In-band full-duplex (IBFD) communication in underwater acoustic channels is challenged by strong and time-varying self-interference (SI). To detect data symbols, the receiver needs to suppress the SI and equalize the resultant signal to compensate for the intersymbol interference (ISI) caused by the remote transmission (RT) channel. In this article, we develop a new receiver that combines adaptive decision feedback equalizer and SI cancellation (ADFE-SIC) to jointly eliminate the ISI and SI. A recursive least squares algorithm adaptively estimates the filters in ADFE-SIC. By conducting simulations and experimental tests, we show that the proposed method outperforms the conventional approach in which equalization and SI cancellation tasks are performed separately and the filter configuration is based on prior estimations of the SI and the RT channels.

With the increasing demand for advanced autonomous driving, the available communication resources may become constrained over different geographic areas. In addition, due to dynamic channel variations and imperfect cell deployments, guaranteeing the required communication resources for data hungry and delay-sensitive applications in autonomous vehicles (AVs), along their entire trips, becomes challenging. To address these issues, the paper investigates the feasibility of a hybrid system-optimum and user-equilibrium AV traffic framework subject to communication constraints, as well as its performance gain. Within such a framework, the paper introduces the problems of communication-constrained routing (CCR) and traffic control (CCTC) in the context of infrastructure-assisted autonomous driving and presents respective solutions. For CCR, an efficient two-layered routing scheme is proposed which can provide optimal trip duration. Simulation results show that the routing scheme achieves a good balance between longer duration of communication coverage and acceptable source-to-destination travel time. For CCTC, it is shown that there exists an optimal AV speed on each road segment, as well as an optimal inter-AV distance and an optimal number of AVs in each cell, to maximize the road-network AV throughput within a single cell. Moreover, spectrum allocation is used to achieve Pareto-optimal road-network throughput across cells, and a new key performance index (KPI) is defined to evaluate the traffic control capability of cellular systems. Simulation results validate the improvement of AV throughput via the proposed CCTC solution.

Estimates of both the self-interference (SI) and the remote transmission (RT) channels are required in underwater acoustic full-duplex (UWA-FD) communication. The former is used for SI cancellation and the latter is needed for equalizing the signal of interest. The idea of adaptive joint channel estimation provides a real-time estimate of both channels without degrading the spectrum efficiency of UWA-FD. In this method, the previously detected data symbols from the remote transmitter are taken as a reference for estimating the RT channel. Nevertheless, when the SI channel is significantly stronger than that of the RT one, even a slight inaccuracy in estimating the SI channel leads to large residuals after cancellation. In this paper, we use the recently proposed multi-layered recursive least squares (m-RLS) algorithm for joint channel estimation with enhanced accuracy in estimating the SI channel. The m-RLS estimator is composed of multiple layers, each of which employs an RLS to estimate and eliminate the remaining SI from the received signal. Simulation results show better SI cancellation achievements by using m-RLS compared to the RLS and the minimum mean square error (MMSE) estimators.

Traditional recursive least squares (RLS) adaptive filtering is widely used to estimate the impulse responses (IR) of an unknown system. Nevertheless, the RLS estimator shows poor performance when tracking rapidly time-varying systems. In this paper, we propose a multi-layered RLS (m-RLS) estimator to address this concern. The m-RLS estimator is composed of multiple RLS estimators, each of which is employed to estimate and eliminate the misadjustment of the previous layer. It is shown that the mean squared error (MSE) of the m-RLS estimate can be minimized by selecting the optimum number of layers. We provide a method to determine the optimum number of layers. A low-complexity implementation of m-RLS is discussed and it is indicated that the complexity order of the proposed estimator can be reduced to ${\mathcal O}(M)$ , where $M$ is the IR length. Through simulations, we show that m-RLS outperforms the classic RLS and the RLS methods with a variable forgetting factor.

In TDD massive MISO systems, user equipments (UEs) send channel measurement pilots to the BS for beamforming. Frequently sending these pilots, although improving beamforming, could consume significant communication resources. In this letter, we investigate how frequent these pilots should be sent for each UE so as to increase overall throughput performance for TDD massive MISO downlink. This real-time resource allocation problem is challenging due to non-trivial performance metric and boundary conditions. Assuming that instantaneous speed and location information of UEs can be obtained by the BS, we propose a reinforcement learning framework in which the BS acts as a learning agent to decide pilot intervals. Simulation results show that, for this multi-terminal setting where UEs compete for resources, using this centralized reinforcement learning framework, performance can be improved by choosing pilot intervals and transmission rates based on the UE information.

By applying a rate-distortion approach, we investigate the relationship between the amount of overhead and the system performance from an information-theoretic point of view. Though the purpose of rate-distortion theory is to find a lower bound on lossy source coding problems, the concepts of distortion and rate can be extended to communication performance measures and the quality of feedback overhead, respectively. In this article, we study the overhead-performance tradeoff for a downlink MU-MISO channel system with limited feedback. The required feedback bits for characterizing the channel state information is represented as a function of tolerable rate loss. The proposed method to derive this tradeoff is valid for any number of transmit antennas and users, and can help in designing practical systems where the impact of channel feedback overhead cannot be neglected. Comparing the obtained rate-distortion curves with vector quantization, it can be concluded that, for each user, by feeding back different numbers of bits for different channel power gains, the optimal performance can be achieved.

