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Passive reflectors mitigate millimeter-wave (mmwave) link blockages by extending coverage to non-line-ofsight (NLoS) regions. However, their deployment often leads to irregular reflected beam patterns and coverage gaps. This results in rapid channel fluctuations and potential outages. In this paper, we propose two LiDAR-aided link enhancement techniques to address these challenges. Leveraging user position information, we introduce a location-dependent link control strategy and a user selection technique to improve NLoS link reliability and coverage. Experimental results validate the efficacy of the proposed techniques in reducing outages and enhancing NLoS signal strength.

The dynamic nature of indoor environments poses unique challenges for next-generation millimeter-wave (mmwave) connectivity. These challenges arise from blockages due to mobile obstacles, mm-wave signal scattering caused by indoor surfaces, and user phased antenna array imperfections. Traditional compressed sensing (CS) based beam alignment techniques enable swift mm-wave connectivity with a limited number of measurements. These techniques, however, rely on prior knowledge of the communication channel model and the user's array manifold to design the sensing matrix and minimize angle quantization errors. This limits their effectiveness in dynamic environments. This paper proposes a novel CS-based beam alignment technique for mm-wave systems operating in indoor environments. Unlike prior work that rely on knowledge of the user's antenna architecture, communication codebook, and channel, the proposed technique is agnostic to these factors. The proposed formulation eliminates angle quantization errors by mapping the recovered angular directions onto the user's specific codebook. This is achieved by exploiting the energy compaction property of the Discrete Cosine Transform (DCT) to compress and identify the strongest cluster locations in the transform domain for robust beamforming. Experimental results at 60 GHz demonstrate successful recovery of the mm-wave power distribution in the angular domain, facilitating accurate beam alignment with limited measurements compared to exhaustive search solutions.

Direction-of-arrival (DOA) information is vital for multiple-input-multiple-output (MIMO) systems to complete localization and beamforming tasks. Switched antenna arrays have recently emerged as an effective solution to reduce the cost and power consumption of MIMO systems. Switch-based array architectures connect a limited number of radio frequency chains to a subset of the antenna elements forming a subarray. This paper addresses the problem of antenna selection to optimize DOA estimation performance. We first perform a subarray layout alignment process to remove subarrays with identical beampatterns and create a unique subarray set. By using this set, and based on a DOA threshold region performance approximation, we propose two antenna selection algorithms; a greedy algorithm and a deep-learning-based algorithm. The performance of the proposed algorithms is evaluated numerically. The results show a significant performance improvement over selected benchmark approaches in terms of DOA estimation in the threshold region and computational complexity.

Communication in the millimeter-wave (mmWave) band has recently been proposed to enable giga-bit-per-second data rates for next generation wireless systems. Physical layer security techniques have emerged as a simple and yet effective way to safeguard these systems against eavesdropping attacks. These techniques make use of the large antenna arrays available in mmWave systems to provide an array gain at the target receiver and degrade the signal quality at the eavesdropper. Despite their effectiveness, majority of these techniques are based on line-of-sight communication links between the transmitter and the receiver, and may fail in the presence of blockages or non-line-of-sight links. This paper builds upon previous work and extends physical layer security to the non-line-of-sight communication case and randomly located eavesdroppers. Specifically, the large dimensional antenna arrays in mmWave systems and the intrinsic characteristics of wireless channel are exploited to induce noiselike signals that jam eavesdroppers with sensitive receivers. Numerical results show that the proposed techniques provide higher secrecy rate when compared to conventional array and physical layer techniques based on line-of-sight links.

Emerging wireless applications such as 5G cellular, large intelligent surfaces (LIS), and holographic massive MIMO require antenna array processing at mm-wave frequencies with large numbers of independent digital transceivers. This paper summarizes the authors' recent progress on the design and testing of 28 GHz and 60 GHz fully-digital array processing platforms based on wideband reconfigurable FPGA-based software-defined radios (SDRs). The digital baseband and microwave interfacing aspects of the SDRs are implemented on single-chip RF system-on-chip (RF-SoC) processors from Xilinx. Two versions of the RF-SoC technology (ZCU-111 and ZCU-1275) were used to implement fully-digital real-time array processors at 28~GHz (realizing 4 parallel beams with 0.8 GHz bandwidth per beam) and 60~GHz (realizing 4 parallel beams with 1.8~GHz bandwidth per beam). Dielectric lenslet arrays fed by a digital phased-array feed (PAF) located on the focal plane are proposed for further increasing antenna array gain., Comment: 6 pages

