analyze the performance of SNR-based hybrid decode-amplify-forward (HDAF) relaying cooperative diversity networks over independent non-identical flat Rayleigh fading channels with maximum ratio combining (MRC) technique. Closed-form expressions for the outage and bit error probability of the HDAF relaying scheme are derived. Computer simulations are carried out to illustrate and validate the correctness of analytical results. Besides, the impacts of the SNR threshold and relay location on the performances of outage and bit error probability are investigated.

We analyze the symbol error probability (SEP) performance of cooperative diversity networks with opportunistic decode-and-forward (ODF) relaying. Assuming that channels suffer from independent nonidentical Rayleigh fading and symbols are M-PSK modulated, the approximate closed-form SEP expressions are derived for opportunistic relaying with adaptive and fixed DF protocols, respectively. Simulations are carried out to validate the theoretical analysis. Results show that the derived approximate SEP is tight particularly at medium and high SNR.