Cost-effective phase noise (PN) compensation is essential for orthogonal frequency division multiplexing (OFDM) systems, especially in millimeter-wave (mmWave) communications. In this paper, we propose an intelligent semi-blind receiver design for PN-affected OFDM systems. Specifically, we first formulate a joint channel estimation, PN compensation, and data detection problem in the time domain, accounting for the constant modulus constraint of PN and discrete constraint of the data. To address the constrained non-convex problem, a low-complexity receiver based on the alternating direction method of multipliers (ADMM) and minorization-maximization (MM) is proposed. Subsequently, by adopting the deep unfolding technique, we present an intelligent receiver to avoid cumbersome parameter selection and also accelerate the algorithm convergence. Simulation results validate the clear performance superiority of our proposed design over existing schemes. Moreover, the complexity per iteration/layer is as low as O(N log2N) with N being the number of subcarriers.

Phase noise introduced by imperfect oscillators can degrade the performance of multiple-input multiple-output (MIMO) systems. To address the issue, this paper investigates a joint estimation of channel and phase noise algorithm by taking advantage of the soft output of the decoder. Specifically, a novel criterion is proposed to select the most reliable symbols as virtual pilots to refine the channel amplitude. Furthermore, we develop a decoding-aided extended Kalman filter (EKF) to track the phase variation. Numerical results demonstrate that the proposed approach can effectively improve the accuracy of the channel and phase noise estimation, thereby enhancing the bit error rate (BER) performance of the MIMO system.

The receiver design for multi-input multi-output (MIMO) ultra-reliable and low-latency communication (URLLC) systems can be a tough task due to the use of short channel codes and few pilot symbols. Consequently, error propagation can occur in traditional turbo receivers, leading to performance degradation. Moreover, the processing delay induced by information exchange between different modules may also be undesirable for URLLC. To address the issues, we advocate to perform joint channel estimation, detection, and decoding (JCDD) for MIMO URLLC systems encoded by short low-density parity-check (LDPC) codes. Specifically, we develop two novel JCDD problem formulations based on the maximuma posteriori(MAP) criterion for Gaussian MIMO channels and sparse mmWave MIMO channels, respectively, which integrate the pilots, the bit-to-symbol mapping, the LDPC code constraints, as well as the channel statistical information. Both the challenging large-scale non-convex problems are then solved based on the alternating direction method of multipliers (ADMM) algorithms, where closed-form solutions are achieved in each ADMM iteration. Furthermore, two JCDD neural networks, called JCDDNet-G and JCDDNet-S, are built by unfolding the derived ADMM algorithms and introducing trainable parameters. It is interesting to find via simulations that the proposed trainable JCDD receivers can outperform the turbo receivers with affordable computational complexities.

A novel joint phase noise compensation and signal detection algorithm based on deep learning (DL) is proposed for orthogonal frequency division multiplexing (OFDM) systems under rapidly time-varying channels. Specifically, we first develop an alternating direction method of multipliers (ADMM) based solution to the difficult mixed-integer maximum likelihood (ML) estimation, along with a low-complexity ADMM solution that exploits the inherent feature of the inter-carrier interference (ICI). Furthermore, by unfolding the ADMM iterations, we present a model-driven network with trainable variables, which avoids cumbersome parameter search and also results in faster convergence. Simulation results demonstrate that our proposed DL-aided ADMM algorithms significantly outperform existing baselines in terms of BER performance with acceptable complexity.

In this paper, we investigate the design of statistically robust detectors for multi-input multi-output (MIMO) systems subject to imperfect channel state information (CSI). A robust maximum likelihood (ML) detection problem is formulated by taking into consideration the CSI uncertainties caused by both the channel estimation error and the channel variation. To address the challenging discrete optimization problem, we propose an efficient alternating direction method of multipliers (ADMM)-based algorithm, which only requires calculating closed-form solutions in each iteration. Furthermore, a robust detection network RADMMNet is constructed by unfolding the ADMM iterations and employing both model-driven and data-driven philosophies. Moreover, in order to relieve the computational burden, a low-complexity ADMM-based robust detector is developed using the Gaussian approximation, and the corresponding deep unfolding network LCRADMMNet is further established. On the other hand, we also provide a novel robust data-aided Kalman filter (RDAKF)-based channel tracking method, which can effectively refine the CSI accuracy and improve the performance of the proposed robust detectors. Simulation results validate the significant performance advantages of the proposed robust detection networks over the non-robust detectors with different CSI acquisition methods.

