This paper studies asynchronous repetition slotted ALOHA (a-ReSA) for massive random access. Each user’s packet is repeated and allocated to randomly selected slots. Then, the users transmit simultaneously and arrive at the base station (BS) receiver with different delays, i.e., without user-synchronization. The BS receiver carries out an over-sampling-based operation, yielding an over-sampled signal space. We show that owing to user-asynchrony, the channel state information (CSI) can be acquired even when some users utilize an identical pilot sequence, i.e., pilot collision happens. Further, we develop an iterative soft cancellation detection and decoding that exploits the interference structure of the over-sampled signal for powerful multi-user decoding. Afterwards, packet cancellation for a-ReSA is utilized to solve packet collision. To characterize the performance of a-ReSA, we analyze the achievable channel parameter region (ACPR) and outage probability. Our analysis shows that the ACPR of the user-asynchronous scenario is considerably larger than that of user-synchronous scenarios. Further, we present an asymptotic analysis of the throughput of a-ReSA system. It is demonstrated that the normalized throughput of a-ReSA exceeds that of traditional ReSA by about 20% ~ 80%. We also show that the a-ReSA scheme is more robust than traditional ReSA in imperfect CSI scenarios. Numerical results are verified to agree with the analyzed results.

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.

This paper studies asynchronous repetition slotted ALOHA (a-RSA) for massive random access. Each user’s packet is repeated and allocated to randomly selected slots. Then, the users transmit simultaneously and arrive at the base station (BS) receiver with different delays, i.e., without user-synchronization. The BS receiver carries out an over-sampling-based operation, yielding an over-sampled signal space. We develop an iterative soft cancellation detection and decoding that exploits the interference structure of the over-sampled signal for powerful multi-user decoding. Afterwards, packet cancellation for a-RSA is utilized to solve packet collision. To characterize the performance of a-RSA, we analyze the achievable channel parameter region (ACPR) and outage probability. Further, we present an asymptotic analysis of the throughput of a-RSA system. It is demonstrated that the normalized throughput of a-RSA exceeds that of traditional RSA by about 20% ~ 30%.

Integrated sensing and communications (ISAC) is a key enabler for 6G, yet it introduces considerable interference that limits its performance. To minimize interference and enhance spectrum efficiency of the ISAC systems, we propose a user-grouping and full non-orthogonal multiple access (GF-NOMA) approach. Specifically, we first group communication users and sensing targets based on their spatial distribution, so that spatial beam orthogonality can be employed to reduce interference between different groups. Then, by taking sensing targets as virtual communication users, each group is fully multiplexed by NOMA in the power domain for interference cancellation. To optimize the performance of the proposed approach, we formulate a beamformer design problem to maximize the weighted sum of the communication throughput and sensing power. Meanwhile, we propose a double-penalty successive convex approximation (DP-SCA) algorithm to solve this problem by iteratively searching for the optimal solution. The simulation results demonstrate that the proposed approach improves the system performance by about 10% on average compared to the non-grouping approach. When compared to Partial-NOMA, the proposed Full-NOMA approach improves the communication users’ sum rate by about 20%.

Integrated communications and localization (ICAL) will play an important part in future sixth generation (6G) networks for the realization of Internet of Everything (IoE) to support both global communications and seamless localization. Massive multiple-input multiple-output (MIMO) low earth orbit (LEO) satellite systems have great potential in providing wide coverage with enhanced gains, and thus are strong candidates for realizing ubiquitous ICAL. In this paper, we develop a wideband massive MIMO LEO satellite system to simultaneously support wireless communications and localization operations in the downlink. In particular, we first characterize the signal propagation properties and derive a localization performance bound. Based on these analyses, we focus on the hybrid analog/digital precoding design to achieve high communication capability and localization precision. Numerical results demonstrate that the proposed ICAL scheme supports both the wireless communication and localization operations for typical system setups.

