arXiv subjects

Hamed Masoumi

Publications and source records attributed to Hamed Masoumi.

6 recordsLinked to original sources

arXiv2023

In-sector Compressive Beam Alignment for MmWave and THz Radios

Beam alignment is key in enabling millimeter wave and terahertz radios to achieve their capacity. Due to the use of large 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 to achieve faster beam alignment. Unfortunately, standard 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 for beam alignment. 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 the CS matrix 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 than comparable benchmarks.

eess.SP↗

arXiv2023

Analysis of Orthogonal Matching Pursuit for Compressed Sensing in Practical Settings

Orthogonal matching pursuit (OMP) is a widely used greedy algorithm for sparse signal recovery in compressed sensing (CS). Prior work on OMP, however, has only provided reconstruction guarantees under the assumption that the columns of the CS matrix have equal norms, which is unrealistic in many practical CS applications due to hardware constraints. In this paper, we derive sparse recovery guarantees with OMP, when the CS matrix has unequal column norms. Finally, we show that CS matrices whose column norms are comparable achieve tight guarantees for the successful recovery of the support of a sparse signal and a low mean squared error in the estimate.

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arXiv2022

Structured Sensing Matrix Design for In-sector Compressed mmWave Channel Estimation

Fast millimeter wave (mmWave) channel estimation techniques based on compressed sensing (CS) suffer from low signal-to-noise ratio (SNR) in the channel measurements, due to the use of wide beams. To address this problem, we develop an in-sector CS-based mmWave channel estimation technique that focuses energy on a sector in the angle domain. Specifically, we construct a new class of structured CS matrices to estimate the channel within the sector of interest. To this end, we first determine an optimal sampling pattern when the number of measurements is equal to the sector dimension and then use its subsampled version in the sub-Nyquist regime. Our approach results in low aliasing artifacts in the sector of interest and better channel estimates than benchmark algorithms.

eess.SP↗

arXiv2020

Weighted Sum-Rate Maximization for Multi-IRS-assisted Full-Duplex Systems with Hardware Impairments

Smart and reconfigurable wireless communication environments can be established by exploiting well-designed intelligent reflecting surfaces (IRSs) to shape the communication channels. In this paper, we investigate how multiple IRSs affect the performance of multi-user full-duplex communication systems under hardware impairment at each node, wherein the base station (BS) and the uplink users are subject to maximum transmission power constraints. Firstly, the uplink-downlink system weighted sum-rate (SWSR) is derived which serves as a system performance metric. Then, we formulate the resource allocation design for the maximization of SWSR as an optimization problem which jointly optimizes the beamforming and the combining vectors at the BS, the transmit powers of the uplink users, and the phase shifts of multiple IRSs. Since the SWSR optimization problem is non-convex, an efficient iterative alternating approach is proposed to obtain a suboptimal solution for the design problem considered and its complexity is also discussed. In particular, we firstly reformulate the main problem into an equivalent weighted minimum mean-square-error form and then transform it into several convex sub-problems which can be analytically solved for given phase shifts. Then, the IRSs phases are optimized via a gradient ascent-based algorithm. Finally, numerical results are presented to clarify how multiple IRSs enhance the performance metric under hardware impairment.

cs.IT↗

arXiv2020

Coexistence of D2D Communications and Cell-Free Massive MIMO Systems With Low Resolution ADC for Improved Throughput in Beyond-5G Networks

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.

cs.IT↗

arXiv2019

Transmission strategies for cell-free massive mimo with limited capacity fronthaul links

We study an uplink scenario of a cell-free massive multiple-input multiple-output (CF-mMIMO) system with limited capacity fronthaul links (LC-FHLs) connecting each access point (AP) to the central unit (CU), where user equipments and APs are subject to hardware impairments. Therefore, to efficiently use the capacity of FHLs to maximize the achievable rate, we analyze three strategies for performing compression and forwarding of channel state information (CSI) and data signals over the LC-FHLs to the CU; Compress-forward-estimate (CFE), estimate-compress-forward (ECF), and estimate-multiply-compress-forward (EMCF). For CFE and EMCF achievable rates are derived, and for ECF one upper and lower bounds are presented which are tight for ideal hardware and FHLs. Also for forwarding the quantized version of CSI and data signals of each user, low-complexity fronthaul capacity allocations are proposed for ECF and EMCF strategies, which considerably improve the performance of the system, especially for limited capacity FHLs. Our results indicate that at high SNR regime and for large enough capacity of FHLs, estimating channels at the CU rather than APs result in smaller estimation error. Then, geometric programming power allocations are developed for CFE and ECF to maximize sum rates. Finally, to highlight the performance characteristics of the system numerical results are presented.

cs.IT↗