By integrating radar services into existing 5G networks, we enable radar applications using 5G waveforms with minimal interference to existing cellular networks and no dedicated infrastructure. Prior work demonstrates that this "radar-as-a-subscriber technology" (RAST) approach achieves reasonable target detection and range estimation performance even with constraints on the number and allocation of time-frequency resource blocks. We previously demonstrated that the specific placement of 5G resource blocks can be manipulated to favor range estimation performance or sidelobe level of a RAST subsystem. In dynamic environments where target position, noise, interference, attenuation, and system priorities are dynamic, the joint detection-estimation performance of the RAST subsystem is significantly constrained if the resource allocation strategy is static or random. To optimize the RAST subsystem performance, we propose an adaptive resource manager that uses reinforcement learning to optimize the selection of 5G resource blocks in response to dynamic environmental conditions. We demonstrate that this strategy achieves performance improvements of up to 40% compared to a random resource allocation strategy in a simple MATLAB simulation environment.

The emergence of non-contact biosensing technologies is transforming the Healthcare Internet-of-Things (IoT) by enabling continuous and unobtrusive monitoring of cardiovascular function, critical for early disease detection and personalized care. Millimeter-wave radar, known for its fine spatial resolution and motion sensitivity, offers a compelling platform for remote cardiac monitoring. However, practical deployment remains challenging due to susceptibility to motion artifacts, respiratory interference, and physiological variability. In this work, we present a novel radar-acoustic cardiac monitoring framework that addresses these challenges by exploiting the spectral separation between high-frequency heart sounds (20-80 Hz) and the lower-frequency thoracic movements (0.7-1.5 Hz). This natural spectral isolation facilitates the suppression of motion artifacts and respiratory interference, enhancing the robustness of cardiac signal extraction. Building on the presented insights, we develop an active motion cancellation technique to effectively estimate and compensate for band-limited multiplicative motion interference even under complex body motions. This method enables seamless signal extraction while preserving the integrity of heart sound signals. Simulations and experimental validations demonstrate significant improvements in heart rate estimation and pulse waveform recovery, even under realistic motion scenarios. This privacy-preserving, non-contact, and clothing-penetrating sensing framework presents a promising avenue for seamless integration of cardiac monitoring into IoT ecosystems, enabling real-time health analytics in free-living environments.

The increasing demand for cost-efficient yet reliable swarms of multi-function unmanned aerial systems (UASs) has tremendous potential in both military and civilian applications. Advancements in ISACs research, such as RF Convergence exclusively provide a promising path toward implementing multi-function UASs, often limited by insufficient front-end capabilities as the network scales up. Because the interconnection bandwidth and computational complexity required to process the data among the ISACs nodes using centralized architectures are high. In this paper, we propose a decentralized waveform co-design method to reduce computational complexity while maximizing the mutual benefits of users in a multi-function UASs network based on the theory of decentralized, partially-observable Markov decision processes (Dec-POMDPs). To address the computational intractability of solving Dec-POMDPs (as with any decision-theoretic framework), we extend an approximate dynamic programming approach we recently developed-nominal belief-state optimization (NBO) in the context of radar-communications waveform co-design. We conduct a numerical study to benchmark the performance of the Dec-POMDP-based waveform design approach against a centralized decision optimization approach, which demonstrates a 50% reduction in computation time at the cost of moderate loss in ranging precision.

This paper presents a novel motion-tolerant heart sound (HS) detection approach using a millimeter-wave (mmWave) Frequency-Modulated Continuous Wave (FMCW) radar sensor. Recent works demonstrate, in ideal stationary scenarios, radar-based HS detection from chest surface vibrations due to sound pressure waves. However, similar to the known motion distortion problem for radar-based vital signs detection, we have observed that HS measurement is severely affected by other motion artifacts as the HS-induced skin vibration is extremely small (a few micrometers) compared to body motion. Therefore, we develop a phase-tracking-based motion cancellation technique to mitigate the effect of body motions and thus radar-based HS detection is feasible. Simulation and measurement results validate the motion-tolerant radar-based HS detection in the presence of random body motions (RBM).