We investigate the problem of low-complexity, high-dimensional channel estimation with beamspace observations, for the purpose of localization. Existing work on beamspace ESPRIT (estimation of signal parameters via rotational invariance technique) approaches requires either a shift-invariance structure of the transformation matrix, or a full-column rank condition. We extend these beamspace ESPRIT methods to a case when neither of these conditions is satisfied, by exploiting the full-row rank of the transformation matrix. We first develop a tensor decomposition-based approach, and further design a matrix-based ESPRIT method to achieve auto-pairing of the channel parameters, with reduced complexity. Numerical simulations show that the proposed methods work in the challenging scenario, and the matrix-based ESPRIT approach achieves better performance than the tensor ESPRIT method.

The future sixth generation (6G) networks will feature great importance on the integration of communications and localization, to realize the Internet of Everything (IoE). In this paper, we investigate the hybrid precoding design for the integrated communications and localization (ICAL) in the massive multiple-input multiple-output (MIMO) low Earth orbit (LEO) systems. In particular, we first derive an upper bound of the communication spectral efficiency (SE) and the squared position error bound (SPEB) of localization. Then, we formulate a multi-objective optimization problem to simultaneously operate communications and localization. Simulation results demonstrate the satisfactory performance of the proposed massive MIMO LEO ICAL system for typical setups.

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.

Reconfigurable intelligent surfaces (RISs) have tremendous potential to boost communication performance, especially when the line-of-sight (LOS) path between the user equipment (UE) and base station (BS) is blocked. To control the RIS, channel state information (CSI) is needed, which entails significant pilot overhead. To reduce this overhead and the need for frequent RIS reconfiguration, we propose a novel framework for integrated localization and communications, where RIS configurations are fixed during location coherence intervals, while BS precoders are optimized every channel coherence interval. This framework leverages accurate location information obtained with the aid of several RISs as well as novel RIS optimization and channel estimation methods. Performance in terms of localization accuracy, channel estimation error, and achievable rate demonstrates the effectiveness of the proposed approach.

Networks in 5G and beyond utilize millimeter wave (mmWave) radio signals, large bandwidths, and large antenna arrays, which bring opportunities in jointly localizing the user equipment and mapping the propagation environment, termed as simultaneous localization and mapping (SLAM). Existing approaches mainly rely on delays and angles, and ignore the Doppler, although it contains geometric information. In this paper, we study the benefits of exploiting Doppler in SLAM through deriving the posterior Cramér-Rao bounds (PCRBs) and formulating the extended Kalman-Poisson multi-Bernoulli sequential filtering solution with Doppler as one of the involved measurements. Both theoretical PCRB analysis and simulation results demonstrate the efficacy of utilizing Doppler.

Radio localization is a key enabler for joint communication and sensing in the fifth/sixth generation (5G/6G) communication systems. With the help of multipath components (MPCs), localization and mapping tasks can be done with a single base station (BS) and single unsynchronized user equipment (UE) if both of them are equipped with an antenna array. However, the antenna array at the UE side increases the hardware and computational cost, preventing localization functionality. In this work, we show that with Doppler estimation and MPCs, localization and mapping tasks can be performed even with a single-antenna mobile UE. Furthermore, we show that the localization and mapping performance will improve and then saturate at a certain level with an increased UE speed. Both theoretical Cramér-Rao bound analysis and simulation results show the potential of localization under mobility and the effectiveness of the proposed localization algorithm.

5G millimeter wave (mmWave) signals have inherent geometric connections to the propagation channel and the propagation environment. Thus, they can be used to jointly localize the receiver and map the propagation environment, which is termed as simultaneous localization and mapping (SLAM). One of the most important tasks in the 5G SLAM is to deal with the nonlinearity of the measurement model. To solve this problem, existing 5G SLAM approaches rely on sigma-point or extended Kalman filters, linearizing the measurement function with respect to the prior probability density function (PDF). In this paper, we study the linearization of the measurement function with respect to the posterior PDF, and implement the iterated posterior linearization filter into the Poisson multi-Bernoulli SLAM filter. Simulation results demonstrate the accuracy and precision improvements of the resulting SLAM filter.

