This paper presents a systematic analysis of inter-symbol and inter-carrier interference (ISCI) modeling in doubly dispersive channels for integrated sensing and communication (ISAC) systems. We propose a generalized OFDM (Weyl-Heisenberg) framework to evaluate four ISCI treatment approaches: (1) explicit estimation and compensation, (2) complete ignorance, (3) uncorrelated colored noise approximation, and (4) correlated colored noise modeling. Through continuous delay-Doppler channel characterization, we derive LMMSE channel estimators and corresponding estimation errors (as sensing metrics) for both pilot-assisted and fully-known symbol scenarios. The communication performance is quantified via ergodic capacity bounds under imperfect CSI. Our theoretical analysis and numerical results reveal fundamental performance-complexity trade-offs, providing insights for practical ISAC waveform and receiver design in doubly dispersive channels.

Motor imagery (MI) based brain–computer interfaces (BCIs) enable active device control through electroencephalography (EEG), offering contactless human–machine interaction. However, realistic MI-EEG decoding is challenged by non-uniform electrode coupling, long-range rhythmic dynamics, unstable second-order statistics, and blurred boundaries among fine-grained motor intentions. These challenges require topology-aware, statistically stable, and boundary-discriminative EEG representations, which remain insufficiently addressed in realistic decoding scenarios. To address these challenges, we propose ASDL-EEG, a coordinated representation learning framework for robust MI-EEG decoding. First, ASDL-EEG introduces a topology-aware first-order encoder that performs asymmetric electrode-axis spatial aggregation with multi-scale receptive fields and dilated temporal modeling to capture non-uniform spatial dependencies and long-range MI rhythms. Second, the Log-Diagonal Riemannian (LDR) extractor constructs aligned log-diagonal covariance descriptors as a compact, stability-oriented alternative to high-dimensional full covariance representations. To further enhance boundary discrimination, we propose RPA-v2, a sample-specific hard-negative prototype alignment method for fused first- and second-order EEG embeddings enlarging the margin between the target prototype and the most confusing non-target prototype. We further construct CW-MI-5, to the best of our knowledge, the first five-class MI-EEG dataset for intelligent-cockpit car-window control, serving as a naturalistic benchmark with synchronized EEG sensing and cockpit interaction cues. Experiments on CW-MI-5 and BCI Competition IV-2a show superior accuracy and Cohen’s kappa, validating ASDL-EEG in both cockpit interaction and standard MI benchmark settings.

this paper, we investigate the Inter Symbol and Carrier Interference (ISCI) of doubly-dispersive channels in highly dynamic scenarios from the continuous and discrete perspectives, respectively, and thoroughly analyze its impact on the performance of communications systems. Due to its robustness against the Doppler effect in time-varying channels, Orthogonal Time Frequency Space (OTFS) modulation has gained significant research interest, with many equalization algorithms proposed. However, existing studies either fail to fully account for ISCI or suffer from prohibitive complexity. In this study, we accurately quantify the ISCI of doubly-dispersive channels and provide an in-depth analysis from continuous and discrete channel models, respectively. Based on this, we propose a low-complexity Minimum Mean Square Error (MMSE) equalization to equalize ISCI in doubly-dispersive channels. The proposed algorithm demonstrates a reduction in complexity of the MMSE equalization fromO(M3N3)toO(MNL2bw), whereLbwdenotes the bandwidth of the time domain channel matrix. Concurrently, it has been demonstrated to significantly reduce the bit error rate (BER), thereby enhancing communication performance.

