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.

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.

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.

Drones are evolving into highly capable and adaptable devices, prompting the development of advanced control frameworks. This paper introduces a novel online control framework tailored for a multi-task drone, explicitly addressing the simultaneous execution of in-situ sensing and goods delivery. To tackle this complex scenario, a finite-horizon Markov decision process (FH-MDP) is formulated to ensure not only the prompt delivery of goods but also the minimization of energy consumption and the maximization of the drone's reward for in-situ sensing. A significant contribution lies in establishing the monotonicity and subadditivity of the FH-MDP. This mathematical foundation provides evidence for the existence of an optimal, monotone, deterministic Markovian policy. The crux of the optimal policy revolves around flight distance- and time-related thresholds, determining the precise points at which the drone should switch its optimal action. This unique feature empowers the multi-task drone to make real-time decisions, such as adjusting flight speed or engaging in in-situ sensing, by comparing its current state with these predefined thresholds. This process can be accomplished with a linear complexity, ensuring efficiency in decision-making. The optimality of our approach is rigorously demonstrated through numerical validation, where it is compared against a computationally expensive, dynamic programming-based alternative. Under the considered simulation settings, our approach reduces drone energy consumption by a substantial 19.8% compared to existing benchmarks. This not only highlights the practical effectiveness of the proposed framework but also underscores its potential for significant advancements in the field of drone operations and energy efficiency.

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.

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.

Cooperation can help unmanned aerial vehicles (UAVs) improve their plans to visit charging stations and avoid congestion, but can be hindered by privacy concerns. We propose a new, privacy preserving, joint routing, and charging scheduling framework which allows multiple cellular-connected UAVs to jointly optimize their routes and charging schedules in a decentralized fashion. The framework allows each UAV to minimize its energy usage and connectivity outage, maximize its recharged energy, ensure its timely arrival, and preserve its privacy concerning its trajectory and destination. The key idea is that we obfuscate probabilistically the destination of each UAV, and design a new noncooperative Bayesian game among the UAVs to find their best routes and charging schedules toward the obfuscated destinations. Another important aspect is that we prove the game is a potential Bayesian game with a pure-strategy Bayesian Nash equilibrium and the best response yielded with the Bellman–Ford algorithm. This new framework preserves the UAVs’ privacy in the sense that an UAV only shares the probability of its visit to a charging station at different times, and its best response is based on an obfuscated destination. Simulations demonstrate that the framework ensures timely arrivals with near-optimal routes and substantially lower complexity than a centralized routing scheme based on brute force.

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.

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.

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.

We investigate the performance of massive ultra-reliable and low-latency communications (mURLLC) under massive active users, and non-uniform small-scale and shadow fading in the uplink (UL) of a next-generation multiple access (NGMA) system that integrates massive multiple-input multiple-output (MIMO) and non-orthogonal multiple access (NOMA) techniques. We first derive new closed-form expressions to accurately approximate the probability density function (PDF) and cumulative distribution function (CDF) of the channel gains in MIMO systems under the$\kappa $-$\mu $shadowed fading. Then, we derive the post-processing signal-to-noise ratio (SNR) and its closed-form PDFs and CDFs in the NGMA system, under both perfect and imperfect channel state information of the$\kappa $-$\mu $shadowed fading channel. Given the post-processing SNRs and their PDFs, the general expressions are established for the error probability (EP) to analyze the mURLLC of NGMA by applying finite blocklength information theory. Corroborated by extensive simulations, our analysis reveals that with the increasing reliability requirements of the users, the relative gaps in EPs enlarge between users experiencing different fading channels, and the feasible system configurations (i.e., the transmit powers of the users, and the numbers of antennas, active users, and subcarriers) also increasingly differ between the users. The impact of different fading on mURLLC implementations cannot be overlooked, and the research of mURLLC under the$\kappa $-$\mu $shadowed fading model is indispensable. The NGMA system considered in this paper is capable of achieving mURLLC under non-uniform small-scale and shadow fading.

This paper puts forth a new application of an unmanned aerial vehicle (UAV) to joint goods delivery and in-situ sensing, and proposes a new algorithm that jointly optimizes the route and sensing task selection to minimize the UAV’s energy consumption, maximize its sensing reward, and ensure timely goods delivery. This problem is new and non-trivial due to its nature of mixed integer programming. The key idea behind the new algorithm is that we interpret the possible waypoints of the UAV as location-dependent tasks to incorporate routing and sensing in one task selection process. Another critical aspect is that we construct a new task-time graph to describe the process, where each vertex corresponds to a task associated with its location, time and reward, and each edge indicates the propulsion energy required for the UAV to travel between two tasks. By redistributing the weight of a vertex to its incoming edges, the new UAV routing and sensing task selection problem can be converted to a weighted routing problem in the new task-time graph and solved optimally using the Bellman-Ford algorithm. Validated by a real-world case study, our approach can outperform its alternatives by over 18% in task reward.

Unmanned aerial vehicles (UAVs) have been extensively applied to goods delivery and in-situ sensing. It becomes increasingly probable that multiple UAVs are delivering goods and carrying out sensing tasks at the same time. The destinations of the UAVs are usually required to jointly design their trajectories and sensing selections, leading to privacy concerns for the UAVs. This paper presents a new game-theoretic routing framework for joint goods delivery and sensing of multiple cellular-connected UAVs, where the UAVs minimize their energy consumption and connectivity outage, maximize their sensing reward, and ensure timely goods delivery and trajectory privacy by optimizing their trajectories and sensing task selections in a decentralized manner. The key idea is that we unify routing and sensing in a single task selection process, which is further transformed into routing on a task-time graph. Another important aspect is that we design a non-cooperative potential game for the routing on the task-time graph. A distributed strategy is developed, where each UAV only reports its sensing task selections and withholds its destination information and its best response produced by the Bellman-Ford algorithm. By this means, the destination and trajectory privacy of the UAVs are protected. Simulations show that the new game-theoretic approach can ensure timely delivery and achieve close-to-optimal solutions with significantly lower complexity compared to a centralized brute-force approach.

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.

An effective and practical joint analog and digital self-interference cancellation (SIC) scheme without additional signalling overhead for an I/Q imbalanced full duplex transceiver is proposed in this paper. This scheme combines an I/Q imbalanced analog least mean square (ALMS) loop at the transceiver radio frequency frontend and a two-stage digital signal processing (DSP) at the digital baseband to achieve excellent SIC performance with low complexity. The steady state weighting coefficients of the I/Q imbalanced ALMS loop with periodical transmitted signal and the loop’s convergence behaviour are firstly analysed. The residual SI is then modelled as the output of a time-varying widely linear system. With a track/hold control mechanism applied to the ALMS loop, the system model for digital SIC is further presented, followed by the DSP algorithms suitable for real-time implementation. The noise enhancement in each stage digital cancellation is also analysed and formulated. Finally, simulation results are provided to verify the theoretical analyses and demonstrate the overall SIC performance.

The proliferation of delay-sensitive Internet-of-Things (IoT) applications has ushered in a need for the statistical delay quality-of-service (QoS) guarantee for the applications. In this paper, we first derive an upper bound for the queuing delay violation probability (UB-QDVP) in uplink non-orthogonal multiple access (NOMA) by applying stochastic network calculus (SNC) to the Mellin transforms of service processes. A closed-form asymptotic approximation of the UB-QDVP is developed by proving the asymptotic convergence of the Mellin transform and its finite-length truncations. Given the closed-form asymptotic UB-QDVP, we propose two power allocation schemes. The first scheme minimizes the transmit power of a NOMA user pair while guaranteeing the statistical delay QoS of the pair. The second maximizes the$\alpha $-utility function of the effective capacity of the user pair, striking a balance between the energy efficiency and user fairness of uplink NOMA systems. Simulations validate the UB-QDVP and show the superiority of the proposed schemes to conventional power allocation schemes in terms of energy efficiency and fairness.

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.

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.

A novel noncoherent microwave imaging principle with periodical or random radio frequency (RF) illumination is proposed in this article. Implemented with circular array synthetic aperture radar (SAR) frontend and low-complexity signal processing algorithms, the imaging device, called RF camera, achieves some desired properties similar to an optical camera, such as the capability to operate with multiple uncoordinated illuminators. Different from conventional multistatic imaging, the RF camera does not require any knowledge about an illuminator’s location or signal waveform. A static illumination sensor (IS) can be used to provide a reference signal for image reconstruction. With periodical illumination, the RF camera can even operate without IS, but the imaging performance can be improved with IS. With random illumination, the IS is necessary for the RF camera operation, and the imaging distortion can be described by a point blur function. Theoretical analyses on the imaging signal-to-noise ratios are performed under different RF camera operation modes. Simulation and experimental tests are conducted using 77-GHz millimeter wave frequency to verify the noncoherent imaging principle and its performance.

