The large bandwidth combined with ultra-massive multiple-input multiple-output (UM-MIMO) arrays enables terahertz (THz) systems to achieve terabits-per-second throughput. The THz systems are expected to operate in the near, intermediate, as well as the far-field. As such, channel estimation strategies suitable for the near, intermediate, or far-field have been introduced in the literature. In this work, we propose a cross-field, i.e., able to operate in near, intermediate, and farfield, compressive channel estimation strategy. For an array-of-subarrays (AoSA) architecture, the proposed method compares the received signals across the arrays to determine whether a near, intermediate, or far-field channel estimation approach will be appropriate. Subsequently, compressed estimation is performed in which the proximity of multiple subarrays (SAs) at the transmitter and receiver is exploited to reduce computational complexity and increase estimation accuracy. Numerical results show that the proposed method can enhance channel estimation accuracy and complexity at all distances of interest.

In this paper, we develop a channel estimation strategy for terahertz (THz) ultra-massive multiple-input multiple-output (UM-MIMO) system with a sub-connected array-of-subarrays architecture, in which one subarray (SA) is connected to one RF chain exclusively. Further, we consider a hybrid spherical-planar wave model (HSPM) for the channel modelling in which the channel between individual transmit and receive SAs is based on the planar wave model, while variation across the SAs is captured via the spherical wave model. Since the channel between different SAs is similar -albeit not identical - we propose a dictionary reduction based compressed sensing method to exploit the spatial information extracted from the estimates of the first SA in channel estimation of subsequent SAs. The proposed method achieves up to 2 dB NMSE improvement over the conventional methods.

In this work, we comparatively analyze the least mean squares (LMS) algorithm and the normalized least mean squares (NLMS) algorithm. We use the input moment matrices for comparison as the mean-square behavior of both algorithms is determined by the input moment matrices. First, we derive the closed-form expressions of the input moment matrices of the NLMS. Second, we do a numerical and theoretical comparison of the input moment matrices of the LMS and the NLMS. The analysis shows why the performance of the NLMS is less sensitive to the changes in eigenvalue-spread (of the input-correlation matrix) than the LMS.

In this work, we study the impact of input-spread on the steady-state excess mean squared error (EMSE) of the normalized least mean squares (NLMS) algorithm. First, we use the concept of majorization to order the input-regressors according to their spread. Second, we use Schur-convexity to show that the majorization order of the input-regressors is preserved in the EMSE. Effectively, we provide an analytical justification of the increase in steady-state EMSE as the input-spread increases.

Millimeter wave (mmWave) has the potential to offer high data rates for vehicle-to-everything (V2X) communication. In this paper, we provide an introduction to important use cases of V2X as they pertain to fifth generation (5G) communication networks. As 5G technology is still evolving to support vehicles, and mmWave is reflected and blocked by vehicles, it remains unclear if the target rates for the use cases can be achieved. Motivated by the different data rate requirements, we introduce a methodology for evaluating rates in 5G mmWave V2X scenarios. Our approach leverages available city CAD models, realistic traffic simulators, and industry standard ray tracing tools to allow site-specific propagation evaluation. This approach may be used to devise insight into the role of traffic density, antenna array placement, and base station density in important urban propagation settings. Results are provided that highlight the application to develop a rate map for an urban intersection. They show that rate increases in dense deployments by more than ten percent per base station, but only decreases about one percent when going from light to heavy traffic.

Spatial channel covariance information can replace full instantaneous channel state information for the analog precoder design in hybrid analog/digital architectures. Obtaining spatial channel covariance estimation, however, is challenging in the hybrid structure due to the use of fewer radio frequency (RF) chains than the number of antennas. In this paper, we propose a spatial channel covariance estimation method based on higher-order tensor decomposition for spatially sparse time-varying frequency-selective channels. The proposed method leverages the fact that the channel can be represented as a low-rank higher-order tensor. We also derive the Cramér-Rao lower bound on the estimation accuracy of the proposed method. Numerical results and theoretical analysis show that the proposed tensor-based approach achieves higher estimation accuracy in comparison with prior compressive-sensing-based approaches or conventional angle-of-arrival estimation approaches.

In high mobility applications of millimeter wave (mmWave) communications, e.g., vehicle-to-everything communication and next-generation cellular communication, frequent link configuration can be a source of significant overhead. We use the sub-6 GHz channel covariance as an out-of-band side information for mmWave link configuration. Assuming: 1) a fully digital architecture at sub-6 GHz and 2) a hybrid analog-digital architecture at mmWave, we propose an out-of-band covariance translation approach and an out-of-band aided compressed covariance estimation approach. For covariance translation, we estimate the parameters of sub-6 GHz covariance and use them in theoretical expressions of covariance matrices to predict the mmWave covariance. For out-of-band aided covariance estimation, we use weighted sparse signal recovery to incorporate out-of-band information in compressed covariance estimation. The out-of-band covariance translation eliminates the in-band training completely, whereas out-of-band aided covariance estimation relies on in-band as well as out-of-band training. We also analyze the loss in the signal-to-noise ratio due to an imperfect estimate of the covariance. The simulation results show that the proposed covariance estimation strategies can reduce the training overhead compared to the in-band only covariance estimation.

