Compressive sensing (CS) is key to reduce the overhead in estimating sparse high dimensional channels at millimeter wave or terahertz frequencies. The channel measurements in CS are usually perturbed by random phase errors, commonly modeled as a Wiener process, at the oscillators. CS algorithms that ignore such phase errors fail to accurately estimate the channel. In practice, the phase errors are similar within a batch of measurements acquired in a short burst and the errors vary significantly across different batches, resulting in partially coherent measurements. We develop a message passing-based channel estimation algorithm that exploits the sparse structure of the channel together with the Wiener statistics of the phase errors. To this end, we absorb the phase errors into the sparse channel, and introduce three hidden variables to model its support, magnitude, and phase. We derive the message flows between these variables while incorporating Wiener phase noise statistics. Finally, we use alternating optimization to decouple the sparse channel and the phase errors from the vector estimated with our message-passing technique. Using simulations, we show that the proposed algorithm achieves better channel reconstruction than comparable benchmarks.

Channel estimation can lead to a substantial training overhead in millimeter wave (mmWave) and terahertz (THz) systems employing large arrays. Prior work has leveraged channel sparsity at these frequencies to reduce this overhead. Most of the sparsity-aware algorithms, however, assume perfect phase coherence in the channel measurements, which is disrupted due to phase noise. Due to the errors induced by phase noise, standard sparse channel estimation algorithms assuming perfect phase coherence can fail. In this paper, we consider a frame structure in which the channel measurements are acquired over multiple packets. Our model assumes that the phase errors remain constant within a packet and vary considerably across different packets, leading to partially coherent channel measurements. We develop a message passing-based technique for sparse channel estimation under such partially coherent phase errors and show that our approach achieves a lower channel reconstruction error than comparable benchmarks.

Beam acquisition is key in enabling millimeter wave and terahertz radios to achieve their capacity. Due to the use of large antenna arrays in these systems, the common exhaustive beam scanning results in a substantial training overhead. Prior work has addressed this issue by developing compressive sensing (CS)-based methods which exploit channel sparsity for faster beam acquisition. Unfortunately, most CS techniques employ wide beams and suffer from a low signal-to-noise ratio (SNR) in the channel measurements. To solve this challenge, we develop an IEEE 802.11ad/ay compatible technique that takes an in-sector approach for CS. In our method, the angle domain channel is partitioned into several sectors, and the channel within the best sector is estimated and then used for beamforming. The essence of our framework lies in the construction of a low-resolution beam codebook to identify the best sector and in the design of a CS matrix optimized for in-sector channel estimation. Our beam codebook illuminates distinct non-overlapping sectors and can be realized with low-resolution phased arrays. We show that the proposed codebook results in a higher received SNR than the state-of-the-art sector sweep codebooks. Furthermore, our optimized CS matrix achieves a better in-sector channel reconstruction and a higher achievable rate than comparable benchmarks.

In this paper, uplink transmission of a cell-free massive multiple-input multiple-output (CF-mMIMO) system coexisting with device-to-device (D2D) communication links is investigated, under the assumption that access points (APs) are equipped with low resolution analog-to-digital converters (ADCs). Lower bounds of achievable rates for both D2D users (DUEs) and CF-mMIMO users (CFUEs) are derived in closed-form, with perfect and imperfect channel state information. Next, in order to reduce pilot contamination, greedy and graph coloring-based pilot allocation algorithms are proposed and analyzed for the considered scenario. Furthermore, to control interference and improve the performance, two power control strategies are designed and their complexity and convergence are also discussed. The first power control strategy aims at maximizing CFUEs’ sum spectral efficiency (SE) subject to quality of service constraints on DUEs, while the second one maximizes the weighted product of CFUEs’ and DUEs’ signal-to-interference-plus-noise-ratios (SINRs). Numerical results show that the proposed pilot and power allocations bring a considerable improvement to the network SE. Also, it is revealed that the activation of D2D links has a positive effect on the system throughput, i.e. the network offloading ensured by the D2D links overcomes the increased interference brought by D2D communications.

We study uplink of cell-free massive multiple-input multiple-output system with limited capacity fronthaul link connecting each access point (AP) to a central processing unit (CPU), under hardware impairments at user equipments and APs. Achievable rates are derived for three transmission strategies at APs, named as compress-forward-estimate (CFE), estimate-compress-forward (ECF), and estimate-multiply-compress-forward (EMCF), to efficiently share the fronthaul capacity for sending channel state information (CSI) and/or data signals of the users to the CPU. For forwarding the compressed version of the signals, low-complexity fronthaul rate allocations are proposed for ECF and EMCF strategies, which considerably improve the performance especially for limited capacity fronthauls. Besides, for CFE and ECF strategies, two power allocations are presented to maximize the sum rates; One approximate solution based on geometric programming and an alternative general iterative algorithm. It is shown that performances of the strategies are not the same, because they require different fronthaul rate allocations for CSI and/or data signals transmission to CPU, and their AP signal processing capabilities are different. Finally, to highlight performance characteristics of the proposed system under different transmission strategies and circumstances such as pilot reuse and multiple-antenna APs scenarios, numerical results are presented and the three strategies are compared.