Bridging the gap between high-level algorithm development and efficient FPGA implementation remains a fundamental challenge, particularly in latency- and resource-constrained domains such as wireless communications. In practice, many signal processing and communication algorithms are first developed and validated at the software level, where rapid iteration and numerical flexibility are critical, making MATLAB a de facto environment for algorithm prototyping and verification. To deploy these algorithms on FPGA platforms, High-Level Synthesis (HLS) is commonly adopted as an intermediate step that enables hardware generation from high-level descriptions. Although HLS significantly improves productivity compared with register-transfer level design, translating MATLAB models into high-quality HLS implementations still requires substantial manual effort and deep domain expertise. Designers must carefully restructure computation patterns, redesign dataflows, and tune synthesis parameters to meet strict performance and resource constraints. Recent advances in large language models (LLMs) suggest a promising direction for automating this translation process. However, current LLM-based approaches remain unreliable in complex hardware design workflows, as they suffer from hallucinations, forgetting, limited domain expertise, and often overlook key performance metrics such as latency, throughput, and resource utilization.

Fingerprint-based indoor localization is a critical enabling technology for Internet of Things (IoT) applications, where the primary challenges stem from complex environmental variability and prohibitive costs of data collection and labeling. This paper introduces a cross-domain multi-modal indoor localization framework that effectively combines visual and WiFi signals using few-shot learning techniques, achieving improved localization performance with minimal training data. We derive an upper bound on the generalized transfer localization error. Based on this bound, our learning-based approach applies feature-level knowledge distillation from pre-trained localization models. This process systematically calibrates discrepancies in feature distributions between the source and target environments. As a result, the proposed method significantly reduces the dependence on large labeled datasets. Experimental results demonstrate that our proposed method achieves substantial improvements over state-of-the-art localization models, with a mean localization error of 0.247 meters across diverse indoor environments, while requiring substantially fewer labeled samples in the target domain.

Chirp spread spectrum (CSS) modulation-based long-range (LoRa) communication has attracted widespread attention owing to its long-range coverage and low-power consumption. However, the inherent trade-off between communication range and data rate in CSS modulation creates a throughput bottleneck, significantly limiting LoRa’s applicability in practical scenarios. To enhance the uplink data rate and reliability in LoRa communications, this paper proposes novel schemes that integrate reconfigurable intelligent surfaces (RIS) and index modulation (IM). Specifically, we develop comprehensive transmission and detection mechanisms for both single-node and multi-node scenarios. To improve the performance of single-node transmission, we introduce an IM scheme based on receiving antenna selection combined with RIS-assisted beamforming, and design a low-complexity sequential detector that exploits unique signal characteristics. To address the multi-node scenario with the co-spreading-factor (co-SF) interference, we propose a power control scheme at transmitters inspired by non-orthogonal access principles, and develop an asymptotically optimal RIS beamforming strategy. We further design an efficient demodulation scheme that leverages statistical features of the received signals across selected antennas. Extensive simulation results demonstrate that our proposed schemes effectively improve both bit error rate and data rate performance compared to conventional LoRa systems and related prior art.

Reconfigurable intelligent surface (RIS)-assisted integrated sensing and communication (ISAC) is emerging as a key enabler for sixth-generation (6G) wireless systems, unifying communication, sensing, and control within a reconfigurable electromagnetic (EM) environment. This article presents a comprehensive review that integrates theoretical foundations, signal processing methodologies, and experimental implementations of RIS-ISAC. It first summarizes core principles of channel modeling, waveform design, and sensing parameter estimation across near-field, broadband, and multihop propagation regimes. Building on these foundations, the article surveys advances in hardware architectures, testbeds, and prototype demonstrations spanning sub-6 GHz to millimeter-wave and terahertz bands. Cross-cutting insights are drawn on system tradeoffs, performance bounds, and learning-aided adaptation. Finally, unified benchmarking metrics and open challenges are identified, covering synchronization, scalability, and standardization, to guide the evolution of RIS-ISAC from conceptual frameworks to practical 6G network deployments.

This paper presents an HLS-based algorithm-hardware co-design methodology and complete FPGA hardware accelerator for multi-user massive MIMO-OFDM receivers operating in contested tactical environments with jamming suppression capabilities. We develop a systematic bi-directional co-design methodology using high-level synthesis (HLS) where algorithms provide functional verification constraints while hardware synthesis feedback drives algorithmic complexity reduction, enabling efficient transformation from signal processing algorithms to optimized circuit implementations on the Xilinx ZCU111 radio frequency system-on-chip (RFSoC) platform. The primary contributions include: 1) hardware architecture innovations featuring QR decomposition-based synchronization achieving 330 MHz post-route operation and optimized frequency-domain minimum mean square error (FD-MMSE) equalization with systematic loop restructuring for data dependency removal in substitution modules, reducing hardware resources by 56% lookup tables (LUTs), 58% flip-flops (FFs), and 67% digital signal processing (DSP) blocks while maintaining real-time throughput; 2) systematic HLS-based co-design framework enabling automated architecture exploration with hardware-oriented algorithm adaptations including silent-period frame structure and Cholesky-based decision-feedback equalization; 3) complete system integration validated through field trials demonstrating 8 dB jamming suppression improvement with reliable spatial division multiple access (SDMA) communications. The presented methodology provides insights applicable to broader signal processing systems with stringent real-time constraints.

Due to discrepancies in clock oscillators between transmitter and receiver, bistatic sensing in integrated sensing and communications (ISAC) systems suffers from the clock asynchronism issue. Previous research has demonstrated that these offsets can be effectively mitigated through cross-antenna techniques. However, such techniques may not always be preferred due to the side effects of restricted spatial degrees of freedom or complicated signal models. In this paper, we introduce cross-frequency techniques to address the clock asynchrony issue for line-of-sight (LOS) dominant bistatic sensing. We begin by uncovering the rotational invariance properly inherent in a time-frequency-domain signal matrix that is typically constructed for bistatic sensing. We then propose two novel methods, i.e., cross-frequency cross-correlation and cross-frequency signal ratio, to effectively suppress clock asynchronism without compromising spatial sensing ability. Furthermore, we evaluate the performance of these methods using key metrics, including the maximum unambiguous delay, delay resolution, and analytical target SNRs. Extensive simulation and experimental results are provided, verifying the effectiveness of the proposed methods and their superiority versus efficiency over prior art in sensing accuracy.

The paper considers monostatic ISAC transceivers relying on in-band full-duplex (IBFD) capability to achieve simultaneous sensing and communication. These systems transmit a waveform for both communication and sensing and receive target echoes and incoming communication signals from other nodes. The major challenge is interference cancellation, suppressing self-interference (SI) from the leaked transmitted signal and the mutual interference (MI) between the echo for sensing and incoming signals for communication. This paper proposes an advanced joint analog and two-stage digital interference cancellation (DIC) structure to address this challenge, enabling simultaneous communication and sensing in IBFD ISAC systems. The analog SI cancellation structure leverages an analog least mean square (ALMS) loop with specific design constraints to preserve the integrity of sensing signals. A track-and-hold mechanism is employed to avoid ALMS weighting coefficient variation caused by the strong reflected sensing signal and uplink communication signal. The novel two-stage DIC first cancels residual SI for sensing and then mitigates echo sensing signals for communication. Doppler effects in the echo signals are compensated during the second stage to ensure effective suppression of sensing signals and accurate retrieval of communication signals. Simulation results validate the proposed approach, showcasing its strong communication and sensing performance and robust interference cancellation capabilities.

Accurate water level sensing is essential for flood monitoring, agricultural irrigation, and water resource optimization. Traditional methods require dedicated sensor deployments, leading to high costs, vulnerability to interference, and limited resolution. In this work, we proposes PMN-WaterSense, a novel scheme that leverages Channel State Information (CSI) from existing communication networks to sense water level variations. We employ a CSI power method to mitigate phase offsets from clock asynchrony in bi-static systems, followed by multi-domain filtering to extract phase features that captures the variations in the reflection path over the water surface. A Kalman filter-based unwrapping technique resolves the phase ambiguity, while transceiver geometry converts the path variations into water level height estimates. The indoor experiments with 28 GHz mmWave and 3.1 GHz LTE testbeds achieve 0.025 cm and 0.198 cm height estimation errors, respectively. Real-world outdoor testing with 2.6 GHz LTE signals yields a 4.8 cm error for a 1-meter water level change, demonstrating practical effectiveness.

User re-identification (Re-ID) plays a crucial role in the research fields of wireless sensing and Internet of things. However, existing vision-based Re-ID approaches often face privacy concerns and are susceptible to variations in lighting conditions. In response to these challenges, in this paper, we propose a novel radio imaging based user Re-ID scheme with a commercial off-the-shelf (COTS) millimeter-wave (mmWave) radar, called RImID. RImID capitalizes on radar signals to initially track individuals in motion. To achieve high-resolution images of tracked subjects, we propose an advanced mmWave Inverse Synthetic Aperture Radar (ISAR) imaging algorithm, which incorporates focusing and nonlinear motion compensation techniques. Importantly, as individuals exhibit unique body movement patterns, RImID captures these distinctions and encodes them within the radio images. Subsequently, RImID utilizes a well-designed convolutional neural network (CNN) that takes these radio images as inputs to identify each registered person. We have implemented RImID using a mmWave radar, specifically the TI IWR1843BOOST, and have collected a comprehensive dataset of over 25.6k mmWave frames for CNN training. Our experimental results confirm the effectiveness of RImID, achieving a remarkable recognition accuracy exceeding 98 percent for 10 individuals and 93 percent for 30 individuals.

Integrated sensing and communication (ISAC) technology empowers WiFi to function as both sensors for wireless sensing and communication devices for data exchange. Currently, achieving accurate object tracking with commercial WiFi devices is still challenging due to the limited bandwidth, a small number of antennas, and the clock asynchronization in a bi-static setup. Many existing methods achieve tracking only via extracting a dominant Doppler frequency shift (DFS) from a moving person. However, since the human body is nonrigid, various body parts generate different DFSs, and different subcarriers can exhibit varying Doppler characteristics in a multipath environment. This work presents WiDFS2.0, an enhanced real-time tracking scheme that leverages the micro-Doppler effect to extract multiple signal features from various body parts of a moving person, represented as WiFi point clouds. Each point cloud consists of Doppler, Angle of Arrival, Range, and signal-to-noise ratio. We design a novel signal processing chain to extract the WiFi point clouds. Then, we refine these point clouds and implement an extended Kalman filter-based algorithm to track the person’s trajectory. Our experiments demonstrate that WiDFS2.0 can achieve real-time tracking with a median position error of 0.55 m, while determining the presence of a moving person with over 98% accuracy during tracking.

With integrated sensing and communications (ISAC) and reconfigurable intelligent surface (RIS) emerging as critical enablers for future mobile communications, their combination has attracted increasing attention lately. To effectively utilize RIS for improving ISAC, we propose two novel designs targeting different scenarios. The first design strikes for a spectral-efficient ISAC by seeking to maximize the weighted sum rate (WSR) of communications and minimize sensing radiation pattern approximation error. The second design aims to achieve an energy-efficient ISAC by optimizing the power allocation between communications and sensing subject to communication quality of service (QoS) constraints. Different optimization problems are formulated for the two designs, with practical constraints of RIS considered, including unit modulus and discrete phase shift. Efficient solutions are developed for the non-convex optimization problems by adeptly employing techniques including weighted minimum mean squared error (WMMSE), fractional programming (FP), second-order cone programming (SOCP), semi-definite relaxing (SDR), and feasibility check. Simulation results demonstrate the non-trivial improvements of WSR, communication energy efficiency and sensing radiation patterns achieved by the proposed designs, also highlighting their superiority over the conventional methods.

Uplink sensing in integrated sensing and communications (ISAC) systems, such as Perceptive Mobile Networks, is challenging due to the clock asynchronism between transmitter and receiver. Existing solutions typically require the presence of a line-of-sight path and the knowledge of the transmitter’s location. In this paper, these requirements, we propose a novel uplink sensing scheme to relieve these requirements by introducing static anchor points for the first time. The scheme consists of two efficient algorithms. The first algorithm estimates the relative timing and carrier frequency offsets, with respect to a randomly selected reference snapshot. Its estimation performance is analyzed with closed-form bias and root mean squared error derived. The estimates from the first algorithm are then used to eliminate clock offsets, enabling the construction of angle-Doppler maps. Using the maps, the second algorithm is developed to identify anchor points and then locate the transmitter and dynamic targets. The impact of the locations of the transmitter and anchor points is also analytically illustrated. Extensive simulation results are provided, demonstrating the effectiveness of the proposed sensing scheme in practical 5G communication setups and its superiority over prior art in terms of noise resilience and asymptotic performances.

When applying integrated sensing and communications (ISAC) in future mobile networks, many sensing tasks have low latency requirements, preferably being implemented at terminals. However, terminals often have limited computing capabilities and energy supply. In this paper, we investigate the effectiveness of leveraging the advanced computing capabilities of mobile edge computing (MEC) servers and the cloud server to address the sensing tasks of ISAC terminals. Specifically, we propose a novel three-tier integrated sensing, communication, and computing (ISCC) framework composed of one cloud server, multiple MEC servers, and multiple terminals, where the terminals can optionally offload sensing data to the MEC server or the cloud server. The offload message is sent via the ISAC waveform, whose echo is used for sensing. We jointly optimize the computation offloading and beamforming strategies to minimize the average execution latency while satisfying sensing requirements. In particular, we propose a low-complexity distributed algorithm to solve the problem. Firstly, we use the alternating direction method of multipliers (ADMM) and derive the closed-form solution for offloading decision variables. Subsequently, we convert the beamforming optimization sub-problem into a weighted minimum mean-square error (WMMSE) problem and propose a fractional programming based algorithm. Numerical results demonstrate that the proposed ISCC framework and distributed algorithm significantly reduce the execution latency and the energy consumption of sensing tasks at a lower computational complexity compared to existing schemes.

The research of faster-than-Nyquist (FTN) signaling has reached a state of maturity for considering practical multipath fading channels, rather than idealized additive white Gaussian noise channels only. To overcome fast-fading multipath propagations, conventional FTN systems tend to rely on channel coding techniques for cleaning up the residual errors, rather than harnessing Doppler effect mitigation. To circumvent this limitation, we propose an adaptive transmit precoding (ATPC) method associated with FTN signaling for applications in fast-fading multipath channels. Upon leveraging real-time channel state information fed back by the receiver, ATPC updates the modulation matrix to improve resilience against Doppler frequency shifts. To mitigate the inter-block interference and multipath effect, a cyclic prefix is inserted at the beginning of each transmission frame. In addition, we employ decision-directed successive interference cancellation for alleviating the inter-symbol interference stemming from FTN signaling and multipath effects. We also analyze the theoretical bit error rate (BER) performance and a pair of closed-form BER expressions are derived for extreme channel conditions, i.e., sufficiently large number of paths and sufficiently large Doppler frequency shift. Simulation results verify the effectiveness of the proposed ATPC method and demonstrate our performance improvements over conventional schemes.