This article presents an universal circular synthetic aperture radar (SAR) (UCSAR) by which the targets to be observed at any radial distance can be imaged, thus making SAR imaging possible in a more general scenario with a circular movement of the radar platform. The UCSAR point spread function (PSF) is firstly analyzed based on the time-domain correlation imaging approach, and thus a three-dimension (3-D) spatial variant PSF of the target can be formulated. The closed-form PSF expressions with single-frequency and frequency-modulated continuous wave (FMCW) transmitted signals are derived respectively to quantify the imaging resolutions, showing that the PSF is a product of a sinc function and a zeroth-order Bessel function when using a wideband FMCW signal. Secondly, a fast UCSAR imaging algorithm and its further simplified version are proposed to reduce the computational cost significantly based on the piecewise constant Doppler (PCD) principle. To quantify the imaging performance, we derive an error function of the slant range approximation for the proposed algorithm, serving as a practical guideline for the UCSAR parameter selection. Finally, the simulation and experimental results are provided to validate the PSF analysis, the fast imaging algorithm, and the implementation of the proposed UCSAR.

This paper proposes a new synthetic aperture radar (SAR), named as panoramic SAR, based on a combination of linear and rotational SARs, by which a large 360-degree panoramic view of the observed scene can be reconstructed. Firstly, the system geometry and its imaging process based on the back-projection algorithm (BPA) are presented. The combined movement constitutes a two-dimensional synthetic aperture and thus higher imaging resolutions can be obtained. The corresponding resolution analysis and the sampling criteria are discussed accordingly. Then, a novel dynamic piecewise compensation (DPC) algorithm, a recursive imaging process, is proposed to reduce the processing complexity significantly. The imaging implementation and the complexity are also studied respectively. Finally, a prototype of panoramic SAR is built based on an frequency modulated continuous wave (FMCW) radar and a moving platform, and the simulation and experimental results are provided to validate the proposed panoramic SAR principle and the DPC algorithm.

Unmanned aerial vehicle (UAV) has demonstrated its usefulness in goods delivery. However, the delivery distances are often restrained by the battery capacity of UAVs. This paper integrates UAVs into intelligent transportation systems for energy-efficient, delay-sensitive goods delivery. Dynamic programming (DP) is first applied to minimize the energy consumption of a UAV and ensure its timely arrival at its destination, by optimizing the control policy of the UAV. The control policy involves decisions including flight speed, hitchhiking (on collaborative ground vehicles), or recharging at roadside charging stations. Another key aspect is that we reveal the conditions of the remaining flight distance or the elapsed time, only under which the optimal action of the UAV changes. Accordingly, thresholds are derived, and the optimal control policy can be instantly made by comparing the remaining flight distance and the elapsed time with the thresholds. Simulations show that the proposed algorithms can improve the flight distance by 48%, as compared with existing alternatives. The proposed threshold-based technique can achieve the same performance as the DP-based solution, while significantly reducing the computational complexity.

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.

Abstract The fifth generation (5G) wireless communication networks are being deployed worldwide from 2020 and more capabilities are in the process of being standardized, such as mass connectivity, ultra-reliability, and guaranteed low latency. However, 5G will not meet all requirements of the future in 2030 and beyond, and sixth generation (6G) wireless communication networks are expected to provide global coverage, enhanced spectral/energy/cost efficiency, better intelligence level and security, etc. To meet these requirements, 6G networks will rely on new enabling technologies, i.e., air interface and transmission technologies and novel network architecture, such as waveform design, multiple access, channel coding schemes, multi-antenna technologies, network slicing, cell-free architecture, and cloud/fog/edge computing. Our vision on 6G is that it will have four new paradigm shifts. First, to satisfy the requirement of global coverage, 6G will not be limited to terrestrial communication networks, which will need to be complemented with non-terrestrial networks such as satellite and unmanned aerial vehicle (UAV) communication networks, thus achieving a space-air-ground-sea integrated communication network. Second, all spectra will be fully explored to further increase data rates and connection density, including the sub-6 GHz, millimeter wave (mmWave), terahertz (THz), and optical frequency bands. Third, facing the big datasets generated by the use of extremely heterogeneous networks, diverse communication scenarios, large numbers of antennas, wide bandwidths, and new service requirements, 6G networks will enable a new range of smart applications with the aid of artificial intelligence (AI) and big data technologies. Fourth, network security will have to be strengthened when developing 6G networks. This article provides a comprehensive survey of recent advances and future trends in these four aspects. Clearly, 6G with additional technical requirements beyond those of 5G will enable faster and further communications to the extent that the boundary between physical and cyber worlds disappears.

This work generalizes the Fourier transform (FT)-based frequency-invariant beamforming (FIB) method to the synthesis of scanned frequency-invariant (FI) beam pattern with space-frequency notching for an array with non-isotropic elements. Wideband FI pattern characteristics are described by using multiple reference sub-band FI patterns. By applying fast Fourier transform (FFT) on the combination of these multiple reference sub-band FI patterns, a wideband excitation distribution can be generated. Based on this excitation distribution, we construct a new wideband excitation distribution that is conjugate-symmetric about zero frequency, so that real-valued finite-impulse-response (FIR) filter coefficients can be obtained by applying FFT on the constructed distribution. Two numerical examples are introduced to show the effectiveness and efficiency of the proposed method.

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.

A novel 3-D time-domain terahertz (THz) imaging system based on piecewise constant Doppler (PCD) algorithm and step-frequency continuous-wave (SFCW) signaling is proposed in this article. First, the SFCW THz imaging system configuration and the Gaussian beam propagation model are introduced. Then, the conventional time-domain correlation imaging algorithm is reviewed, and the closed-form expression of its point spread function (PSF) is derived to quantify the range and lateral resolutions. To reduce the computational complexity, a 2-D recursive imaging process based on the plane approximation of the range surface is proposed, by which the original PCD algorithm is extended for 3-D imaging with 2-D aperture synthesis. The 3-D PCD imaging principle, implementation, and complexity analysis are discussed afterward. Finally, simulation and experimental results are provided to validate the theoretical analysis of the 3-D time-domain THz imaging and demonstrate the high quality of the proposed imaging algorithm at a low computational cost.

Atmospheric scattering model (ASM) is one of the most widely used model to describe the imaging processing of hazy images. However, we found that ASM has an intrinsic limitation which leads to a dim effect in the recovered results. In this paper, by introducing a new parameter, i.e., light absorption coefficient, into ASM, an enhanced ASM (EASM) is attained, which can address the dim effect and better model outdoor hazy scenes. Relying on this EASM, a simple yet effective gray-world-assumption-based technique called IDE is then developed to enhance the visibility of hazy images. Experimental results show that IDE eliminates the dim effect and exhibits excellent dehazing performance. It is worth mentioning that IDE does not require any training process or extra information related to scene depth, which makes it very fast and robust. Moreover, the global stretch strategy used in IDE can effectively avoid some undesirable effects in recovery results, e.g., over-enhancement, over-saturation, and mist residue, etc. Comparison between the proposed IDE and other state-of-the-art techniques reveals the superiority of IDE in terms of both dehazing quality and efficiency over all the comparable techniques.

Online social networks (OSNs) suffer from forged messages. Current studies have typically been focused on the detection of forged messages and do not provide the analysis of the behaviors of message publishers and network strategies to suppress forged messages. This paper carries out the analysis by taking a game theoretic approach, where infinitely repeated games are constructed to capture the interactions between a publisher and a network administrator and suppress forged messages in OSNs. Critical conditions, under which the publisher is disincentivized to publish any forged messages, are identified in the absence and presence of misclassification on genuine messages. Closed-form expressions are established for the maximum number of forged messages that a malicious publisher could publish. Confirmed by the numerical results, the proposed infinitely repeated games reveal that forged messages can be suppressed by improving the payoffs for genuine messages, increasing the cost of bots, and/or reducing the payoffs for forged messages. The increasing detection probability of forged messages or decreasing misclassification probability of genuine messages also has a strong impact on the suppression of forged messages.

The performance of transmit beamforming for both optimized precoding and self-interference cancellation (SIC) in full duplex multiple input multiple output (MIMO) transceivers is analysed in this paper. With sub-space dimension larger than that of the null-space of the self-interference channels, the precoding error is reduced but the interference suppression ratio (ISR) is degraded, resulting in a trade-off between multibeam communication and MIMO SIC. An analytical approach for the ISR evaluation is proposed assuming known eigenvalue distribution of the self-interference channels, and a closed-form ISR expression is derived after applying a uniform distribution approximation. The ISR and precoding error trade-off curves are also formulated. Joint SIC by transmit beamforming and beam-based analog adaptive filters over both propagation and analog domains is proposed to achieve better SIC performance and enable more flexible receive antenna selection. Simulation results verify the theoretical analyses.

Joint analog and digital self-interference cancellation (SIC) is essential for enabling in-band full duplex (IBFD) communications. Analog least mean square (ALMS) loop is a promising low-complexity high-performance analog SIC technique with multi-tap adaptive filtering capability, but its properties on the tap coefficient variation have not been fully understood. In this paper, analysis based on higher-order statistics of the transmitted signal is performed to solve the problem of evaluating the variance of the ALMS loop’s weighting coefficient error, which reveals two additional types of irreducible residual self-interference (SI) produced by an ALMS loop if it runs freely. The residual SI channel impulse response in digital baseband is also analysed and its unique properties are investigated. By introducing a simple track and hold control to the ALMS loop’s tap coefficients, a joint analog and digital SIC scheme is proposed to stop the tap coefficient variation and achieve very low residual SI close to the IBFD receiver’s noise floor. In a coordinated application scenario, the noise figure of the digital SIC algorithm is proved to be only 1.76 dB at most. Simulation results are provided to verify the theoretical analyses.