Millimeter wave (mmWave) signals are useful for simultaneous localization and mapping (SLAM), due to their inherent geometric connection to the propagation environment and the propagation channel. To solve the SLAM problem, existing approaches rely on sigma-point or particle-based approximations, leading to high computational complexity, precluding real-time execution. We propose a novel low-complexity SLAM filter, based on the Poisson multi-Bernoulli mixture (PMBM) filter. It utilizes the extended Kalman (EK) first-order Taylor series based Gaussian approximation of the filtering distribution, and applies the track-oriented marginal multi-Bernoulli/Poisson (TOMB/P) algorithm to approximate the resulting PMBM as a Poisson multi-Bernoulli (PMB). The filter can account for different landmark types in radio SLAM and multiple data association hypotheses. Hence, it has an adjustable complexity/performance trade-off. Simulation results show that the developed SLAM filter can greatly reduce the computational cost, while it keeps the good performance of mapping and user state estimation.

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.

Simultaneous localization and communication (SLAC) is a desirable feature of 5G and beyond 5G wireless networks. To be able to implement SLAC, efficient high dimensional channel estimation methods are critical. This work presents a low-complexity multidimensional channel parameter estimation via rotational invariance techniques (MD-ESPRIT). We use both the spatial smoothing and forward-backward averaging techniques to further explore data samples to extract multipath components (MPCs). We propose a one-dimensional Fast-Fourier-Transform- (FFT) and inverse-FFT-based approach to obtain the signal subspaces for angular frequency estimation. The geometry relationship between MPCs and positions is utilized for simultaneous positioning and mapping. Numerical results demonstrate the improved identifiability and low complexity performance of the proposed scheme.

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.

To alleviate the blockage effects involved in millimeter-wave propagation, we investigate network massive multiple-input multiple-output (MIMO) transmission where only statistical channel state information is available at base stations (BSs). We first establish a network massive MIMO transmission model over millimeter-wave bands using per-beam synchronization. We Figure out that the beam domain is in favor of performing transmission in this scenario. We also demonstrate that BSs can work individually when sending signals to user terminals. Based on these insights, the network massive MIMO precoding design is reduced to a network sum-rate maximization problem with respect to beam domain power allocation. By exploiting the sequential optimization method and random matrix theory, an iterative algorithm with guaranteed convergence is further proposed to solve the problem. Numerical results reveal that the proposed network massive MIMO transmission approach can effectively alleviate the blockage effects and provide substantial performance gains over the existing transmission approaches.

Mobility and blockage are two critical challenges in wireless transmission over millimeter-wave (mmWave) and Terahertz (THz) bands. In this paper, we investigate network massive multiple-input multiple-output (MIMO) transmission for mmWave/THz downlink in the presence of mobility and blockage. Considering the mmWave/THz propagation characteristics, we first propose to apply per-beam synchronization for network massive MIMO to mitigate the channel Doppler and delay dispersion effects. Accordingly, we establish a transmission model. We then investigate network massive MIMO downlink transmission strategies with only the statistical channel state information (CSI) available at the base stations (BSs), formulating the strategy design as an optimization problem to maximize the network sum-rate. We show that the beam domain is favorable to perform transmission, and demonstrate that BSs can work individually when sending signals to user terminals. Based on these insights, the network massive MIMO precoding design is reduced to a network sum-rate maximization problem with respect to beam domain power allocation. By exploiting the sequential optimization method and random matrix theory, an iterative algorithm with guaranteed convergence performance is further proposed for beam domain power allocation. Numerical results reveal that the proposed network massive MIMO transmission approach with the statistical CSI can effectively alleviate the blockage effects and provide mobility enhancement over mmWave and THz bands.

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.

