Unmanned aerial vehicles (UAVs) fill coverage holes as wireless relays during emergency situations. Fixed-wing UAVs offer longer flight duration and larger coverage in such situations than rotary-wing counterparts. Maximizing the effectiveness of fixed-wing UAV relay systems requires careful tuning of system and flight parameters. This process is challenging because factors including flight trajectory, timeshare, and user scheduling are not easily optimized. In this paper, we propose an optimization for UAV-based wireless relaying networks based on a setup which is applicable to arbitrary spatial user positions. In the setup, a fixed-wing UAV flies over a circular trajectory and relays data from ground users in a coverage hole to a distant base station (BS). Our optimization iteratively maximizes the average achievable spectral efficiency (SE) for the UAV trajectory, user scheduling, and relay timeshare. The simulation results show that our optimization is effective for varying user distributions and that it performs especially well on distributions with a high standard deviation.

Efficient channel estimation and beamforming in the upper mid-band spectrum pose a fundamental challenge for 6G base stations (BSs). This requires practical beam management techniques that account for the propagation characteristics of this frequency range and the increasing number of BS antennas. To address the channel estimation bottleneck in Frequency Division Duplex (FDD) systems, this paper proposes an integrated framework combining channel synthesis and AI-based beam management. We first introduce partial statistical reciprocity (PSR) by extending the concept of partial reciprocity into the statistical domain, enabling a novel model-based online channel synthesis method. The proposed approach uses uplink channel parameters collected during BS operation to generate synthetic downlink channels that reflect the spatial distribution of active user equipment (UE), enabling the creation of high-quality, site-specific datasets for AI training. Furthermore, we propose a joint optimization framework for scalable codebook design and downlink channel estimation. A U-Net Transformer model is trained to estimate full beam-domain channel state information (CSI) using a single snapshot of uplink CSI and feedback from codebook-based beam sweeping. The model iteratively updates the codebook via backpropagation to align with current UE distributions and channel conditions, while channel estimation is performed through forward inference. The proposed AI-based framework achieves accurate channel reconstruction with minimal feedback, offering a scalable and practical solution for 6G spatial multiple access in the upper mid-band FDD system.

Multiple-input multiple-output (MIMO) communication has led to immense enhancements in data rates and efficient spectrum management. The evolution of MIMO, though, has been accompanied by increased hardware complexity and array sizes, causing the system power consumption to increase. Despite past advances in power-efficient hybrid architectures, new solutions are needed to enable extremely large-scale MIMO deployments for 6G and beyond. In this paper, we introduce a novel architecture that integrates low-power reconfigurable antennas with both digital and analog precoding. Thistri-hybridapproach addresses key limitations in traditional and hybrid MIMO systems by improving power consumption and adds a new layer for signal processing. We provide an analysis of the proposed architecture and compare its performance with existing solutions, including fully-digital and hybrid MIMO systems. The results demonstrate significant improvements in energy efficiency, highlighting the potential of the tri-hybrid system to meet the growing demands of future wireless networks. We conclude the paper with a summary of design and implementation challenges, including the need for technological advancements in reconfigurable array hardware and tunable antenna parameters.

Dynamic metasurface antennas (DMAs) are leaky-wave antennas with frequency-reconfigurable slots. While resonant frequency of individual slots on the DMA can be independently configured in a wide frequency range, the DMA’s beamforming performance across different operating frequencies in the available range has not been studied yet. We show that the dominant beam direction and the operating frequency of a DMA are interdependent. Using this observation, we derive a closed-form expression for line-of-sight (LOS) beamforming gain maximization to jointly configure the operating frequency and per-slot resonant frequency configuration of the DMA. A benefit of this approach in the LOS channel case is that all slots are tuned to the same resonant frequency, eliminating the need for a computationally expensive search across all feasible resonant frequency configurations. We also apply this approach to mitigate beam training overhead in large arrays by proposing a single-shot beam training algorithm that estimates the optimal resonant frequency configuration through simultaneous probing of multiple angular directions across frequencies. Simulation results show that our proposed approach offers a comparable achievable rate to true-time-delay (TTD) based systems, but with lower power consumption and hardware cost.

