Millimeter-wave (mmWave) communication enables multi-gigabit data rates but suffers from severe blockage and propagation loss in dynamic indoor environments. Unlike outdoor environments, indoor spaces are filled with naturally occurring surfaces such as walls, furniture, and fixtures that exhibit diverse scattering properties. These surfaces can serve as opportunistic passive reflectors to support non-line-of-sight (NLoS) links, and potentially eliminate the need for dedicated passive or reconfigurable surfaces. This paper presents a novel receiver-side beam-selection scheme that exploits LiDAR backscatter information to identify favorable NLoS reflection paths. We hypothesize that optically smooth, low-LiDAR-backscatter surfaces likewise scatter minimally at mmWave and yield strong forward reflections. Accordingly, the receiver probes only beam directions aligned with these low-backscatter returns and selects the one that maximizes received signal strength (RSS). Experimental results across 55 receiver locations in an indoor lab environment demonstrate near-optimal average RSS with a substantial reduction in beam training overhead. Statistical analysis confirms a strong correlation between LiDAR scattering patterns and mmWave direction selection performance. These results highlight the feasibility of passive, LiDAR-assisted beam prediction using natural indoor reflectors and offers a pathway toward efficient NLoS mmWave communication without relying on specialized reflective infrastructure, prior environment mapping, or online learning.

Millimeter-wave (mmWave) links promise multi-gigabit rates but degrade sharply in non-line-of-sight (NLoS) indoor settings. This paper introduces a LiDAR-guided adaptive beamforming framework that dynamically steers the transmit beam to optimize NLoS coverage based on user location. A LiDAR sensor co-located with the transmitter detects the user position in an L-shaped corridor and determines the corresponding angle of departure (AoD) for reflection toward the user. A mirror and glossy metallic reflector placed at the corridor corner provide a common reflection path for both LiDAR and mmWave signals, enabling precise beam alignment without exhaustive angular scanning. Experimental results show that the proposed LiDAR-assisted adaptive beamforming enhances the minimum received signal strength by up to 12 dB compared to fixed-beam transmission. The findings demonstrate that common indoor surfaces capable of reflecting both LiDAR and mmWave signals can be effectively leveraged to extend and optimize coverage.

Reliable millimeter-wave (mmWave) communication in non-line-of-sight (NLoS) environments remains a major challenge for next-generation wireless networks, particularly in indoor and infrastructure-constrained scenarios. This paper presents a proof-of-concept vision-guided passive reflector system that enhances mmWave connectivity by autonomously steering signal reflections using a motorized metallic plate. A lightweight monocular camera detects fiducial ArUco markers on transmitter and receiver nodes, estimates their relative orientation, and aligns the reflector in real time, without relying on RF feedback, external infrastructure, or Reconfigurable Intelligent Surfaces (RIS). Beyond dynamic beam alignment, the use of uniquely identifiable markers enables selective targeting of authorized users, addressing the user identification problem largely overlooked in prior vision-aided RIS research. A low-cost prototype, implemented on a Raspberry Pi 5, demonstrates operation in dynamic 60 GHz NLoS indoor environments, achieving received power gains of up to 17 dB (average ≈10 dB) compared to the no-reflector baseline. The results establish vision-guided reflectors as a practical, low-power alternative to active relays or RIS panels, with strong potential for short-range Industry 4.0/5.0 applications such as IoT networks, AR/VR systems, autonomous robotics, and adaptive factory communications.

We present a shoe-mounted 60GHz Doppler radar for continuous, contactless gait monitoring. Foot-level micro-Doppler signatures quantify timing, symmetry, and clearance linked to neurological and musculoskeletal disorders. Trials with controlled patterns (normal, hemiplegic, festinating, freezing) show separable spectrogram features and derived metrics. The unobtrusive, privacy-preserving form factor supports early screening, objective tracking, and rehabilitation monitoring.