Novel use of radar acoustics for noncontact cardiac parameter estimation is presented as a potentially invaluable addition to wireless sensor networks in Healthcare IoT. Here, radar acoustics refers to the radar captured high-frequency mechanical motion, beyond 20 Hz, on the skin surface induced by heart sound (HS) acoustic waves. Conventional radar-based heart rate (HR) detection methods rely on detecting the heartbeat motion, around 1 Hz, while the unaddressed respiration motion coupling issue prevents consistent heartbeat estimation accuracy in the presence of stronger respiration interference, below 1 Hz. Another issue common to prior methods is the nonadaptive spectral filter design for separating heartbeat signal. As a consequence, these methods cannot be generalized to measurement spaces with high heartbeat variability as demonstrated in this study. To effectively address these limitations, a new HR detection method using radar is proposed to derive a high-fidelity pulse template from the smoothed envelogram of HS measurements. What is more, the feasibility study of radar HS-based HR variability analysis is performed, in which inter-beat-internal is derived from the first and second HS signatures. To that end, automatic identification of HS signatures and signal denoising techniques are developed accordingly. Multiple validation experiments are conducted on human subjects. Our results imply that the new methodology effectively improves remote cardiac sensing performance using the radar technology and presents an exciting opportunity to expand Healthcare IoT, improving patient experiences and outcomes.

We implement and experimentally demonstrate a distributed, phase-coherent, mesh relay network that executes spatiotemporal beamforming on a communications signal. Each single-antenna node of this mesh network amplifies, predistorts, and forwards its reception to a receiver. In this configuration, an incoherent network ofNnodes enhances the received power of a signal of interest by a factor ofNcompared to a single-input single-output communications link. By synchronizing these distributed nodes and constructing a spatiotemporal beamformer, we increase this factor to a maximum ofN2and enable significant interference rejection capabilities. To achieve phase-coherence across the network elements, we execute a distributed synchronization algorithm using training data from the source node. We construct spatiotemporal beamformers by solving an MMSE optimization, which we continually reoptimize using new observations of training sequences and updated channel estimates. We present results from two over-the-air experimental demonstrations, one without and one with an external interferer. In the former, we demonstrate a 17.4 dB signal-to-noise ratio (SNR) improvement compared to the 18.1 dB theoretical bound for an eight-element network. In the latter, we demonstrate an 11.3 dB SNR improvement and a 14.6 dB interference reduction.

Remote vibrometry using wireless signals is a recently introduced novel technique with a wide range of applications such as remote microphones and structural health monitoring. These use cases require high sensitivity and coherence in the sensing system. In this context, radar is a suitable all-weather sensor compared to conventional acoustic acquisition. We investigate human vocal sound and heart sound detection and separation using a single millimeter-wave radar sensor and advanced array processing techniques to achieve superior motion sensitivity.

Practical, in-band, full-duplex (IBFD) systems typically require more than 100 dB of self-interference cancellation (SIC). Digital processing alone is insufficient for achieving this target, which drives us towards supplementary analog mitigation techniques. We propose an analog-domain, self-interference cancellation circuit to enable pass-band, analog SIC in an IBFD system. Analog SIC is limited by several hardware constraints and design choices, including finite tap-delay resolution, non-negative tap constraints, and bit precision quantization. We characterize the performance impact of each of these limitations as a function of signal bandwidth, carrier frequency, bit precision, and other system design parameters. We further characterize the achievable system performance under all of these limitations combined. We simulate several realistic examples to illustrate the relationship between the achievable self-mitigation performance and various system design choices. We implement a simple constrained optimization algorithm informed by these results to optimize the tap-delay weights of the analog circuit under these system constraints. We simulate the achievable mitigation performance and demonstrate as much as 45 dB of analog-domain, self-interference mitigation of a wide-band signal with realistic system configurations.