This paper presents a novel and effective deep reinforcement learning (DRL)-based approach to addressing joint resource management (JRM) in a practical multi-carrier non-orthogonal multiple access (MC-NOMA) system, where hardware sensitivity and imperfect successive interference cancellation (SIC) are considered. We first formulate the JRM problem to maximize the weighted-sum system throughput. Then, the JRM problem is decoupled into two iterative subtasks: subcarrier assignment (SA, including user grouping) and power allocation (PA). Each subtask is a sequential decision process. Invoking a deep deterministic policy gradient algorithm, our proposed DRL-based JRM (DRL-JRM) approach jointly performs the two subtasks, where the optimization objective and constraints of the subtasks are addressed by a new joint reward and internal reward mechanism. A multi-agent structure and a convolutional neural network are adopted to reduce the complexity of the PA subtask. We also tailor the neural network structure for the stability and convergence of DRL-JRM. Corroborated by extensive experiments, the proposed DRL-JRM scheme is superior to existing alternatives in terms of system throughput and resistance to interference, especially in the presence of many users and strong inter-cell interference. DRL-JRM can flexibly meet individual service requirements of users.

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.

To enable ultrareliable and low-latency communications (URLLCs) in the Internet of Things (IoT), a sparse-code multiple-access (SCMA)-enhanced full-duplex (FD) scheme (FD-SCMA) is proposed in this article. FD-SCMA can support short-packet transmissions of several SCMA users in the uplink (UL) and downlink (DL) simultaneously by an FD next generation node B (gNB). First, the gNB and UL users can generate and superpose signals according to the preconfigured SCMA codebooks, and simultaneously transmit the signals via occupied subcarriers in a joint SCMA pattern. The receivers at the gNB and DL users can demodulate and decode the signals with multiuser detection (MUD). With the imperfect self-interference suppression (SIS) of FD considered, the effective signal-to-noise ratio (SNR) of FD-SCMA at the gNB and DL users is formulated. The error probability of FD-SCMA in the UL and DL is also derived under a given transmission latency constraint of short-packet transmissions. In the stationary flat-fading channel, it is proved that FD-SCMA can achieve better reliability than the existing FD and SCMA schemes. In the time-invariant frequency-selective fading channel, the upper bounds for error probability of the UL and DL users in FD-SCMA are derived, respectively. Through the theoretical calculation and Monte Carlo simulation, it is verified that the superiority of FD-SCMA in supporting ultrareliable and low-latency short-packet transmissions in IoT.

It is challenging to satisfy the critical requirements of ultrareliable and low-latency communications (URLLCs) in the Internet of Things (IoT) under severe channel fading. The emerging massive multiuser multiple-input-multiple-output (MU-MIMO) concept is applied in IoT networks under shadow fading, enabling URLLC with pilot-assisted channel estimation (PACE) and zero-forcing (ZF) detection. Assuming users are uniformly and randomly deployed under log-normal shadow fading, the probability density function (pdf) of postprocessing signal-to-noise ratios (SNRs) is derived for the uplink (UL) of massive MU-MIMO with perfect channel state information (CSI) and imperfect CSI obtained by PACE. Then, finite blocklength (FBL) information theory is utilized to derive the error probability of accessing users with a given latency, thereby evaluating the reliability of massive MU-MIMO for short-packet transmissions. Further, the length of pilots to minimize the error probability can be decided by the golden section search method (GSSM), which can converge rapidly. Numerical results verify that massive MU-MIMO can support a large number of UL URLLC users even when users are randomly deployed under shadow fading.

A novel generalized continuous-wave synthetic aperture radar (GCW-SAR) based on deramp-on-receive operating in millimeter-wave frequency is proposed in this article. With deramp-on-receive, the receiver sampling rate is drastically reduced, and the downsampled 1-D raw data can be obtained from the received beat signal. Further adopting piecewise constant Doppler (PCD) imaging in the digital domain, a GCW-SAR image can be easily reconstructed by using the existing frequency-modulated continuous-wave (FMCW) radar system. The effects of deramp-on-receive in PCD imaging are analyzed accordingly. The short wavelength of the millimeter-wave carrier used in the proposed GCW-SAR enables high azimuth resolution as well as a short synthetic aperture, which, in turn, significantly reduces the imaging computational complexity. Simulation and experimental results confirm the advantages of the proposed GCW-SAR.

Disconnecting impaired or suspicious nodes and rewiring to those reliable, adaptive networks have the potential to inhibit cascading failures, such as DDoS attack and computer virus. The weights of disconnected links, indicating the workload of the links, can be transferred or redistributed to newly connected links to maintain network operations. Distinctively different from existing studies focused on adaptive unweighted networks, this paper presents a new mean-field model to analyze the reliability of adaptive weighted networks against cascading failures. By taking mean-field approximation, we develop a new continuous-time Markov model to capture the propagations of cascading failures and the rewiring actions that individual nodes can take to bypass failed neighbors. We analyze the stability of the model to identify the critical conditions, under which the cascading failures can be eventually inhibited or would proliferate. The conditions are evaluated under different link weight distributions and rewiring strategies. Our model reveals that preferentially disconnecting suspicious peers with high weights can effectively inhibit virus and failures.

This paper introduces a novel and effective image prior, i.e., gamma correction prior (GCP), which leads to an efficient image dehazing method, i.e., IDGCP. A step-by-step procedure of the proposed IDGCP is as follows. First, an input hazy image is preprocessed by the proposed GCP, resulting in a homogeneous virtual transformation of the hazy image. Then, from the original input hazy image and its virtual transformation, the depth ratio is extracted based on atmospheric scattering theory. Finally, a "global-wise" strategy and a vision indicator are employed to recover the scene albedo, thus restoring the hazy image. Unlike other image dehazing methods, IDGCP is based on the "global-wise" strategy, and it only needs to determine one unknown constant without any refining process to attain a high-quality restoration, thereby leading to significantly reduced processing time and computation cost. Each step of IDGCP is tested experimentally to validate its robustness. Moreover, a series of experiments are conducted on a number of challenging images with IDGCP and other state-of-the-art technologies, demonstrating the superiority of IDGCP over the others in terms of restoration quality and implementation efficiency.

Frame-based listen-before-talk (FB-LBT) has been adopted as one of the channel access mechanism for Wi-Fi/LTE coexistence.We aim to explore the limits of FB-LBT by developing theoretical models to characterise the FB-LBT channel access performance under the coexistence of LTE and Wi-Fi.We first derive a steady-state model to calculate the spectrum share occupied by LTE under the assumption that the Wi-Fi transmissions have stationary distributions.The assumption does not hold when the time between two LTE transmissions is short, where the system is dominated by a dynamic phenomenon.A second model is developed that accounts for the dynamics of the Wi-Fi channel access mechanism.Our models, validated by simulation results, accurately calculate the spectrum share occupied by LTE over a range of FB-LBT frame periods and Wi-Fi traffic loads.We obtain upper bounds on the FB-LBT spectrum share when competing with heavy Wi-Fi traffic, which confirm the weakness of FB-LBT.Moreover, we demonstrate that our models can be used to control the FB-LBT spectrum share within a modest range.

Self-interference (SI) cancellation for full-duplex (FD) multiple input multiple output (MIMO) systems is challenging due to both hardware and signal processing complexity. In this paper, a beam-based adaptive filter structure with analog least mean square (ALMS) loops is proposed to significantly reduce the complexity of SI cancellation for FD MIMO systems. With this structure, the number of adaptive filters required for SI cancellation scales linearly with the number of transmit beams rather than quadratically with the number of antennas. Furthermore, to avoid additional transmit chains used to up-convert the beam signals to generate reference signals for the ALMS loops, a novel method is proposed to select the optimized reference signals from all transmitted signals. In addition, our stationary analysis shows that the proposed structure for FD MIMO systems outperforms the ALMS loop employed for an FD single input single output system. Simulations are conducted to confirm the theoretical analyses.

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.

© 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.

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.

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.

Antenna design and angle-of-arrival (AoA) estimation are critical to the efficiency of wireless information and power transfer. The AoA estimation is challenging for energy-efficient lens antenna arrays (LAAs), due to discrete sets of fixed discrete Fourier transform (DFT) beams. This paper presents a novel fast and accurate approach for the AoA estimation of LAAs. The key idea is that we prove the two differential outputs of three adjacent lens beams, referred to as “DFT beam differences (DBDs),” that are the strongest at the two sides of an AoA. They are easy to identify and robust to noises, and their powers are proved to provide an accurate estimate of the AoA. Another important aspect is a new beam synthesis technique which produces different beam widths based on DFT beams and practical 1-bit phase shifts in real time. As a result, the angular region containing the AoA can exponentially narrow down, and the two strongest DBDs can be quickly identified. The proposed approach can operate in coupling with successive interference cancellation to estimate the AoAs of multiple paths. Simulations show that the proposed approach is able to outperform the state of the art by orders of magnitude in terms of accuracy. The power transfer efficiency can be dramatically improved.