Leveraging communications signals for localization has attracted increasing attention. However, most existing studies face two limitations. First, timing-frequency synchronization (TiFSync), essential for reliable demodulation, is often ignored in modeling, leading to over-optimistic bounds and degraded accuracy in localization. Second, joint position and velocity estimation (PaVE) has not been fully investigated, including the analysis of the requirement for local identifiability and the corresponding low-complexity algorithm design. To address these issues, we propose an uplink orthogonal frequency division multiplexing multiple-input-multiple-output (OFDM-MIMO) PaVE framework under imperfect TiFSync and multipath conditions. An equivalent system model is first established to demonstrate the potential of PaVE. We then derive the Fisher information matrix (FIM) and Cramér-Rao lower bound (CRLB). By analyzing the FIM rank, we examine the local identifiability of our PaVE framework. The results show that four synchronous base stations (BSs) are required to ensure the local identifiability of PaVE of an asynchronous mobile device. We also develop a decentralized two-step method for PaVE: (i) estimating the channel parameters through the multi-dimensional discrete Fourier transform (DFT)-based interpolation; (ii) performing PaVE based on a weighted least squares (WLS) estimator, which is solved by the Gauss-Newton method with a good initial guess. This algorithm only requires the exchange of channel parameter estimates and TiFSync adjustments among BSs, resulting in a very low communications overhead. Simulation results demonstrate that our proposed PaVE framework achieves fast convergence and near-CRLB accuracy. Comparisons with existing works confirm its reliability in practical scenarios.

Multi-target detection and estimation present critical challenges in 5G NR-based multi-input multi-output (MIMO) integrated sensing and communications (ISAC) systems. In this paper, we investigate a 5G NR-based MIMO-ISAC system, leveraging 5G NR pilots, in a practical multi-target scenario with an unknown target number. We propose a novel joint target detection and 4-dimensional (4D) estimation (azimuth, elevation, distance, and velocity) algorithm, JTD4DE, by exploiting MIMO radar gain. First, we derive the Cramér–Rao lower bounds (CRLBs) for 4D estimation to establish theoretical estimation limits. Then, we propose a low-complexity virtual array-based 4D estimation method, 4DE, which successfully improves the estimation accuracy. Furthermore, we apply the generalized maximum likelihood (GML) rule to effectively determine the target number. Numerical results demonstrate that our proposed algorithm accurately detects the number of targets and significantly improves the 4D estimation accuracy, highlighting its substantial value for 5G NR-based MIMO-ISAC systems.

Nowadays, localization functionality is becoming increasingly critical in Internet of Things (IoT) applications. However, the localization accuracy of long-range IoT systems is fundamentally constrained by the limitations of narrowband transmission, low power consumption, and imperfect timing-frequency synchronization. To overcome these problems, this paper proposes a frequency-hopping (FH)-assisted localization system based on the existing LoRa technique under imperfect synchronization. The core idea is that localization accuracy can benefit from an equivalent wider bandwidth by transmitting symbols over multiple frequency bands and jointly processing the collectively received symbols. Specifically, we first develop a system model for the FH-assisted LoRa localization, incorporating the effects of imperfect timing-frequency synchronization. We then derive the Cramér-Rao Lower Bound (CRLB) for localization accuracy in the continuous-time domain, quantifying the theoretical performance limits with an infinitely wide sampling bandwidth. Furthermore, we propose a low-complexity 2D discrete Fourier transform (DFT)-based channel estimation algorithm. The estimation results are then used to localize the target device in a time-difference-of-arrival (TDoA)-based trilateration scheme. Numerical results demonstrate that our proposed FH-assisted LoRa localization system can achieve tens-of-meter-level accuracy in both LoS and multipath environments, significantly outperforming current LoRa localization systems with an accuracy of around 200 m.

Activity detection plays an important role in grantfree random access, a promising approach for handling a large number of users in use cases like massive machine type communication (mMTC). Existing activity detection algorithms cover various scenarios but overlook wideband distributed antenna systems, a practical configuration for next-generation wireless networks. When following conventional activity detection methods, sparse Bayesian learning (SBL) could be an option in this case. However, SBL-based methods for wideband systems lack the consistency of maximum likelihood estimation (MLE), resulting in unsatisfactory detection performance. This paper proposes a novel distributed activity detection framework for wideband cell-free multiple-input and multiple-output (MIMO). Specifically, we provide a novel uplink channel model for activity detection in wideband cell-free MIMO, accounting for asynchronous reception. Additionally, we present possible SBLbased methods, identifying their limitations, which motivates the development of a new approach for activity detection. Next, we propose a covariance-matching distributed activity detection framework that matches the sample covariance matrix to the estimated covariance matrix. Simulation results demonstrate the effectiveness of the proposed distributed algorithm.