Traditional antenna arrays with a half-wavelength spacing between elements are capable of achieving a power gain proportional to the number of antennasM. Superdirective antenna arrays, however, leverage smaller antenna spacing to approach an achievable power gain ofM2, which could provide a significant performance improvement to the spectral efficiency in wireless communication systems. In this paper, we study the power scaling law of superdirective beamforming in multi-user communication systems using a uniform linear array (ULA). First, we extend superdirective precoding from single-user to multi-user multipath scenarios. Employing the basis of Legendre polynomials, we prove that the scaling laws of both the power gain and signal-to-interference-plus-noise ratio (SINR) are betweenMandM2, whereM2is achieved in the end-fire direction. To further enhance user power gains and effectively manage interference, we formulate and solve an optimization problem that maximizes the directivity gain while nullifying interference to other users. We demonstrate that this scheme can significantly improve spectral efficiency in multi-user settings, even when antenna spacing approaches zero. Moreover, we address the narrow directivity bandwidth issue, showing that the directivity of superdirective arrays decreases sharply as the frequency moves away from the center frequency, necessitating the use of multi-carrier technology to overcome this limitation. Simulation results verify the proposed power scaling law and show significant improvements in spectral efficiency with our proposed methods compared to a traditional antenna array with half-wavelength spacing.

Dynamic metasurface antennas (DMA) provide low-power beamforming through reconfigurable radiative slots. Each slot has a tunable component that consumes low power compared to typical analog components like phase shifters. This makes DMAs a potential candidate to minimize the power consumption of multiple-input multiple-output (MIMO) antenna arrays. In this paper, we investigate the use of DMAs in a wideband communication setting with practical DMA design characteristics. We develop approximations for the DMA beamforming gain that account for the effects of waveguide attenuation, element frequency-selectivity, and limited reconfigurability of the tunable components as a function of the signal bandwidth. The approximations allow for key insights into the wideband performance of DMAs in terms of different design variables. We develop a simple successive beamforming algorithm to improve the wideband performance of DMAs by sequentially configuring each DMA element. Simulation results for a line-of-sight (LOS) wideband system show the accuracy of the approximations with the simulated DMA model in terms of spectral efficiency. We also find that the proposed successive beamforming algorithm increases the overall spectral efficiency of the DMA-based wideband system compared with a baseline DMA beamforming method.

A parasitic reconfigurable antenna array is a low-power approach for beamforming using passive tunable elements. Prior work on reconfigurable antennas in communication theory is based on ideal radiation pattern abstractions. Beamforming with parasitic elements is inherently difficult because mutual coupling creates non-linearity in the beamforming gain objective. We develop a multi-port circuit-theoretic model of the hybrid array with parasitic elements and antennas with active RF chain validated through electromagnetic simulations with a dipole array. Based on this formulation, we derive the beamforming weight of the parasitic element using the theoretical beam pattern expression for the case of a single active antenna and multiple parasitic elements. The analysis shows that the parasitic beamforming is challenging because the weights are subject to coupled magnitude and phase constraints. To overcome this, a shift-of-origin transformation simplifies the optimization, leading to a closed-form expression for the parasitic reactance. The solution generalizes to arrays with multiple active and parasitic elements operating in multipath channels. The proposed hybrid architecture with parasitic elements outperforms conventional architectures in terms of energy efficiency.

Learning-based downlink power control in cell-free massive multiple-input multiple-output (CFmMIMO) systems offers a promising alternative to conventional iterative optimization algorithms, which are computationally intensive due to online iterative steps. Existing learning-based methods, however, often fail to exploit the intrinsic structure of channel data and neglect pilot allocation information, leading to suboptimal performance, especially in large-scale networks with many users. This paper introduces the pilot contamination-aware power control (PAPC) transformer neural network, a novel approach that integrates pilot allocation data into the network, effectively handling pilot contamination scenarios. PAPC employs the attention mechanism with a custom masking technique to utilize structural information and pilot data. The architecture includes tailored preprocessing and post-processing stages for efficient feature extraction and adherence to power constraints. Trained in an unsupervised learning framework, PAPC is evaluated against the accelerated proximal gradient (APG) algorithm, showing comparable spectral efficiency fairness performance, while significantly improving computational efficiency. Simulations demonstrate PAPC’s superior performance over fully connected networks (FCNs) that lack pilot information, its scalability to large-scale CFmMIMO networks, and its computational efficiency improvement over APG. PAPC is further validated through ablation studies and evaluated across several representative CFmMIMO scenarios, demonstrating robustness to pilot contamination, scalability, and adaptability to varying user counts without retraining.