Future mobile networks are projected to support integrated sensing and communications in high-speed communication scenarios. Nevertheless, large Doppler shifts induced by time-varying channels may cause severe inter-carrier interference (ICI). Frequency domain shows the potential of reducing ISAC complexity as compared with other domains. However, parameter mismatching issue still exists for such sensing. In this paper, we develop a novel sensing scheme based on sparse Bayesian framework, where the delay and Doppler estimation problem in time-varying channels is formulated as a 3D multiple measurement-sparse signal recovery (MM-SSR) problem. We then propose a novel two-layer variational Bayesian inference (VBI) method to decompose the 3D MM-SSR problem into two layers and estimate the Doppler in the first layer and the delay in the second layer alternatively. Subsequently, as is benefited from newly unveiled signal construction, a simplified two-stage multiple signal classification (MUSIC)-based VBI method is proposed, where the delay and the Doppler are estimated by MUSIC and VBI, respectively. Additionally, the Cramér-Rao bound (CRB) of the considered sensing parameters is derived to characterize the lower bound for the proposed estimators. Corroborated by extensive simulation results, our proposed method can achieve improved mean square error (MSE) than its conventional counterparts and is robust against the target number and target speed, thereby validating its wide applicability and advantages over prior arts.

Perceptive mobile networks (PMN) have been widely recognized as a pivotal pillar for the sixth generation (6G) mobile communication systems. However, the asynchronicity between transmitters and receivers results in velocity and range ambiguity, which seriously degrades the sensing performance. To mitigate the ambiguity, carrier frequency offset (CFO) and time offset (TO) synchronizations have been studied in the literature. However, their performance can be significantly affected by the specific choice of the window functions harnessed. Hence, we set out to find superior window functions capable of improving the performance of CFO and TO estimation algorithms. We firstly derive a near-optimal window, and the theoretical synchronization mean square error (MSE) when utilizing this window. However, since this window is not practically achievable, we then test a practical “window function” by utilizing the multiple signal classification (MUSIC) algorithm, which may lead to excellent synchronization performance.

Perceptive mobile networks (PMNs), integrating ubiquitous sensing capabilities into mobile networks, represent an important application of integrated sensing and communication (ISAC) in 6G. In this paper, we propose a practical framework for uplink sensing of angle-of-arrival (AoA), Doppler, and delay in millimeter-wave (mmWave) communication systems, which addresses challenges posed by clock asynchrony and hybrid arrays, while being compatible with existing communication protocols. We first introduce a beam scanning method and a corresponding AoA estimation algorithm, which utilizes frequency smoothing to effectively estimate AoAs for both static and dynamic paths. We then propose several methods for constructing a “clean” reference signal, which is subsequently used to cancel the effect caused by the clock asynchrony. We further develop a signal ratio-based joint AoA-Doppler-delay estimator and propose an AoA-based 2D-FFT-MUSIC (AB2FM) algorithm that applies 2D-FFT operations on the signal subspace, which accelerates the computation process with low complexity. Our proposed framework can estimate parameters in pairs, removing the complicated parameter association process. Simulation results validate the effectiveness of our proposed framework and demonstrate its robustness in both low and high signal-to-noise ratio (SNR) conditions.

Current research on faster-than-Nyquist (FTN) systems mainly focuses on baseband digital signal processing without considering the impact of I/Q imbalance (IQI) caused by hardware impairments in the signal chain. To address this problem, this paper considers frequency-dependent IQI and applies the frame-based decision-directed successive interference cancellation (DDSIC) algorithm after minimum mean square error (MMSE) equalization to jointly mitigate inter-symbol interference (ISI) and IQI. We introduce extended-dimension signal models, which use both original and image signals to describe the impact of IQI. Based on the models, a two-stage iterative DDSIC algorithm is then proposed, achieving effective interference cancellation. Furthermore, the theoretical bit error rate (BER) for each iteration of DDSIC and the BER lower bound of the proposed system are derived. Simulation results demonstrate the superiority of DDSIC over some existing algorithms under both additive white Gaussian noise (AWGN) and multipath fading channels. These results also validate the derived theoretical BER expressions and the robustness of our scheme under various ISI and IQI scenarios, respectively.

Asynchronous radio transceivers often lead to significant range and velocity ambiguity, posing challenges for precise positioning and velocity estimation in passive-sensing perceptive mobile networks (PMNs). To address this issue, carrier frequency offset (CFO) and time offset (TO) synchronization algorithms have been studied in the literature. However, their performance can be significantly affected by the specific choice of the utilized window functions. Hence, we set out to find superior window functions capable of improving the performance of CFO and TO estimation algorithms. We first derive a near-optimal window, and the theoretical synchronization mean square error (MSE) when utilizing this window. However, since this window is not practically achievable, we then develop a practical window selection criterion and test a special window generated by the super-resolution algorithm. Numerical simulation has verified our analysis.

In indoor integrated sensing and communication (ISAC) systems, angle of arrival (AoA) estimation for moving targets via reflected signals plays an important role in passive sensing. It is a challenging task in the presence of multiple static paths which cause significant interference to AoA estimation for dynamic paths. In this paper, we propose a subspace algorithm that achieves accurate AoA estimation for multiple targets using static communication transceivers in general indoor scenarios. We first use a complex vector to model static paths as a merged static path. Then we design a conditional pseudo-likelihood probability density function (PDF) based on the projection matrix of the signal subspace to bridge the signal subspace with the merged static path and AoAs of dynamic paths. We propose to utilize the Particle Filter to track merged static path and AoAs of dynamic paths via signal subspace. Thereby, the interference caused by static path signals can be eliminated and the accurate AoA estimation results are obtained. Simulation results are provided and validate the proposed algorithm.

Joint communications and sensing (JCAS) have the potential to improve the overall energy, cost and frequency efficiency of Internet-of-Things (IoT) systems. As a first effort, we propose to optimize the MIMO-OFDM data symbols carried by sub-carriers for better time-and spatial-domain signal orthogonality. This can reduce inter-target and inter-antenna interference, enabling high-quality sensing. We establish an optimization problem that modifies data symbols on sub-carriers to enhance the above-mentioned signal orthogonality. We also develop an efficient algorithm to solve the problem based on the majorization-minimization framework. Moreover, we discover unique signal structures and features from the newly modeled problem, which substantially reduce the complexity of majorizing the objective function. We also develop new projectors to enforce the feasibility of the obtained solution. Simulations show that to achieve the same sensing performance, the optimized waveform can reduce the signal-to-noise ratio (SNR) requirement by 3~4.5 dB compared with the original waveform, while the SNR loss for the uncoded bit error rate is only 1~1.5 dB.

In this paper, we propose a novel Kalman Filter (KF)-based uplink (UL) joint communication and sensing (JCAS) scheme, which can significantly reduce the range and location estimation errors due to the clock asynchronism between the base station (BS) and user equipment (UE). Clock asynchronism causes time-varying time offset (TO) and carrier frequency offset (CFO), leading to major challenges in uplink sensing. Unlike existing technologies, our scheme does not require knowing the location of the UE in advance, and retains the linearity of the sensing parameter estimation problem. We first estimate the angle-of-arrivals (AoAs) of multipaths and use them to spatially filter the CSI. Then, we propose a KF-based CSI enhancer that exploits the estimation of Doppler with CFO as the prior information to significantly suppress the time-varying noise-like TO terms in spatially filtered CSIs. Subsequently, we can estimate the accurate ranges of UE and the scatterers based on the KF-enhanced CSI. Finally, we identify the UE’s AoA and range estimation and locate UE, then locate the dumb scatterers using the bi-static system. Simulation results validate the proposed scheme. The localization root mean square error of the proposed method is about 20 dB lower than the benchmarking scheme.

For wideband transceivers operating at millimeter wave and terahertz frequencies, the implementation of conventional digital predistortion for nonlinearity mitigation faces significant challenges due to the limited availability and/or complexity of high-speed digital signal processing. In this paper, a simple received signal model is proposed for wideband system with nonlinearity and other practical impairments, such as transmitter (Tx) and receiver (Rx) I/Q imbalances (IQIs), carrier frequency offset (CFO), and phase noise, to enable low-complexity impairment mitigation. An expanded memory polynomial (EMP) model is firstly proposed to capture Tx IQI and the nonlinearity over the entire transceiver chain. Exploiting the CFO and a novel transmission protocol, a blind Rx IQI estimation is also proposed. The noise enhancement after Rx IQI and CFO compensation is then evaluated as a noise factor related to the mean-square-error of the Rx IQI estimation. As a result, the received signal of the wideband system is finally modelled as an EMP plus additive noises followed by a band-limited noisy receiver filter. Simulation results using a millimeter wave system with 2.5 GHz bandwidth and 73.5 GHz carrier frequency are presented to verify the accuracy of the EMP modelling and validate the theoretical analyses.

Integrated sensing and communication (ISAC) is regarded as the enabling technology in the future 5th-Generation-Advanced (5G-A) and 6th-Generation (6G) mobile communication system. ISAC waveform design is critical in ISAC system. However, the difference of the performance metrics between sensing and communication brings challenges for the ISAC waveform design. This paper applies the unified performance metrics in information theory, namely mutual information (MI), to measure the communication and sensing performance in multicarrier ISAC system. In multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) ISAC system, we first derive the sensing and communication MI with subcarrier correlation and spatial correlation. Then, we propose optimal waveform designs for maximizing the sensing MI, communication MI and the weighted sum of sensing and communication MI, respectively. The optimization results are validated by Monte Carlo simulations. Our work provides effective closed-form expressions for waveform design, enabling the realization of MIMO-OFDM ISAC system with balanced performance in communication and sensing.

Integrated sensing and communications (ISAC) is an emerging technique for the next generation of communication systems. However, due to multiple performance metrics used for communication and sensing, the limited number of degrees-of-freedom (DoF) in optimizing ISAC systems poses a challenge. Reconfigurable intelligent surfaces (RISs) can introduce new DoF for beamforming in ISAC systems, thereby enhancing the performance of communication and sensing simultaneously. In this paper, we propose two optimization techniques for beamforming in RIS-assisted ISAC systems. The first technique is an alternating optimization (AO) algorithm based on the semidefinite relaxation (SDR) method and a one-dimension iterative (ODI) algorithm, which can maximize the radar mutual information (MI) while imposing constraints on the communication rates. The second technique is an AO algorithm based on the Riemannian gradient (RG) method, which can maximize the weighted ISAC performance metrics. Simulation results verify the effectiveness of the proposed schemes. The AO-SDR-ODI method is shown to achieve better communication and sensing performance, than the AO-RG method, at a higher complexity. It is also shown that the mean-squared-error (MSE) of the estimates of the sensing parameters decreases as the radar MI increases.

Gridless methods show great superiority in line spectral estimation. These methods need to solve an atomic$l_{0}$norm (i.e., the continuous analog of$l_{0}$norm) minimization problem to estimate frequencies and model order. Since this problem is NP-hard to compute, relaxations of the atomic$l_{0}$norm, such as the nuclear norm and reweighted atomic norm, have been employed for promoting sparsity. However, the relaxations give rise to a resolution limit, subsequently leading to biased model order and convergence error. To overcome the above shortcomings of relaxation, we propose a novel idea of simultaneously estimating the frequencies and model order using the atomic$l_{0}$norm. To accomplish this idea, we build a multiobjective optimization model. The measurement error and the atomic$l_{0}$norm are taken as the two optimization objectives. The proposed model directly exploits the model order via the atomic$l_{0}$norm, thus breaking the resolution limit. We further design a variable-length evolutionary algorithm to solve the proposed model, which includes two innovations. One is a variable-length coding and search strategy. It flexibly codes and interactively searches diverse solutions with different model orders. These solutions act as steppingstones that helpfully exploring the variable and open-ended frequency search space and provide extensive potentials toward the optima. Another innovation is a model-order pruning mechanism, which heuristically prunes less contributive frequencies within the solutions, thus significantly enhancing convergence and diversity. Simulation results confirm the superiority of our approach in both frequency estimation and model-order selection.

Contact-free vital sign monitoring, which uses wireless signals for recognizing human vital signs (i.e, breath and heartbeat), is an attractive solution to health and security. However, the subject’s body movement and the change in actual environments can result in inaccurate frequency estimation of heartbeat and respiratory. In this paper, we propose a robust mmWave radar and camera fusion system for monitoring vital signs, which can perform consistently well in dynamic scenarios, e.g., when some people move around the subject to be tracked, or a subject waves his/her arms and marches on the spot. Three major processing modules are developed in the system, to enable robust sensing. First, we utilize a camera to assist a mmWave radar to accurately localize the subjects of interest. Second, we exploit the calculated subject position to form transmitting and receiving beamformers, which can improve the reflected power from the targets and weaken the impact of dynamic interference. Third, we propose a weighted multi-channel Variational Mode Decomposition (WMC-VMD) algorithm to separate the weak vital sign signals from the dynamic ones due to subject’s body movement. Experimental results show that, the 90th percentile errors in respiration rate (RR) and heartbeat rate (HR) are less than 0.5 RPM (respirations per minute) and 6 BPM (beats per minute), respectively.

Bi-static sensing is crucial for exploring the potential of networked sensing capabilities in integrated sensing and communications (ISAC). However, it suffers from the challenging clock asynchronism issue. Channel state information (CSI) ratio-based sensing is an effective means to address the issue. Its performance bounds, particular for Doppler sensing, have not been fully understood yet. This work endeavors to fill the research gap. Focusing on a single dynamic path in high-SNR scenarios, we derive the closed-form Cramér-Rao bounds (CRB). Then, through analyzing the mutual interference between dynamic and static paths, we simplify the CRB results by deriving close approximations, further unveiling new insights of the impact of numerous physical parameters on Doppler sensing. Moreover, utilizing the new CRB and analyses, we propose novel waveform optimization strategies for noise- and interference-limited sensing scenarios, which are also empowered by closed-form and efficient solutions. Extensive simulation results are provided to validate the preciseness of the derived CRB results and analyses, with the aid of the maximum-likelihood estimator. The results also demonstrate the substantial enhanced Doppler sensing accuracy and the sensing capabilities for low-speed target achieved by the proposed waveform design.

Wideband millimeter wave and terahertz systems face severe nonlinearity and other practical impairments such as transmitter and receiver in-phase/quadrature imbalances (IQIs), carrier frequency offset, and phase noise. Based on a simplified yet effective received signal model including an expanded memory polynomial (EMP) and a noisy receiver filter, this paper proposes a low-complexity digital post-cancellation (DPC) framework for transmitter IQI and overall system nonlinearity mitigation. The nonlinearity parameters with reduced nonlinearity order are firstly estimated with low-complexity using a novel transmission protocol incorporating both frame rotation and preamble power scaling. Through widely linear system equalization and interference cancellation, the signal distortion caused by frequency-dependent IQI and nonlinearity is then mitigated with significant performance improvement. The mean-squared-error measurement of the EMP-modelled signals also provides a practical means for the nonlinear system identification and characterization. Both simulation and experiment results obtained from a millimeter wave system with 2.125 GHz bandwidth and 73.5 GHz carrier frequency are presented to verify the theoretical analyses and demonstrate the effectiveness of the DPC technology.

In this paper, we investigate the realization of covert communication in a general radar-communication cooperation system, which includes integrated sensing and communications as a special example. We explore the possibility of utilizing the sensing ability of radar to track and jam the aerial adversary target attempting to detect the transmission. Based on the echoes from the target, the extended Kalman filtering technique is employed to predict its trajectory as well as the corresponding channels. Depending on the maneuvering altitude of adversary target, two channel state information (CSI) models are considered, with the aim of maximizing the covert transmission rate by jointly designing the radar waveform and communication transmit beamforming vector based on the constructed channels. For perfect CSI under the free-space propagation model, by decoupling the joint design, we propose an efficient algorithm to guarantee that the target cannot detect the transmission. For imperfect CSI due to the multi-path components, a robust joint transmission scheme is proposed based on the property of the Kullback-Leibler divergence. The convergence behaviour, tracking MSE, false alarm and missed detection probabilities, and covert transmission rate are evaluated. Simulation results show that the proposed algorithms achieve accurate tracking. For both channel models, the proposed sensing-assisted covert transmission design is able to guarantee the covertness, and significantly outperforms the conventional schemes.