Indoor humidity influences comfort, health, and material longevity, yet conventional hygrometers offer only localized point measurements. This paper explores millimeter-wave (mmWave) radar as a non-intrusive approach for estimating indoor humidity by leveraging the hygroscopic response of common building materials. Wood, in particular, absorbs ambient moisture until reaching an equilibrium moisture content (EMC), increasing its dielectric constant and thus enhancing radar reflectivity as humidity rises. To decouple this material-driven effect from humidity-dependent atmospheric attenuation, a differential reference method is proposed that compares radar returns from a wooden target against a colocated metallic (non-hygroscopic) reference under identical propagation conditions. Experiments using a 60GHz Texas Instruments radar confirm that wood reflections increase monotonically with relative humidity, consistent with EMC theory, while the differential approach effectively compensates for propagation attenuation. These results demonstrate the feasibility of radar-based indoor humidity monitoring and highlight the potential for non-localized sensing using existing mmWave communication infrastructure (e.g., IEEE 802.11ad/ay and 5G FR2).

Reliable connectivity in millimeter-wave (mmWave) and sub-terahertz (sub-THz) networks depends on reflections from surrounding surfaces, as high-frequency signals are highly vulnerable to blockage. The scattering behavior of a surface is determined not only by material permittivity but also by roughness, which governs whether energy remains in the specular direction or is diffusely scattered. We present a LiDAR-based framework to classify indoor surfaces as semi-or low-specular using optical reflectivity as a proxy for scattering. A dataset of over 78,000 points from 16 representative indoor materials was collected and partitioned into 3 cm × 3 cm patches to enable classification from partial views. Patch-level features capturing geometry and intensity, including elevation angle, natural-log–scaled intensity, and max-to-mean ratio, were extracted and used to train Random Forest, XGBoost, and neural network classifiers. The proposed framework enables the generation of scatter-aware environment maps and digital twins, supporting adaptive beam management, blockage recovery, and environment-aware connectivity in beyond-5G and 6G networks.

This paper presents a Light Detection and Ranging (LiDAR) based technique for identifying non-line-of-sight (NLoS) communication paths in indoor millimeter-wave (mmWave) environments. This is achieved by using LiDAR distance and backscattered intensity measurements to i) create a map of the communication environment, and ii) identify a set of potential reflective surfaces for NLoS communication. Experimental results demonstrate that the proposed technique, which combines environmental geometry and LiDAR scatter data for ray-tracing, achieves performance comparable to traditional exhaustive search beam training methods, particularly for first-ranked (top1) alternative NLoS paths. This offers a promising solution for enhancing indoor mmWave communications in dynamic environments prone to line-of-sight link blockages without the need for a fully dedicated beam training stage.

Passive reflectors mitigate millimeter-wave (mm-wave) link blockages by extending coverage to non-line-of-sight (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 demonstrate that silver-coated mirror reflectors achieve comparable performance to silver reflectors and validate the efficacy of the proposed techniques in reducing outages and enhancing NLoS signal strength.

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. Unlike prior work that rely on knowledge of the user's antenna architecture, beamforming codebook, and channel, this paper introduces a CS-based beam alignment technique that is agnostic to these factors. The proposed formulation eliminates angle quantization errors by mapping the recovered angular directions directly onto the user's specific beamforming codebook. Experimental results at 60 GHz demonstrate successful recovery of the mm-wave power distribution in the angular domain. This facilitates accurate beam alignment with limited measurements when compared to exhaustive search solutions.

Millimeter-wave (mm-wave) vehicular communication networks are expected to revolutionize intelligent transportation systems by enabling ultra-reliable, low-latency, and high-speed data exchange. A major challenge that needs to be addressed in these networks is the presence of malicious user activity that can disrupt the network through various means. These malicious activities include manipulating safety messages or launching location spoofing attacks. To tackle this challenge, a physical-layer-based location spoofing attack detection technique for mm-wave vehicular networks is proposed in this paper. The proposed technique makes use of the additional channel measurements available in systems with hybrid antenna architectures to enable joint beamforming and channel sensing. Spoofing attack detection is achieved by verifying a cluster of estimated angles of arrivals with the reported location information. The numerical results show that the proposed technique is highly accurate in detecting location spoofing attacks using just a few communication packets at the receiver without the need for a dedicated channel sensing stage or extra communication overhead.

Large antenna arrays enable directional precoding for Millimeter-Wave (mmWave) systems and provide sufficient link budget to combat the high path-loss at these frequencies. Due to atmospheric conditions and hardware malfunction, outdoor mmWave antenna arrays are prone to blockages or complete failures. This results in a modified array geometry, distorted far-field radiation pattern, and system performance degradation. Recent remote array diagnostic techniques have emerged as an effective way to detect defective antenna elements in large antenna arrays with few diagnostic measurements. These techniques, however, are dependent on full and perfect channel state information (CSI), which can be challenging to acquire in the presence of antenna faults. This paper proposes a new remote array diagnosis technique that relaxes the need for full CSI and only requires knowledge of the incident angles-of-arrival, i.e. partial channel knowledge. Numerical results demonstrate the effectiveness of the proposed technique and show that fault detection can be achieved with comparable number of diagnostic measurements required by diagnostic techniques based on full channel knowledge. In presence of channel estimation errors, the proposed technique is shown to out-perform recently proposed array diagnostic techniques.