RF system development is traditionally constrained by a restrictive trade-off between power efficiency and programmatic flexibility. We outline a path towards achieving both, thereby enabling a range of new system concepts that better utilize limited resources. As an example, for many future applications, we consider RF convergence – reusing the same spectrum and waveforms to achieve multiple distributed system functions and goals, simultaneously. To enable this next step in processing, we develop a novel framework that includes both software and the system-on-chip (SoC) design.

Implementing new RF applications has traditionally required significant time and expertise, even for relatively simple algorithms. Software-defined radios (SDRs) have recently enabled rapid implementation and validation of RF applications without specialized hardware or advanced programming skills. We further facilitate this rapid application development by constructing WISCANet – a diverse network of commercial SDRs with specialized control software. WISCANet automatically configures these SDRs and controls transmit and receive events with minimal user input. This allows users to rapidly implement over-the-air RF applications by simply defining the baseband processing chain in software. Furthermore, WISCANet emulates real-time operations, which allows users to test real-time applications without the usual complications such as processing speed or hardware limitations. In this paper, we present recent improvements to the WISCA Software-Defined Radio Network (SDR-N). These improvements include configurable and flexible multi-channel phase coherence and support for both MATLAB and Python applications. The open source release of this software may be found on GitHub at: https://github.com/WISCA

In the automotive sector, both radars and wireless communication are susceptible to interference. However, combining the radar and communication systems, i.e., radio frequency (RF) communications and sensing convergence, has the potential to mitigate interference in both systems. This article analyses the mutual interference of spectrally coexistent frequency modulated continuous wave (FMCW) radar and communication systems in terms of occurrence probability and impact, and introduces RadChat, a distributed networking protocol for mitigation of interference among FMCW based automotive radars, including self-interference, using radar and communication cooperation. The results show that RadChat can significantly reduce radar mutual interference in single-hop vehicular networks in less than 80 ms.

Image sensors with programmable region-of-interest (ROI) readout are a new sensing technology important for energyefficient embedded computer vision. In particular, ROIs can subsample the number of pixels being readout while performing single object tracking in a video. In this paper, we develop adaptive sampling algorithms which perform joint object tracking and predictive video subsampling. We utilize an object detection consisting of either mean shift tracking or a neural network, coupled with a Kalman filter for prediction. We show that our algorithms achieve mean average precision of 0.70 or higher on a dataset of 20 videos in software. Further, we implement hardware acceleration of mean shift tracking with Kalman filter adaptive subsampling on an FPGA. Hardware results show a 23 × improvement in clock cycles and latency as compared to baseline methods and achieves 38FPS real-time performance. This research points to a new domain of hardware-software co-design for adaptive video subsampling in embedded computer vision.

Current day commercial aircrafts are equipped with numerous communications and navigation systems that are considered in isolation, leading to a stove-pipe design. Antennae supporting these services, protrude from the aircraft's body increasing drag and, as a result, fuel consumption. Therefore, we redesign deployment of the antennae to support multiple services with shared physical elements that conforms to the exterior of an aircraft to mitigate drag. Conformal arrays are, however, susceptible to structural changes in the fuselage that manifest as pointing errors and side lobe degradation. We propose an on-line calibration algorithm that leverages cooperative satellites in direct line-of-sight of the aircraft to optimally steer beams. These external calibration sources supplement an in situ source placed on the aircraft's tail. We establish models for potential sources of mismatch and demonstrate the hybrid calibration method via simulations.