Zero-forcing precoding is a commonly-used multiple-user, multiple-input multiple-output beamforming technique. Applying such precoding, the signal-to-interference-plus-noise ratio (SINR) statistics with outdated channel state information, which involve various projections related to the multi-dimensional channel vectors and precoding vectors, have never been explicitly derived. In this paper, for the multiple-input single-output scenario with uniform power allocation, the distributions of these projections are derived, and then the SINR distribution and the conditional outage probability can be computed using those distributions.

Virtual reality (VR) is becoming prevalent with a plethora of applications in education, healthcare, entertainment, etc. To increase the user mobility, and to reduce the energy consumption and production cost of VR head mounted displays (HMDs), wireless VR with edge-computing has been the focus of both industry and academia. However, transferring large video frames of VR applications with their stringent Quality of Service (QoS) requirements over wireless network requires innovations and optimizations across different network layers. In order to develop efficient architectures, protocols and scheduling mechanisms, the traffic characteristics of various types of VR applications are required. In this paper, we first compute the theoretical throughput requirements of an ideal VR experience as well as a popular VR HMD. We then examine the traffic characteristics of a set of VR applications using an edge-enabled Wi-Fi network. Our results reveal interesting findings that can be considered in developing new optimizations, protocols, access mechanisms and scheduling algorithms.

Due to the limited available bandwidth and dynamic channel, data rates are extremely limited in underwater acoustic (UWA) communications. Addressing this concern, in-band full-duplex (IBFD) has the potential to double the efficiency in a given bandwidth. In an IBFD scheme, transmission and reception are performed simultaneously in the same frequency band. However, in UWA-IBFD, because of reflections from the surface and bottom and the inhomogeneity of the water, a significant part of the transmitted signal returns back to the IBFD receiver. This signal contaminates the desired signal from the remote end and is known as the self-interference (SI). With an estimate of the self-interference channel impulse response (SCIR), a receiver can estimate and eliminate the SI. A better understanding of the statistical characteristics of the SCIR is necessary for an accurate SI cancellation. In this article, we use an orthogonal frequency division multiplexing (OFDM) signal to characterize the SCIR in a lake water experiment. To verify the results, SCIR estimation is performed by using estimators in both the frequency and time domains. We show that, in our experiment, regardless of the depth of hydrophone, the direct path of SCIR is strong, stable and easily tracked; however, the reflection paths are weaker and rapidly time-varying making SI cancellation challenging. Among the reflections, the first bounce from the water surface is the prevalent path with a short coherence time around 70 ms.

Full-duplex (FD) communication is a promising candidate to address the data rate limitations in underwater acoustic (UWA) channels. Because of transmission at the same time and on the same frequency band, the signal from the local transmitter creates self-interference (SI) that contaminates the the signal from the remote transmitter. At the local receiver, channel state information for both the SI and remote channels is required to remove the SI and equalize the SI-free signal, respectively. However, because of the rapid time-variations of the UWA environment, real-time tracking of the channels is necessary. In this paper, we propose a receiver for UWA-FD communication in which the variations of the SI and remote channels are jointly tracked by using a recursive least squares (RLS) algorithm fed by feedback from the previously detected data symbols. Because of the joint channel estimation, SI cancellation is more successful compared to UWA-FD receivers with separate channel estimators. In addition, due to providing a real-time channel tracking without the need for frequent training sequences, the bandwidth efficiency is preserved in the proposed receiver.

In cellular communications, deploying a larger number of antennas at the base station, also called massive multiple-input multiple-output (MIMO), can offer a significant improvement in system throughput. In this paper, we exploit the spatial fading correlations in massive MIMO to reduce the downlink training and the corresponding feedback overhead in frequency division duplexing systems. We first study the user clustering, where the users with similar spatial channel correlations are clustered together. In the study, we provide the optimal metric and prove the convergence of the user clustering. Then, we propose an efficient eigenspace training and precoding (EETP) framework, where two different prebeamforming matrices are designed to minimize the channel estimation error and to manage the inter-user interference, respectively. In the results, we show that the channel estimation error for EETP decreases monotonically when either the number of prebeamforming vectors or the number of clusters increases. The spectral efficiency of the new algorithms is evaluated extensively with different user distributions, errors in channel correlations, different numbers of clusters, and different coherence block lengths, as well as with dynamic user scheduling for a large number of users. The new EETP not only achieves significant savings in the downlink training and the corresponding feedback, but also offers significantly higher system throughput compared with the existing schemes in the literature.