We develop the optimal economical caching schemes in cache-enabled heterogeneous networks, while delivering multimedia video services with personalized viewing qualities to mobile users. By applying scalable video coding (SVC), each video file to be requested is divided into one base layer (BL) and several enhancement layers (ELs). In order to assign different transmission tasks, the serving small-cell base stations (SBSs) are grouped into $K$ clusters. The SBSs are able to cache and cooperatively transmit BL and EL contents to the user. We analytically derive expressions for successful transmission probability and ergodic service rate, and then the closed-form expression for EConomical Efficiency (ECE) is obtained. In order to enhance the ECE performance, we formulate the ECE optimization problems for two cases. In the first case, with equal cache size equipped at each SBS, the layer caching indicator is determined. Since this problem is NP-hard, after the l0-norm approximation, the discrete optimization variables are relaxed to be continuous, and this relaxed problem is convex. Next, based on the optimal solution derived from the relaxed problem, we devise a greedy-strategy based heuristic algorithm to achieve the near-optimal layer caching indicators. In the second case, the cache size for each SBS, the layer size, and the layer caching indicator are jointly optimized. This problem is a mixed integer programming problem, which is more challenging. To effectively solve this problem, the original ECE maximization problem is divided into two subproblems. These two subproblems are iteratively solved until the original optimization problem is convergent. Numerical results verify the correctness of the theoretical derivations. Additionally, compared to the most popular layer placement strategy, the performance superiority of the proposed SVC-based caching schemes is testified.

Epidemic models trade the modeling accuracy for complexity reduction. This paper proposes to group vertices in directed graphs based on connectivity and carries out epidemic spread analysis on the group basis, thereby substantially reducing the modeling complexity while preserving the modeling accuracy. A group-based continuous-time Markov SIS model is developed. The adjacency matrix of the network is also collapsed according to the grouping, to evaluate the Jacobian matrix of the group-based continuous-time Markov model. By adopting the mean-field approximation on the groups of nodes and links, the model complexity is significantly reduced as compared with previous topological epidemic models. An epidemic threshold is deduced based on the spectral radius of the collapsed adjacency matrix. The epidemic threshold is proved to be dependent on network structure and interdependent of the network scale. Simulation results validate the analytical epidemic threshold and confirm the asymptotical accuracy of the proposed epidemic model.

Analog least mean square (ALMS) loop is a promising method to cancel self-interference (SI) in in-band full-duplex (IBFD) systems. In this paper, the steady state analyses of the residual SI powers in both analog and digital domains are firstly derived. The eigenvalue decomposition is then utilized to investigate the frequency domain characteristics of the ALMS loop. Our frequency domain analyses prove that the ALMS loop has an effect of amplifying the frequency components of the residual SI at the edges of the signal spectrum in the analog domain. However, the matched filter in the receiver chain will reduce this effect, resulting in a significant improvement of the interference suppression ratio (ISR). It means that the SI will be significantly suppressed in the digital domain before information data detection. This paper also derives the lower bounds of ISRs given by the ALMS loop in both analog and digital domains. These lower bounds are joint effects of the loop gain, tap delay, number of taps, and transmitted signal properties. The discovered relationship among these parameters allows the flexibility in choosing appropriate parameters when designing the IBFD systems under given constraints.

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.

Atmospheric scattering model (ASM) has been widely used in hazy image restoration. However, the recovered albedo might deviate from the real scene once the input hazy image cannot fully satisfy the model's assumptions such as the homogeneous atmosphere and even illumination. In this paper, we break these limitations and redefine a more reliable ASM (RASM) that is extremely adaptable for various practical scenarios. Benefiting from RASM, a simple yet effective Bayesian dehazing algorithm (BDPK) is further proposed based on the prior knowledge. Our strategy is to convert the single image dehazing problem into a maximum a-posteriori probability one that can be approximated as an optimization function using the existing priori constraints. To efficiently solve this optimization function, the alternating minimizing technique is introduced, which enables us to directly restore the scene albedo. Experiments on a number of challenging images reveal the power of BDPK on removing haze and verify its superiority over several state-of-the-art techniques in terms of quality and efficiency.

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.

Arrays of Butler matrices provide a promising front-end design for massive MIMO transceivers with low cost and low complexity. However, this advanced design does not necessarily translate to effective applications, unless the angle-of-arrival (AoA) of signals avails to the Butler matrices. This paper presents an efficient approach to the unprecedented AoA estimation for the arrays of Butler matrices. Specifically, we design a new beam synthesis method to recursively narrow down and increasingly focus on the angular region of interest, and hence achieving robust estimation of the phase offset between Butler matrices. With the phase offset canceled in the received signals, we are able to identify the set of critical Butler beams with the dominating effect on the AoA estimation, and estimate the AoA accordingly with minimum signaling. The mean squared error of the proposed estimation is analyzed in the presence of non-negligible noises, with closed-form lower bounds derived. Validated by simulations, the proposed algorithm is able to indistinguishably approach the lower bounds, and significantly outperforms the state-of-the-art developed for discrete antenna arrays by orders of magnitude in terms of accuracy, especially in low signal-to-noise regimes.

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.

The superiority of exploring millimeter wave (mmWave) frequencies for future wireless communication systems has pushed forward the development of large-scale antenna arrays for achieving sufficient array gain and high spectral efficiency. In this paper, we study the matrix normalization (MN) based zero-forcing (ZF) hybrid precoding in multi-user multi-input-multi-output (MU-MIMO) mmWave systems. We derive the upper bounds of the achievable rate for two representative hybrid array structures, i.e., fully-connected structure and partially-connected structure. Analytical and simulation results validate the tightness of the proposed performance upper bounds for both hybrid structures using massive array, and provide a comparison of the achievable rate using MN and vector normalization (VN).

Generalized continuous wave synthetic aperture radar (GCW-SAR) is a promising new imaging radar system since it applies the full-duplex (FD) transmission technique to achieve continuous signaling in order to overcome several fundamental limitations of the conventional pulsed SARs. As in any FD wireless communication system, self-interference (SI) is also a key problem which can impact on the GCW-SAR system. In this paper, the analog least mean square (ALMS) loop in the radio frequency domain is adopted to cancel the SI for a GCW-SAR system with periodic chirp signaling. The average residual SI power after the ALMS loop is analyzed theoretically by a stationary analysis. It is found that the ALMS loop not only works with random signals in general FD communication systems, but also works well with the periodic signal in GCW-SAR systems. Simulation results show that over 45 dB SI cancellation can be achieved by the ALMS loop which ensures the proper operation of the GCW-SAR system.

Decentralized orchestration of the control plane is critical to the scalability and reliability of software-defined network (SDN). However, existing orchestrations of SDN are either one-off or centralized, and would be inefficient the presence of temporal and spatial variations in traffic requests. In this paper, a fully distributed orchestration is proposed to minimize the time-average cost of SDN, adapting to the variations. This is achieved by stochastically optimizing the on-demand activation of controllers, adaptive association of controllers and switches, and real-time request processing and dispatching. The proposed approach is able to operate at multiple timescales for activation and association of controllers, and request processing and dispatching, thereby alleviating potential service interruptions caused by orchestration. A new analytic framework is developed to confirm the asymptotic optimality of the proposed approach in the presence of non-negligible signaling delays between controllers. Corroborated from extensive simulations, the proposed approach can save up to 73% the time-average operational cost of SDN, as compared to the existing static orchestration.

Accurate estimation of angle-of-arrival (AoA) is critical to wideband satellite communications, but is susceptible to receive noises and can be ambiguous due to space/cost-effective hybrid antenna array designs with localized analog phased subarrays. As a matter of fact, there has yet to be an unambiguous estimator even for narrow-band systems. This paper proposes a new design of subarray-specific time-varying phase shifts, which enables unambiguous and noise-tolerant estimation of AoA in localized hybrid arrays. Particularly, the new phase shifts deliver deterministic phase changes in the cross-correlations of receive signals between subarrays, and enable the cross-correlations to be coherently accumulated across subarrays and sub-carriers to eliminate ambiguities and tolerate noises. Another important contribution of the paper is that we optimize the frequency interval for coherent accumulation across sub-carriers, leveraging between estimation errors, and accumulation gains. Evident from simulations, our approach is able to dramatically improve the estimation accuracy by orders of magnitudes with significantly reduced requirements of complexities and training symbols, as compared with the state of the art. The approach is robust against noises, with estimation errors asymptotically achieving a rigorously developed lower bound.

By suppressing repeated content deliveries, wireless caching has the potential to substantially improve the energy efficiency (EE) of the fifth-generation communication networks. In this paper, we propose two novel energy-efficient caching schemes in heterogeneous networks, namely, scalable video coding (SVC)-based fractional caching and SVC-based random caching, which can provide on-demand video services with different perceptual qualities. We derive the expressions for successful transmission probabilities and ergodic service rates. Based on the derivations and the established power consumption models, the EE maximization problems are formulated for the two proposed caching schemes. By taking logarithmic approximations of the l0-norm, the problems are efficiently solved by the standard gradient projection method. Numerical results validate the theoretical analysis and demonstrate the superiority of our proposed caching schemes, compared to three benchmark strategies.