Spectral congestion limits the opportunities and performance of radio frequency (RF) systems. Spectral isolation sufficiently mitigates this congestion for a small number of users but does not offer a scalable solution once the entire spectrum is occupied. Dynamic resource management supports higher user densities by constantly renegotiating spectral access depending on need and opportunity. This approach promises efficient spectral access but is predicated on cooperation between different types of RF systems, which is a significant paradigm shift for many legacy technologies. Intelligent transportation systems (ITS) rely on several different types of RF services such as radar, communications, and positioning, navigation, and timing (PNT). RF Convergence demonstrates that many of these systems can be executed simultaneously using efficient cooperation strategies, which improves performance and limits spectral access. In this study, we demonstrate a simultaneous positioning, navigation, timing, and communications system that cooperatively executes multiple RF services. We define a "constant-information ranging" strategy that maintains constant information learned about an incoherent moving target by modulating the revisit interval to minimize the number of interactions. This significantly reduces spectral congestion and offers a control mechanism to dynamically manage spectral access. We validate the constant-information ranging algorithm in a simulation environment where we observe a 91% reduction in spectral access for a particular flight path while maintaining a 3 cm precision in ranging.

We analyze the effects of radar stretch processing on a system that jointly receives communications waveforms and radar waveforms. This effort is motivated by the growing interest in RF convergence, for which spectrum is cooperatively used for multiple functions simultaneously. In this paper, we first introduce and motivate the use of chirp radar waveforms and review stretch processing for radar applications. We then consider communications signaling under the constraint of a stretch processing radar receiver that de-chirps pulses at the first stage of the processing chain. Since it may not be possible in some systems to perform communications reception prior to the de-chirp processing block, we analyze the effects of de-chirp processing as part of the communications channel.

The increasing availability of low cost, commercial off-the-shelf unmanned aerial systems (UAS) technology has made it more accessible for researchers to investigate air-to-ground wireless communications channel phenomenology. Furthermore, using software defined radio (SDR) transceivers as the UAS communications platform provides the flexibility for researchers to characterize the air-to-ground channel across multiple frequency bands. In this paper we investigate the time and frequency dispersion characteristics of UAS air-to-ground wireless channel at C band (5.8 GHz), with signal bandwidth of 20 MHz, using empirical measurements collected in outdoor residential and mountainous desert terrains. We characterize the time and frequency dispersion of the UAS air-to-ground channel by evaluating the root mean square (RMS) delay and Doppler spread. For the residential environment, the median RMS delay spread was approximately 0.03μs. For the mountainous desert environment, the median RMS delay spread was approximately 0.06μs.

In this paper, the self-interference mitigation performance of in-band full-duplex multiple-input multiple-output (MIMO) nodes is considered in the context of models for realistic hardware characteristics in which antennas are reused to transmit and receive simultaneously. The use of MIMO indicates a self-interference channel with spatially diverse inputs and outputs. Consequently, there are both MIMO channels for self interference and MIMO channels between the intended transmit array and receive array. Furthermore, physical transceivers suffer from nonlinearities and other nonidealities including IQ mismatch associated with direct conversion RF. Approaches to address self-interference mitigation under this model are presented and performances are detailed.

Millimeter-wave remote sensing technology can significantly improve measurements of volcanic eruptions, yielding new insights into eruption processes and improving forecasts of drifting volcanic ash for aviation safety. Radiometers can measure water vapor density and temperature inside eruption clouds, improving on existing measurements with infrared cameras that are limited to measuring the outer cloud surface. Millimeter-wave radar can measure the 3-D mass distribution of volcanic ash inside eruption plumes and their nearby drifting ash clouds. Millimeter wavelengths are better matched to typical ash particle sizes, offering better sensitivity than longer wavelength existing weather radar measurements, as well as the unique ability to directly measure ash particle size in situ. Here we present sensitivity calculations in the context of developing the water and ash millimeter-wave spectrometer (WAMS) instrument. WAMS, a radar/radiometer system designed to use off-the-shelf components, would be able to measure water vapor and ash throughout an entire eruption cloud, a unique capability.