Millimeter-wave Integrated Sensing and Communications (ISAC) with multi-beam design holds significant promise for vehicular networks, offering multi-target omnidirectional sensing and high-capacity communication services concurrently. Nonetheless, the considerable challenge of potential mutual interference arises due to the high mobility and density of transmitters in such networks. To address this challenge effectively, we propose leveraging inter-vehicle communication to schedule communication and sensing signals for vehicles, thereby enhancing networked sensing capabilities. We first introduce an analytical framework to characterize the mutual interference among multiple vehicles. Subsequently, we evaluate the effectiveness of our proposed interference mitigation method in terms of interference probability, duration, and the achievable detectable density. Additionally, recognizing the different performance requirements of communication and sensing functions, we investigate a joint resource allocation problem catering to both aspects. Simulation results demonstrate a notable enhancement in the proposed ISAC-based interference mitigation, with a 58% reduction in interference probability compared to benchmarking schemes.

Sensing in Integrated Sensing and Communications (ISAC) systems with clock asynchronism between the transmitter and receiver poses significant challenges. Understanding the fundamental limits of sensing performance in such setups, which remain largely unknown, is crucial. This paper investigates the sensing performance bounds in the presence of clock asynchronism. In both single-carrier and multi-carrier models, we derive the Cramér-Rao bounds (CRB) for estimating dynamic channel path parameters including angle of arrival, delay, and complex gain sequence (CGS). Through mathematical analyses and numerical simulations, we conduct a comprehensive study on how these bounds depend on various system parameters and the impact of clock asynchronism. Our findings highlight the degradation of parameter estimation performance due to clock asynchronism and reveal low-accuracy zones for CGS estimation in strong-line-of-sight scenarios. Additionally, we observe asymptotic mitigation in performance degradation with larger bandwidth, providing valuable insights for system design and optimization.

Integrating sensing into wifi platforms, referred to as wifi sensing, provides an efficient and device-free means for indoor monitoring/localization with low cost. Clock asynchrony is one of the most challenging issues in wifi sensing. A mainstream solution to date employs cross-antenna processing to suppress the clock offsets that are common to all antennas. Such methods, however, may suffer from issues such as mirrored targets and noise enhancement etc. This paper develops a novel wifi sensing scheme. It embodies accurate and low-complexity methods for estimating the timing and frequency offsets as well as the angle-of-arrival (AoA), all from the LoS path. It also involves a coherent Doppler processing method which effectively suppresses static paths and accurately estimates the Doppler by enjoying the coherent processing gain. Corroborated by experimental results using open Widar2.0 data, the proposed design is able to precisely recover the velocity traces in various indoor scenarios.

Effective wireless communications are increasingly important in maintaining the successful closed-loop operation of mission-critical Industrial Internet of Things (IIoT) applications. To meet the ever-increasing demands on better wireless communications for IIoT, we propose an orthogonal time-frequency space (OTFS) waveform-based joint communication and radio sensing (JCAS) scheme—an energy-efficient solution for not only reliable communications but also high-accuracy sensing. OTFS has been demonstrated to have higher reliability and energy efficiency than the currently popular IIoT communication waveforms. JCAS has also been highly recommended for IIoT, since it saves cost, power, and spectrum compared to having two separate radio frequency systems. Performing JCAS based on OTFS, however, can be hindered by a lack of effective OTFS sensing. This article is dedicated to filling this technology gap. We first design a series of echo preprocessing methods that successfully remove the impact of communication data symbols in the time-frequency domain, where major challenges, such as intercarrier and intersymbol interference and noise amplification, are addressed. Then, we provide a comprehensive analysis of the signal-to-interference-plus-noise ratio (SINR) for sensing and optimize a key parameter of the proposed method to maximize the SINR. The extensive simulations show that the proposed sensing method approaches the maximum-likelihood estimator with respect to the estimation accuracy of target parameters and manifests applicability to wide ranges of key system parameters. Notably, the complexity of the proposed method is only dominated by a 2-D Fourier transform.

The spectrally efficient multicarrier faster-than-Nyquist (MFTN) signaling provides an efficient and robust solution for enhancing transmission rate and resisting channel impairments. In this paper, an evolutionary non-orthogonal physical waveform is proposed for simultaneously achieving high spectral and energy efficiency via combining MFTN signaling with index modulation (IM). Exploiting the implicit transmission of IM, the inactivated subcarriers alleviate the inherent two-dimensional interferences imposed by time-frequency packing in MFTN. Then, we develop a pair of iterative equalization algorithms based on vector approximate message passing (VAMP). For the first time-domain equalization (TDE), an extended constellation set is constructed for uniformly characterizing the activated and inactivated subcarriers. To further reducing the computational complexity, we establish a subcarrier-based segment-wise frequency-domain received signal model and accordingly develop low-complexity VAMP-based frequency-domain equalization (FDE) combined with interference elimination. Corroborated by simulations, the novel MFTN-IM waveform achieves superior bit error rate (BER) performance over benchmark waveforms in the high signal noise ratio (SNR) regions. Interestingly, while the proposed VAMP-TDE outperforms the proposed VAMP-FDE in BER performance, the latter is more competitive in balancing demodulation performance and computational complexity.

Full-duplex (FD) integrated sensing and communication (ISAC) has great potential in future vehicular networks. However, the FD requirement and the ISAC functions make the receiver processing extremely complicated, particularly when multiple transmissions are uncoordinated. In this paper, we study frequency-hopping (FH) based receivers in an FD ISAC system, where the arrivals of backscattered signals from one node may overlap with those of signals from another node. To mitigate the interferences caused by the overlapping signals, we consider two receiver options based on either conventional communications or frequency-modulated continuous-wave radars, and two signal modulations based on either fast FH or un-slotted ALOHA FH. Based on the different signal modulations, we develop two parameter estimation schemes via using FH-decoding and de-chirp operations, respectively. To further improve the sensing accuracy, we proceed to propose an iterative algorithm, which refines the estimates of all parameters via using short-time-Fourier transform and maximizing the received power in desired frequency bands. After obtaining all channel parameters in sensing, bilateral communications between two nodes are realized by differential phase-shift keying. Finally, simulation results are provided and verify that the proposed FD ISAC can obtain parameters in high resolution and realize robust communication links.

Faster-than-Nyquist (FTN) signaling can improve spectral efficiency and enable high-speed transmission for next-generation communication systems. One of the most significant challenges in FTN transmission is how to remove the inter-symbol interference (ISI). In this paper, we propose a novel decision-directed successive interference cancellation (DDSIC) based on frequency-domain minimum-mean-square-error (MMSE) equalization for practical FTN systems. To reduce the computational complexity, the detection process is performed frame-by-frame in the frequency domain. In addition, we derive the theoretical bit error rate (BER) expression for each iteration in DDSIC as well as the BER lower bound for$M$-ary quadrature amplitude modulated FTN systems. The simulation results verify the theoretical analyses and demonstrate that our proposed method enables lower complexity and better performance compared with state-of-the-art methods.

Beamspace millimeter-wave (mmWave) and terahertz (THz) massive MIMO constitute attractive schemes for next-generation communications, given their abundant bandwidth and high throughput. However, their user and beam selection problem has not been efficiently addressed yet. Inspired by this challenge, we develop low-complexity solutions explicitly. In contrast to the zero forcing in the prior art, we introduce the dirty paper coding (DPC) into the joint user and beam selection problem. We unveil the compelling properties of the DPC sum rate in beamspace massive MIMO, showing its monotonic evolution against the number of users and beams selected. We then exploit its beneficial properties for substantially simplifying the joint user and beam selection problem. Furthermore, we develop a set of algorithms striking unique trade-offs for solving the simplified problem, facilitating simultaneous user and beam selection based on partial beamspace channels for the first time. Additionally, we derive the sum rate bound of the algorithms and analyze their complexity. Our simulation results validate the effectiveness of the proposed design and analysis, confirming their superiority over prior solutions.

Device-free human tracking is an essential ingredient for ubiquitous wireless sensing. Recent passive WiFi tracking systems face the challenges of inaccurate separation of dynamic human components and time-consuming estimation of multi-dimensional signal parameters. In this work, we present a scheme namedWiFiDopplerFrequencyShift (WiDFS), which can achieve single-target real-time passive tracking using channel state information (CSI) collected from commercial-off-the-shelf (COTS) WiFi devices. We consider the typical system setup including a transmitter with a single antenna and a receiver with three antennas; while our scheme can be readily extended to another setup. To remove the impact of transceiver asynchronization, we first apply CSI cross-correlation between each RX antenna pair. We then combine them to estimate a Doppler frequency shift (DFS) in a short-time window. After that, we leverage the DFS estimate to separate dynamic human components from CSI self-correlation terms of each antenna, thereby separately calculating angle-of-arrival (AoA) and human reflection distance for tracking. In addition, a hardware calibration algorithm is presented to refine the spacing between RX antennas and eliminate the hardware-related phase differences between them. A prototype demonstrates that WiDFS can achieve real-time tracking with a median position error of 72.32 cm in multipath-rich environments.

Network Function Virtualization (NFV), which decouples network functions from hardware and transforms them into Virtual Network Functions (VNFs), is a crucial technology for data center (DC) networks. A service function chain (SFC) is composed of an ordered set of VNFs and virtual links (VLs) connecting them. To optimize the resource allocation in DC networks, we need to efficiently map SFCs onto the physical network. Nevertheless, the dynamics and diversity of SFC requests in multi-datacenter (MDC) networks pose a significant challenge in embedding SFCs. To overcome this challenge, we design a two-stage graph convolutional network (GCN) assisted deep reinforcement learning (DRL) scheme. This framework aims to maximize the overall acceptance ratio of SFC requests while minimizing the total cost in an MDC network. In the first stage, we propose a GCN-based DRL algorithm as a coarse granularity solution to the SFC embedding problem from the macro perspective. This solution outlines a local observation scope (LOS) for each agent in the multi-agent system of the second stage, where all agents simultaneously handle SFC requests from their respective DCs using a multi-agent framework from the micro perspective. Numerical evaluations show that, compared to state-of-the-art methods, the proposed scheme improves the acceptance ratio by approximately 13% compared with the Kolin algorithm and 18% compared with the DQN algorithm and saves the cost by around 28% compared with the Kolin and the DQN.

Joint communication and sensing (JCS) has become a promising technology for mobile networks because of its higher spectrum and energy efficiency. Up to now, the prevalent fast Fourier transform (FFT)-based sensing method for mobile JCS networks is on-grid based, and the grid interval determines the resolution. Because the mobile network usually has limited consecutive OFDM symbols in a downlink (DL) time slot, the sensing accuracy is restricted by the limited resolution, especially for velocity estimation. In this paper, we propose a multiple signal classification (MUSIC)-based JCS system that can achieve higher sensing accuracy for the angle of arrival, range, and velocity estimation, compared with the traditional FFT-based JCS method. We further propose a JCS channel state information (CSI) enhancement method by leveraging the JCS sensing results. Finally, we derive a theoretical lower bound for sensing mean square error (MSE) by using perturbation analysis. Simulation results show that in terms of the sensing MSE performance, the proposed MUSIC-based JCS outperforms the FFT-based one by more than 20 dB. Moreover, the bit error rate (BER) of communication demodulation using the proposed JCS CSI enhancement method is significantly reduced compared with communication using the originally estimated CSI.

Employing cyclic prefixed OFDM (CP-OFDM) communication waveform for sensing has attracted extensive attention in vehicular integrated sensing and communications (ISAC). A unified sensing framework is developed recently, greatly extending the ranging capability of CP-OFDM sensing. However, a false target issue still remains unsolved. In this paper, we investigate and solve this issue. Specifically, we unveil that false targets are caused by periodic cyclic prefixes (CPs) in CP-OFDM waveform. We also derive the relation between the locations of false and true targets, and other features, e.g., strength, of false targets. Moreover, we develop an effective solution to removing false targets. Simulations are provided to confirm the validity of our analysis and the effectiveness of the proposed solution.

For wireless networks under jamming attacks, suppressing the jammer is essential to guarantee a rehable communication link. However, it can be problematic to nullify the jamming signal when the correlations between transmitted jamming signals are deliberately varied over tone. Specifically recent studies reveal that the time-varying correlations create a "virtual change" m the jamming channel and thus their nullspace, even when the physical channels remain unchanged Unlike existing studies that only consider unchanged correlations or merely propose a heuristic solution to the "virtual change" problem by continuously monitoring the residual jamming signal then updating the beam-forming matrix, we develop a deep dueling Q-learning technique to minimize the magnitude of the "virtual change" by choosing a suitable allocated time for different phases of each communication frame. Extensive simulations show that the proposed techniques can suppress the jamming signal, even when the correlations vary over time, and the correlations’ trajectory is unrevealed. Moreover, our techniques do not require monitoring the residual jamming signals then updating the beam-forming matrix. Therefore, our technique can improve the system’s spectral efficiency and reduce the outage probability.

Network slicing is one of the core techniques of the current 5G networks. To accommodate as many network slices as possible with limited hardware resources, service providers need to avoid over-provisioning of resources. In this paper, we first propose a Deep Q-Network (DQN) based network slicing algorithm to maximize the acceptance ratio and ensure prior placement of higher-priority requests for Ultra-Reliable Low-Latency Communication (URLLC) services. Specifically, we model the network slicing as a Markov Decision Process (MDP), where we consider Virtual Network Function (VNF) placements to be the actions of the MDP, and define a reward function based on service priority. For every service request, we use the DQN to choose an MDP action for performing the VNF placement. The placement results in an MDP reward that we can use to train the DQN. Once trained, the DQN approximates the optimal solution of the MDP. Considering the over-provisioning of resources, we then propose a Binary Search Assisted Transfer Learning algorithm (BSATL), in which the available hardware resources are scaled down/up and the knowledge learned from the source task is transferred to the target task in each iteration, to achieve automated and closed-loop optimization for the ever changing infrastructure, a scenario of 6G Event Defined uRLLC (EDuRLLC). Numerical evaluations show that our proposed scheme can significantly improve cost-utility while maintaining the optimal acceptance ratio.

Abstract Due to difficulties in manufacturing, data conversion devices with extremely high sampling rate are becoming the bottleneck in realising high‐speed communication systems with a large bandwidth. Dual pulse shaping (DPS) transmission allows half‐symbol‐rate conversion devices to be used for two parallel data streams to achieve full‐rate transmission, and is proved to be an effective solution. Here, two sets of ideal sinc‐function based complementary Nyquist pulses for DPS transmission are proposed. Theoretically, it is shown that the proposed pulses satisfy the inter‐symbol and cross‐symbol interference‐free conditions, and can achieve full‐Nyquist‐rate transmission with half of the sampling rate. With reference to commercially available D/As, two sets of practical dual spectral shaping pulses are further proposed, and the close relationship between the ideal and practical pulses are disclosed. Performance analysis for linear equalisation is provided in the presence of both timing offset between dual shaping pulses and carrier‐frequency offset. Two approaches are then proposed to improve the system robustness by adjusting the clock phase of the D/As and A/Ds. Simulation results are presented to provide a comparison between the proposed DPS transmission schemes and the state of the art, in terms of the performance metrics of peak‐to‐average power ratio and bit error rate.