In this letter, a concise gamma-correction-based dehazing model (GDM) is proposed. This GDM explicitly describes the inner relationship between the gamma correction (GC) and the traditional scattering model. Combined with the existing priori constraints, GDM is further approximated into a one-dimensional (1-D) function to seek the only unknown constant that is used for haze removal. Using the determined constant, the scene albedo can be recovered, eliminating the haze from single hazy images. The proposed GDM is able to suppress the halo/blocking artifacts in the recovered results due to the scene albedo, which is less sensitive to the determined constant. Simulation results on different types of benchmark images verify that the proposed technique outperforms state-of-the-art methods in terms of both recovery, quality, and real-time performance.

A generalized continuous wave synthetic aperture radar (GCW-SAR) concept is proposed in this paper. By using full-duplex radio frontend and continuous wave signaling, the GCW-SAR system can overcome a number of limitations inherent within the existing SAR systems and achieve high-resolution and wide-swath remote sensing with low-power signal transmission. Unlike the conventional pulsed SAR and the frequency-modulated continuous-wave SAR, the GCW-SAR reconstructs a radar image by directly correlating the received 1-D raw data after self-interference cancellation with predetermined location-dependent reference signals. A fast imaging algorithm, called the piecewise constant Doppler (PCD) algorithm, is also proposed, which produces the radar image recursively in the azimuth direction without any intermediate step, such as range compression and migration compensation, as required by conventional algorithms. By removing the stop-and-go assumption or slow-time sampling in azimuth, the PCD algorithm not only achieves better imaging quality but also allows for more flexible waveform and system designs. Analyses and simulations show that the GCW-SAR tolerates significant self-interference and works well with a large selection of various system parameters. The work presented in this paper establishes a solid theoretical foundation for next-generation imaging radars.

A major challenge in distributed mobile networks is network integrity, resulting from short link duration and severe transmission collisions. This paper analyzes the impact of link duration and transmission collisions on a range of on-the-fly authentication protocols, which operate based on predistributed keys and can instantly verify and forward messages. All unexpired messages within a link duration can be verified retrospectively, once the keys are matched on-the-air. We develop a new general 4D Markov model which, apart from the first three dimensions modeling a cycle of the protocols, is able to unprecedentedly capture unexpired messages between cycles in the fourth dimension. Validated by simulation, our analysis reveals that the on-the-fly authentication is efficient under short link duration, but is susceptible to transmission collisions. The authentication requires holistic cross-layer designs of retransmission and rekeying. The proposed model is able to facilitate the design of the protocol parameters, which allows the protocols to significantly outperform the state of the art.

Hybrid array is able to leverage array gains, transceiver sizes, and costs for massive multiple-input-multiple-output systems in millimeter-wave frequencies. Challenges arise from the estimation of angle-of-arrival (AoA) in localized hybrid arrays, due to the array structure and the resultant estimation ambiguities and susceptibility to noises. This paper eliminates the ambiguities and enhances the tolerance to the noises based on our new discoveries. Particularly, by designing new subarray-specific time-varying phase shifts, we discover that the cross-correlations between the gains of consecutive subarrays have consistent signs except the strongest. This enables the cross-correlations to be deterministically calibrated and constructively combined for the noise-tolerant estimation of the propagation phase offset between adjacent subarrays. Given the phase offset, the AoA can be estimated unambiguously with few training symbols. We also derive a closed-form lower bound for the mean square error of AoA estimation. Corroborated by simulations, our approach is able to dramatically improve estimation accuracy by orders of magnitude while reducing complexity and training symbols, as compared to the state of the art. With the ambiguities eliminated, the estimation errors of our method asymptotically approach the lower bound, as training symbols increase.

Attention has been devoted to Synthetic Aperture Radar (SAR) for half a century. Though it is a well-proven remote sensing technique, conventional pulsed SAR has several inherent limitations. In this paper, we present a new SAR concept, called Generalized Continuous Wave SAR (GCW-SAR). By using continuous wave signaling, the GCW-SAR system achieves better performance and overcomes the limitations such as the minimum antenna area in conventional SAR. Unlike the frequency modulated continuous wave SAR (FMCW-SAR) system, the GCW-SAR image is reconstructed by correlation between the sampled raw data and the location dependent reference signals. A fast image reconstruction algorithm is also presented in the paper. The principle of GCW-SAR and the effectiveness of the proposed algorithm are validated by numerical simulation results.

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.

The rapid growth of mobile broadband wireless services in recent years demands high speed data transmission for both access and backhaul networks. With the increase of data rate for 5G access to tens of Gigabits per second (Gbps), higher speed transmission for backhaul network is necessary. Current wireless backhaul systems have been able to achieve the data rate of multiple Gbps, but the ability to deal with significant practical impairments such as large carrier frequency offset and IQ mismatch is still a technical challenge. In this paper, a 20 Gbps digital modem for wireless backhaul applications is proposed. Simulation and field programmable gate array implementation show that the the proposed design and signal processing algorithms meet the targeted system performance.

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 heterogeneous networks (HetNets), the network energy efficiency is critically determined by the base station (BS) deployment density. In this paper, we consider a BS density optimization problem by turning on only a fraction of micro BSs according to an activation ratio to minimize the network average power consumption per area in a 2- tier HetNet. In contrast to previous studies where a BS is assumed to be transmitting packets all the time, such that the network power consumption monotonically increases as the BS density increases, we assume that each BS can be busy or idle depending on the dynamic packet arrivals. The network power consumption is thus closely related to the average traffic intensity of each tier. With the assumption of universal spectrum reuse, the average traffic intensity of each tier is found to be uniquely determined by a set of fixed-point equations, based on which the network average power consumption per area is characterized. Simulation results demonstrate that the network average power consumption per area can be minimized by properly tuning the activation ratio. It is further revealed that the optimal activation ratio increases as the mean packet arrival rate of each user increases.

The existence of relatively long LTE data blocks within the licensed-assisted access (LAA) framework results in bursty machine-type communications (MTC) packet arrivals, which cause system performance degradation and present new challenges in Markov modeling. We develop an embedded Markov chain to characterize the dynamic behavior of the contention arising from bursty MTC and Wi-Fi data traffic in the LAA framework. Our theoretical model reveals a high-contention phenomenon caused by the bursty MTC traffic, and quantifies the resulting performance degradation for both MTC and Wi-Fi data traffic. The Markov model is further developed to evaluate three potential solutions aiming to alleviate the contention. Our analysis shows that simply expanding the contention window, although successful in reducing congestion, may cause unacceptable MTC data loss. A TDMA scheme instead achieves better MTC packet delivery and overall throughput, but requires centralized coordination. We propose a distributed scheme that randomly spreads the MTC access processes through the available time period. Our model results, validated by simulations, demonstrate that the random spreading solution achieves a near TDMA performance, while preserving the distributed nature of the Wi-Fi protocol. It alleviates the MTC traffic contention and improves the overall throughput by up to 10%.

This paper studies non-orthogonal transmission over a K-user fading multiple access channel. We propose a new reliable physical-layer network coding and cascade-computation decoding scheme. In the proposed scheme, K single-antenna users encode their messages by the same practical channel code and QAM modulation, and transmit simultaneously. The receiver chooses K linear coefficient vectors and computes the associated K layers of finite-field linear message combinations in a cascade manner. Finally, the K users' messages are recovered by solving the K linear equations. The proposed can be regarded as a generalized onion peeling. We study the optimal network coding coefficient vectors used in the cascade computation. Numerical results show the performance of the proposed approaches that of the iterative maximum a posteriori probability detection and decoding scheme, but without using receiver iteration. This results in considerable complexity reduction, processing delay, and easier implementation. Our proposed scheme significantly outperforms the iterative detection and decoding scheme with a single iteration, for example, by 1.7 dB for the two user case. The proposed scheme provides a competitive solution for non-orthogonal multiple access.

A rotation parameter extraction method based on temporal differencing and image edge detection from range-Doppler images is presented in this letter. The proposed method first detects the motion trail of the moving pixels caused by the rotating parts in temporal differential range-Doppler images using image edge detection. A Doppler-slow-time image is then generated from the edge pixels on the motion trail. Finally, the rotation parameters are extracted from the Doppler-slow-time image. The proposed method is simple, rapid, and practical. Computer simulations and experimental results demonstrate its effectiveness in terms of computation time compared with existing methods.

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 blind learning algorithm, namely, the Benveniste-Goursat input-output decision (BG-IOD), to enhance the convergence performance of neural network-based equalizers for nonlinear channel equalization. In contrast to conventional blind learning algorithms, where only the output of the equalizer is employed for updating system parameters, the BG-IOD exploits a new type of extra information, the input decision information obtained from the input of the equalizer, to mitigate the influence of the nonlinear equalizer structure on parameters learning, thereby leading to improved convergence performance. We prove that, with the input decision information, a desirable convergence capability that the output symbol error rate (SER) is always less than the input SER if the input SER is below a threshold, can be achieved. Then, the BG soft-switching technique is employed to combine the merits of both input and output decision information, where the former is used to guarantee SER convergence and the latter is to improve SER performance. Simulation results show that the proposed algorithm outperforms conventional blind learning algorithms, such as stochastic quadratic distance and dual mode constant modulus algorithm, in terms of both convergence performance and SER performance, for nonlinear equalization.

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.