The Orthogonal Frequency Division Multiplexing (OFDM) has been very successful in terrestrial communication, but it is challenged in underwater acoustic (UWA) channels. This is because OFDM was engineered for under-spread channels, while UWA channels are intrinsically over-spread. In this paper, we will represent the channel in the scale-delay (S-D) domain and propose the Mellin-Frequency Division Multiplexing (MFDM) modulation scheme. When represented in the S-D domain, the received signal is equal to the$\omega$-convolution of the transmitted signal and the channel response. We can then convert the signals and channel response to the Mellin-Frequency (M-F) domain through the Mellin-Fourier transform. The$\omega$-convolution thus becomes point-wise multiplication, leading to low-complexity detection. Based on this motivation, we model the system in a continuous form and then propose a discretized approach for practical system implementation. The symbol error rate (SER) performance of MFDM is compared with two existing modulation schemes: Orthogonal Delay Scale Space (ODSS) and Cyclic Prefix-OFDM (CP-OFDM). It is not surprising to see that CP-OFDM performs the worst. As for the other two modulation schemes, ODSS is based on scale-delay domain equalization, and the interference among symbols resulting from the$\omega$-convolution was ignored. In comparison, the equalization was conducted in the Mellin-Frequency domain for the proposed MFDM, leading to improved SER performance.

The rise of spy cameras has become a global concern regarding personal privacy. Unfortunately, affordable and easily accessible detection methods are lacking, leaving individuals vulnerable to these intrusive devices. Currently, most research focuses on detecting cameras based on their wireless transmission. Some of the latest research can detect other types of emissions from cameras but require costly equipment like universal software radio peripherals (USRP) or thermal cameras. However, this paper introduces a ubiquitous, cost-effective (less than $2) system, CamFirm, to identify and locate spy cameras regardless of their transmission mode. CamFirm only requires wired headphones and a Frequency Modulation (FM) module. Despite limitations of the FM module, such as narrow bandwidth, distortion, and vulnerability to interference, CamFirm remains capable of accurately identifying camera signals and pinpointing their locations. By utilizing the harmonics of digital circuits for signal screening, CamFirm can detect the photosensitive sensor signature of cameras and ascertain its direction through the attenuation of electromagnetic waves by the human body. The camera’s position can be further confirmed by activating a phone’s flashlight. Through extensive testing on 18 different cameras, CamFirm can achieve a median detection distance of 2.6 meters, effectively guiding users in locating the hidden camera.

Fast underwater localization serves as a critical application of Internet-of-Underwater-Things (IoUT), such as underwater search and rescue. Narrowband sinusoidal pulses are very commonly used in locator beacons installed on flight recorders. A mobile anchor (e.g., autonomous underwater vehicle (AUV)) has to keep receiving the signal and measuring Doppler shift for a long time, so that enough information can be collected for reliable localization. The need of a long observation window is deeply rooted in the fact that the Doppler shift measurements are highly correlated when they are taken at closely located spots. In this paper, we will show that by replacing the narrowband beacon signal with a wideband one, high-accuracy positioning can be achieved within a short period of time. The basic idea is to simultaneously measure Doppler shift and time of arrival (ToA) from wideband signals, and the errors spaces corresponding to these two measurements are complementary even when they are taken at the same position. The Cramér-Rao bound (CRB) will be derived for such a system. In low-signal-to-noise ratio (SNR) regime, the observation window has to be prolonged for effective information extraction, and we will see that the wideband signals still have an edge over the narrowband signals in positioning accuracy. Efficient algorithms are designed for positioning and the closed-form positioning error is derived. We also show that the localization accuracy will experience a significant drop when ToA is replaced by time difference of arrival (TDoA), because the perfect complementation no longer holds. The performance of the proposed algorithm, along with corresponding comparisons, is verified through simulations over various parameters such as SNR, number of measurements, and length of observation window, etc.