Recent popular emphasis on exercise for personal wellbeing has created a demand for techniques which monitor and classify human activities. Previous studies have shown promising results in applying various classification and feature extraction methods for identifying unique physical activities on various datasets. We apply learning techniques to GENEactiv accelerometer recordings to identify and monitor a wide range of daily activities. The dataset is composed of 92 participants, of ages 20-65, performing 25 unique activities, both ambulatory and non-ambulatory. The algorithm identified 130 different time and frequency domain features and selected the most efficient features with the sequential forward selection algorithm. With classification in two stages with both Gaussian mixture model (GMM) and hidden Markov model (HMM) we have combined the activities with similar features. We have also shown a comparative study between the two classifiers. We achieved an accuracy of 95.5% while classifying 10 unique activities with HMM and 89.7% while classifying 9. The most efficient result is obtained using HMM in 2-D feature space, where it is able to classify 15 unique activities at an accuracy of 90.12%.

With the recent interest in physical therapy through sufficient physical activity, considerable efforts have been made to monitor and classify daily human activities, especially for people who need physical rehabilitation. In our previous study, we designed a classifier to identify 25 unique physical activities performed by 92 healthy participants between the ages of 20 and 65. In this study, with the use of a GENEActiv accelerometer to monitor a wide range of daily activities, we present a learning approach to identify unique activities performed by a varied group of participants with various health conditions. The dataset is comprised of 99 senior participants and 23 participants who are significantly taller in height than the general population, performing 8 unique activities. We have extracted 130 different features in time and frequency domain and selected the most efficient features with the sequential forward selection algorithm. With two stages of classification, the first is utilized for combining similar classes, and the second determines the final decision. We have tested two classifiers for our learning approach, the Gaussian mixture models (GMMs) and the hidden Markov models (HMMs) and compared their performances. We have improved the GMM classifier from our previous study and it has shown more promising results for this dataset. We achieved an accuracy of 88.92% when classifying the 8 unique activities with GMM and 93.5% with HMM when classifying 7 activities.

In this paper, we consider the tracking of a radar target with unknown range and range rate at low signal-to-noise ratio (SNR). For this nonlinear estimation problem, the Cramér-Rao lower bound (CRLB) provides a bound on an unbiased estimator's mean-squared error (MSE). However, there exists a threshold SNR at which the estimator variance deviates from the CRLB. We consider the Barankin bound (BB) on the range and range-rate variance in order to obtain a tighter lower bound at low SNR, and we use the BB to predict the SNR threshold for a transmitted signal. We demonstrate that the BB with the additional information provided by the threshold SNR has an advantage over the CRLB in selecting the optimal transmit waveform at low SNRs. We also develop a waveform parameter configuration method that uses the BB and the ambiguity function resolution cell measurement model to optimize the SNR threshold.

The articles in this special issue focus on the technology and applications supported by in-band full duplex wireless services.

In-band full-duplex (IBFD) operation has emerged as an attractive solution for increasing the throughput of wireless communication systems and networks. With IBFD, a wireless terminal is allowed to transmit and receive simultaneously in the same frequency band. This tutorial paper reviews the main concepts of IBFD wireless. One of the biggest practical impediments to IBFD operation is the presence of self-interference, i.e., the interference that the modem's transmitter causes to its own receiver. This tutorial surveys a wide range of IBFD self-interference mitigation techniques. Also discussed are numerous other research challenges and opportunities in the design and analysis of IBFD wireless systems.

Apnea of prematurity is a common developmental disorder in preterm infants that is implicated in a number of acute and long-term complications. Therapeutic stochastic resonance (TSR) is a noninvasive preventative intervention for stabilizing breathing patterns and reducing the incidence of apnea and hypoxia. Because the stabilizing effect of TSR lags its initiation, it can be used most effectively if it is linked to a system for apnea prediction. We present a real-time algorithm for generating apnea predictions based on cardio-respiratory and movement features extracted from multiple physiological sensors. The features are used to create patient-specific statistical models of apnea precursors. The state parameters generated by these models are evaluated over time to form apnea predictions. The algorithms predictions are evaluated using a short, 5.5 minute prediction horizon. The algorithm obtains highly accurate predictions, with statistical significance obtained on five out of the six patients that it is evaluated on.