Channel estimation for an orthogonal frequency-division multiplexing (OFDM) broadband system operating with high mobility is very challenging. This is mainly due to the significant Doppler spread, inherent in a time-frequency doubly-selective (DS) channel. Consequently, a large number of channel coefficients must be estimated, forcing the need for allocating a large number of pilot subcarriers. To address this problem, we propose a novel channel estimation method based on basis expansion models (BEMs) and distributed compressive sensing (DCS) theory. To be specific, we develop a two-stage sparse BEM coefficients estimation method, which can effectively combat the Doppler spread and enable accurate channel estimation with dramatically reduced number of pilot subcarriers. The numerical results reveal that, in a typical LTE system configuration, the proposed scheme can increase the spectral efficiency by 40% and achieve a 6 dB gain in terms of normalized mean square error (NMSE), both compared to the conventional scheme.

Carrier-frequency offset (CFO) estimation for orthogonal frequency-division multiplexing access (OFDMA) systems operating in multiuser uplink transmission is very challenging due to the presence of a multiple-parameter estimation problem. In this paper, we propose a novel blind CFO estimation method for interleaved OFDMA uplink based on distributed Bayesian compressive sensing (DBCS) theory. Considering the received signal structure, the new method first constructs a measurement matrix associated with a sparse signal matrix weight, which sets up the stage for the application of CS theory in tackling the original estimation problem. Then, the DBCS theory that exploits a common sparse profile of the sparse signal matrix weight is employed to distributively estimate a sparse hyperparameter vector, whose significant peaks are linked to the correct estimation of the multiple CFOs. Compared with the existing subspace theory based methods, the proposed scheme offers a significant enhancement in estimation accuracy, in specific in the low signal-to-noise ratio (SNR) region. The numerical results validate the effectiveness of the proposed scheme.

Limited feedback precoding (LFP) significantly improves multiple-input multiple-output (MIMO) spatial multiplexing link reliability with a small amount of feedback from the receiver back to the transmitter. One of the key problems linked to LFP is how to select an optimal precoder from a pre- determined unitary codebook. We find that the conventional precoder selection criteria are not applicable to the stream maximization transmission (SMT) mode with linear receivers, including zero forcing (ZF) and minimum mean square error (MMSE) decoders. To solve this issue, a novel singular value decomposition (SVD) based precoder selection criterion is proposed in this paper. This criterion features a unified structure for all the linear receivers such as ZF and MMSE decoders, and is shown by simulation to provide significant coding gains in various SMT systems. With the same complexity as the conventional one, the proposed criterion could find its applications in next generation systems employing SMT spatial multiplexing, significantly improving system performance with affordable feedback requirement.

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.

Recently, the performance bounds of the compressed sensing (CS) recovery algorithms have been investigated in the noisy setting. However, most of the papers only focus on the noisy measurement model where the signal is noiseless and the noise enters after the CS operation. The noisy signal model where both the signal and the compressed measurements are contaminated by the different noises is not considered. This paper works on the noisy signal model and provides the performance bounds for the following popular recovery algorithms: thresholding and orthogonal matching pursuit (OMP), Dantzig selector (DS) and basis pursuit denoising (BPDN). The performance of the recovery algorithms is quantified as the ℓ2distance between the reconstructed signal and the true noisy signal. Next, the impacts of the noise are analyzed on the basis of the quantified performance. The analysis results show that the effective way to restrain the impact of the noise is to choose the measurement matrix with low correlation between the columns or the rows. Finally, the theoretical bounds are verified with numerical simulations by calculating the mean-squared-error for the different noise variances. The simulation results show that OMP owns the better performance than the other three recovery algorithms under the noisy signal model.

It is well known that traditional water-filling provides a closed form solution for capacity maximization in orthogonal frequency division multiplex (OFDM) system. In this paper, cooperative power allocation is investigated in a two-transmitter multi-receiver model for OFDM systems. The local full channel state information (CSI) is available at the two transmitters respectively, where each transmitter has an individual power constrain. The transmitters first cooperate by sharing CSI, and then jointly optimize power allocation in the metric of sum throughput, which can be modeled as a convex optimization problem. Through an application of Karush-Kuhn-Tucker (KKT) conditions, the convex optimization problem is reformulated as a simplified convex one. Then the closed form solution is derived, which takes a form similar to classic water-filling principle. Based on the solution, the optimal cooperative power allocation algorithm is constructed, the structure of which can be explained as a cooperative water-filling relative to the traditional water-filling. Finally, numerical simulation is given to evaluate and demonstrate the performance of the optimal cooperative water-filling scheme.

Channel estimation for an orthogonal frequency-division multiplexing (OFDM) broadband system over a doubly selective channel is very challenging. This is mainly due to the significant Doppler shift, which results in a time-frequency doubly-selective (DS) channel. The DS channel features a large number of channel coefficients, which introduces inter-carrier interference (ICI) and forces the need for allocating a large number of pilot subcarriers. To tackle this problem, in this paper we propose a novel channel estimation scheme based on distributed compressive sensing (DCS) theory. Taking advantage of the basis expansion model (BEM) and the channel sparsity in the delay domain, we transform the original DS channel into a novel two-dimensional channel model, where several jointly sparse BEM coefficient vectors become the estimation goal. Then a special decoupling form originating from a novel sparse pilot pattern is designed for such estimation, which results in an ICI-free structure and enables the DCS application to make joint estimation of these vectors accurately. Combined with a smoothing treatment process, the proposed scheme can achieve significantly higher estimation accuracy than the existing ones, although with a much smaller number of pilot subcarriers. Theoretical analysis and simulation results both confirm its performance merits.

Recently, distributed data storage (DDS) for Wireless Sensor Networks (WSNs) has attracted great attention, especially in catastrophic scenarios. Since power consumption is one of the most critical factors that affect the lifetime of WSNs, the energy efficiency of DDS in WSNs is investigated in this paper. Based on Compressed Sensing (CS) and network coding theories, we propose a Compressed Network Coding based Distributed data Storage (CNCDS) scheme by exploiting the correlation of sensor readings. The CNCDS scheme achieves high energy efficiency by reducing the total number of transmissions Nttot and receptions Nrtot during the data dissemination process. Theoretical analysis proves that the CNCDS scheme guarantees good CS recovery performance. In order to theoretically verify the efficiency of the CNCDS scheme, the expressions for Nttotand Nrtotare derived based on random geometric graphs (RGG) theory. Furthermore, based on the derived expressions, an adaptive CNCDS scheme is proposed to further reduce Nttot and Nrtot. Simulation results validate that, compared with the conventional ICStorage scheme, the proposed CNCDS scheme reduces Nttot, Nrtot, and the CS recovery mean squared error (MSE) by up to 55%, 74%, and 76% respectively. In addition, compared with the CNCDS scheme, the adaptive CNCDS scheme further reduces Nttotand Nrtotby up to 63% and 32% respectively.

Analog Compressed Sensing (CS) has attracted considerable research interest in sampling area. One of the promising analog CS technique is the recently proposed Modulated Wideband Converter (MWC). However, MWC has a very high hardware complexity due to its parallel structure. To reduce the hardware complexity of MWC, this paper proposes a novel Random Circulant Orthogonal Matrix based Analog Compressed Sensing (RCOM-ACS) scheme. By circularly shifting the periodic mixing function, the RCOM-ACS scheme reduces the number of physical parallel channels from m to 1 at the cost of longer processing time, where m is in the order of several dozen to several hundred in MWC. It is proved that the m×M measurement matrix of RCOM-ACS scheme satisfies the Restricted Isometry Property (RIP) condition with probability 1-M-O(1)when m = O(rlog2Mlog3r), where M is the length of the periodic mixing function, r denotes the sparsity of the input signal. Furthermore, to make a good tradeoff between processing time and hardware complexity, a short processing time RCOM-ACS scheme is proposed in this paper. Simulation results show that, the proposed schemes outperform MWC in terms of recovery performance.

Compressed sensing (CS) has recently emerged as a powerful signal acquisition paradigm. CS enables the recovery of high-dimensional sparse signals from much fewer samples than usually required. Further, quite a few recent channel measurement experiments show that many wireless channels also tend to exhibit sparsity. In this case, CS theory can be applicable to sparse channel estimation and its effectiveness has been validated in point-to-point (P2P) communication. In this work, we study sparse channel estimation for two-way relay networks (TWRN). Unlike P2P systems, applying CS theory to sparse channel estimation in TWRN is much more challenging. One issue is that the equivalent channels (terminal-relay-terminal) may be no longer sparse due to the linear convolutional operation. On this basis, novel schemes are proposed to solve this problem and effectively improve the accuracy of TWRN channel estimation when using CS theory. Extensive numerical results are provided to corroborate the proposed studies.

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.

The papers in this special issue focus on research and field trials of unmanned autonomous vehicles on land, in the air and underwater. The suite of selected papers covers key challenges that impact on the communications dynamics and behaviour of unmanned autonomous vehicles of varying size and resource capability and address UAV bridging, topology maintenance,path planning and link performance optimisation in highly changeable deployments.

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.

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.