Multi-mode fiber (MMF) significantly increases data rates by spatially multiplexing over parallel optical channels. Analog radio-over-fiber (A-RoF) achieves lower latency and greater bandwidth as compared to digital systems but is limited by nonlinear interference in single-mode fiber (SMF). MMF reduces this interference by transmitting signals across multiple spatial modes. In this work we present a MMF aided A-RoF model for uplink MIMO systems. The linear model considers fiber impairments and noise propagation over the optical and wireless link, assuming an optimum receiver at the baseband unit. We compare the spectral efficiency of MMFaided A-RoF with wavelength division multiplexing (WDM) aided A-RoF, showing MMF reduces nonlinear interference and supports higher data rates, though it is limited by a number of optical modes and wireless channels.

A dynamic metasurface antenna (DMA) is a slottedwaveguide antenna with tunable components that allow for beamforming with low power consumption. The resonant frequencies of the individual DMA slot elements are reconfigurable, which can be leveraged for wideband communications to support larger bandwidths. The vast majority of prior work on DMAs focuses on narrowband applications for DMAs. In this paper, we develop a beamforming algorithm to improve the performance of a multicarrier DMA-based wireless system with large signal bandwidths. We build upon prior DMA signal models to incorporate different frequency-selective aspects of the physical DMA elements, and investigate the impact of different DMA designs on wideband performance. We find that the proposed beamforming algorithm outperforms a baseline beamforming algorithm in terms of spectral efficiency and data rates under significant frequencyselectivity in the multi-carrier channel and DMA elements.

Dynamic metasurface antennas (DMAs) enable beamforming through low-power components that reconfigure each radiating element. Previous studies on a single user multiple-input-single-output (MISO) system with transmit DMA focused on maximizing beamforming gain in narrowband scenarios. In this paper, we analyze the beamforming performance of DMAs in wideband scenarios. By leveraging DMA's frequency reconfigurability, our proposed approach dynamically adjusts both the transmission frequency and element configuration, achieving a higher beamforming gain than the narrowband method. We also propose the optimal waveguide refractive index and DMA element spacing to obtain an equal beamforming gain as the power hungry true-time-delay (TTD) architecture in the desired angular range. We evaluate the achievable rate performance and show that the rate approaches that of TTD when the DMA tuning range increases.

We consider the problem of Multiple-Input SingleOutput (MISO) communication with limited feedback, where the transmitter relies on a limited number of bits associated with the channel state information (CSI), available at the receiver (CSIR) but not at the transmitter (no CSIT), sent via the feedback link. We demonstrate how character-polynomial (CP) codes, a class of analog subspace codes (also, referred to as Grassmann codes) can be used for the corresponding quantization problem in the Grassmann space. The proposed CP codebook-based precoding design allows for a smooth trade-off between the number of feedback bits and the beamforming gain, by simply adjusting the rate of the underlying CP code. We present a theoretical upper bound on the mean squared quantization error of the CP codebook, and utilize it to upper bound the resulting distortion as the normalized gap between the CP codebook beamforming gain and the baseline equal gain transmission (EGT) with perfect CSIT. We further show that the distortion vanishes asymptotically. The results are also confirmed via simulations for different types of fading models in the MISO system and various parameters.

Antenna behaviors such as mutual coupling, near-field propagation, and polarization cannot be neglected in signal and channel models for wireless communication. We present an electromagnetic-based array manifold that accounts for several complicated behaviors and can model arbitrary antenna configurations. We quantize antennas into a large number of Hertzian dipoles to develop a model for the radiated array field. The resulting abstraction provides a means to predict the electric field for general non-homogeneous array geometries through a linear model that depends on the point source location, the position of each Hertzian dipole, and a set of coefficients obtained from electromagnetic simulation. We then leverage this model to formulate a beamforming gain optimization that can be adapted to account for polarization of the receive field as well as constraints on the radiated power density. Numerical results demonstrate that the proposed method achieves accuracy that is close to that of electromagnetic simulations. By leveraging the developed array manifold for beamforming, systems can achieve higher beamforming gains compared to beamforming with less accurate models.