The demand for wireless access continues to increase rapidly in both military and civilian communities. The modern internet and modern personal-area devices have made billions of users around the world accustomed to data-hungry applications such as videos. This has an inevitable effect on the users' desire for the same through wireless media. The articles in this special issue focus on new theories and methods for advanced wireless relay technologies.

The spectral efficiency of a representative uplink of a given length, in interference-limited, spatially-distributed wireless networks with hexagonal cells, simple power control, and multiantenna linear Minimum-Mean-Square-Error receivers is found to approach an asymptote as the numbers of base-station antennas N and wireless nodes go to infinity. An approximation for the area-averaged spectral efficiency of a representative link (averaged over the spatial base-station and mobile distributions), for Poisson distributed base stations, is also provided. For large N, in the interference-limited regime, the area-averaged spectral efficiency is primarily a function of the ratio of the product of N and the ratio of base-station to wireless-node densities, indicating that it is possible to scale such networks by linearly increasing the product of the number of base-station antennas and the relative density of base stations to wireless nodes, with wireless-node density. The results are useful for designers of wireless systems with high inter-cell interference because it provides simple expressions for spectral efficiency as a function of tangible system parameters like base-station and wireless-node densities, and number of antennas. These results were derived combining infinite random matrix theory and stochastic geometry.

In this paper, the problem of informed-transmitter cooperative MIMO communications is addressed. The informed-transmitter link assumes that the distributed transmit nodes have access to channel state information. The channel state information includes the channel between the transmit and receive antenna arrays and a statistical model for interference impinging upon the receive array. In principle, the channel will have resolvable delay spread. In addition, because the distributed sets of nodes do not have a common local oscillator and move independently, the receiving set of nodes may observe resolvable independent frequency offsets and frequency spread from each of the transmit nodes. To compensate for this doubly dispersive channel, a space-time-frequency channel estimate is constructed. The frequency components of the channel estimate enable improved channel prediction capabilities. Space-time-frequency transmit adaptive processing approaches are developed. These techniques are demonstrated in simulation.

In this paper we consider the problem of full-duplex multiple-input multiple-output (MIMO) relaying between multi-antenna source and destination nodes. The principal difficulty in implementing such a system is that, due to the limited attenuation between the relay's transmit and receive antenna arrays, the relay's outgoing signal may overwhelm its limited-dynamic-range input circuitry, making it difficult-if not impossible-to recover the desired incoming signal. While explicitly modeling transmitter/receiver dynamic-range limitations and channel estimation error, we derive tight upper and lower bounds on the end-to-end achievable rate of decode-and-forward-based full-duplex MIMO relay systems, and propose a transmission scheme based on maximization of the lower bound. The maximization requires us to (numerically) solve a nonconvex optimization problem, for which we detail a novel approach based on bisection search and gradient projection. To gain insights into system design tradeoffs, we also derive an analytic approximation to the achievable rate and numerically demonstrate its accuracy. We then study the behavior of the achievable rate as a function of signal-to-noise ratio, interference-to-noise ratio, transmitter/receiver dynamic range, number of antennas, and training length, using optimized half-duplex signaling as a baseline.

The 46 papers focusing on the theme of "Theories and Methods for Advanced Wireless Relays" have been divided into two groups to be published in two separate issues. This first issue includes 23 papers on relay performance bound, MIMO relay beamforming, relay channel estimation, two-way and shared relays, full-duplex relays, and security for relay networks. The second issue includes papers on coding for relay networks, medium access control for relays, implementation ans system performance studies.