A novel iterative receiver for polar-coded massive multiuser multiple-input-multiple-output (MU-MIMO) systems is proposed. In the receiver, the feedbacks of the polar decoder, including the correctly decoded data and the soft outputs generated using Pyndiah's Chase algorithm, are available to the detector and the channel estimator, respectively. Moreover, interference cancellation (IC) schemes are employed to improve the overall performance and reduce the computation complexity. Simulation results show that the proposed receiver with polar codes can bring about considerable performance gains through iterations and can always outperform the one with LDPC codes.

The replica method originated from statistical mechanics has been successfully applied to analyzing performance of the global maximum-likelihood (GML) MIMO detector in the large-system limit. In this paper, the analysis is extended to the local maximum-likelihood (LML) detectors. A bit error rate (BER) formula for the LML detectors with a fixed neighborhood size is obtained by the replica method and interestingly by the method of Gaussian approximation as well. It is shown that the LML BER is always one of the solutions to the GML BER in any system configuration. Furthermore, the LML BER is the only solution of the GML BER in a broad range of system parameters of practical interest. In the high signal-to-noise ratio regime, both LML and GML detectors achieve the AWGN channel performance when the channel load is up to 1.51 bits/dimension with an equal-energy distribution, and the load can be higher with an unequal-energy distribution. This analytical result is verified by simulation that the sequential likelihood ascent search detector, which is a linear-complexity LML detector, can approach the BER of the NP-hard GML detector predicted by the analysis. This result might be practically useful in large MIMO systems.

This paper proposes a novel assistive navigation system based on simultaneous localization and mapping (SLAM) and semantic path planning to help visually impaired users navigate in indoor environments. The system integrates multiple wearable sensors and feedback devices including a RGB-D sensor and an inertial measurement unit (IMU) on the waist, a head mounted camera, a microphone and an earplug/speaker. We develop a visual odometry algorithm based on RGB-D data to estimate the user's position and orientation, and refine the orientation error using the IMU. We employ the head mounted camera to recognize the door numbers and the RGB-D sensor to detect major landmarks such as corridor corners. By matching the detected landmarks against the corresponding features on the digitalized floor map, the system localizes the user, and provides verbal instruction to guide the user to the desired destination. The software modules of our system are implemented in Robotics Operating System (ROS). The prototype of the proposed assistive navigation system is evaluated by blindfolded sight persons. The field tests confirm the feasibility of the proposed algorithms and the system prototype.

This study analyses power spectral density (PSD) of a pulse train where the pulses take from a prototype pulse but randomly take an independently and identically distributed time scaling and an independent stationary amplitude. A closed‐form expression of PSD is obtained, which is an implicit function of the Fourier transform of the prototype pulse without time scaling, the probability distribution of time scaling, and the first and the second moment means of amplitude. In the special case when the time scaling and amplitude are fixed with probability one, the PSD is degenerated to the well‐known PSD of a periodic signal. Results of numerical evaluation and simulation for pulse trains with three rates as well as with Gaussian rates demonstrate that the analytical formula well predicts the data PSD.

The searching for a location-unknown radio transmitter is a challenging task for autonomous robot. We propose an adaptive searching algorithm named theseus gradient guide (TGG) which is designed for solving the searching problem in indoor environments using received signal strength (RSS). While the RSS gradient serves as the main guide, the robot prefers to move to the places which have never been traveled. Thus the robot will not get stuck in the local maxima. Moreover, unlike the commonly used random kick strategy the TGG drives the robot escaping the local maxima with low cost in terms of travel distance. Meanwhile, TGG is not sensitive to motion errors. Simulation results show that the searches using TGG cost much less compared with those using other gradient based methods in our testing indoor environment. Guided by TGG, the robot can successfully reach the location-unknown radio transmitter with a ratio over 97% when the standard deviation of motion error is up to 20% of the step length.

Biologically-inspired swarm of robots with collaboration towards a common mission has a broad range of applications. However, the required dynamic localization among autonomous robots for such swarm collaboration, though usually implicitly assumed, has not been properly studied. In this paper, we address the roles of multiple antennas in localization and energy-efficient wireless communications for a swarm of robots. Following the gradient of signal powers along a trajectory, a robot can track the direction of a source robot. With three or more properly placed antennas that sense different phase shifts of carrier, a robot can localize a source. By lateration, three collaborative robots can localize a source with known distances to it. Via angulation technique, three robots can determine their geometric relationship with knowing two angles and one distance between them. The techniques can be extended from the 2-D to the 3-D space for application of wall-climbing robots. On the basis of knowledge of robot locations, beamforming techniques can be employed to receive and transmit signal towards the desired robot therefore improving energy efficiency and prolonging robot lifetime.