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.

Millimeter wave (mmWave) systems rely on accurate channel state information (CSI) for the design of the precoding and combining matrices. Acquiring accurate CSI, however, is challenging due to the large number of antennas, the low signal-to-noise (SNR) ratio before beamforming, and possible interference from neighboring base stations (BSs). Prior work on channel estimation focused on the first two challenges and did not address inter- cell interference. Interference from neighboring BSs deteriorates the already low SNR and introduces errors in the channel estimate. This leads to additional interference in the system. This paper studies the effects of inter- cell interference on compressed sensing (CS) mmWave channel estimation techniques. A CS measurement matrix design is then proposed to jointly estimate the mmWave channel and null interference from neighboring BSs. Simulation results show that channel estimate errors strongly depend on the interference power and the number of interfering BSs. Moreover, in the presence of interference, the proposed techniques are shown to achieve channel estimates comparable to interference-free systems.

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, modify the array geometry, and distort the far-field radiation pattern of the array. This paper studies the effects of blockages on the far-field radiation pattern of linear arrays and proposes several 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 given knowledge of the angles of arrival/departure. 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.

The radiation pattern of an antenna array depends on the excitation weights and the geometry of the array. Due to mobility, some vehicular antenna elements might be subjected to full or partial blockages from a plethora of particles like dirt, salt, ice, and water droplets. These particles cause absorption and scattering to the signal incident on the array, and as a result, change the array geometry. This distorts the radiation pattern of the array mostly with an increase in the sidelobe level and decrease in gain. In this paper, we propose a blockage detection technique for millimeter wave vehicular antenna arrays that jointly estimates the locations of the blocked antennas and the attenuation and phase-shifts that result from the suspended particles. The proposed technique does not require the antenna array to be physically removed from the vehicle and permits real-time array diagnosis. Numerical results show that the proposed technique provides satisfactory results in terms of block detection with low detection time provided that the number of blockages is small compared to the array size.

Millimeter wave (mmWave) vehicular communication 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.

This paper presents a comprehensive performance analysis of full-duplex multiuser relay networks employing opportunistic scheduling with noisy and compressive feedback. Specifically, two feedback techniques based on compressive sensing (CS) theory are introduced and their effect on the system performance is analyzed. The problem of joint user identity and signal-to-noise ratio (SNR) estimation at the base-station is casted as a block sparse signal recovery problem in CS. Using existing CS block recovery algorithms, the identity of the strong users is obtained and their corresponding SNRs are estimated using the best linear unbiased estimator (BLUE). To minimize the effect of feedback noise on the estimated SNRs, a backoff strategy that optimally backsoff on the noisy estimated SNRs is introduced, and the error covariance matrix of the noise after CS recovery is derived. Finally, closed-form expressions for the end-to-end SNRs of the system are derived. Numerical results show that the proposed techniques drastically reduce the feedback air-time and achieve a rate close to that obtained by scheduling techniques that require dedicated error-free feedback from all network users. Key findings of this paper suggest that the choice of half-duplex or full-duplex SNR feedback is dependent on the channel coherence interval, and on low coherence intervals, full-duplex feedback is superior to the interference-free half-duplex feedback.

We propose a distributed user selection strategy in a network MIMO setting with M base stations serving K users. Each base station is equipped with L antennas, where LM ≪ K. The conventional selection strategy is based on a well known technique called semi-orthogonal user selection when the zero-forcing beamforming (ZFBF) is adopted. Such technique, however, requires perfect channel state information at the transmitter (CSIT), which might not be available or need large feedback overhead. This paper proposes an alternative distributed user selection technique where each user sets a timer that is inversely proportional to his channel quality indicator (CQI), as a means to reduce the feedback overhead. The proposed strategy allows only the user with the highest CQI to respond with a feedback. Such technique, however, remains collision free only if the transmission time is shorter than the difference between the strongest user timer and the second strongest user timer. To overcome the situation of longer transmission times, the paper proposes another feedback strategy that is based on the theory of compressive sensing, where collision is allowed and all users encode their feedback information and send it back to the base-stations simultaneously. The paper shows that the problem can be formulated as a block sparse recovery problem which is agnostic on the transmission time, which makes it a good alternative to the timer approach when collision is dominant.