Deep learning has demonstrated its great potential in channel state information (CSI)-based human activity recognition (HAR), and hence has attracted increasing attention in both the industry and academic communities. While promising, most existing high-accuracy methodologies require to retrain their models when applying the previous-trained ones to a new/unseen environment. This issue has limited their practical usabilities. In order to overcome this challenge, this article proposes an innovative scheme, which combines an activity-related feature extraction and enhancement (AFEE) method and matching network (AFEE-MatNet). The proposed scheme is “one-fits-all,” meaning that the trained model can be directly applied in new/unseen environments without any retraining. We introduce the AFEE method to enhance CSI quality by eliminating noise. Specifically, the approach mitigates environmental noises unrelated to activity while better compressing and preserving the behavior-related information. Moreover, the size of feature signals generated by AFEE are reduced, which in turn significantly shortens the training time. For effective feature extraction, we propose to use the MatNet architecture to learn transferable features shared among source environments. To further improve the recognition performance, we introduce a prediction checking and correction scheme to rectify some classification errors that do not abide by the state transition of human behaviors. Extensive experimental results demonstrate that our proposed AFEE-MatNet significantly outperforms existing state-of-the-art HAR methods, in terms of both recognition accuracy and training time.

Angle-of-Arrival (AoA) estimation is an important problem in passive radio-frequency identification (RFID) systems. Affixing an RFID tag array to an object enables to acquire its orientation information. However, the electromagnetic interaction between the tags can induce mutual coupling interference, distorting the RFID fingerprint measurements used for AoA estimation. Moreover, RFID reader modes with radio-frequency (RF) noise-tolerant Miller encoding can induce$\pi $-radians phase jump. In this article, we propose a scheme called RF-Mirror that can resolve the mutual coupling and phase jump problems and achieve accurate AoA estimation for an array with two or more tags. First, we characterize the impact of mutual coupling on a tag’s signal fingerprint and develop novel RSSI/phase-distance models. We then develop new experimental methods and signal processing techniques to verify the effectiveness of the proposed models. Based on the validated models, we develop new AoA estimation algorithms for tag arrays that deal with the mutual coupling effect explicitly. We provide extensive experimental results, which demonstrate that RF-Mirror can achieve significantly improved performance compared to baseline schemes, with median AoA estimation errors of 11.65° and 6.29° for two- and four-tag arrays, respectively.

Integrating sensing into standardized communication systems can potentially benefit many consumer applications that require both radio frequency functions. However, without an effective sensing method, such integration may not achieve the expected gains of cost and energy efficiency. Existing sensing methods, which use communication payload signals, either have limited sensing performance or suffer from high complexity. In this paper, we develop a novel and flexible sensing framework which has a complexity only dominated by a Fourier transform and also provides the flexibility in adapting to different sensing needs. We propose to segment a whole block of echo signal evenly into sub-blocks; adjacent ones are allowed to overlap. We design a virtual cyclic prefix (VCP) for each sub-block that allows us to employ two common ways of removing communication data symbols and generate two types of range-Doppler maps (RDMs) for sensing. We perform a comprehensive analysis of the signal components in the RDMs, proving that their interference-plus-noise (IN) terms are approximately Gaussian distributed. The statistical properties of the distributions are derived, which leads to the analytical comparisons between the two RDMs as well as between the prior and our sensing methods. Moreover, the impact of the lengths of sub-block, VCP and overlapping signal on sensing performance is analyzed. Criteria for designing these lengths for better sensing performance are also provided. Extensive simulations validate the superiority of the proposed sensing framework over prior methods in terms of signal-to-IN ratios in RDMs, detecting performance and flexibility.

Joint communication and radar sensing (JCAS) integrates the two functions into one system, sharing one transmitted signal. In this paper, we investigate JCAS waveform optimization in communication-centric systems, where a base station (BS) detects radar targets and communicates with mobile users simultaneously. Different from existing works, we study multi-metric optimizations for a practical low-cost system and establish their connections. To relax the requirement of full-duplex technology, we add a single receive antenna for sensing at the BS, which is synchronized with and spatially separated from the JCAS transmit array. We first optimize precoders for communications and radar, individually. Then, we formulate a JCAS waveform optimization problem that constrains either mutual information (MI) or Cramér-Rao bound (CRB) of radar and maximizes the relaxed signal-to-interference-plus-noise rate (SINR) of communications. Exploiting the geometric characteristic of the relaxed SINR, we provide a closed-form solution under certain conditions and propose a numerical iteration algorithm that works in all situations. We also disclose the connections between optimizations with constraining MI and CRB, using numerical results. Finally, simulation results are provided and validate the proposed optimization solutions.

Beamforming design is an important technique for enhancing the performance of integrated sensing and communication (ISAC) systems. However, related research based on the hybrid analog-digital (HAD) architecture is still limited. In this paper, we investigate the partially-connected hybrid beamforming design for multi-user ISAC systems. Instead of the commonly used beampattern related metric, the Cramér-Rao bound (CRB) is employed as the sensing performance metric for direction of arrival (DOA) estimation. We aim to minimize the CRB while satisfying the signal-to-interference-plus-noise ratio (SINR) constraints for individual communication users by jointly optimizing the digital and analog beamformers. Subsequently, we propose an alternating optimization based framework, which is significantly different from the conventional methods based on the approximation of the optimal fully-digital beamformer with a hybrid one. We also consider an alternative formulation of optimizing the SINR of radar echo signals. Based on optimal receive beamformer design, we transform the SINR based joint transmitter and receiver optimization problem to a series of problems sharing a similar form with the CRB based transmitter optimization problem, which can be efficiently solved via the proposed algorithm. Simulation results show that the proposed designs provide significant performance gains in DOA estimation over the existing beampattern approximation based design.

Deep Learning plays an increasingly important role in device-free WiFi Sensing for human activity recognition (HAR). Despite its strong potential, significant challenges exist and are associated with the fact that one may require a large amount of samples for training, and the trained network cannot be easily adapted to a new environment. To address these challenges, we develop a novel scheme using matching network with enhanced channel state information (MatNet-eCSI) to facilitate one-shot learning HAR. We propose a CSI correlation feature extraction (CCFE) method to improve and condense the activity-related information in input signals. It can also significantly reduce the computational complexity by decreasing the dimensions of input signals. We also propose novel training strategy which effectively utilizes the data set from the previously seen environments (PSE). In the least, the strategy can effectively realize human activity recognition using only one sample for each activity from the testing environment and the data set from one PSE. Numerous experiments are conducted and the results demonstrate that our proposed scheme significantly outperforms state-of-the-art HAR methods, achieving higher recognition accuracy and less training time.

Dual-function radar-communication (DFRC) based on frequency hopping (FH) MIMO radar (FH-MIMO DFRC) achieves symbol rate much higher than radar pulse repetition frequency. Such DFRC, however, is prone to eavesdropping due to the spatially uniform illumination of an FH-MIMO radar. In this paper, we reveal the potential of using permutations of hopping frequencies to achieve secure and high-speed FH-MIMO DFRC. Specifically, we identify the angle-dependent issue in detecting permutations and develop an element-wise phase compensation (EPC) to solve the issue for a legitimate user (Bob). EPC makes the demodulation at an eavesdropper (Eve) conditioned on knowing the angle-of-departure (AoD) of Bob. We also propose the random sign reversal (RSR) technique which randomly selects several antennas over hops and reverses their signs. Owing to EPC, there is a sign rule available for Bob. We employ the rule and develop a low-complexity algorithm for Bob to remove RSR. We further prove that, given the same signal-to-noise ratio, RSR plus EPC make the demodulation performance of Eve inferior to that of Bob in most angular regions. Confirmed by simulation, our design achieves substantially high physical layer security for FH-MIMO DFRC, improves demodulation performance compared with existing designs, and reduces mutual interference among radar targets.

Achieving better performance in high mobility scenarios has become an emerging topic for next generation wireless communications. Compared with traditional modulation techniques, the recently proposed orthogonal time frequency space (OTFS) shows outstanding performance over fast fading channels. In this paper, the OTFS system is first represented in the form of precoded orthogonal frequency division multiplexing (OFDM), enabling traditional estimation and equalization techniques to work under fast fading channels. Then, a novel frequency-domain pilot-aided channel estimation scheme is proposed to obtain the channel state information at the receiver. Simulation results show that the new channel estimation scheme works efficiently in different channel scenarios. Meanwhile, the overhead of the proposed scheme is also lower than those of the current popular schemes.

Reducing cost and power consumption while maintaining high network access capability is a key physical-layer requirement of massive Internet of Things (mIoT) networks. Deploying a hybrid array is a cost- and energy-efficient way to meet the requirement, but would penalize system degree of freedom (DoF) and channel estimation accuracy. This is because signals from multiple antennas are combined by a radio frequency (RF) network of the hybrid array. This article presents a novel hybrid uniform circular cylindrical array (UCyA) for mIoT networks. We design a nested hybrid beamforming structure based on sparse array techniques and propose the corresponding channel estimation method based on the second-order channel statistics. As a result, only a small number of RF chains are required to preserve the DoF of the UCyA. We also propose a new tensor-based two-dimensional (2-D) direction-of-arrival (DoA) estimation algorithm tailored for the proposed hybrid array. The algorithm suppresses the noise components in all tensor modes and operates on the signal data model directly, hence improving estimation accuracy with an affordable computational complexity. Corroborated by a Cramér-Rao lower bound (CRLB) analysis, simulation results show that the proposed hybrid UCyA array and the DoA estimation algorithm can accurately estimate the 2-D DoAs of a large number of IoT devices.

A low-complexity parametric bilinear generalized approximate message passing (PBiGAMP)-based receiver is conceived for multi-carrier faster-than-Nyquist (MFTN) signaling over frequency-selective fading channels. To mitigate the inherent ill-conditioning problem of MFTN signaling, we construct a segment-based frequency-domain received signal model in the form of a block circulant linear transition matrix, which can be efficiently calculated by applying a two dimensional fast Fourier transform. Based on the eigenvalue decomposition of the block circulant matrices, we can diagonalize the covariance matrix of the complex-valued colored noise process imposed by the associated two dimensional non-orthogonal matched filtering. Building on this model, a PBiGAMP-based parametric joint channel estimation and equalization (JCEE) algorithm is proposed for MFTN systems. In this algorithm, we introduce a pair of additive terms for characterizing the interferences arising from adjacent segments and employ the exact discretea prioriprobabilities of the transmitted symbols for improving the bit error rate (BER) performance. To further enhance the system’s robustness in the presence of ill-conditioned matrices, we develop a refined PBiGAMP-based JCEE algorithm by introducing a series of scaled identity matrices. Moreover, the proposed PBiGAMP-based JCEE algorithms may be readily decomposed into GAMP-based equalization algorithms, when the channel state information is perfectly known. The overall complexity of the proposed algorithms only increases logarithmically with the total number of transmitted symbols. Our simulation results demonstrate the benefits of the proposed PBiGAMP-based iterative message passing receiver conceived for MFTN signaling.

In this paper, we investigate the network utility maximization problem in a dual-functional radar-communication multi-unmanned aerial vehicle (multi-UAV) network where multiple UAVs serve a group of communication users and cooperatively sense the target simultaneously. To balance the communication and sensing performance, we formulate a joint UAV location, user association, and UAV transmission power control problem to maximize the total network utility under the constraint of localization accuracy. We then propose a computationally practical method to solve this NP-hard problem by decomposing it into three sub-problems, i.e., UAV location optimization, user association and transmission power control. Three mechanisms are then introduced to solve the three sub-problems based on spectral clustering, coalition game, and successive convex approximation, respectively. The spectral clustering result provides an initial solution for user association. Based on the three mechanisms, an overall algorithm is proposed to iteratively solve the whole problem. We demonstrate that the proposed algorithm improves the minimum user data rate significantly, as well as the fairness of the network. Moreover, the proposed algorithm increases the network utility with a lower power consumption and similar localization accuracy, compared to conventional techniques.

Frequency-hopping (FH) MIMO radar-based dual-function radar communication (FH-MIMO DFRC) enables communication symbol rate to exceed radar pulse repetition frequency, which requires accurate estimations of timing offset and channel parameters. The estimations, however, are challenging due to unknown, fast-changing hopping frequencies and the multiplicative coupling between timing offset and channel parameters. In this article, we develop accurate methods for a single-antenna communication receiver to estimate timing offset and channel for FH-MIMO DFRC. First, we design a novel FH-MIMO radar waveform, which enables a communication receiver to estimate the hopping frequency sequence (HFS) used by radar, instead of acquiring it from radar. Importantly, the novel waveform incurs no degradation to radar ranging performance. Then, via capturing distinct HFS features, we develop two estimators for timing offset and derive mean squared error lower bound of each estimator. Using the bounds, we design an HFS that renders both estimators applicable. Furthermore, we develop an accurate channel estimation method, reusing the single hop for timing offset estimation. Validated by simulations, the accurate channel estimates attained by the proposed methods enable the communication performance of DFRC to approach that achieved based on perfect timing and ideal knowledge of channel.

Frequency-hopping (FH) MIMO radar is recently introduced as an underlying system for realizing dual-function radar-communication (DFRC), increasing communication symbol rates to multiples of the radar pulse repetition frequency. As a newly conceived DFRC system, many realistic issues, such as channel estimation and synchronization, are not effectively solved yet. In this paper, we develop a multi-antenna receiver-based downlink communication scheme for the FH-MIMO DFRC, addressing the above issues in multi-path channels. By exploring the unique FH-MIMO radar waveform, we suppress both inter-antenna and inter-hop interference, and introduce minimal constraints on the radar waveform to facilitate DFRC. We then develop accurate estimation methods for timing offset and channel parameters. These methods are further employed to design reliable demodulation methods. We also derive performance bounds for the proposed estimation methods and embedded communications. Simulation results validate the efficacy of our receiving scheme, showing that the performance of estimators and data communications approaches analytical bounds.

Accurate channel parameter estimation is challenging for wideband millimeter-wave (mmWave) large-scale hybrid arrays, due to beam squint and much fewer radio frequency (RF) chains than antennas. This article presents a novel joint angle and delay estimation (JADE) approach for wideband mmWave fully-connected hybrid uniform cylindrical arrays. We first design a new hybrid beamformer to reduce the dimension of received signals on the horizontal plane by exploiting the convergence of the Bessel function, and to reduce the active beams in the vertical direction through preselection. The important recurrence relationship of the received signals needed for subspace-based angle and delay estimation is preserved, even with substantially fewer RF chains than antennas. Then, linear interpolation is generalized to reconstruct the received signals of the hybrid beamformer, so that the signals can be coherently combined across the whole band to suppress the beam squint. As a result, efficient subspace-based algorithm algorithms can be developed to estimate the angles and delays of multipath components. The estimated delays and angles are further matched and correctly associated with different paths in the presence of non-negligible noises, by putting forth perturbation operations. Simulations show that the proposed approach can approach the Cramér-Rao lower bound (CRLB) of the estimation with a significantly lower computational complexity than existing techniques.