Obtaining accurate and timely channel state information (CSI) is a fundamental challenge for large MIMO systems. Mobile cellular systems like 5G use a beam management framework that joins the initial access, beamforming, CSI acquisition, and data transmission. The design of codebooks for these stages, however, is challenging due to their interrelationships, varying array sizes, and site-specific channel and user distributions. Furthermore, beam management is often focused on single-sector operations while ignoring the overarching network- and system-level optimization. In this paper, we proposed an end-to-end learned codebook design algorithm, network beamspace learning (NBL), that captures and optimizes codebooks to mitigate interference while maximizing the achievable performance with extremely large hybrid arrays. The proposed algorithm requires limited shared information yet designs codebooks that outperform traditional codebooks by over 10dB in beam alignment and achieve more than 25% improvements in network spectral efficiency.

Fluid antenna systems (FASs) can reconfigure their antenna locations freely within a spatially continuous space. To keep favorable antenna positions, the channel state information (CSI) acquisition for FASs is essential. While some techniques have been proposed, most existing FAS channel estimators require several channel assumptions, such as slow variation and angular-domain sparsity. When these assumptions are not reasonable, the model mismatch may lead to unpredictable performance losses. In this paper, we propose the successive Bayesian reconstructor (S-BAR) as a general solution to estimate FAS channels. Unlike model-based estimators, the proposed S-BAR is prior-aided, which builds the experiential kernel for CSI acquisition. Inspired by Bayesian regression, the key idea of S-BAR is to model the FAS channels as a stochastic process, whose uncertainty can be successively eliminated by kernel-based sampling and regression. In this way, the predictive mean of the regressed stochastic process can be viewed as a Bayesian channel estimator. Simulation results verify that, in both model-mismatched and model-matched cases, the proposed S-BAR can achieve higher estimation accuracy than the existing schemes.

In this paper, we characterize the effect of radar-to-radar interference in a system setting in terms of the probability of missed detection for two common driving scenarios: Highway traffic and a 4-way intersection. We focus on the frequency modulated continuous wave (FMCW) and phase modulated continuous wave (PMCW) radar waveforms and try to answer the question: Is one waveform inherently better than the other for automotive applications? In contrast to most results available in published literature, we show that PMCW does not have an advantage over FMCW in a system setting. For the scenarios considered in this paper, our results show that a typical FMCW radar unit actually outperforms a typical PMCW radar unit in terms of the probability of missed detection by using coordination across the radar network.

Incorporating the sub-6 GHz band into 5G networks can improve data rates by leveraging the benefits of propagation across frequency ranges. Sequential decision making algorithms such as deep reinforcement learning (DRL) can adaptively select a band over time to take full advantage of the multi-band operation. The distinctive beam management procedure between the sub-6 GHz band and the millimeter wave (mmWave) band, though, pose a sample efficiency challenge for DRL algorithms. In this paper, we use hierarchical reinforcement learning (HRL) to divide and conquer the joint band assignment and beam management problem. The proposed HRL-based method uses rate feedback for intermittent band determination and frequent beam management mode decisions. We show with numerical evaluation that the proposed algorithm achieves a quicker increase in data rate compared to baselines and identify off-policy correction methods as a key factor for this enhancement.

Fluid antenna systems (FASs) can reconfigure their locations freely within a spatially continuous space. To keep favorable antenna positions, the channel state information (CSI) acquisition for FASs is essential. While some techniques have been proposed, most existing FAS channel estimators require several channel assumptions, such as slow variation and angular-domain sparsity. When these assumptions are not reasonable, the model mismatch may lead to unpredictable performance loss. In this paper, we propose the successive Bayesian reconstructor (S- BAR) as a general solution to estimate FAS channels. Unlike model-based estimators, the proposed S- BAR is prior-aided, which builds the experiential kernel for CSI acquisition. Inspired by Bayesian regression, the key idea of S- BAR is to model the FAS channels as a stochastic process, whose uncertainty can be successively eliminated by kernel-based sampling and regression. In this way, the predictive mean of the regressed stochastic process can be viewed as the maximum a posterior (MAP) estimator of FAS channels. Simulation results verify that, in both model-mismatched and model-matched cases, the proposed S-BAR can achieve higher estimation accuracy than the existing schemes.