An asymptotic technique is presented for finding the spectral efficiency of multiantenna links in spatially distributed wireless networks where transmitters have channel-state-information (CSI) corresponding to their target receiver. Transmitters are assumed to transmit independent data streams on a limited number of channel modes which limits the rank of transmit covariance matrices. An approximation for the spectral efficiency in the interference-limited regime as a function of link-length, interferer density, number of antennas per receiver and transmitter, number of transmit streams, and path-loss exponent is derived. It is found that targeted-receiver CSI, which can be acquired with low overhead in duplex systems with reciprocity, can increase spectral efficiency several fold, particularly when link lengths are large, node density is high, or both. Additionally, the per-link spectral efficiency is found to be a function of the ratio of node density to the number of receiver antennas, and it can often be improved if nodes transmit using fewer streams. These results are validated for finite-sized systems by Monte-Carlo simulation and are asymptotic in the regime where the number of users and antennas per receiver approach infinity.

A patient-specific seizure prediction algorithm is proposed that extracts novel multivariate signal coherence features from ECoG recordings and classifies a patient's pre-seizure state. The algorithm uses space-delay correlation and covariance matrices at several delay scales to extract the spatiotemporal correlation structure from multichannel ECoG signals. Eigenspectra and amplitude features are extracted from the correlation and covariance matrices, followed by dimensionality reduction using principal components analysis, classification using a support vector machine, and temporal integration to produce a seizure prediction score. Evaluation on the Freiburg EEG database produced a sensitivity of 90.8% and false positive rate of .094.

An asymptotic technique is developed to find the Signal-to-Interference-plus-Noise-Ratio (SINR) and spectral efficiency of a link with N receiver antennas in wireless networks with non-homogeneous distributions of nodes. It is found that with appropriate normalization, the SINR and spectral efficiency converge with probability 1 to asymptotic limits as N increases. This technique is applied to networks with power-law node intensities, which includes homogeneous networks as a special case, to find a simple approximation for the spectral efficiency. It is found that for receivers in dense clusters, the SINR grows with N at rates higher than that of homogeneous networks and that constant spectral efficiencies can be maintained if the ratio of N to node density is constant. This result also enables the analysis of a new scaling regime where the distribution of nodes in the network flattens rather than increases uniformly. It is found that in many cases in this regime, N needs to grow approximately exponentially to maintain a constant spectral efficiency. In addition to strengthening previously known results for homogeneous networks, these results provide insight into the benefit of using antenna arrays in non-homogeneous wireless networks, for which few results are available in the literature.

A MIMO (multi-input multi-output) radar system, unlike standard phased-array radar, can transmit via its antennas multiple probing signals. This waveform diversity offered by MIMO radar enables superior capabilities compared with a standard phased-array radar. We consider MIMO radar waveform optimization for parameter estimation for the general case of multiple targets in the presence of spatially colored interference and noise. Numerical examples are provided to demonstrate the effectiveness of the approaches we consider herein.

Receivers with N antennas in single-hop, ad-hoc wireless networks with nodes randomly distributed on an infinite plane with uniform area density are studied. Transmitting nodes have single antennas and transmit simultaneously in the same frequency band with power P that decays with distance via the commonly-used inverse-polynomial model with path-loss- exponent (PLE) greater than 2. This model applies to shared spectrum systems where multiple links share the same frequency band. In the interference-limited regime, the average spectral efficiency of a representative link E[C] (b/s/Hz/link) is found to grow as log(N) and linearly with PLE, and its variance decays as 1/N. The average signal-to-interference-plus-noise-ratio (SINR) on a representative link is found to grow faster than linearly with N. With multiple-input-multiple-output (MIMO) links where transmit nodes have multiple antennas without Channel- State-Information, it is found that E[C] in the network can be improved if nodes transmit using the optimum number of antennas compared to the optimum selfish strategy of transmitting equal-power streams from every antenna. The results are extended to random code-division-multiple-access systems where the optimum spreading factor for a given link length is found. These results are developed as asymptotic expressions using infinite random matrix theory and are validated by Monte-Carlo simulations.