In this paper, the emerging orthogonal time frequency space (OTFS) modulation is firstly restructured as a precoded orthogonal frequency division multiplexing (OFDM) system, so that the well-established frequency-domain approach can be applied to perform signal in fast fading channels. Then a frequency-domain minimum mean squared error (MMSE) equalizer for OTFS is introduced and its performance is analyzed based on the eigenvalue decomposition of the channel matrix. Inspired by the frequency-domain precoding structure, an adaptive transmission scheme with frequency-domain precoding matrix composed of the eigenvectors of the channel matrix is proposed to improve the system performance under MMSE equalization, and its optimized performance is derived with simple expression. Finally, considering two extreme channel conditions, the lower and upper bounds for the diversity performance of the adaptive transmission scheme are derived. Simulation results show that the proposed adaptive transmission achieves significantly better performance for short signal frames and can work well with imperfect channel state information (CSI). The derived performance bounds can serve as benchmarks for OTFS and other precoded OFDM systems.

Deep learning has shown a strong potential in device-free human activity recognition (HAR). However, a fundamental challenge is ensuring accuracy, without re-training, when exposing a previously trained architecture to a new or unseen environment. To overcome the aforementioned challenge, this paper proposes an environment-robust channel state information (CSI) based HAR by leveraging the properties of a matching network (MatNet) and enhanced features (HAR-MN-EF). To improve the CSI quality, we propose a CSI cleaning and enhancement method (CSI-CE) that includes two key stages: activity-related information extraction (ARIE) and correlation feature extraction based on principal component analysis (CFE-PCA). The ARIE stage is able to effectively enhance the activity-dependent features whilst mitigating behavior-unrelated information. The CFE-PCA stage further improves the extracted features by filtering out the residual activity-unrelated data and the residual noise contained in signals from the former stage. The extracted features are then sequenced into the MatNet to create an environment-robust HAR. Experimental results confirm that an architecture trained by the proposed HAR-MN-EF can be directly adapted to a new environment, achieving reliable sensing accuracies without requiring additional effort.

Highly accurate localization is important for wire-less communications. In this paper, we propose a new tensor-based positioning method for 5G wideband mmWave massive MIMO systems. We first develop an extended multidimensional interpolation (E-MI)-based method as the preprocessing step to suppress the frequency-dependence of the array steering vectors. By using this method, the data across the whole frequency band can be processed jointly, and the high temporal resolution offered by wideband mmWave signals can be exploited. Then, we propose a parameter decoupling (PD)-based tensor multiparameter estimation algorithm. This algorithm can suppress the noises in all of temporal, spatial and frequency domains, and thus all the parameters can be precisely estimated. A simplified perturbation term (S-PT)-based method is also presented to match the estimated parameters at low complexity. Based on the quasi-optical property of mmWave signals, we propose a novel method to compute the 3D coordinates of the target. Simulation results demonstrate the effectiveness of the proposed positioning method in the end.

The sixth generation (6G) mobile systems will enable high mobility applications in both space and ground based networks. In this paper, we investigate low-complexity equalization and adaptive transmission schemes to combat fast fading channels due to high mobility. We first derive signal and channel models in fast fading channels, which allow low complexity minimum mean square error (MMSE) equalization. We then analyze the output signal-to-noise ratio (SNR) using eigenvalue decomposition for a generalized modulation representation. Assuming the channel state information (CSI) is known at the transmitter, we propose an adaptive transmission technique which utilizes the CSI to precode data symbols in order to improve the output SNR at the receiver. Simulation results show that the adaptive transmission scheme effectively improves the MMSE equalization performance in non-line-of-sight channels especially when the transmission signal frame is short.

Radio sensing can be integrated with communication in what the authors call future perceptive mobile networks. Due to the complicated signal structure, it is challenging to estimate sensing parameters such as delay, angle of arrival, and Doppler when joint communication and radar/radio sensing is applied in perceptive mobile networks. Radio sensing with signals compatible with a fifth‐generation (5G) new radio standard using one‐dimension (1D) to 3D compressive sensing (CS) techniques under 5G channel conditions is studied. In the case of 1D–3D CS techniques, they formulate the parameter estimation as a sparse signal recovery problem. These algorithms demonstrate respective advantages, but also show shortcomings in dealing with clustered channels. To effectively exploit the cluster structure in multipath channels, they also propose a 2D cluster Kronecker CS algorithm for significantly improved sensing parameter estimation via introducing a prior probability distribution. Simulation results are provided and they focus the respective advantages and disadvantages of these techniques that validate the effectiveness of the proposed algorithms.

Channel estimation is challenging for hybrid millimeter wave (mmWave) large-scale antenna arrays which are promising in 5G/B5G applications. The challenges are associated with angular resolution losses resulting from hybrid front-ends, beam squinting, and susceptibility to the receiver noises. Based on tensor signal processing, this paper presents a novel multi-dimensional approach to channel parameter estimation with large-scale mmWave hybrid uniform circular cylindrical arrays (UCyAs) which are compact in size and immune to mutual coupling but known to suffer from infinite-dimensional array responses and intractability. We design a new resolution-preserving hybrid beamformer and a low-complexity beam squinting suppression method, and reveal the existence of shift-invariance relations in the tensor models of received array signals at the UCyA. Exploiting these relations, we propose a new tensor-based subspace estimation algorithm to suppress the receiver noises in all dimensions (time, frequency, and space). The algorithm can accurately estimate the channel parameters from both coherent and incoherent signals. Corroborated by the Cramér-Rao lower bound (CRLB), simulation results show that the proposed algorithm is able to achieve substantially higher estimation accuracy than existing matrix-based techniques, with a comparable computational complexity.

Angle of Arrival (AoA) estimation with localized hybrid arrays is challenging in millimeter-wave (mmWave) communication systems. Most existing solutions quantize AoAs into limited values with relatively low accuracy. This paper presents a multi-AoA estimation scheme which is capable of estimating multiple AoAs from multiple users with low complexity. Specifically, we design a path filter via combining the received signals for each subarray. Each path filter enables a certain range of AoAs to pass through while suppressing the rest. Then we can use low-complexity cross-correlation operations to obtain continuous AoA estimates. Association of paths to users is further achieved by a follow-up pseudo-random codes based correlation operation. The scheme is first presented for a narrowband system and then extended to wideband with frequency selectivity. We also introduce new metrics and derive the lower bound of mean square error for evaluating the accuracy of AoA estimates, as conventional metrics face difficulties in the presence of multiple closely located AoAs. Extensive simulation results are provided and validate the effectiveness of the proposed multi-AoA estimation scheme.

Motivated to understand the increasingly severe threat of unmanned aerial vehicles (UAVs) to the confidentiality of terrestrial radio links, this paper analyzes the ergodic and E-outage secrecy capacities of the links in the presence of multiple cooperative aerial eavesdroppers flying autonomously in three-dimensional (3D) spaces and exploiting selection combining (SC) or maximal ratio combining (MRC). The “cut-off” density of the eavesdroppers under which the secrecy capacities vanish is identified. By decoupling the analysis of the random trajectories from the random channel fading, closed-form approximations with almost sure convergence to the secrecy capacities are devised. The analysis is extended to study the impact of the oscillator phase noises and finite memories of the aerial eavesdroppers on the secrecy performance of the ground link. Validated by simulations, the cut-off density only depends on the range of the link in the case of SC eavesdropping, while it depends on the flight region of the eavesdroppers in the case of MRC eavesdropping.

Angle-of-arrival (AoA) estimation is a challenging problem for analog antenna arrays. Typical schemes use time-consuming beam scanning, and the resolution is limited to the scanning beam width. In this paper, we propose a virtual-subarray based AoA (ViSA) estimation scheme, which divides an analog array into two virtual subarrays and exploits phase difference between one pair of measurements for AoA estimation. The basic ViSA algorithm can obtain a direct AoA estimate from every two temporal measurements. We propose different subarray constructions which can lead to different accuracy of estimation. We provide closed-form expressions for the statistics of the estimation error. Based on the basic ViSA estimator, we develop two methods to combine multiple pairs of measurements, when they are obtained via sequential and multi-resolution scanning, respectively. Near-optimal estimators are derived for both methods, employing the maximum likelihood principle. Novel techniques are also proposed to address the typical phase ambiguity problem due to the periodic phase function. Simulation results demonstrate that the proposed scheme significantly outperforms existing ones.

The sparse code multiple access (SCMA) is a promising candidate for bandwidth-efficient next generation wireless communications, since it can support more users than the number of resource elements. On the same note, faster-than-Nyquist (FTN) signaling can also be used to improve the spectral efficiency. Hence in this paper, we consider a combined uplink FTN-SCMA system in which the data symbols corresponding to a user are further packed using FTN signaling. As a result, a higher spectral efficiency is achieved at the cost of introducing intentional inter-symbol interference (ISI). To perform joint channel estimation and detection, we design a low complexity iterative receiver based on the factor graph framework. In addition, to reduce the signaling overhead and transmission latency of our SCMA system, we intrinsically amalgamate it with grant-free scheme. Consequently, the active and inactive users should be distinguished. To address this problem, we extend the aforementioned receiver and develop a new algorithm for jointly estimating the channel state information, detecting the user activity and for performs data detection. In order to further reduce the complexity, an energy minimization based approximation is employed for restricting the user state to Gaussian. Finally, a hybrid message passing algorithm is conceived. Our Simulation results show that the FTN-SCMA system relying on the proposed receiver design has a higher throughput than conventional SCMA scheme at a negligible performance loss.

This paper proposes a novel 3D localization method for wideband mmWave massive MIMO systems. A high dimensional linear interpolation (HDLI)-based preprocessing is first proposed to transform the frequency-associated dynamical array response vectors into the common counterparts at the reference frequency. Through this method, the received data in all frequency bands can be processed jointly, and thus the high temporal resolution provided by wideband mmWave systems can be fully exploited for position estimation. To reduce the computational complexity in the process of the parameter estimation, we then present a wideband beamspace (WBS)-based parameter estimation algorithm to estimate the angle and delay in the low-dimensional beamspace. By exploiting the quasi- optical propagation at the mmWave frequencies, a novel positioning scheme is also designed to determine the 3D location of the target. According to our analysis and simulation results, the proposed method is capable of achieving significantly reduced computational complexity, while maintaining high localization accuracy.

Multibeam technology has recently been proposed for joint communication and radio sensing (JCAS) in millimeter wave systems using analog antenna arrays. Generation of the multibeam satisfying both communication and sensing requirements is yet to be developed. In this paper, we develop closed-form solutions for optimizing the coefficient that combines communication and sensing subbeams to generate a multibeam. Our solutions maximize the received signal power for communication, in the cases (1) without constraint on sensing beamforming (BF) waveform, (2) with minimum BF gain constraints on discrete sensing directions, and (3) with a minimum total power constraint on a range of sensing directions. Simulation results are provided and validate the effectiveness of the proposed solutions.

We propose a novel joint quantization scheme for hybrid antenna array systems using the two-phase-shifter (2-PS) structure, where two phase shifters are combined to represent one beamforming weight. Conventional quantization using a single phase shifter for each beamforming weight cannot represent the magnitude. We propose a new codebook design that combines the two codebooks of the two phase shifters in the recently proposed 2-PS structure. We also study the scaling problem of the beamforming vector and propose a low-complexity searching algorithm for finding a near-optimal scalar based on element-wise quantization. The mean squared quantization error and signal-to-noise ratio (SNR) degradation are derived analytically. Simulation results validate the accuracy of the analytical results and the effectiveness of the proposed quantization methods.

Device free WiFi Sensing using channel state information (CSI) has been shown great potentials for human activity recognition (HAR). However, extracting reliable and concise feature signals remains as a challenging problem, especially in a dynamic and complex environment. In this paper, we propose a novel scheme for CSI-based HAR using deep learning network (CH-DLN), with an innovative CSI correlation feature extraction (CCFE) method. The CCFE method pre-processes the signals input to the DLN in two steps. Firstly, it uses a recursive algorithm to reduce non-activity-related information from the signal and hence enhance the activity-dependent signals. Secondly, it computes the correlation over both the time and frequency domain to disclose better signal structure and compress the signal. From such enhanced and compressed signals, we utilize the recurrent neural networking (RNN) to automatically extract deeper features, and then apply the softmax regression algorithm for classifying activities. Through extensive experimental results, our proposed scheme is shown to outperform state-of-the-art methods in recognition accuracy, with much less training time.

© 2019 IEEE A millimeter wave system with commercially available and affordable data conversion devices is presented in this paper for achieving high-speed and low-cost wireless communications. By adopting the proposed dual pulse shaping (DPS) transmission scheme, the system can achieve full Nyquist rate transmission with only half of the sampling rate required by conventional Nyquist pulse shaping. Structures of the DPS transmitter and receiver are described and effective symbol rate equalization techniques suitable for DPS transmission are presented. Simulation results with two sets of practical dual spectral shaping pulses are also provided to compare system performance with the conventional Nyquist pulse shaping system.

In a dense urban area, conventional cellular V2X communications require frequent and heavy resource allocation, which can lead to processing congestion and large delay. In this paper, we propose a semi- persistent resource allocation scheme using the least minimum mean square error (LMMSE) traffic prediction in a two-tier network. The two-tier network architecture includes a central macro base station (MBS) and multiple roadside units (RSU). In the proposed scheme, the MBS pre-allocates persistent resource to RSUs based on predicted traffic, and then allocate dynamic resource upon real-time requests from vehicles through RSUs. We formulate an optimization problem for minimizing the total bandwidth under latency constraints and provide an optimal solution to the problem. Simulation is conducted for both artificially generated and real-world data, and the results validate the effectiveness of the proposed semi- persistent scheme.

Massive MIMO can dramatically improve capacity and spectral efficiency. However, it is not very clear whether it can significantly improve the signal blockage problem that exists in single antenna systems. In this paper, we investigate the impact of the human body on indoor massive MIMO channels, using practically measured channel data for a 32x8 massive MIMO system in a complex office environment. We introduce a parameter of Power Imbalance (PI) indices to estimate the wide-sense none-stationarity in multiple domains and another parameter of Channel Popularity Indices (CPI) to predict the popularity of MIMO channel. We find that in most cases, the presence of the human body still has a non- negligible negative impact. It decreases the ergodic capacity by about 8% and increases the path loss exponent by 1. In average, the ergodic capacity for NLOS channels are 15% higher than that for LOS.

The concept of complementary Nyquist pulse is introduced in this paper. Making use of a half rate Nyquist pulse and its complementary one, a dual pulse shaping transmission scheme is proposed, which achieves full Nyquist rate transmission with only a half of the sampling rate required by conventional Nyquist pulse shaping. This is essential for realizing high-speed digital communication systems with available and affordable data conversion devices. The condition for cross-symbol interference free transmission with the proposed dual pulse shaping is proved in theory, and two classes of ideal complementary Nyquist pulses are formulated assuming raised-cosine pulse shaping. Simulation results are also presented to demonstrate the improved spectral efficiency with dual pulse shaping and compare other system performance against conventional Nyquist pulse shaping.

With balanced system performance, implementation complexity and hardware cost, hybrid antenna array is regarded as an enabling technology for massive multiple-input and multiple-output communication systems in millimeter wave (mmWave) frequencies. Angle-of-arrival (AoA) estimation using a localized hybrid array faces the challenges of the phase ambiguity problem due to its localized nature of array structure and susceptibility to noises. This paper discusses AoA estimation in an mmWave system employing dual-polarized antennas. We propose an enhanced AoA estimation algorithm using a localized hybrid dual-polarized array for a polarized mmWave signal. First, the use of dual-polarized arrays effectively strengthens the calibration of differential signals and resulting signal-to-noise ratio with coherent polarization combining, leading to an enhanced estimate of the phase offset between adjacent subarrays. Second, given the phase offset, an initial AoA estimate can be obtained, which is used to update the phase offset. By employing the updated one, the AoA is re- estimated with improved accuracy. The closed-form mean square error (MSE) lower bounds of AoA estimation are derived and compared with simulated MSEs. The simulation results show that the proposed algorithm in combination with hybrid dual- polarized arrays significantly improves the estimation accuracy compared with the state of the art.