For underwater localization, mobile anchors are generally preferred because they are easy to deploy and retrieve, such as the autonomous underwater vehicle (AUV). However, a big disadvantage is that we can only install small sonar arrays on AUVs due to their limited size. This leads to poor angular domain resolution. On the other hand, the mobility makes it possible for us to capture multiple snapshots of the acoustic signals when the AUV is located at different positions, and synthesize a virtual array, i.e., the Doppler effect. In this case we can equivalently obtain a large virtual uniform planar array (UPA), which gives much higher spatial resolution and positioning accuracy. Mathematically, we have not only the DoA (Direction of Arrival) measurements, but also the Doppler shift measurements for positioning. In this paper, we propose a low-complexity algorithm to jointly estimate Doppler shift and DoA. Based on these estimates, we can obtain a series of nonlinear equations concerning the target's position. To solve these equations, a two-phase localization algorithm of linear computational complexity will be presented. We also derive Cramer-Ran lower bound (CRLB) of the system as a benchmark. The theoretical analysis will be verified through extensive simulations.

Efficient target localization is critical to many marine applications in the Internet of Underwater Things (IoUT). Doppler effect becomes more predominant in the underwater environment when the relative speed of the moving object, i.e., the observer, to the signal propagation speed in water is much larger than that in the air. This along with the observer-target geometry will bring in a notable impact on the localization performance. In this article, we derive the Cramér–Rao lower bound (CRLB) and formulate the A-optimality criterion-based observer trajectory optimization problem to improve the localization performance based on time delay and Doppler shift measurements. We show that in the worst case scenario, there will be a conflict between the nonaccessible zone constraint and the observer dynamics constraint, which will lead to an erroneous result. To address this problem, we propose a warning zone-based augmented Lagrange multiplier method (ALMM) where the nonaccessible zone constraint is relaxed to resolve the conflict and ensure the nonaccessible requirement of the targeted zone is maintained. Performance evaluations are conducted through extensive simulations for different scenarios, and the results are compared to other methods with or without trajectory optimization. We demonstrate that trajectory optimization using time delays and Doppler shifts can greatly improve the target localization accuracy in underwater networks.

Next generation cellular network is expected to provide the simultaneous high-accuracy localization and ultra-reliable communication services, even in high mobility scenarios. To that end, the novel orthogonal time frequency space (OTFS) modulation has been developed as a promising physical-layer transmission technique, evident by the outstanding performance in terms of robustness against time-frequency selective fading over the orthogonal frequency division multiplexing (OFDM) counterpart. However, when OTFS meets massive multiple-input multiple-output (MIMO), the specific conditions, under which the delay-Doppler (DD) domain channel model holds, are not identified. In addition, the channel estimation and localization performance in such system is rarely studied. In this work, we target at these new challenges, and conduct comprehensive modelling, performance analysis, and algorithm design for massive MIMO-OTFS based simultaneous localization and communications. Specifically, we derive new channel models for the massive MIMO-OTFS system, which captures both time-frequency dispersion and spatial wideband effects. The specific conditions, under which the new models hold has been unveiled as well. Based on the new models, we establish the theoretical foundations for channel estimation and localization, by deriving the Cramér-Rao lower bounds of channel parameter and location estimation errors. Such bounds have been achieved with the newly designed low-complexity channel estimation and localization algorithms. Numerical simulations of the proposed framework with prevailing pulse functions are also conducted and the results validate the proposed designs and analysis.

Massive multiple-input-multiple-output (MIMO) and millimeter wave have been adopted as the enabling technologies for the 5G and beyond 5G (B5G) systems. A challenging problem introduced by the use of large antenna size and wide bandwidth is beam squint, i.e., spatial-wideband effect. Beam squint can significantly degrade the channel estimation performance for conventional channel estimators. Research effort on channel estimation under beam squint conditions has been very limited. For the few available work that attempts to address this problem, they require either all subcarriers or multiple symbols used as pilot for channel estimation, so large overhead becomes inevitable. Therefore, in this paper, we propose an efficient channel estimation method that only requires a small number of subcarriers. The channel estimation problem is formulated as a nonlinear least squares optimization problem. Initial parameter estimation is critical, which will affect the efficiency and convergence of the proposed algorithm. Using a densely-spaced antenna structure and consecutive subcarriers assignment approach, we can effectively avoid the aliasing effect and reduce the ambiguity during the initialization phase. A subcarrier assignment criterion is proposed to achieve the optimal performance. Closed-form expressions of the Cramér-Rao lower bound (CRLB) and the achievable rate are derived to evaluate the performance. Both simulation results and theoretical analysis show that even with a small number of subcarriers, the estimation error closely approaches the CRLB, and its effect is negligible compared with the noise when evaluating the signal-to-noise ratio with a simple linear detector. Furthermore, the number of pilot subcarriers has little impact on the achievable rate.