High speed long range millimetre-wave (mm-wave) links can be achieved by using wideband hybrid antenna arrays of sub-arrays. However, conventional wideband angle-of-arrival (AoA) estimation and beamforming algorithms are not applicable to the wideband hybrid arrays due to the array architecture difference. In this paper, an adaptive frequency-domain AoA estimation and beamforming algorithm suitable for practical wideband hybrid array of side-by-side sub-arrays is proposed. Mean square error bounds under simplified array configuration and extreme array imperfection condition are also given. Simulation results show that the proposed algorithm is robust with low complexity and fast convergence.

A solution to the power leakage minimization problem in discrete Fourier transform (DFT) based communication systems is presented. In a conventional DFT based system, modulated subcarriers exhibit high sidelobe levels, which leads to significant out-of-band power leakage. Existing techniques found in the literature either do not achieve sufficient sidelobe suppression or suffer from significant spectral efficiency loss. Precoding can be seen as a general linear processing method for power leakage reduction, however, how to design the optimal linear precoder is still an open problem. In this paper, the power leakage suppression is first treated as a matrix Frobenius norm minimization problem, and then the optimal orthogonal precoding matrix design for the power leakage suppression is proposed based on singular value decomposition (SVD). By further exploiting the extra degrees of freedom in the precoding matrix, two kinds of optimized precoding matrices, one with multi-carrier property and the other with single-carrier property, are developed to take the advantages of orthogonal frequency division multiplexing (OFDM) and single carrier frequency division multiple access (SC-FDMA), respectively. Simulation results show that both the multi-carrier and the single-carrier precoding schemes achieve significant power leakage suppression, and have similar peak-to-average power ratio (PAPR) and bit-error-rate (BER) to those of OFDM and SC-FDMA systems, respectively.

High speed long range millimetre-wave (mm-wave) links can be achieved by using wideband hybrid antenna arrays of sub-arrays. Due to the array architecture difference, conventional wideband angle-of-arrival (AoA) estimation and beamforming techniques are not applicable to such wideband hybrid arrays. Targeted at point-to-point line-of-sight wireless transmission in the 70/80 GHz E bands, a unified frequency-domain AoA estimation and beamforming algorithm suitable for large scale wideband hybrid arrays of both interleaved and side-by-side sub-arrays is proposed in this paper. The AoA estimation performance is analyzed by deriving a recursive modified Cramér-Rao bound (MCRB). The effect of mutual coupling among antenna elements on the estimation performance is also considered for the hybrid array of side-by-side sub-arrays. The analytical results can be used to determine system parameters according to required system specifications. Simulation results show that the proposed AoA estimation algorithm is robust against practical impairments, and the frequency dependency of the array pattern is significantly reduced after digital beamforming. Simulated mean square errors of AoA estimation are also compared with the analytical bounds, showing that the derived recursive MCRB provides a meaningful indication to the AoA estimation performance.

This paper presents a joint channel and mutual coupling estimation technique for wideband antenna array to achieve high speed wireless communications in the millimetre-wave frequency bands. The estimated channel frequency responses and mutual coupling matrix can be used to digitally calibrate a wideband antenna array in the frequency-domain, followed by digital beamforming. Experiments are carried out using a four-element receive array prototype in the E-band (71-76 GHz) to demonstrate the frequency-domain digital calibration and beamforming performance. The results show that both the mutual coupling and wideband effects are effectively mitigated by the proposed technique and a 11.5 dBi array gain is achieved which is very close to that of an ideal four-element antenna array.

A novel combination of block spread orthogonal frequency division multiple access (BS-OFDMA) with space-time coded multiple input multiple output (STC MIMO) scheme is proposed for broadband wireless access uplink transmission. Using complex exponential spreading sequences, the block spreading technique can efficiently generate precoded OFDMA signal to exploit frequency diversity. An Alamouti STC MIMO encoding technique is incorporated with the block spreading to achieve further spatial diversity. The performance of the proposed STC-BS-OFDMA system using minimum mean squared error equalization is analyzed, and a closed-form asymptotical bit error rate expression is derived. Simulation results are also given to demonstrate the improved diversity performance as compared with other OFDMA schemes. The proposed techniques are well suited for future broadband wireless access systems such as 3G LTE and 4G.

We consider the robust linear precoding (LP) and Tomlinson-Harashima precoding (THP) schemes for multiuser MIMO-OFDM downlink channels with limited feedback. Benefiting from the correlation of spatial channels, the mobile terminal compresses and feeds back the time-domain channel vectors instead of the corresponding frequency-domain vectors to substantially reduce the feedback signalling overhead. A compression and restoration method and a codebook design for channel state information at the transmitter (CSIT) feedback are proposed in the time domain. By treating the partial CSIT as a random quantity, we develop the robust precoders to combat the truncation and quantization errors introduced in the feedback procedure. In comparison with the non-robust designs, both the robust LP and THP have better bit-error rate performance especially in high signal-to-noise ratio region.

The power allocation problem is addressed for time division duplex (TDD) LTE-A uplink systems in this paper. Due to the IDFT de-spreading in LTE-A uplink, the channel frequency responses in an IDFT de-spreading block will be tangled together. After analyzing the equivalent signal to interference plus noise ratio (SINR) in the time domain, a Truncated Squared norm of channel equalization Coefficients based Power Allocation (TSCPA) method is proposed to improve the final SINR performance after the IDFT de-spreading block. The proposed TSC-PA algorithm is verified for the clustered DFT-s-OFDM system in eigen-model block diagonalization multi-user MIMO uplink environment by simulations. The results demonstrate that the proposed TSC-PA algorithm can further improve the system block error rate (BLER) performance by selecting a proper truncation threshold.

Owing to the excessive demand on signal processing and space constraint, a full digital implementation of a large adaptive antenna array at millimeter wave frequencies is very challenging. Targeted at long range high data rate point-to-point link in the 70/80 GHz bands, a novel hybrid adaptive antenna array which consists of analogue subarrays followed by a digital beamformer is presented in this paper to overcome the digital implementation difficulty. Two subarray configurations, the interleaved subarray and the side-by-side subarray, are proposed, and two Doppler resilient adaptive angle-of-arrival estimation and beamforming algorithms, the differential beam tracking (DBT) and the differential beam search (DBS), are developed. Simulation results on the DBT and DBS performance are provided using a 64 element hybrid planar array of four 4 by 4 element subarrays with the two subarray configurations, respectively. Recursive mean square error (MSE) bounds of the developed algorithms are also analyzed and compared with simulated MSEs.

The mean squared error (MSE) is commonly used to measure and compare the performance of various phase estimation techniques in communications and signal processing systems. When the received signal contains recursive nuisance parameters, the MSE is extremely difficult to obtain and even the conventional modified Cramer-Rao bound (MCRB) can not be readily applied. In this paper, a recursive MSE bound and its simplified calculation method are proposed to solve the problem. As an application example, an adaptive hybrid antenna array and its associated angle-of-arrival (AoA) estimation technique are presented. The MSE of the AoA estimation is simulated and compared with the recursive MSE bound and MCRB. The results show that the proposed recursive MSE bound provides a tighter lower MSE bound than the recursive MCRB.

A new type of hybrid antenna array consisting of analogue subarrays followed by a digital beamformer is proposed for practical implementation of long range high data rate millimetre wave communications systems. An adaptive algorithm, referred to as the differential beam search (DBS), is proposed for the angle of arrival (AoA) estimation to control the phase shifters in the analogue subarrays and to perform digital beamforming. This algorithm does not need the knowledge of a reference signal and effectively solves the phase ambiguity problem in AoA estimation inherent to the practical subarray configuration. The performance of the proposed DBS algorithms is demonstrated by simulations.

This paper proposes a parallel packet transmission (PPT) scheme based on orthogonal frequency division multiplexing (OFDM). The principle of the PPT scheme is to divide a packet into a number of smaller parallel packets, and transmit each smaller packet over an individual subcarrier of the OFDM symbols instead of spreading the data bits in a packet across a number of different subcarriers. It is proved theoretically that the proposed PPT scheme has higher average throughput than the conventional serial packet transmission without preceding. Furthermore, simulation results show that the OFDM system with PPT outperforms the preceded OFDM system with minimum mean squared error equalization in both uncoded and coded cases in terms of average throughput. The PPT scheme provides an alternative and simpler means to combat frequency-selective fading.

A new frequency and space preceding scheme for multiple input multiple output (MIMO) orthogonal frequency division multiplexing (OFDM) systems is presented. For frequency preceding, the data symbols to be transmitted are divided into multiple substreams, and a predefined unitary matrix is applied to each substream to obtain different linear combinations of data symbols in the substream to gain frequency diversity. For space preceding, different preceding matrices selected from a predefined orthogonal matrix are used to allocate each frequency precoded data symbol to all transmit antennas to gain spatial diversity. The number of substreams and the corresponding data symbol mapping scheme are also adaptively determined at the receiver under varying received signal strength and MIMO channel conditions, and are made available to the transmitter through a low-rate feedback channel. Simulation results show that the proposed MIMO OFDM system with adaptive substream selection can effectively exploit both frequency and spatial diversity, and deliver the maximum system throughput.