Relay selection is a simple technique that achieves spatial diversity in cooperative relay networks. Nonetheless, relay selection algorithms generally require error-free channel state information (CSI) from all cooperating relays. Practically, CSI acquisition generates a great deal of feedback overhead that could result in significant transmission delays. In addition to this, the fed back channel information is usually corrupted by additive noise. This could lead to transmission outages if the central node selects the set of cooperating relays based on inaccurate feedback information. In this paper, we propose a relay selection algorithm that tackles the above challenges. Instead of allocating each relay a dedicated channel for feedback, all relays share a pool of feedback channels. Following that, each relay feeds back its identity only if its effective channel (source-relay-destination) exceeds a threshold. After deriving closed-form expressions for the feedback load and the achievable rate, we show that the proposed algorithm drastically reduces the feedback overhead and achieves a rate close to that obtained by selection algorithms with dedicated error-free feedback from all relays.

Relay selection is a simple technique that achieves spatial diversity in cooperative relay networks. Generally, relay selection algorithms require channel state information (CSI) feedback from all cooperating relays to make a selection decision. This requirement poses two important challenges, which are often neglected in the literature. Firstly, the fed back channel information is usually corrupted by additive noise. Secondly, CSI feedback generates a great deal of feedback overhead (air-time) that could result in significant performance hits. In this paper, we propose a compressive sensing (CS) based relay selection algorithm that reduces the feedback overhead of relay networks under the assumption of noisy feedback channels. The proposed algorithm exploits CS to first obtain the identity of a set of relays with favorable channel conditions. Following that, the CSI of the identified relays is estimated using least squares estimation without any additional feedback. Both single and multiple relay selection cases are considered. After deriving closed-form expressions for the asymptotic end-to-end SNR at the destination and the feedback load for different relaying protocols, we show that CS-based selection drastically reduces the feedback load and achieves a rate close to that obtained by selection algorithms with dedicated error-free feedback.

Relay selection is a simple technique that achieves spatial diversity in cooperative relay networks. However, for relay selection algorithms to make a selection decision, channel state information (CSI) from all cooperating relays is usually required at a central node. This requirement poses two important challenges. Firstly, CSI acquisition generates a great deal of feedback overhead (air-time) that could result in significant transmission delays. Secondly, the fed back channel information is usually corrupted by additive noise. This could lead to transmission outages if the central node selects the set of cooperating relays based on inaccurate feedback information. In this paper, we introduce a limited feedback relay selection algorithm for a multicast relay network. The proposed algorithm exploits the theory of compressive sensing to first obtain the identity of the "strong" relays with limited feedback. Following that, the CSI of the selected relays is estimated using linear minimum mean square error estimation. To minimize the effect of noise on the fed back CSI, we introduce a back-off strategy that optimally backs-off on the noisy estimated CSI. For a fixed group size, we provide closed form expressions for the scaling law of the maximum equivalent SNR for both Decode and Forward (DF) and Amplify and Forward (AF) cases. Numerical results show that the proposed algorithm drastically reduces the feedback air-time and achieves a rate close to that obtained by selection algorithms with dedicated error-free feedback channels.

Opportunistic schedulers rely on the feedback of the channel state information of users in order to perform user selection and downlink scheduling. This feedback increases with the number of users, and can lead to inefficient use of network resources and scheduling delays. We tackle the problem of feedback design, and propose a novel class of nonorthogonal codes to feed back channel state information. Users with favorable channel conditions simultaneously transmit their channel state information via non-orthogonal beams to the base station. The proposed formulation allows the base station to identify the strong users via a simple correlation process. After deriving the minimum required code length and closed-form expressions for the feedback load and downlink capacity, we show that: the proposed algorithm reduces the feedback load while matching the achievable rate of full feedback algorithms operating over a noiseless feedback channel; and the proposed codes are superior to the Gaussian codes.