Internet of underwater things (IoUT) attracts many interests in these years both in academia and industry, such as marine data collection, pollution monitoring, and offshore exploration. As a fundamental issue of IoUT, the underwater acoustic channel experiences long delay and temporal-spatial uncertainty compared with terrestrial communications and networks. It is difficult to capture full characteristics of the underwater acoustic channel by statistical models. In this paper, we investigate the properties of acoustic propagation in seawater and different underwater acoustic channel models. Moreover, we survey five underwater acoustic channel models, including ray-theoretical model, normal mode model, multipath expansion model, fast-field model, and parabolic equation model, which are the corresponding solutions of the wave equation. We conclude the paper with the characteristics of each model in terms of different aspects.

Wireless sensor network (WSN), consisting of several sensor nodes, is one of the most promising technologies emerged in the past decade. The positioning system for WSN is particularly meaningful and widely used in the military surveillance, air-sea rescue, traffic monitoring, and etc. However, the traditional positioning system always suffers from deployment and maintenance of anchors. In this paper, we propose a positioning system employing a Raspberry Pi platform attached to a DJI drone as a mobile anchor. The DJI drone can serve as multiple virtual anchors by moving and broadcasting its location information periodically. Thus, it is possible to localize sensor node by itself when the sensor node collects the drone's position. A Gauss-Newton method is applied to improve the accuracy of the proposed positioning system. We also elaborate the adaption of the Gauss-Newton method with the geodetic coordinates. The goal of the proposed positioning system is to achieve higher accuracy and higher coverage at lower cost.

As the data traffic in future wireless communications will explosively grow up to 1000-fold by the deployment of 5G, several technologies are emerging to satisfy this demand, including multiple-input multiple-output (MIMO), millimeter wave communications, Non-orthogonal Multiple Access (NOMA), etc. Millimeter wave communication is a promising solution since it can provide tens of GHz bandwidth by fundamentally exploring higher unoccupied spectrum resources. As the wavelength of higher frequency shrinks, it is possible to design more compact antenna array with large number of antennas with independent RF (Radio Frequency) chains, causing high cost and complexity. By exploring the spatial sparsity of the millimeter wave channels, lens antenna array has been investigated recently as a promising choice with limited RF chains and low complexity. In this paper, we investigate the outage probability for highway communication systems with lens antenna array, under overtaking scenario, where high mobility of users is expected. When a vehicle is trying to pass another one, the channels between these two vehicles and the RSU (Road Side Unit) are unresolvable, thus causing outage for at least tens of symbol durations. We apply power-domain NOMA in this scenario, where these two users are paired by a threshold derived with the QoS of each user, to alleviate this problem and achieve low outage probability.

Millimeter wave communications show great potentials in many applications, one of which is the high-speed railway(HSR) communication system. However, a major challenge is the Doppler effect caused by the relative-movement between the train and the base station (BS), which leads to fast channel variation. To compensate for the Doppler shift, an accurate channel model is indispensable, and the far-field channel model is generally employed, which assumes that the dimensions of the antenna arrays are negligible compared to the distance between transmitter and receiver. This model is widely used in Cellular systems, but the underlining assumption is not always true for railway communication systems. In this paper, the modeling of the Doppler effect for millimeter wave in HSR communications is conducted, and data-aided Doppler estimation and compensation algorithms are designed based on the new model. We show that the conventional far-field channel model is based on the first-order Taylor expansion of the actually channel, and the second-order component cannot be ignored for HSR communications. Extensive simulations are conducted to verify the validity of the new model and the effectiveness of the proposed algorithms.