Autonomous vehicles (AVs) have the potential to transform road transportation by improving safety and increasing traffic/fuel efficiency. Currently, AVs rely primarily on line-of-sight sensing technologies to gather information about their surroundings. Some of the surrounding objects (e.g., vehicles, infrastructure, and pedestrians), however, can potentially communicate with the AV to help it create a better digital map of the world around it. In this work, we quantify the gains vehicle-to-vehicle (V2V) communication can provide for AVs. Specifically, we focus on the safety and maneuver planning of the AVs. Our preliminary findings indicate that V2V can reduce a significant fraction of AV collisions. Further, V2V can considerably reduce the maneuver completion time.

Millimeter wave (mmWave) communication is one feasible solution for high data-rate applications like vehicular-to-everything communication and next generation cellular communication. Configuring mmWave links, which can be done through channel estimation or beam-selection, however, is a source of significant overhead. In this paper, we propose using spatial information extracted at sub-6 GHz to help establish the mmWave link. Assuming a fully digital architecture at sub-6 GHz; and an analog architecture at mmWave, we outline a strategy to extract spatial information from sub-6 GHz and its use in mmWave compressed beam-selection. Specifically, we formulate compressed beam-selection as a weighted sparse signal recovery problem, and obtain the weighting information from sub-6 GHz channels. In addition, we outline a structured precoder/combiner design to tailor the training to out-of-band information. We also extend the proposed out-of-band aided compressed beam-selection approach to leverage information from all active subcarriers at mmWave. To simulate multi-band frequency dependent channels, we review the prior work on frequency dependent channel behavior and outline a multi-frequency channel model. The simulation results for achievable rate show that out-of-band aided beam-selection can considerably reduce the training overhead of in-band only beam-selection.

Compressed beam-selection (CBS) exploits the limited scattering of the millimeter wave (mmWave) channel using compressed sensing and finds the best beam-pair with limited overhead. The CBS procedure can further benefit from the knowledge of some additional structure in the channel. As mmWave systems are envisioned to be deployed in conjunction with sub-6 GHz systems, we use the spatial information extracted at sub-6 GHz as out-of-band side information about the mmWave channel. In particular, we formulate beam-selection as a weighted sparse signal recovery problem, and obtain the weights using sub-6 GHz angular information. Furthermore, we formalize the notion of spatial congruence between sub-6 GHz and mmWave, and numerically evaluate the degree of spatial congruence necessary for the success of the proposed approach. The simulation results illustrate that the proposed approach reduces the training overhead of the CBS approach by 3×.

By virtue of large antenna arrays, massive MIMO systems have a potential to yield higher spectral and energy efficiency in comparison with the conventional MIMO systems. This paper addresses uplink channel estimation in massive MIMO-OFDM systems with frequency selective channels. We propose an efficient distributed minimum mean square error (MMSE) algorithm that can achieve near optimal channel estimates at low complexity by exploiting the strong spatial correlation among antenna array elements. The proposed method involves solving a reduced dimensional MMSE problem at each antenna followed by a repetitive sharing of information through collaboration among neighboring array elements. To further enhance the channel estimates and/or reduce the number of reserved pilot tones, we propose a data-aided estimation technique that relies on finding a set of most reliable data carriers. Furthermore, we use stochastic geometry to quantify the pilot contamination, and in turn use this information to analyze the effect of pilot contamination on channel MSE. The simulation results validate our analysis and show near optimal performance of the proposed estimation algorithms.

This paper presents a novel narrowband interference (NBI) mitigation scheme for SC-FDMA systems. The proposed scheme exploits the frequency domain sparsity of the unknown NBI signal and adopts a low complexity Bayesian sparse recovery procedure. In practice, however, the sparsity of the NBI is destroyed by a grid mismatch between NBI sources and SC-FDMA system. Towards this end, an accurate grid mismatch model is presented and a sparsifying transform is utilized to restore the sparsity of the unknown signal. Numerical results are presented that depict the suitability of the proposed scheme for NBI mitigation.

This paper addresses the problem of narrowband interference (NBI) in SC-FDMA systems by using tools from compressed sensing and stochastic geometry. The proposed NBI cancellation scheme exploits the frequency domain sparsity of the unknown signal and adopts a Bayesian sparse recovery procedure. This is done by keeping a few randomly chosen sub-carriers data free to sense the NBI signal at the receiver. As Bayesian recovery requires knowledge of some NBI parameters (i.e., mean, variance and sparsity rate), we use tools from stochastic geometry to obtain analytical expressions for the required parameters. Our simulation results validate the analysis and depict suitability of the proposed recovery method for NBI mitigation.

High peak-to-average power ratio is one of the major drawbacks of orthogonal frequency division multiplexing (OFDM). Clipping is the simplest peak reduction scheme, however, it requires clipping mitigation at the receiver. Recently compressed sensing has been used for clipping mitigation (by exploiting the sparse nature of clipping signal). However, clipping estimation in multi-user scenario (i.e., OFDMA) is not straightforward as clipping distortions overlap in frequency domain and one cannot distinguish between distortions from different users. In this work, a collaborative clipping removal strategy is proposed based on joint estimation of the clipping distortions from all users. Further, an effective data aided channel estimation strategy for clipped OFDM is also outlined. Simulation results are presented to justify the effectiveness of the proposed schemes.

In this paper, we design and analyze a Newton-like blind equalization algorithm for the APSK system. Specifically, we exploit the principle of minimum entropy deconvolution and derive a blind equalization cost function for APSK signals and optimize it using Newton׳s method. We study and evaluate the steady-state excess mean square error performance of the proposed algorithm using the concept of energy conservation. Numerical results depict a significant performance enhancement for the proposed scheme over well established blind equalization algorithms. Further, the analytical excess mean square error of the proposed algorithm is verified with computer simulations and is found to be in good conformation.