Angle-of-arrival (AoA) estimation is a challenging problem for analog antenna arrays. Typical algorithms use beam scanning and sweeping, which can be time-consuming, and the resolution is limited to the scanning step. In this paper, we propose a virtual-subarray based AoA estimation scheme, which divides an analog array into two virtual subarrays and can obtain a direct AoA estimate from every two temporal measurements. We propose different subarray constructions which lead to different range and accuracy of estimation. We provide detailed beamforming vector designs for these constructions and provide a performance lower bound for the estimator. We also present how to apply the estimator to AoA acquisition and tracking. Simulation results demonstrate that the proposed scheme significantly outperforms existing ones when the signal-to-noise ratio is not very low.

This paper studies the threat that an aerial eavesdropper can pose to terrestrial wireless communications, from an information-theoretic point of view. The achievable ergodic and the average ε-outage secrecy rates with no channel state information at the transmitter (i.e., with no CSIT) are analyzed for a transmitter-receiver pair on the ground, in the presence of an aerial eavesdropper which flies a random trajectory following a smooth turn (ST) mobility model in a three-dimensional (3D) space. The ST mobility model induces a uniform distribution (of the eavesdropper's waypoints) within the considered 3D volume. Closed-form asymptotic approximations of the achievable secrecy rates are derived based on the almost sure convergence and non-trivial mathematical manipulations. Validated by simulations, our analysis is tight and reveals that the ground transmission is particularly vulnerable to aerial eavesdropping which can be carried out in a distance without being noticed. 3D spherical regions are identified, within which the secrecy rates vanish. This sheds useful insights to protect terrestrial wireless networks from aerial eavesdropping.

In this paper, we study the low complexity reception of multiuser signals in uplink millimeter wave (mmWave) communications using a partially connected hybrid antenna array. Exploiting the mmWave channel property, we propose a low-complexity user-directed multiuser receiver with three novel schemes for allocating subarrays to users. This receiver only requires the knowledge of angles-of-arrival (AoAs) for dominating paths and a small amount of equivalent channel information instead of perfect channel state information. For comparison, we also derive a successive interference cancellation-based solution as a performance benchmark. We design two types of reference signals with the channel estimation method to enable efficient and simple estimation for AoA and equivalent baseband channel. Also, we provide analytical results for the performance of the AoA estimation, using the lower bounds of mean square errors in line-of-sight dominated mmWave channels. The simulation results validate that the proposed channel estimation method is effective when employed in combination with a zero-forcing equalizer.

Joint communication and radio sensing (JCAS) in millimeter-wave (mmWave) systems requires the use of a steerable beam. For analog antenna arrays, a single beam is typically used, which limits the sensing area within the direction of the communication. Multibeam technology can overcome this limitation by separately generating package-level direction-varying sensing subbeams and fixed communication subbeams and then combine them coherently. In this paper, we investigate the optimal combination of the two subbeams and the quantization of the beamforming (BF) vector that generates the combined beam. When either the full channel matrix or only the angle of departure (AoD) of the dominating line-of-sight (LOS) path is known at the transmitter, we derive the closed-form expressions for the optimal combining coefficients that maximize the received communication signal power. For the quantization of the BF vector, we focus on the two-phase-shifter array where two phase shifters are used to represent each BF weight. We propose novel joint quantization methods by combining the codebooks of the two phase shifters. The mean squared quantization error is derived for various quantization methods. Extensive simulation results validate the accuracy of the analytical results and the effectiveness of the proposed multibeam optimization and joint quantization methods.

Time-domain wireless channels are generally modeled by Tapped Delay Line (TDL) model and its variants. These models are not effective for channel representation and estimation when the number of multipath taps is large. Compressive sensing (CS) provides a powerful tool for sparse channel modeling and estimation. Most of the research has been focusing on sparse channel estimation, while sparse channel modeling (SCM) is rarely considered for centimetre-wave channels. In this paper, we investigate statistical sparse channel modeling, using both measured and simulated channels over a frequency range of 6 to 8.5 GHz. We first introduce the triple equilibrium principle to explore the trade-off between sparsity, modeling accuracy, and algorithm complexity in SCM, and provide a methodology for characterizing the sparsity of time-domain channels using single-measurement-vector compressive sensing algorithms. Using mainly the selected wavelet dictionary and various CS reconstruction (aka recovery) algorithms, we then present comprehensive statistical sparse channel models, including channel sparsity, magnitude decaying profile, sparse coefficient distribution and atomic index distribution. Connections between the parameters of conventional TDL and sparse channel models are mathematically established. We also propose three methods for generating simulated channels from the developed sparse channel models, which validates their effectiveness.

Channel sparsity is well exploited for channel estimation, but there is very limited work on sparse channel modelling, which studies and characterizes the statistical properties of sparse channel coefficients. In this paper, we study sparse channel modelling using real measured channel data in off-body signal propagation. We propose multi-measurement vector based compressive sensing algorithms for extracting sparse channel coefficients, study the statistical properties of these extracted coefficients, and develop an algorithm for generating simulated channels using the statistical sparse model. The proposed method can be directly applied to other channel measurements, and is very useful for channel simulation and developing advanced sparse channel estimation schemes.

Generalized spatial modulation (GSM) activates 'it out of Nt (1 ≤ 'it <; Nt) available transmit antennas, and information is conveyed through 'it modulated symbols as well as the index of the 'it activated antennas. GSM strikes an attractive tradeoff between spectrum efficiency and energy efficiency. Linear precoding that exploits channel state information at the transmitter enhances the system error performance. For GSM with 'it = 1 (the traditional SM), the existing precoding methods suffer from high computational complexity. On the other hand, GSM precoding for 'it ≥ 2 is not thoroughly investigated in the open literature. In this paper, we develop a unified precoding design for GSM systems, which universally works for all 'it values. Based on the maximum minimum Euclidean distance criterion, we find that the precoding design can be formulated as a large-scale nonconvex quadratically constrained quadratic program problem. Then, we transform this challenging problem into a sequence of unconstrained subproblems by leveraging augmented Lagrangian and dual ascent techniques. These subproblems can be solved in an iterative manner efficiently. Numerical results show that the proposed method can substantially improve the system error performance relative to the GSM without precoding and features extremely fast convergence rate with a very low computational complexity. I'idex Terms-

In this paper, we propose a new caching scheme for a random wireless device-to-device (D2D) network of$n$nodes with local caches, where each node intends to download files from a prefixed library via D2D links. Our proposed caching delivery includes two stages, employing distributed MIMO and hierarchical cooperations, respectively. The distributed MIMO is applied to the first stage between source nodes and neighbors of the destination node. The induced multiplexing gain and diversity gain increase the number of simultaneous transmissions, improving the throughput of the network. The hierarchical cooperations are applied to the second stage to facilitate the transmissions between the destination node and its neighbors. The two stages together exploit spatial degrees of freedom as well as spatial reuse. We develop an uncoded random caching placement strategy to serve this cooperative caching delivery. Analytical results show that the average aggregate throughput of the network scales almost linearly with$n$, with a vanishing outage probability. Furthermore, we derive an explicit expression of the optimal throughput as a function of system parameters, such as pathloss factor under a target outage probability. Analytical and numerical results demonstrate that our proposed scheme outperforms existing ones when the local cache size is limited.

In this paper, we propose a new caching scheme for a random wireless device-to-device (D2D) network of n nodes with local caches, where each node intends to download files from a prefixed library via D2D links. Our proposed caching delivery includes two stages, employing distributed MIMO and hierarchical cooperations respectively. The distributed MIMO is applied to the first stage between source nodes and neighbours of the destination node. The induced multiplexing gain and diversity gain increase the number of simultaneous transmissions, improving the throughput of the network. The hierarchical cooperations are applied to the second stage to facilitate the transmissions between the destination node and its neighbours. The two stages together exploit spatial degrees of freedom as well as spatial reuse. We develop an uncoded random caching placement strategy to serve this cooperative caching delivery. Analytical results show that the average aggregate throughput of the network scales almost linearly with n, with a vanishing outage probability.

In this paper, we investigate linear precoding for spatial modulation (SM) over multiple-input-multiple-output (MIMO) fading channels. With channel state information avail- able at the transmitter, our focus is to maximize the minimum Eu- clidean distance among all candidates of SM symbols. We prove that the precoder design is a large-scale non-convex quadratically constrained quadratic program (QCQP) problem. However, the conventional methods, such as semi- definite relaxation and it- erative concave-convex process, cannot tackle this challenging problem effectively or efficiently. To address this issue, we leverage augmented Lagrangian and dual ascent techniques, and transform the original large-scale non-convex QCQP problem into a sequence of subproblems. These subproblems can be solved in an iterative manner efficiently. Numerical results show that the proposed method can significantly improve the system error performance relative to the SM without precoding, and features extremely fast convergence rate with very low computational complexity.

Due to the enormous needs for signal processing and hardware constraints, the full digital implementation for a large antenna array at mmWave frequencies becomes intractable. Hence, receiver design for MIMO system with massive hybrid array is very demanding, particularly for subarray structure. In this paper, we propose two low- complexity uplink multiuser receiver design schemes (single-beam and multi-beam) under the circumstances of only users' angles of arrival (AOAs) available at base station, which greatly simplify the analog beamforming structure and reduce the complexity of channel estimation especially when the number of antenna elements is considerably large. In the single-beam scheme, subarrays are mapped to different users one by one such that each subarray serves a specific user. In the multibeam scheme, all subarrays use the same analog beamforming and any subarray's beamforming signal potentially points at all users. Digital beamforming is then employed to combine all subarray signals followed by equalization. Simulations are performed to compare the proposed schemes with other schemes that need channel information available at the subarray output. Since only AOAs are required for analog and digital beamformings, the proposed schemes are more suitable for mmWave MIMO system with massive hybrid array in spite of slight sum-rate loss.

In this paper, we develop a framework for an innovative perceptive mobile (i.e. cellular) network that integrates sensing with communication, and supports new applications widely in transportation, surveillance and environmental sensing. Three types of sensing methods implemented in the base-stations are proposed, using either uplink or downlink multiuser communication signals. The required changes to system hardware and major technical challenges are briefly discussed. We also demonstrate the feasibility of estimating sensing parameters via developing a compressive sensing based scheme and providing simulation results to validate its effectiveness.

Beam-steering has great potentials for joint communications and sensing, which is becoming a demanding feature on many emerging platforms such as unmanned aerial vehicles and smart cars. Although beam-steering has been extensively studied for communications and radar sensing respectively, its application in the joint system is not straightforward due to different beamforming requirements by communications and sensing. In this paper, we propose a low-cost system framework which allows seamless operation of communications and sensing, using two small- size steerable analog antenna arrays. We provide system architecture, high-level protocols, detailed signal model, novel beamforming design and advanced 1D compressive sensing algorithms for joint communications and sensing. We also provide preliminary simulation results which validate the effectiveness of the proposed technique in resolving closely located objects.

Beamforming design for millimeter-Wave hybrid array with the subarray structure is very challenging. There is neither known optimal solution that maximizes the sum rate capacity nor near-optimal solution. This paper proposes some low-complexity user-directed analog radio- frequency (RF) beamforming design schemes. The basic idea is to iteratively allocate different subarrays to different users such that users' channel correlation can be efficiently reduced via RF beamforming. Several new but less efficient schemes are also presented to shed light on RF beamforming design, and to serve as comparisons for the user-directed schemes. Simulation results are provided for these proposed schemes, existing ones in the literature and an upper-bound for hybird array with a fully-connected structure. The user-directed schemes demonstrate significantly better sum-rate and BER performance over other schemes, although the gap to the upperbound is still large.

In this paper, we propose a new linear physical- layer network coding (NC) scheme for the fading Y- channel, assuming that the channel state information (CSI) is not available at transmitters. In this scheme, each user transmits one message to a relay and intends to obtain both other two users' messages. Based on the receiver- side CSI, the relay determines two NC generator vectors for linear network coding, and reconstructs the associated two linear NC codewords. For the case when there is one time- slot in the uplink phase, we present an explicit solution for the generator vectors that minimizes the error probability at a high SNR, and a lower bound of the error performance of the proposed scheme using our optimized generator vectors. Extending to multiple time-slots in the uplink, two typical scenarios are discussed. Numerical results show that the proposed scheme significantly outperforms existing schemes, and match well with our analytical results.

Frequency-dependent I/Q imbalance estimation and compensation are of significant practical importance to low-cost wideband systems with an I/Q modulation architecture. To enable multichannel transmission without inter-channel interference, transmitter I/Q imbalance must be pre-compensated to meet stringent transmit mask requirement. In this paper, a simple frequency domain joint transmitter and receiver I/Q imbalance estimation method is proposed for self-calibration of such wideband multichannel transceivers. Using two frequency domain training signals and a phase shifter inserted in the transceiver local loopback channel, the transmitter and receiver I/Q imbalances can be estimated separately. The estimation errors are also analyzed and the mean square error lower bounds are derived. Simulation results are in good agreement with analytical ones. Compared with existing methods, the proposed technique demonstrates better image rejection performance and quicker adaptation to parameter changes, making it more applicable to many wireless systems, especially the multichannel microwave backhaul, for achieving high data rates with high-order modulation and wide transmission bandwidth.

In this paper, we propose a new linear physical-layer network coding scheme for spatial-multiplexing MIMO two-way relay channels (TWRCs), where the transmitters lack the channel state information (CSI). In the uplink, each user transmits independent signal streams from its multiple antennas, and the two users transmit simultaneously. The relay selects a finite-field coefficient matrix based on its receiver CSI. It then jointly computes the associated linear combinations of all messages. In the downlink, the resultant message-combinations are forwarded to the users, which recover their desired messages. We derive an asymptotic expression for the coefficient matrix used by the relay that minimizes the error probability. We show by numerical results that for Rayleigh fading channel, the proposed linear PNC scheme outperforms existing schemes by up to 4.5 dB and that the proposed scheme approaches an interference-free lower bound at a sufficiently high SNR.

A distributed multiple-input-multiple-output (MIMO) system consists of M users served by L distributed base stations (BSs), where the BSs are connected to a central unit (CU) via L independent backhaul (BH) links. In this paper, we consider the design of an uplink distributed MIMO system where 1) the channel state information is not available at the transmitters and 2) the BH links are rate constrained. We propose a new linear network coding (LNC)-based protocol: the M users transmit simultaneously. Each BS generates N linear functions of the M users' messages, based on a preassigned LNC coefficient matrix. The CU collects N · L linear functions from the L BSs and recovers all M users' messages by solving these linear functions. The decoding becomes successful if the linear functions has full rank M and fails if the linear functions are rank deficient. We derive the preassigned LNC coefficient matrix that minimizes the probability of rank deficiency. We then analyze the outage probability (OP) of the proposed scheme over a Rayleigh fading channel. We analytically show that as long as the BH rate is greater than the individual data rate of one user, the OP of the proposed scheme decays like 1/SNRLat high SNR. This is in contrast to the existing scheme whose OP decays like 1/SNR. As the BH rate constraint approaches M times the data rate of one user, the performance of the proposed scheme is 10/L log10(L!) dB away from that of the full MIMO scenario at high SNR. We also develop a structured way to efficiently construct the preassigned LNC coefficient matrix that yields the optimized OP performance. Numerical results show that the proposed scheme has significantly improved performance over existing schemes.