The autonomous underwater vehicle(AUV)-aided localization techniques for underwater acoustic devices show promising applications in many scenarios, and most researches in this area are based on the time of arrival (ToA) or the time difference of arrival (TDoA) measurements. However, these measurements are not readily available. To develop a more universally applicable scheme, we investigate the possibility of employing the Doppler shift measurements for underwater localization of acoustic devices in this paper. To be specific, we employ a low-complexity algorithm for Doppler estimation, and prove that the estimation error can be well approximated by zero-mean Gaussian distribution. Based on the Doppler estimates, we can obtain a series of nonlinear equations. To solve them, we propose a two-phase linear algorithm to obtain high-accuracy position information of the target devices. Compared with the conventional iterative algorithms, the proposed one does not require initial estimate. Both the closed-form localization error and the Cramér-Rao lower bound are presented. They prove to be consistent for reasonably small Doppler estimation error. Besides, we conduct simulations to verify the theoretical analysis. Moreover, the complexity of the proposed algorithm only grows linearly with the number of Doppler shift measurements.

Underwater event/object detection is an enabling technique for many marine applications. for the surveillance of target water areas, the future underwater network can serve as a backbone system, and every sensor in this network is an agent. When the target moves into the target area or when an event happens, the agents will detect acoustic signals from the target or event. The acoustic waves arrive at different agents at different time. Based on the correlation of the received signals between two agents, the time difference of arrival (TDoA) can be estimated, which locks the target/event’s position on one branch of a hyperbola, represented by a nonlinear equation. With three or more agents, the target/event’s position can be uniquely decided. To make this system universally applicable, the average underwater acoustic velocity is also assumed to be unavailable, and a two-phase linear algorithm is proposed. A coarse estimation is obtained in Phase I, and the result is further refined in the Phase II. Extensive simulations are provided to verify the effectiveness of the proposed system.

The deployment of a large-scale array of hydrophones in underwater acoustic (UWA) communications brings numerous benefits, in terms of high spectrum and energy efficiency, and the high data rate communications. However, along with the merits, large array signal processing is known to be computationally costly and long processing delay required. Even with linear detection methods such as minimum mean-square error (MMSE) based schemes, the computational complexity is still considerable as the matrix inversion operations are involved. With Gauss-Seidel method, the matrix inversion operations are avoided, while the iterative processing achieves comparable system performance to the MMSE-based schemes. However, Gauss-Seidel method introduces successive data detection, causing significant processing delay. Meanwhile, the successive detection structure is inefficient in hardware implementation. In this paper, we propose a block Gauss-Seidel method for large array signal detection in UWA communications. In the proposed scheme, Gauss-Seidel method is performed on a set of small size block matrices, and the processing on each block can be parallelized. As a result, the total processing delay can be greatly reduced. Moreover, the parallel processing structure is quite efficient for hardware implementation. We also utilize the UWA channel model developed in recent work to investigate the performance of the proposed scheme, and the results are promising.

It is known that the acquisition of the complete channel state information at receivers is difficult in multiple‐input multiple‐output (MIMO) systems. Channel estimation error is unavoidable in practical applications. Under imperfect channel conditions, the channel estimate is directly applied to the equalisation process in the conventional minimum mean‐square error (MMSE)‐based turbo equalisation scheme. A few studies treat the channel estimation error as an independent component from the channel estimate and slightly enhanced performance is achieved. Unlike the existing work, the authors derive the MMSE‐based iterative processing conditioned on channel estimate. Moreover, they note that in low‐density parity check coded systems, the parity‐check procedure is also involved. The pass in parity check indicates that the message bitstream is successfully recovered. This information can be utilised to reduce the overall computational complexity by degrading the MIMO size since the unknown parameters are reduced. By extending the analysis in a small‐scale MIMO system to a large‐scale one, they propose to utilise the normalised transmission power in the development. Numerical results show the proposed schemes outperform the existing schemes in terms of system bit error rate and computational complexity performance.

Pilot contamination has been known as one of the most challenging issues in massive multiple-input multiple-output (MIMO) systems. Every user will experience interferences from users in adjacent cells who employ the same pilot sequence. For cell-edge users, pilot contamination is particularly detrimental, because their signals might be overwhelmed by the interference. In this paper, we propose a pilot decontamination method based on a spatial filter, which exploits the spatial sparsity of massive MIMO channels. In massive MIMO systems, the communication protocols are generally divided into four phases: pilot transmission, processing, uplink data transmission, and downlink data transmission. In the first phase, the base station (BS) receives both the desired signal and the pilot contaminated signal. In the second phase, all users in the target cell stay silent for one symbol period, and the BS only receives interference from adjacent cells. The fast Fourier transform can then be employed to analyze the spatial spectrums of the received signals. The spatial sparsity of the massive MIMO channels makes it possible to identify the pilot contamination components by comparing the two spectrums on different spatial signatures (or angles of arrival). A spatial filter can then be constructed to eliminate pilot contamination. Both the theoretical analysis and simulation results demonstrate the effectiveness of the proposed method, whose complexity is comparable to that of the traditional matched filter-based channel estimator.