Compressed Sensing (CS) based channel estimation techniques have recently emerged as an effective way to acquire the channel of millimeter-wave (mmWave) systems with a small number of measurements. These techniques, however, are based on prior knowledge of transmit and receive array manifolds, and assume perfect antenna arrays at both the transmitter and the receiver. In the presence of antenna imperfections, the geometry and response of the arrays are modified. This distorts the CS measurement matrix and results in channel estimation errors. This paper studies the effects of both transmit and receive antenna imperfections on the mmWave channel estimate. A relay-aided solution which corrects for errors caused by faulty transmit arrays is then proposed. Simulation results demonstrate the effectiveness of the proposed solution and show that comparable channel estimates can be obtained when compared to systems with perfect antennas without the need for additional training overhead., Comment: Accepted for publication in IEEE ICC 2019

The radiation pattern of an antenna array depends on the excitation weights and the geometry of the array. Due to wind and atmospheric conditions, outdoor millimeter wave antenna elements are subject to full or partial blockages from a plethora of particles like dirt, salt, ice, and water droplets. Handheld devices are also subject to blockages from random finger placement and/or finger prints. These blockages cause absorption and scattering to the signal incident on the array, and change the array geometry. This distorts the far-field radiation pattern of the array leading to an increase in the sidelobe level and decrease in gain. This paper studies the effects of blockages on the far-field radiation pattern of linear arrays and proposes two array diagnosis techniques for millimeter wave antenna arrays. The proposed techniques jointly estimate the locations of the blocked antennas and the induced attenuation and phase shifts. Numerical results show that the proposed techniques provide satisfactory results in terms of fault detection with reduced number of measurements (diagnosis time) provided that the number of blockages is small compared to the array size.

Millimeter wave (mmWave) vehicular communication systems will provide an abundance of bandwidth for the exchange of raw sensor data and support driver-assisted and safety-related functionalities. Lack of secure communication links, however, may lead to abuses and attacks that jeopardize the efficiency of transportation systems and the physical safety of drivers. In this paper, we propose two physical layer (PHY) security techniques for vehicular mmWave communication systems. The first technique uses multiple antennas with a single RF chain to transmit information symbols to a target receiver and noise-like signals in non-receiver directions. The second technique uses multiple antennas with a few RF chains to transmit information symbols to a target receiver and opportunistically inject artificial noise in controlled directions, thereby reducing interference in vehicular environments. Theoretical and numerical results show that the proposed techniques provide higher secrecy rate when compared to traditional PHY security techniques that require digital or more complex antenna architectures.

Millimeter wave (mmWave) vehicular communica tion systems have the potential to improve traffic efficiency and safety. Lack of secure communication links, however, may lead to a formidable set of abuses and attacks. To secure communication links, a physical layer precoding technique for mmWave vehicular communication systems is proposed in this paper. The proposed technique exploits the large dimensional antenna arrays available at mmWave systems to produce direction dependent transmission. This results in coherent transmission to the legitimate receiver and artificial noise that jams eavesdroppers with sensitive receivers. Theoretical and numerical results demonstrate the validity and effectiveness of the proposed technique and show that the proposed technique provides high secrecy throughput when compared to conventional array and switched array transmission techniques.

In this paper, we propose a feedback reduction scheme for full-duplex relay-aided multiuser networks. The proposed scheme permits the base station (BS) to obtain channel state information (CSI) from a subset of strong users under substantially reduced feedback overhead. More specifically, we cast the problem of user identification and CSI estimation as a block sparse signal recovery problem in compressive sensing (CS). Using existing CS block recovery algorithms, we first obtain the identity of the strong users and then estimate their CSI using the best linear unbiased estimator (BLUE). To minimize the effect of noise on the estimated CSI, we introduce a back-off strategy that optimally backs-off on the noisy estimated CSI and derive the error covariance matrix of the post-detection noise. In addition to this, we provide exact closed form expressions for the average maximum equivalent SNR at the destination user. Numerical results show that the proposed algorithm drastically reduces the feedback air-time and achieves a rate close to that obtained by scheduling schemes that require dedicated error-free feedback from all the network users.