In this paper, we propose a new linear vector physical-layer network coding (NC) scheme for spatial multiplexing multiple-input multiple-output (MIMO) two-way relay channel (TWRC) where the channel state information (CSI) is not available at the transmitters. In this scheme, each user transmits M independent quadrature amplitude modulation signal streams respectively from its M antennas to the relay. Based on the receiver-side CSI, the relay determines a NC generator matrix for linear vector network coding, and reconstructs the associated M linear combinations of all messages. We present an explicit solution for the generator matrix that minimizes the error probability at a high SNR, as well as an efficient algorithm to find the optimized solution. We propose a novel typical error event analysis that exploits a new characterization of the deep fade events for the TWRC. We derive a new closed-form expression for the average error probability of the proposed scheme over a Rayleigh fading MIMO TWRC. Our analysis shows that the proposed scheme achieves the optimal error rate performance at a high SNR. Numerical results show that the proposed scheme significantly outperforms existing schemes, and match well with our analytical results.

This paper proposes a comprehensive solution to reduce peak-to-average power ratio (PAPR), cancel out-of-band emission (OOBE), and alleviate the impact of phase noise for precoded orthogonal frequency-division multiplexing (OFDM) systems. Making use of the cancellation and pilot symbols and subcarriers in both data and frequency domains, this solution integrates a number of novel schemes to overcome OFDM's inherent drawbacks and mitigate practical impairments for high speed wireless communications. These schemes include a layered precoding structure, a low complexity OOBE cancellation using both data domain cancellation symbols and frequency domain cancellation subcarriers, and an effective phase noise compensation using data domain pilot symbols. The improved overall system performance of the proposed solution is verified by simulation results.

This paper investigates discrete Fourier Transform (DFT)-precoded OFDM systems (DFT-OFDM) for intensity modulated direct-detection optical communications. Such scheme can be used to support both single and multiple user communications. Two DFT-OFDM systems using real modulation and DC-biasing are proposed. Performance analysis based on signal-to-interference-and-noise ratio (SINR) is provided. Insights are provided for designing choices between different modulations, and between using DC-biasing and asymmetrical clipping approaches. Simulation results show that the proposed schemes largely outperform systems using asymmetrical clipping, particularly in the case of using higher order modulations.

In the cooperative multiple-input/multiple-output (MIMO), multiple access point (AP)-user links form a cluster to increase achievable throughput by cooperatively mitigating inter-user interference within the cluster. In this paper, we propose a clustered cooperative MIMO endowing a constraint on the cluster size such that the clustered MIMO can be implemented as a distributed version of downlink multiuser MIMO in existing standards with minimal modifications. New greedy algorithm and coalition formation algorithm are proposed using matching theory for establishing clusters and allocating frequencies. Simulation results shows that the proposed algorithms achieve almost 20% higher throughput than the fixed cell planning scheme, with very low searching complexity.

This paper explores an idea of demand-supply balance for smart grids in which consumers are expected to play a significant role. The main objective is to motivate the consumer, by maximizing their benefit both as a seller and a buyer, to trade their surplus energy with the grid so as to balance the demand at the peak hour. To that end, a Stackelberg game is proposed to capture the interactions between the grid and consumers, and it is shown analytically that optimal energy trading parameters that maximize customers' utilities are obtained at the solution of the game. A novel distributed algorithm is proposed to reach the optimal solution of the game, and numerical examples are used to assess the properties and effectiveness of the proposed approach.

Based on the sequential sample rate conversion (SRC) structure using B-spline interpolation for orthogonal frequency division multiplexing (OFDM) based software defined radios, a parallel processing SRC structure is proposed in this paper to achieve high speed data transmission for multiband OFDM systems. By deriving an impulse response matrix from the sequential SRC structure, the state vectors of the SRC structure can be calculated from a block of input samples with less complexity than conventional Farrow structure. Real-time SRC implementation combined with local feedback and stuffing is also presented. Performance in terms of state buffer pointer offset caused by clock variation and finite precision in digital hardware is analyzed to provide guidance for practical system design such as determining clock stability and word-length requirements.

Single carrier systems with frequency domain equalization (SC-FDE) have been recently proposed for optical wireless systems as alternatives to optical orthogonal frequency division multiplexing (OFDM) to reduce the peak-to-average power ratio (PAPR) of the transmitted signal and improve the system performance. However, these SC-FDE systems have either higher complexity or lower spectrum efficiency. In this paper a low complexity SC-FDE system based on on-off-keying (OOK) modulation is proposed. Theoretical bit-error-rate (BER) analysis is provided based on minimum mean square error (MMSE) equalization for the proposed system and typical optical SC-FDE and OFDM systems. Both analytical and numerical results show that the proposed system significantly outperforms existing SC-FDE and OFDM systems in terms of PAPR, BER and implementation complexity.

Generating and feeding back beamforming vector are very challenging tasks in distributed transmit beamforming (DTB) systems. Phases of DTB nodes may vary rapidly due to residual carrier frequency offset and hence frequent updating of beamforming vector is required. Existing iterative training schemes that only require one bit training and one or two bits feedback in each iteration have low convergence speed and are not robust in noisy channels due to the lack of structure in the training sequences. In this paper, we consider a DTB system where the number of training bits N sent from each node is no more than the number of source nodes M, and propose a concatenated training scheme based on optimal design of training sequences in this case. For spatially uncorrelated channels, we show that the concatenated training scheme can optimally combine the N latest training signals and achieve beamforming gain approximately proportional to N/M. An algorithm which can adaptively determine the length of the combination in time-varying channels is also proposed. Simulation results demonstrate the proposed scheme can work efficiently even at very low signal-to-noise ratio, with the total feedback bits much less than those required in the iterative schemes.

This letter extends our previous work on layered inverse Fast Fourier Transform (IFFT) structure to a multistage layered IFFT structure where data symbols can input at different stages of the IFFT. We first show that part of the IFFT in the transmitter of an OFDM system can be shifted to the receiver, while a conventional one-tap frequency-domain equalizer is still applicable. We then propose two IFFT split schemes based on decimation-in-time and decimation-in-frequency IFFT algorithms to enable interference-free symbol recovery with simple linear equalizers. Applications of the proposed schemes in multiple access communications are investigated. Simulation results demonstrate the effectiveness of the proposed schemes in improving bit-error-rate performance.

In, we introduced a novel layered inverse Fast Fourier Transform (IFFT) structure based on the Divide-andConquer approach for computing the IFFT. Based on the structure we proposed asymmetric OFDM systems which bridge OFDM and single carrier systems. Its multiuser access version, Quadrature OFDMA, is developed in. In the presented form, the basic transceiver of asymmetric OFDM is mathematically similar to the vector OFDM system. We regret for not citing the vector OFDM and related work in our papers. As a response to, we argue that there are actually significant differences between our work and the vector OFDM work.

Channel matrix inversion, which requires significant hardware resource and computational power, is a very challenging problem in MIMO-OFDM systems. Casting the frequency-domain channel matrix into a polynomial matrix, interpolation-based matrix inversion provides a promising solution to this problem. In this paper, we propose novel algorithms for interpolation based matrix inversion, which require little prior information of the channel matrix and enable the use of simple low-complexity interpolators such as spline and low pass filter interpolators. By invoking the central limit theorem, we show that a Gaussian approximation function well characterizes the power of the polynomial coefficients. Some low-complexity and efficient schemes are then proposed to estimate the parameters of the Gaussian function. With these estimated parameters, we introduce phase shifted interpolation and propose two algorithms which can achieve good interpolation accuracy using general low-complexity interpolators. Simulation results show that up to 85% complexity saving can be achieved with small performance degradation.

Distributed transmit beamforming (DTB) can significantly boost the signal-to-noise ratio (SNR) of a wireless communication system. To realize the benefits of DTB, generating and feeding back beamforming vector are very challenging tasks. Existing schemes have either enormous overhead or weak robustness in noisy channels. In this paper, we investigate the design of training sequences and beamforming vector estimators in DTB systems. We consider an under-determined case, where the length of training sequence N sent from each node is smaller than the number of source nodes M. We derive the optimal estimation of the beamforming vector that maximizes the beamforming gain and show that it can be well approximated as the linear minimum mean square error (LMMSE) estimator. Based on the LMMSE estimator, we investigate the optimal design of training sequences and propose efficient DTB schemes. We analytically show that these schemes can achieve approximately N times increased SNR in uncorrelated channels, and even higher gain in correlated ones. We also propose a concatenated training scheme which optimally combines the training signals over multiple frames to obtain the beamforming vector. Simulation results demonstrate that the proposed DTB schemes can yield significant gains even at very low SNRs, with total feedback bits much less than those required in the existing schemes.

In this paper an efficient energy curtailment scheme is studied, which enables the power users of a smart grid network to decide on the reduction in energy supplied to them in the event of a power outage in the system. Considering the advantages of a two-way communications infrastructure for any future smart grid, a non-cooperative generalized Nash game is proposed where the players are users of power in the network. They adopt a strategy to choose the amount of reduction in energy supplied to them based on their energy requirements so as to minimize the total cost incurred to the system due to the power outage (i.e., social optimality). The game is modeled as a variational inequality problem, and it is shown that the socially optimum solution is obtained at the variational equilibrium of the energy curtailment game. An algorithm that enables the users to efficiently reach this equilibrium is proposed. Simulation results show that the proposed game yields an improvement of about 15% on average, in terms of average total cost reduction, compared to a standard equal power curtailment scheme.

A low-complexity and efficient sidelobe suppression with orthogonal projection (SSOP) scheme is proposed in [1] for OFDM systems. This paper provides comprehensive performance analysis for the zero-forcing receiver for the SSOP scheme. Via rigorous proof, we show the independence of the orthogonal projection matrix on the ordering of the suppression distances, and the monotonicity of the SNR with the suppression distance and the number of reserved subcarriers. We also characterized the SNR degradation of the single-side and double-side suppression schemes analytically. These analytical results match with the numerical results well.

Channel matrix inversion, which requires significant hardware resource and computational power, is a very challenging problem in MIMO-OFDM systems. Casting the frequency-domain channel matrix into a polynomial matrix, interpolation-based matrix inversion provides a promising solution to this problem. In this paper, by showing that the polynomial coefficients can be well approximated by a Gaussian function, we propose an efficient algorithm, which relaxes the requirement for knowing the maximum multipath delay spread and enables the use of simple low-complexity interpolators by introducing a phase shift term to the signal to be interpolated. Simulation results show that significant complexity saving can be achieved with little equalization performance degradation.

Channel prediction, which predicts a future channel gain based on current and past observations, is very useful for power control and resource optimization in wireless communication systems. However, a low-complexity predictor with trustworthy prediction accuracy is yet to be developed. This paper proposes a linear predictor and two linear Markov predictors, which achieve a good balance between complexity and accuracy.

Transmission of signals using multiple antennas can significantly improve the energy efficiency of a wireless network, and the proper alignment of the transmitted signals' phases at the receiver is one of the key factors so this efficiency improvement can be realized. In a time-varying channel, due to the relative motion between the transmitters and the receiver, the development of a scheme that guarantees such alignment is very challenging. In this paper, considering a distributed transmit beamforming scenario, an algorithm to achieve such phase alignment of signals in a time-varying channel is proposed. A simple formula is derived, which can be adopted by each transmitter to compute its beamforming weight's phase, and it is shown that the use of this perturbation results in a significant improvement in terms of speed of convergence at the receiver. With simulation it is shown that, using the proposed scheme, the transmitted signals' phase aligned at the receiver 33% faster than the one-bit feedback scheme. The average theoretical bit error rate is achieved 50% faster relative to the one-bit feedback scheme with on average 18% less number of antennas at the transmitter.

This paper proposes arbitrary ratio sample rate conversion (SRC) architectures and a simpler B-spline interpolation algorithm for orthogonal frequency division multiplexing (OFDM) based software defined radios (SDRs) with multiband and multi-channel capabilities. Different from conventional standalone digital front-end designs for SDRs, the proposed SRC architectures combine the B-spline interpolation with OFDM modulation and equalization for OFDM transmitter and receiver respectively. With this combined design, the passband droop introduced by the B-spline interpolation can be more efficiently compensated using frequency-domain pre-distortion, instead of conventional time-domain pre-filtering, and hence an overall system complexity reduction is achieved. A novel multi-period B-spline interpolation and re-sampling structure is then constructed, and an interpolation algorithm with lower implementation complexity than that of the conventional Farrow structure is further developed. The SRC performance is also analysed by deriving the signal-to-peak distortion ratio formulas which can be used as design tools for determining the required orders of B-splines in the OFDM transmitter and receiver respectively. Finally, SRC examples used in a high-speed multiband multi-channel microwave backhaul system are given and compared with conventional polyphase filterbank interpolation to demonstrate the practicality and performance of the proposed SRC architectures and interpolation algorithm.

To alleviate the high peak-to-average power ratio (PAPR), high complexity in user terminal and sensitivity to carrier frequency offset (CFO) problems in current orthogonal frequency division multiple access (OFDMA) systems, a Quadrature OFDM (Q-OFDMA) system has been recently proposed in the single-input single-output environment. In this paper we study the realization of multi-input multi-output (MIMO) diversity- and multiplexing- oriented methods for Q-OFDMA systems. An Alamouti-like space-time block code (STBC) and simple detection for spatial multiplexing (SM) for Q-OFDMA systems are constructed, both zero forcing (ZF) and minimum mean square error (MMSE) equalizers are investigated. The proposed STBC is a full diversity scheme, which encodes in intermediate domain and decodes in frequency domain. Analytical and empirical results demonstrate that the Q-OFDMA systems can be implemented flexibly and efficiently in a MIMO framework, and the proposed scheme can be easily applied in OFDMA and Single-Carrier Frequency Division Multiple Access (SC-FDMA) by adjusting the parameters of Q-OFDMA.

Sidelobe suppression, or out-of-band emission reduction, in multicarrier systems is conventionally achieved via time-domain windowing which is spectrum inefficient. Although some sidelobe cancellation and signal predistortion techniques have been proposed for spectrum shaping, they are generally not well balanced between complexity and suppression performance. In this paper, an efficient and low-complexity sidelobe suppression with orthogonal projection (SSOP) scheme is proposed. The SSOP scheme uses an orthogonal projection matrix for sidelobe suppression, and adopts as few as one reserved subcarrier for recovering the distorted signal in the receiver. Unlike most known approaches, the SSOP scheme requires multiplications as few as the number of subcarriers in the band, and enables straightforward selection of parameters. Analytical and simulation results show that more than 50dB sidelobe suppression can be readily achieved with only a slight degradation in receiver performance.

Two novel differential normalization factors, depending on the severity of carrier frequency offset, are proposed for autocorrelation based coarse timing scheme. Compared with the conventional normalization factor based on signal energy, they improve the robustness of the timing metric to signal-to-noise ratio (SNR), improve the mainlobe sharpness of the timing metric and reduce both missed detection and false alarm probabilities.

Coexistence of multiple wireless body area networks (WBAN) is a very challenging problem because piconets move frequently and each piconet can have hundreds of sensors. The IEEE 802.15.6 task group is developing a standard based on superframe-level frequency-hopping scheme, which is a good solution to the piconet coexistence problem without requiring coordination between piconets. However, its spectrum efficiency is very low when only a single piconet is operating. In this paper we propose a superframe-level time-hopping scheme with variable contention access period. Compared to the frequency-hopping scheme, the proposed scheme has similar interference mitigation capability, but can achieve significant improvement in energy consumption, latency and spectrum efficiency.