In multi-antenna broadcast networks, the base stations (BSs) rely on the channel state information (CSI) of the users to perform user scheduling and downlink transmission. However, in networks with large number of users, obtaining CSI from all users is arduous, if not impossible, in practice. This paper proposes channel feedback reduction techniques based on the theory of compressive sensing (CS), which permits the BS to obtain CSI with acceptable recovery guarantees under substantially reduced feedback overhead. Additionally, assuming noisy CS measurements at the BS, inexpensive ways for improving post-CS detection are explored. The proposed techniques are shown to reduce the feedback overhead, improve CS detection at the BS, and achieve a sum-rate close to that obtained by noiseless dedicated feedback channels.

In this paper we present our experiences with a new software-tool model for program assessment and evaluation, and engineering programs improvement in assessing learning effectiveness. In the past, the assessment plan was based on the typical practice of assigning a person in the department for overseeing the whole process. The non-collaborative evaluation that is managed by a single program leader is not effective and problematic. To involve more faculty and in an effort to prepare for the most recent ABET visit, we decided to adopt EvalTools® in fall of 2010. EvalTools® is designed and developed according to ABET standards to provide a mechanism for collecting and analyzing data about the program, students' performance and their learning achievements. In addition, EvalTools® is instrumental in providing a mechanism to simplify the process of inspecting the assessment results as well as identifies strengths and shortcomings of the program before ABET review. More importantly getting faculty members excited about results and involved in the process of program improvement is a major accomplishment. Our experience via first-time implementation of EvalTools® shows very useful results for this model that can be easily disseminated for various programs in various disciplines. In this paper we will show: process of best use of relevant features in aid of streamlining faculty's time in data collection as well as evaluation was achieved; our results and how we succeeded in improving our program quality in an effective, efficient and systematic way; that simple curriculum revisions for multiple programs as a result of using EvalTools® for programs under going ABET is possible; that capturing the process of effective trainings needed for faculty and staff in a simple manner; faculty's experience in a constructive and engaging manner.

In this paper, we extend the results of [12] by proposing a joint polling and contention scheduling scheme that reduces the feedback load in orthogonal frequency division multiple access (OFDMA) systems employing adaptive modulation. This feedback reduction is achieved by grouping users into categories, and requesting feedback from users that belong to a certain category only. The feedback process comprises of two modes. The first mode is a polling only mode and the second mode consists of a contention stage followed by a polling stage. The contention stage is used to find the i-th category where the best user is likely to be found. All users belonging to that category are polled sequentially until an acceptable user with channel quality above a predefined threshold is found. Numerical results show that the proposed scheme reduces the feedback load and the guard-time without any loss in the spectral efficiency when compared to the optimal selective diversity with full feedback algorithm shown in [11]. We, further, derive closed-form expressions for the feedback load, average guard time and the spectral efficiency and show that the proposed scheme improves the system spectral efficiency, the feedback load and the average guard time when compared to the schemes proposed in [12].

The static staging heuristics proposed in the literature for staging the data items associated with real-time distributed applications adhere to a method by which only one data item is transferred in each communication step to optimize a specific cost function. In this paper, we first propose the Extended Partial Path (EPP) algorithm based on the same method. In terms of maximizing the number of satisfied requests, we have analytically shown that EPP has a performance that is equal to or greater than the Partial Path Heuristic (PPH) introduced previously [CHECK END OF SENTENCE], thanks to excluding the data items that cannot be satisfied by PPH from scheduling and scheduling the satisfiable data-items along their extended paths. In contrast to EPP and other data staging heuristics proposed, we develop the concurrent scheduling (CS) heuristic which allows simultaneous transfer of more than one data item in an organized fashion, thereby improving the overall performance of the staging system. At the heart of the CS heuristic are EPP and the local priority assignment method devised for solving the conflicts between data items at the intermediate nodes. The extensive simulation results further confirm the superiority of the CS heuristic over PPH.

Large-scale data intensive applications with real-time requirements are currently emerging in many disciplines of science and engineering. Such applications can benefit from a grid environment provided that an efficient solution to the following data scheduling problem can be found: schedule the transfer of a set of large-scale data objects of distributed applications in a grid environment so as to meet real-time constraints associated with these data transfers. Based on this premise, This work focuses on the aforementioned problem and proposes a new effective path-selection based scheduling algorithm. The algorithm performs its optimization based on a schedule reflection model; a new cost criterion that takes into account the satisfiability of each application as a whole. We show, by simulation, that our algorithm improves the performance of data intensive real-time applications.