The random access systems, exploiting retransmission diversity (RD), over large random spreading CDMA channel subject to fading is investigated, under the assumption of infinite number of users and infinite spreading gain but with their ratio converging to a constant. The SIR, throughput and spectrum efficiency are analyzed. Simulation and numerical results show that RD exploitation can significantly increase the throughput and spectrum efficiency in channels both with and without fading.

This paper analyzes the spectral efficiency as a function of medium access control (MAC) for large random spreading CDMA random access systems that employ a linear receiver. It is shown that located at higher than the physical layer, MAC along with spreading and power allocation can effectively perform spectral efficiency maximization and near-far mitigation.

In this paper, the bit error performance of a family of likelihood ascent search (LAS) multiuser detectors is analyzed. An upper bound on the BER of any LAS detector is obtained by bounding the fixed point region with the worst initial detector. The concept of indecomposable errors developed by Verdu is applied to tighten the upper bound. In a special instance, the upper bound is reduced to that for all the local maximum likelihood detectors. The upper bound is comparable with that of the optimum detector obtained by Verdu. A lower bound on the asymptotic multiuser efficiency (AME) is then obtained. It is shown that there are nontrivial CDMA channels such that a LAS detector can achieve unit AME regardless of user number. The AME lower bound provides a means for further seeking a good set of spreading sequences and power distribution for spectral and power efficient CDMA.

Tse and Verdu proved that the global maximum likelihood (GML) detector achieves unit asymptotic multiuser efficiency (AME) in the limit of large random spreading CDMA. In this paper, we show that the wide-sense sequential likelihood ascent search (WSLAS) detectors achieve unit AME with channel load < 1/2 - 1/(4 ln 2) bits/s/Hz. In simulations for random multicode equal-power systems, the WSLAS with a linear per-bit complexity approaches the GML BER in all SNR with channel load as high as 1.05 bits/s/Hz.

In this paper, we propose a novel approach of signal power gradient by which a robot adaptively searches a location-unknown sensor. While moving, the robot measures signal strength and estimates the direction of power gradient along which the robot moves in the next step. The correctness of estimated direction is analyzed and the probability of correct direction is obtained. Since the robot continuously measures signal strength while moving, it can effectively overcome the motion errors. Simulation results demonstrate that the robot can successfully reach the location-unknown sensor with probability close to one when the signal to noise ratio at the initial location is as low as 0 dB and the standard deviation of motion error is 10% step size.

In this paper, the effect of user cooperation on network performance is investigated in terms of throughput and stability region for CDMA random access networks where a large number of users randomly access an access point (AP). The network considered consists of two classes of users depending on the received power at the AP. A cooperation strategy with very low overheads is proposed, and the network performance gain obtained by adopting this strategy is characterized in closed-form formulas. The validity of our analysis is confirmed by simulation results.

The vague assumption of a fixed communication range has facilitated the development of major areas of study in mobile sensor networks such as connectivity, coverage, and self-deployment algorithms by providing a stable element as a foundation for their analysis, but more precise communication model is required in order to convert theoretical simulations into realistic applications. This paper studies the effects of more realistic communication models on mobile sensor networks/ad hoc networks taking into consideration multi-access interference and noise and their impact on the communication range of the nodes. The study shows that the communication range changes with the characteristics of the network (network parameters and node distribution). Simulation results justify our findings and future research directions are discussed

Due to ever-increasing bandwidth demands in future wireless service, the radio frequency band becomes more and more invaluable. In this paper, we address channel equalization for bandwidth-efficient wireless orthogonal frequency-division multiplexing (OFDM). First, we show that in order to be free of both interchannel interference and interblock interference, wireless OFDM has to occupy a bandwidth wider than the Nyquist rate and use insufficient statistics in symbol demodulation. Thus, the conventional OFDM gains computational efficiency using the discrete Fourier transform (DFT) in demodulation at the cost of low efficiency of bandwidth usage and degradation in symbol error performance. Then we consider the OFDM that achieves high efficiency of bandwidth usage, allows interchannel interference and interblock interference to exist, and uses sufficient statistics in symbol demodulation. A one-tap decision feedback equalizer (DFE) is proposed for equalization of the bandwidth efficient OFDM system. Simulation and numerical evaluation for an indoor wireless asynchronous transfer mode network are carried out. It is demonstrated that though occupying a narrower bandwidth, the one-tap DFE-based bandwidth-efficient OFDM system achieves lower symbol error rate and higher mutual information than the conventional DFT-based OFDM system. The proposed OFDM system presents a monotonically increasing symbol error rate, and convex-down achieved mutual information with respect to increasing efficiency of bandwidth usage.