This paper investigates the impact of carrier frequency offset (CFO) on Single Carrier wireless communication systems with Frequency Domain Equalization (SC-FDE). We show that CFO in SC-FDE systems causes irrecoverable channel estimation error, which leads to inter-symbol-interference (ISI). The impact of CFO on SC-FDE and OFDM is compared in the presence of CFO and channel estimation errors. Closed form expressions of signal to interference and noise ratio (SINR) are derived for both systems, and verified by simulation results. We find that when channel estimation errors are considered, SC-FDE is similarly or even more sensitive to CFO, compared to OFDM. In particular, in SC-FDE systems, CFO mainly deteriorates the system performance via degrading the channel estimation. Both analytical and simulation results highlight the importance of accurate CFO estimation in SC-FDE systems.

Coexistence of multiple wireless body area networks (WBAN) is a very challenging problem because each piconet can have a large number of sensors and their movement is unpredictable. Moreover, suitable global coordination schemes do not exist as there is no natural choice of coordinator between piconets. Adaptive schemes that work well with low-occupancy channels, such as listen before transmit, are not a wise global solution because of the potential for high levels of traffic in any one area. In this paper we investigate the performance of three classic multiple-access schemes - namely TDMA, FDMA and CDMA - for (inter-network) piconet coexistence. We first consider a theoretical analysis of these schemes and then simulate each scheme using real-world interference measurements. It is found that co-channel interference could significantly degrade system performance if left unchecked, and that TDMA and FDMA are better choices than CDMA in terms of co-channel interference mitigation.

We propose an efficient and low-complexity scheme for estimating and compensating clipping noise in OFDMA systems. Conventional clipping noise estimation schemes, which need all demodulated data symbols, may become infeasible in OFDMA systems where a specific user may only know his own modulation scheme. The proposed scheme first uses equalized output to identify a limited number of candidate clips, and then exploits the information on known subcarriers to reconstruct clipped signal. Simulation results show that the proposed scheme can significantly improve the system performance.

A characterization of the dynamic narrowband on- body to off-body area channel is presented based on realtime measurements of the time domain channel response at carrier frequencies near the 900 MHz and 2400 MHz Industrial, Scientific and Medical (ISM) bands. A statistical characterization is presented of received signal amplitude when the subject's body is standing and walking, transmitted from the body to a receiver, Rx, off the body, with various orientations of the subject's body with respect to the receiver, and various distances from the receiver. Two locations of the transmitter, Tx, on the human body are considered. The Lognormal distribution provides a good fitting model with and without movement. Further, the stability is characterized based on a measure of channel response variance, which is called here the channel variation factor and can characterize the channel coherence time. The on-body to off-body area channel is determined to be generally stable over a period of 25 ms, but the amount of stability is found to be dependent both on movement, Tx location on-body, and carrier frequency.

We propose a low complexity noncoherent receiver operating at twice the symbol-rate for systems where each data symbol consists of multiple frames/chips. The receiver does not require explicit timing and channel estimation. It implements simple delayed-autocorrelation, followed by sampling at twice the symbol-rate. Simulation results show the receiver achieves performance close to a conventional one with perfect timing.

To alleviate the high peak-to-average power ratio (PAPR), high complexity in user terminal and sensitivity to carrier frequency offset (CFO) problems in current OFDMA systems, a Quadrature OFDM (Q-OFDMA) system has been recently proposed in the single-input single-output environment. In this paper we investigate the realization of multi-input multioutput (MIMO) diversity- and capacity- oriented methods in Q-OFDMA systems. We construct an Alamouti-like spacetime block code (STBC) for Q-OFDMA signal symbols, and investigate the ZF and MMSE equalizers to realize the simple detection for spatial multiplexing (SM) Q-OFDMA systems.

Network-to-network interference is a challenging problem in wireless body area networks which move frequently and operate in a non-coordinated way. In this paper, we characterize interference based on field measurements. We examine the correlation and independence between the desired (signal) and interference channels. The results show that over long periods, the two channels are statistically un-correlated. Over a short periods, the channels may become correlated, particularly when the channel variation is small.

In current OFDMA systems three major problems arise due to the large number of subcarriers, including high peak to average power ratio (PAPR), sensitivity to carrier frequency offset (CFO), and high complexity in users terminals. In this paper, based on an innovative concept of layered FFT structure, we propose novel quadrature OFDMA (Q-OFDMA) systems which can overcome these problems. In particular, the proposed systems can achieve the same guard-interval overhead and same bandwidth occupation to conventional OFDMA systems, while with reduced PAPR and improved CFO robustness and frequency diversity. Q-OFDMA systems also promise low complexity in downlink receivers. Parameter configuration is investigated for both predefined and adaptive users data rates. Theoretical comparison of bit error rate (BER) performance between Q-OFDMA and OFDMA is conducted, and validated by simulation results. It is shown that Q-OFDMA systems could achieve better performance than OFDMA when signal to noise ratio (SNR) is above a threshold depending on the channel condition, and advanced equalizers, such as minimum mean square error equalizer, can significantly decrease this threshold due to the large frequency diversity in Q-OFDMA systems.

To alleviate the high peak-to-average power ratio (PAPR) and sensitivity to carrier frequency offset (CFO) problems, an asymmetric OFDM (A-OFDM) system has been recently proposed. In this paper, the theoretical bit error rate (BER) analysis of the A-OFDM systems is presented. Various modulation methods, i.e., BPSK and M-ray QAM, and equalization techniques, i.e., zero forcing (ZF) and minimum mean square error (MMSE) criteria are considered. Our analysis shows that the BER performance of the A-OFDM system bridges that of conventional OFDM and single carrier systems. Certain advanced equalizers, such as MMSE equalizer, are found to significantly improve the BER performance of the A-OFDM systems. The analysis approach is generally applicable to most modulation schemes and can be extended to any preceded OFDM systems.

We present BEND, a MAC layer solution to practical network coding in multi-hop wireless networks. It is the first exploration of the broadcasting nature of wireless channels to proactively capture more coding opportunities. In BEND, any node can code and forward a packet even when the node is not the intended MAC receiver of the packet, if the node believes that in doing so it can lead the packet to its ultimate destination. Essentially, BEND considers the union of all interface queue contents at the nodes within a neighborhood, i.e. a "neighborhood coding repository", whereas traditional mixing methods only process "individual coding repositories" at separate nodes.

Quadrature OFDMA (Q-OFDMA) systems have been recently proposed to reduce the complexity and peak-to-average power ratio (PAPR), and improve carrier frequency offset (CFO) robustness for OFDMA systems. However, Q-OFDMA receiver obtains frequency diversity at the cost of noise enhancement. This paper proposes an iterative (turbo) equalization in conjunction with channel estimation for Q-OFDMA systems to mitigate the noise enhancement effect and improve the BER performance. In the proposed scheme, the channel estimation technique makes use of both training symbols and soft coded data information to suppress the inter-symbol interference (ISI) caused by channel estimation errors in Q-OFDMA systems. Simulation results show that performance improvement can be achieved with the proposed algorithms.

In precoded OFDM systems, zero-forcing (ZF) equalizers suffer from noise enhancement and error spreading problems. A minimum mean square error (MMSE) equalizer can mitigate the problems and improve system performance, however, it has relatively higher complexity and is sensitive to the estimation accuracy of noise variance. In this paper, based on the principle of reduced-rank signal processing, we propose reduced-rank equalizers for precoded OFDM systems. These equalizers can achieve performance close to MMSE equalizers while with complexity similar to ZF equalizers.

Frequency domain equalization (FDE) is an attractive solution for wireless broadband transmission because of its strong capability in handling multipath environment. However, frequency domain channel estimation suffers from the inter-carrier interference(ICI) caused by carrier frequency offset (CFO). In this paper, we proposed new training sequence structure for channel estimation to reduce the estimation errors caused by residual CFO. The proposed new training sequence structure can be easily obtained from any existing channel training sequences without introducing significant changes to their original property. Simulation results show that, this scheme can efficiently reduce the channel estimation errors in FDE system, and improve the system performance.

In current OFDMA systems, three major problems arise due to the large number of subcarriers, including high peak to average power ratio (PAPR), sensitivity to carrier frequency offset (CFO), and high complexity in user terminal. In this paper, based on an innovative concept ofLayeredFFTstructure, we propose novel quadrature OFDMA system which can overcome these problems. In particular, the proposed system can achieve same guard-interval overhead and same bandwidth occupation to conventional OFDMA schemes, while with significantly reduced complexity and power consumption in user terminals. Simulation results show that the proposed quadrature OFDMA system can achieve similar and even better performance than conventional OFDMA systems, particularly when a minimum mean square error (MMSE) equalizer is applied.

One disadvantage with current OFDM systems is, the system complexity associated with FFT module, equaliser, etc. is almost fixed and varies little with information bit rate. In this paper, based on an innovative "layered" concept in FFT algorithms, we propose a novel OFDM system whose complexity is adaptive to the desired bit rate and system performance by exploiting transmitter diversity. Different diversity options in the transmitter and receiver are discussed, and verified in simulations.

In this paper, we present a modified version of the maximum shortening signal to noise ratio (MSSNR) algorithm for channel shortening in a time hopping (TH) pulse position modulated (PPM) ultra wideband (UWB) communication system. The proposed algorithm introduces two additional UWB channel related parameters in the optimization problem along with the conventional energy criterion. This modification significantly improve the performance of the conventional MSSNR algorithm and enables it to handle the extreme nature of channel shortening needed in UWB systems. We also derive a lower bound for bit error rate (BER) as a comparison bench mark. The proposed algorithm does not need any training or channel estimation and outperforms the conventional MSSNR algorithm in terms of different comparative parameters.

In multiband OFDM (MB-OFDM) systems, frequency hopping (FH) is proposed to achieve multiple access, mitigate interference and extend the range of communications. The introduction of frequency hopping significantly increases the complexity of the transceiver design, both in radio frontend (RF) part and digital baseband side. In this paper, by exploiting the IF-sampling principle, we propose a novel receiver architecture, which typically consists of a mixer with local input of a fixed down-converting frequency, followed by some filter banks. This solution can ease the development of frequency synthesizer greatly, reduce the system complexity and cost significantly with little performance degradation.

It is well known that Orthogonal Frequency Division Multiplexing (OFDM) systems are sensitive to carrier frequency offset (CFO), hence frequency offset estimation and compensation is critical in OFDM systems. The performance of general CFO estimation methods will degrade in the presence of hardware distortions, for example, I/Q mismatch. With higher carrier frequencies, both CFO and I/Q mismatch will increase and the performance of CFO estimation will degrade significantly. CFO estimation robust to I/Q mismatch becomes particularly important today because of the demand of broadband wireless communication at higher carrier frequencies. In this paper, we propose a low complexity CFO estimation algorithm which is robust to a large range of I/Q mismatch. The proposed estimator can achieve similar performance with the general CFO estimation method when smaller I/Q mismatch is present while improve the system performance significantly when a larger mismatch is present.

In this paper, we present a novel channel shortening algorithm developed in particular for ultra wideband (UWB) communication systems. This algorithm makes use of exponentially decaying characteristics of the UWB channel models. It optimizes the channel decaying factor within a desired temporal window in the effective channel while keeping the channel decaying factor constrained elsewhere. The presence of the proposed channel shortening equalizer before rake reception greatly simplifies the rake receiver architecture by significantly reducing the number of channel taps. We compare its performance with all, selective and partial rakes in terms of captured channel energy, rake combining delay and bit error rate (BER). Simulation results show that the proposed algorithm improves the performance of the Rake receiver in dense multipath UWB channels and considerably reduces its structure complexity.

In this letter, we first extend the convolution theory of discrete Fourier transform (DFT) and introduce a structure of layered fast Fourier transform (FFT). Based on this framework, we propose novel asymmetric orthogonal frequency-division multiplexing (OFDM) systems that bridge general OFDM and single carrier systems. Adaptive to the capability of the transceiver, asymmetric OFDM systems provide significant flexibility in system design and operation. We show how effects of noise enhancement and frequency diversity counteract each other in asymmetric OFDM systems. Performance comparison with general OFDM and single carrier systems is also given.

Both multiband orthogonal frequency division multiplexing (MB-OFDM) ultra wideband (UWB) technology and multiple-input multiple output (MIMO)-OFDM technology have been proposed for future wireless communications systems, supporting data-rates in excess of 100 Mbps. These two technologies use two different mechanisms to boost the data-rate. In this paper we analyse the relative advantage, performances and implementation issues of MIMO-OFDM based next generation wireless LAN systems (IEEE 802.11n) and MB-OFDM UWB based next generation wireless PAN systems (OFDM-UWB). In this comparison, we will see that the distinctive differences between OFDM-UWB and IEEE 802.11n technologies lead to significant variations in the performances and different hardware requirements

This paper investigates the spatial-frequency channel characterization of ultra-wideband (UWB) wireless communication systems. First, a novel frequency dependent UWB channel model is constructed based on the theory of electromagnetic diffraction mechanism, which causes the field strength to vary with the frequency in each multipath. Then, we build a space-frequency model, which includes spatial characteristics such as angular power spectrum, and physical sampling points in space. The space-frequency model has two special cases (i) discrete multipath model, and (ii) cluster model, which can be readily used to generate channel data for any arbitrary set of sensor locations. The reconstruction results from channel measurements show the accurateness of the novel frequency dependent model, with reconstruction error decreasing by 40%, compared to the traditional Turin model

In this paper, performance and parameter optimization of RAKE reception for time-hopping ultra wideband (TH-UWB) systems is investigated when interchip interference (ICI) is taken into consideration. For a TH-UWB system, ICI is closely related to the relationship between the number of chips in a frame (N/sub c/), and the period of the TH code (N/sub f/). In a fixed data-rate case, larger N/sub f/ implies higher transmitted symbol signal-to-noise ratio (SNR) and larger ICI in RAKE fingers. So there is a tradeoff between N/sub c/ and N/sub f/ to optimize the RAKE performance. In this paper, two models are suggested to describe the ICI, and this tradeoff is investigated in the single user case based on the traditional Gaussian approximation method and other flexible methods. Given the lack of explicit knowledge of the relationship among N/sub f/, N/sub c/ and the interference, a rule of thumb is proposed to configure N/sub f/ and N/sub c/.

In this paper, we present the Cramer-Rao lower bounds (CKLBs) for the time delay estimation of UWB signals which could be tight lower bounds for the theoretical performance limits of UWB synchronizers. The CRLBs are investigated for both single pulse systems and time hopping systems in AWGN and multipath channels. Insights are given into the relationship between CRLBs for different Gaussian monocycles. It is found that larger number of multipath signals implies higher CRLBs and inferior performance of synchronizers, and multipath interference on CRLBs can not be eliminated completely except in very special cases. As every estimate of time delay could not be perfect, the least influence of the synchronization error on the performance of receivers is quantified.

This paper investigates the performance of ultra-wideband (UWB) correlator receivers for Gaussian monocycles under the condition of equal mean power and provides constructive reference to the selection of pulses. Several channel situations are examined including ideal single user AWGN channel, non-ideal synchronous, multipath fading and multiple access interference. Both numerical and analytical techniques show that the shapes of pulses have notable impact on the performance of correlator receivers, especially on the interference resistance ability and signal-to-noise ratio (SNR) of the output. he results are also extended to the field of fractional bandwidth to better understand the possible relationship between fractional bandwidth and correlator receivers.