We investigate downlink scheduling in two-hop relay access networks with a central controller. To address the time-varying nature of wireless channels, a channel-aware scheduling scheme is proposed to exploit both spatial and multihop diversity. Given instantaneous channel state information, scheduling decisions are made at every time slot based on the designed priority indexes. The proposed scheme improves throughput and ensures fair transmission among all the active mobile stations in the system. In addition, with optimized scaling factors, the proposed scheme is shown to be close to Pareto-optimal.

Autonomous vehicles (AV) is an advanced technology that can bring convenience, improve the road-network throughput, and reduce traffic accidents. To enable higher levels of automation (LoA), massive amounts of sensory data need to be uploaded to the network for processing, and then, maneuvering decisions must be returned to the AV. Furthermore, passengers might have a higher transmission rate demands for various data-hungry and delay-sensitive applications.

In-band full duplex (IBFD) communications attracts increasing attention in the underwater acoustic communication community due to its potential to increase spectral efficiency. The suppression of the self-interference remains the main obstacle to achieve IBFD in the ocean. Limited work in the literature has been done to characterize the self-interference in underwater acoustics. Here we analyze the characteristics of the self-interference based on the fieldwork, where self-interference measurements have been collected for multiple acoustic frequencies ranging from 20 to 180 kHz. The interference cancellation (IC) gain, as the performance metric of the digital self-interference suppression, is found to decrease when the acoustic carrier frequency increases. We propose to use the channel variation ratio (CVR) to characterize channel variability. Experimental results show that the CVR is larger at higher acoustic frequencies and high CVRs lead to the performance degradation in the self-interference suppression. Computer simulations have also been conducted to explain the experimental observations.

This letter proposes a higher-order-moment-based hypothesis testing algorithm to estimate the transmit-antenna number for multiple-input multiple-output systems. Exploiting the asymptotic normal distribution of the moments composed by noise eigenvalues, the proposed algorithm improves the estimation performance for low signal-to-noise ratios. Moreover, since the empirical distribution of the moments converges quickly to the normal distribution when the number of samples increases, our algorithm can make a reliable estimation in a sample starved condition. Computer simulations are provided to demonstrate that the proposed algorithm outperforms the conventional algorithms.

Multiple-input multiple-output technology brings new challenges for detecting signal parameters in some intelligent systems. An accurate estimation of the transmit-antenna number is the prerequisite for estimating other signal parameters. Employing random matrix theory, we propose two hypothesis testing based algorithms to blindly estimate the transmit-antenna number. By exploiting hypothesis testing, the blind estimation problem is converted to a location problem, which seeks the location of the boundary between the signal subspace and the noise subspace of the received sample covariance matrix. Then, the estimated transmit-antenna number is determined by the dimension of the signal subspace. Extensive simulations verify that the two proposed algorithms perform better under a wide range of signal-to-noise ratios and sample lengths, compared with conventional algorithms based on Akaike information criterion, minimum description length, and predicted eigenvalue threshold.

Licensed-assisted access (LAA) of the long term evolution (LTE) has been standardized by the Third Generation Partnership Project (3GPP) in Release 13 to address the urgent issues of ever-increasing traffic demands in cellular systems. However, challenges arise for the efficient coexistence of Wi-Fi and LAA in the same unlicensed spectrum. In this paper, the impact of LAA's energy detection thresholds on such coexistence are investigated from the perspective of collisions occurring during downlink transmissions. To facilitate the efficient coexistence of Wi-Fi and LAA, a distributed algorithm is proposed to adaptively change the energy detection thresholds of LAA per user or per base station, so that the system encourages more concurrent transmissions without introducing too many collisions. Simulation results validate the effectiveness of the proposed adaptive algorithm.

In this study, the authors propose a non-parametric algorithm to implement the estimation of transmit-antenna number, which is a prerequisite for blind interception process of multiple-input multiple-output orthogonal frequency division multiplexing signals in frequency selective fading. Specifically, a series of test statistics are constructed by exploiting the eigenvalues of the sample covariance matrices from each subcarrier, followed by a combination of these test statistics. As a consequence, the number of transmit antennas can be determined after a serial binary hypothesis testing. The theoretical analysis and simulation results verify the rapid convergence and high reliability of the proposed algorithm at a relatively low signal-to-noise ratio.

In cellular systems, deploying a large number of antennas at the base station (BS), also called massive multiple-input multiple-output (MIMO), can offer a huge improvement in system throughput. This performance gain achieved through coherent beamforming highly depends on having accurate channel state information (CSI) at the BS. Initially, massive MIMO was considered promising only for time division duplexing (TDD) systems because the downlink training overhead in frequency division duplexing (FDD) systems could be large. To reduce the overhead in FDD systems, we propose efficient eigenspace training and precoding. In the downlink training, the prebeamforming matrix is designed based on the spatial fading correlation to probe the channel and minimize the mean squared error (MSE) in channel estimation. In data transmission, the precoding matrix is designed based on instantaneous CSI to manage the inter-user interference. Our algorithms achieve significant savings in the downlink training because the overhead is associated with the dimension of prebeamforming matrix and no longer limited by the number of BS antennas. Compared with joint spatial division and multiplexing, our algorithms offer much lower MSE and, under some conditions, higher sum rates.