Massive MIMO has been viewed as one of the most promising techniques for 5G communications. However, its potential is highly confined by the so called pilot contamination issue. For cell-edge users, this problem is particularly critical, because their signals might be overwhelmed by their peers in adjacent cells. In this paper, we propose an innovative pilot decontamination method based on spatial filter, which can greatly improve the channel estimation accuracy for cell-edge users. There are two phases in the proposed method: pilot transmission phase and idle phase. During the first phase, users transmit pilot sequences to BS, and the BS employs matched filter to obtain channel estimation, which contains both desired signal and pilot contamination. In the second phase, all users in the target cell stay silent for one symbol period, and the BS receives signal from adjacent cells. Then, fast Fourier transform can be employed to analyze the spatial spectrums of received signals in these two phases. By comparing these two spectrums, pilot contamination components can be identified, and a spatial filter can be constructed to eliminate them. Both theoretical analysis and simulation results are presented to justify the efficacy of the proposed method.

For the purpose of localization and time synchronization of underwater sensor networks, buoys are generally distributed on the sea surface of the area of interest, serving as fixed anchors. However, this method is not economical and has poor scalability. An alternative is to employ an autonomous underwater vehicle (AUV) as a mobile anchor. By receiving the periodical broadcast signals from the AUV, any sensor in the communication range can measure time of arrival of received packets and obtain a series of nonlinear equations. In this letter, we proposed an efficient linear algorithm to solve the nonlinear equations, and gave closed-form positioning and synchronization error analysis. Besides, we show that the proposed method can approach the Cramér-Rao lower bound by both theoretical analysis and simulation.

Most analytical work for zero-forcing (ZF) receivers are conducted for small-scale multiple-input multiple-output (MIMO) systems in large signal-to-noise ratio (SNR) region and under small channel estimation error conditions. Using large receiving antennas, systems are expected to work in the low-SNR region and under large channel estimation error. In these conditions, we observe an obvious mismatch between the existing analytical results and the simulations. In this letter, we derive an accurate analytical bit error rate (BER) expression for ZF receivers under imperfect channel estimation. We show that our results match nicely with the simulations in small-scale and large-scale MIMO systems, even when large channel estimation error presents.

In this paper, we investigate low-complexity data detection scheme for massive multiple-input multiple-output (MIMO) uplink transmission. We propose to utilize the stair matrix, instead of diagonal matrix in existing proposals, for the development, and achieve near linear minimum mean-square error detection performance. We first demonstrate the applicability of the proposed method by showing that the probability (that the convergence conditions are met) approaches one as long as sufficiently large number of antennas are equipped at the base station. We then propose an iterative method to perform data detection and show that much improved performance can be achieved with the computational complexity remaining at the same level of existing iterative methods, where the diagonal matrix is adopted. Furthermore, we conduct numerical simulations, and the results validate the significant performance enhancement of using the stair matrix over the diagonal matrix in all performance aspects. Moreover, we apply the proposed scheme to a massive MIMO system, where the extended vehicular A channel data are generated. The performance improvement of the proposed scheme over existing proposals is also validated.

Vehicular ad hoc networks (VANET) have gained increasing interests due to the development of the intelligent transport systems (ITS), aiming to improving road safety, traffic efficiency, and providing in-vehicle entertainment. Meanwhile, the fast developing 5G cellular networks have brought innovative techniques to support the demand of ITS such as high rate communications, low latency and high energy efficiency. Massive multiple-input multiple-output (MIMO), as one of the key technologies in future 5G, is to deploy hundreds of antennas at base station, serving up to tens of users simultaneously in shared time-frequency resources. This technique, is attractive for the wireless vehicle-to-infrastructure (V2I) access for multiple vehicles on the road. However, in massive MIMO, the computational complexity is costly even with linear detection methods. The iterative methods, such as Gauss-seidel based signal detection method, are preferred as the computational complexity is low, and near-optimal system performance can be achieved. In this paper, we propose block Gauss- Seidel method based signal detection in V2I massive MIMO uplink transmission. The proposed scheme utilizes the properties of block diagonal matrix, and the Gauss-Seidel method is applied to each block. By doing that, the processing at each block can be paralleled, hence the new structure is much efficient for hardware implementation. In addition, we demonstrate that the system performance is quite close to the original Gauss-Seidel method but at low complexity and fast processing time.