The performance of the peak and leading edge detection methods for time-of-arrival (TOA) estimation in band-limited systems is examined. Analytical expressions for the detection performance in the presence of both random noise and multipath interference are derived. A dimensionless performance factor is presented that allows simple comparisons of the TOA estimation algorithms. These equations allow the performance tradeoff analysis to be undertaken without the need for simulations. It is shown that the leading edge detection method has significantly better multipath mitigation characteristics than the peak detection one, but at the expense of inferior noise performance.

A hybrid anchor-free localisation scheme for multihop wireless sensor networks is presented. First, a relatively dense group of nodes is selected as a base, which are localised by using the multidimensional scaling method. Secondly, the robust quads (RQ) method is employed to localise other nodes, following which the robust triangle and radio range (RTRR) approach is used to perform the localisation task. The RQ and the RTRR methods are used alternately until no more nodes can be localised by the two approaches. Simulation results demonstrate that the proposed hybrid localisation algorithm performs well in terms of both accuracy and the success rate of localisation. To evaluate the accuracy of anchor-free localisation algorithms, the authors derive two different accuracy measures: the Cramer–Rao lower bound (CRLB) to benchmark the coordinate estimation errors and the approximate lower bound to benchmark the distance errors. Simulation results demonstrate that both the CRLB and the distance error lower bound provide references for the accuracy of the location algorithms.

Accuracy of ordinary sensor node localization in wireless sensor networks mainly depends on the signal parameter such as time-of-arrival and signal strength estimation errors and the accuracy of the anchor node locations. In this paper a low- complexity but efficient algorithm is derived to improve anchor location accuracy in the presence of both anchor-to-anchor distance and AOA estimates and GPS measurements. Also, a Lenvenberg-Marquardt (LM) optimization based algorithm is developed for accuracy improvement when anchor-to-anchor distance estimates and GPS measurements are provided. Further, we derive the Cramer-Rao lower bound (CRLB) to benchmark the anchor position accuracy. To our knowledge, improving anchor node location accuracy and deriving the CRLB in the presence of both GPS and anchor-to-anchor measurements in 3-D scenarios are not reported in the literature. Simulation results demonstrate that the proposed approaches can improve the anchor position accuracy substantially and that the accuracy of the two developed algorithms approaches the corresponding CRLB.

This paper addresses the problem of identifying NLOS propagation by applying the statistical decision theory. A time-of-arrival (TOA) based method is developed under idealized conditions to provide a performance reference. In the presence of both TOA and received signal strength (RSS) measurements, a joint identification method is derived to efficiently exploit both the TOA and RSS measurements. Analytical expressions for the probability of detection (POD) and the probability of false alarm (PFA) are derived. Simulation results demonstrate that the proposed methods perform well and the joint TOA and RSS based method outperforms the TOA based methods considerably. It is also shown that the analytical results agree with the simulated ones.

Thin paper presents the performance analysis of time-of-arrival (TOA) measurements by employing bandlimited radio signals. We choose one of the practical TOA estimation methods, peak tracking for study. First, two simplified models, i.e. the hyperbolic and Gaussian models are introduced to approximate the correlation diagram (correlogram) for ease of performance analysis. It is shown that the two models accurately approximate the true bandlimited correlogram especially around the peak. Concise expressions of the TOA estimation errors are derived for either Gaussian measurement noise or multipath interference when using bandlimited signals. The analytical results can be readily exploited to assist the design of TOA based positioning systems using peak tracking algorithm under bandwidth constraints.

In this paper a hybrid localization scheme for multihop wireless sensor networks is presented. At first a relatively dense group of nodes is selected as a base. Next, the multidimensional scaling (MDS) method is applied to localize the group of nodes. Then, the robust quads (RQ) method is employed to localize other nodes, following which we make use of the robust triangle and radio range (RTRR) approach to perform the localization task. The RQ and the RTRR methods are used alternately until no more nodes can be localized by the two approaches. Simulation results demonstrate that the proposed hybrid localization algorithm performs well in terms of both accuracy and success rate of localization.

In this paper, we propose a modified two stage Taylor series (TS) method for position estimation in a 3-D environment when either the time-difference-of-arrival (TDOA) or the distance measurements are available. It is aimed to improve the convergence performance of the traditional Taylor series method. Simulation results demonstrate that the modified TS method can improve the position estimation convergence considerably.

In the paper we consider location estimation in an non-line-of- sight (NLOS) environment. A constrained optimization based location algorithm is proposed to jointly estimate the unknown location and bias by using the sequential quadratic programming (SQP) algorithm. This method does not rely on any prior statistics information, and simulation results show that the proposed method outperforms the existing related methods considerably. To reduce the complexity of the SQP based algorithm, we further propose a Taylor-series expansion based linear quadratic programming (TS-LQP) algorithm. Simulation results demonstrate that the computational complexity of the TS-LQP algorithm is only a fraction of that of the SQP algorithm while the accuracy loss is marginal.

Most radio positioning methods are based on the measurements of distance between different wireless nodes. Owing to the existence of non-line-of-sight (NLOS) radio propagation, unfortunately, not all the measured distances are reliable. One way to tackle the problem of positioning is therefore to take two-steps: (i) identifying the NLOS measurements; (ii) smart signal processing of the mixed LOS and NLOS measurements. This paper is focused on the second issue. Under the assumption that the NLOS measurements have been identified, we first propose a simple method to suppress the effect of the NLOS error. Simulation results demonstrate that the proposed method achieves similar or better accuracy than several other known methods and the computational complexity is reduced considerably. We also present an optimal location estimator under the assumption of Gaussian distributed measurement noise and Rayleigh distributed NLOS error. Although it is difficult to achieve the optimal performance in practice due to modeling uncertainties, the optimal estimator provides a performance benchmark.

This article presents a near-far resistant detection scheme for asynchronous code-division multiple access with frequency offset. Based on a one-shot technique and Taylor expansion, a zeroth-order and a first-order one-shot linear decorrelating detector (LDD) are proposed. The zeroth-order LDD has simple architecture but suffers performance degradation for large frequency offset. The first-order one-shot LDD, with increased complexity, has very good near-far resistant property even for large frequency offset. Two versions of the first-order one-shot LDD are investigated according to different Taylor expansion approaches. The feasibility of the proposed detectors is demonstrated by computer simulations.

The well-known linear decorrelating detector (LDD) for direct-sequence code-division multiple-access (CDMA) systems provides near-far resistant performance when the timing of each user is accurately known. Traditional CDMA acquisition techniques suffer from high differences in power levels. The estimation accuracy for a user overwhelmed by stronger ones is likely to be unsatisfactory; at the same time, the interference from a user undergoing acquisition or tracking is not removed by the standard LDD. In this paper, a fully near-far resistant technique for acquisition and tracking for asynchronous CDMA systems applying the LDD is proposed, considering realistic band-limited signals. This technique is based on the adoption of a pair of special sequences equivalent to a dedicated access channel and is shown to provide a relatively fast and robust means to perform channel estimation both in case of single- and multipath channels.

Frequency-offset correction is considered for a HIPERLAN (HIgh-PErformance Radio LAN) system over the indoor radio channel. Since the multipath channel response is not known a priori, a viable frequency-offset estimator should not depend on such knowledge. Such an estimator, using a single sample per symbol, is derived for HIPERLAN. The estimator is shown to approach the Cramer-Rao bound for frequency-offset estimation over a multipath channel. A HIPERLAN system simulation example shows that the performance with an offset of 150 kHz is within 0.5 dB of that of a system with zero frequency offset.

This paper addresses the problem of selecting the optimum training sequence for channel estimation in communication systems over time-dispersive channels. By processing in the frequency domain, a new explicit form of search criterion is found, the gain loss factor (GLF), which minimizes the variance of the estimation error and is easy to compute. Theoretical upper and lower bounds on the GLF are derived. An efficient directed search strategy and optimal sequences up to length 42 are given. These sequences are optimal only for frequency domain estimation, not for time domain estimation.

This paper presents a new approach to reduced complexity detection of partial response continuous phase modulated signals over channels with severe intersymbol interference (ISI). The detector combines the overall channel response for the CPM modulator with that of a multipath channel to calculate the branch metrics of a breadth-first sequential detection algorithm known as the T-algorithm. To our knowledge the reported work is the first attempt to make use of such a technique for equalisation of quaternary partial response CPM signals transmitted over multipath channels. Simulation of the performance of the receiver in multipath channels is reported.

A fast computation method for the tap weights of a linear equaliser is developed. By approximating the channel autocorrelation matrix, which has a Toeplitz structure, by a circulant matrix, the tap weights can be solved via discrete fourier transform (DFT) techniques. For an equaliser with N taps, the approximate tap weights approach the exact tap weights as N/spl rarr//spl infin/. Excess mean-square error and bit error rate (BER) degradation caused by the approximate tap weights are computed for a representative channel.

To reduce the effects of intersymbol interference resulting from the dispersive nature of the indoor radio channel most HIPERLAN receivers will incorporate an adaptive equaliser. The computational complexity of several equaliser algorithms is estimated. The effect of frequency offset between the transmitter and receiver on the performance of such an equaliser is investigated. By employing a decision feedback equaliser incorporating a second order phase locked loop, the effect of both the intersymbol interference and frequency offset can be significantly reduced. Using such a technique, the packet error ratio (PER) of a HIPERLAN radio link in a multipath channel is found by simulation.