In this paper, a turbo equalization scheme for MIMO-OFDM systems under imperfect channel estimation based on soft-input soft-output (SISO) minimum mean-square error (MMSE) sorted QR decomposition (SQRD) is proposed. A turbo structure consists of a SISO detector and a SISO decoder where extrinsic information is exchanged between the two SISO modules. Turbo equalization schemes are preferable in practical communication systems due to their good performance and acceptable computational complexity. MMSE-SQRD based SISO detection derives from SISO MMSE detection, and successive interference cancellation (SIC) is performed using a posteriori information obtained from previous detected symbols. Compared to SISO MMSE detection, MMSE-SQRD based SISO detection is of low complexity but has significant bit error rate (BER) performance enhancement. However, the derivation of the MMSE-SQRD based SISO detection scheme is under perfect knowledge of channel information at receivers. When channel estimation errors are presented, it has been pointed out that the system performance will degrade. In this paper, we studied this practical issue, and proposed the SISO MMSE-SQRD based turbo equalization under imperfect channel estimation. We first model the channel estimation error as added random Gaussian noise over the channel estimation matrix; based on that, we rederive the SISO MMSE detection for the data, and then redefine the extended channel matrix and receive vector by taking into account of channel estimation errors; after that, the SQRD algorithm is adjusted in accordance; MMSE-SQRD based data detection algorithm is finally performed. Numerical simulation results show that the proposed SISO MMSE-SQRD based turbo equalization for MIMO-OFDM systems under imperfect channel estimation outperforms the traditional MMSE based SISO detection with imperfect channel estimation in terms of BER performance and computational complexity.

In this paper, we propose a new precoding scheme that can introduce correlation between the original transmitted symbols. The precoding process is based on the operations in Galois Field with size q = 2m(GF (q)). In the existing precoding schemes such as orthogonal space-time block code, the generated symbols carry the information of all coded symbols; this correlation can be utilized by the receiver to provide diversity in space and time domain. Similarly, the proposed precoding scheme that utilizes the operations defined in GF (q) can also introduce such correlation. We evaluate the mutual information of the system and conclude that mutual information is always no less than that in the system without the proposed precoding scheme regardless of the source distribution. In addition, when the source is uniformly distributed, the proposed GF (q) precoding scheme achieves the maximum mutual information of the channel, i.e. channel capacity. Hence, we can derive that the proposed precoding scheme in GF (q) can preserve source information during the transmission in the channel. Convinced by the potential benefit of the increased mutual information, the proposed GF (q) precoding scheme is promising in approaching channel capacity.

In this paper, we investigate multi-user interference cancellation (MUI) schemes for deployment at roadside unit in vehicular ad hoc networks. Generally, MUI schemes can be divided into linear and nonlinear groups. In linear MUI schemes, successive and parallel interference cancellation schemes are widely used. In successive interference cancellation (SIC) schemes, the receiver will detect the user's data on a per user base, and immediately cancel the interference of the detected user for the next detection. On the contrary, parallel interference cancellation (PIC) schemes detect a group of users' data simultaneously, then cancel the interference of all users in the next round of operation. There exists error propagation problem in both successive and parallel interference cancellation schemes. To address the problem, ordered successive interference cancellation scheme has been proposed to improve the bit error rate (BER) performance by the receiver detecting the user with the highest instantaneous signal-to-interference-plus-noise ratio (SINR) and canceling the interference of that user for another continuously. The process repeats until all users' data are detected. Besides, iterative processing techniques are also introduced to further improve the system BER performance. In this paper, we study and compare the interference cancellation schemes for uplink transmission in vehicular ad hoc networks. Simulation results show that the BER performance is much better in ordered successive interference cancellation schemes than both the SIC and PIC schemes, especially in high signal-to-noise ratio regions where the multi-user interference becomes dominant. It is also shown through the study that the ordering procedure can efficiently avoid the error propagation problem in vehicular networks.