In massive multiple-input multiple-output (MIMO) systems, linear minimum mean-square error (MMSE) detection can achieve near-optimal performance. However, it suffers from high computational complexity due to the involvement of matrix inversion. This issue becomes severer when user number (U) and receive antenna number (S) increase. Existing approaches such as Neumann series expansion method, Gauss-Seidel and Jacobi methods, can partly address this issue by approaching the matrix inversion with matrix multiplications or solving linear equations with iterative methods, respectively. However, matrix multiplications and the initialization for iterative methods are still costly. In this paper, we propose a further improved Jacobi method based soft-output massive MIMO detection scheme. The contributions include the use of matrix-vector product and a new approach to compute the log likelihood ratio (LLR). By using the matrix-vector product, the overall computational complexity is reduced from O (B × U2) to O(B × U). The new approach uses the noise-plus-interference (NPI) from the MMSE estimation, instead of using that from the first iteration. We then propose an approximation method to obtain the covariance of the NPI from MMSE estimation. Finally, we demonstrate through numerical simulations that the proposed scheme outperforms the existing schemes in terms of computational complexity and system bit error rate performance.

Compared with the traditional multi‐user MIMO (multiple‐input and multiple‐output), massive MIMO aims to serve tens of users with hundreds of antennas on each base station. All users can use the same time–frequency resources through space division multiple access, leading to vast improvement on spectral efficiency. However, to achieve the benefits, channel state information is usually required, and the acquisition is difficult in massive MIMO systems. Theoretically, each user should be assigned with orthogonal pilot sequences to avoid interference; however, due to the huge number of users (much more than available orthogonal pilot sequences) in service, pilot reuse in adjacent cells is inevitable, causing inter‐cell interference. This phenomenon is often referred to as pilot contamination (PC) and is believed to be the fundamental limit on system capacity of massive MIMO systems. To solve this problem, many methods have been proposed since 2010, when the concept of massive MIMO was first proposed. In this study, the authors reviewed these methods, categorised them into four groups and compared their advantages and limitations. Although a survey on PC has been conducted by Elijah et al ., where they tried to cover various aspects of the PC issue, their work focuses on the analysis of rationale and limitations of different contamination mitigation methods. Besides, performance evaluations are conducted and presented.

Abstract Wireless local area network fingerprint‐based indoor location system is a hot topic these years because it needs no extra hardware and is very easy to deploy. However, it demands a database containing the distribution of received signal strength (RSS) of the area of interest,called radio map. Conventionally, we need to grid the area densely and manually measure RSS values on intersections, which will consume a lot of time and human resources. What is worse, change of the environment may render this database totally useless. Our consideration is to measure RSS on a small amount of these intersections and use them to build a radio propagation model. Then, this model can be deployed to predict RSS values of other intersections and reconstruct the radio map. In other words, we only need to collect a very small part the radio map and utilize the radio propagation model to recover the whole one. So far, many models have been proposed, among which the one suggested by Seidel, named floor attenuation factor propagation model, achieves great balance between computational request and accuracy. But it is not compatible with environments in some scenarios. So as to compensate for this deficiency, we take into account the angles formed by signal and surfaces of obstacles, and the results show better compatibility. The proposed model has four parameters that are related to the environments, and our second contribution in this paper is to propose a method to determine them. In fact, after collecting a small part of the radio map, we can estimate these parameters with least square method. Then, these parameters can be used to predict the signal strength at any other points in the same environment, and the whole radio map is rebuilt. According to practical experiments, performance of the radio map built by the proposed model is not as good as the manually collected one, but 80% of collecting labor is saved. Copyright © 2015 John Wiley & Sons, Ltd.