From an information theoretic perspective, joint communication and sensing (JCAS) represents a natural generalization of communication network functionality. However, it requires the re-evaluation of network performance from a multi-objective perspective. We develop a novel mathematical framework for characterizing the sensing and communication coverage probability and ergodic rate in JCAS networks. We employ a formulation of sensing parameter estimation based on mutual information to extend the notions of coverage probability and ergodic rate to the radar setting. We define sensing coverage probability as the probability that the rate of information extracted about the parameters of interest associated with a typical radar target exceeds some threshold, and sensing ergodic rate as the spatial average of the aforementioned rate of information. Using this framework, we analyze the downlink sensing and communication coverage and rate of a mmWave JCAS network employing a shared waveform, directional beamforming, and monostatic sensing. Leveraging tools from stochastic geometry, we derive upper and lower bounds for these quantities. We also develop several general technical results including: i) a generic method for obtaining closed form upper and lower bounds on the Laplace Transform of a shot noise process, ii) a new analog of Hölder’s Inequality to the setting of harmonic means, and iii) a relation between the Laplace and Mellin Transforms of a non-negative random variable. We use the derived bounds to numerically investigate the performance of JCAS networks under varying base station and blockage density. Among several insights, our numerical analysis indicates that network densification improves sensing SINR performance – in contrast to communications.

Dynamic metasurface antennas (DMA) provide a solution to form compact, cost-effective, energy-efficient multiple-input-multiple output (MIMO) arrays. In this paper, we implement a practical hierarchical codebook with a realistic DMA design through electromagnetic simulations. We leverage existing DMA models to derive a novel method for enhancing the beamforming gain. We find that the proposed method provides better coverage and spectral efficiency results than prior methods. We also present and verify a new technique for creating wide beamwidths through the DMA and hierarchical codebook. Additionally, we use a detailed transmitter architecture model to determine the power consumption savings of the DMA compared to a typical phased array. The DMA largely outperforms a passive phased array in terms of spectral and energy efficiency due to high component loss from a high-resolution passive phase shifter. While the DMA provides lower spectral efficiency results than the active phased array, the DMA achieves a higher energy efficiency because of the significant power consumption for the active phase shifters. Therefore, we find that DMAs in a realistic wireless environment provide sufficient coverage and spectral efficiency compared to typical phased arrays while maintaining a substantially lower power consumption.

Reconfigurable arrays mold the propagation environment to benefit wireless systems. We use single-port polarization-reconfigurable antennas in a wideband multiple-input multiple-output (MIMO) system and demonstrate the efficacy of reconfiguration techniques based on analytical channel models. We apply a double-directional channel model to show that polarization reconfiguration acts as an additional precoding step on an unpolarized channel. We use Jensen’s inequality to upper bound the spectral efficiency and leverage the relaxed objective to derive closed-form expressions for the optimal polarization angles at each antenna. We also derive upper bounds on the performance of a polarization reconfigurable system and develop an efficient procedure for polarization reconfiguration that aims to maximize these upper bounds. Numerical results show that the proposed simplified methods achieve near-optimal in wideband MIMO settings.

Impedance-matching networks affect power transfer from the radio frequency (RF) chains to the antennas. Their design impacts the signal to noise ratio (SNR) and the achievable rate. In this paper, we maximize the information-theoretic achievable rate of a multiple-input-single-output (MISO) system with wideband matching constraints. Using a multiport circuit theory approach with frequency-selective scattering parameters, we propose a general framework for optimizing the MISO achievable rate that incorporates Bode-Fano wideband matching theory. We express the solution to the achievable rate optimization problem in terms of the optimized transmission coefficient and the Lagrangian parameters corresponding to the Bode-Fano inequality constraints. We apply this framework to a single electric Chu’s antenna and an array of dipole antennas. We compare the optimized achievable rate obtained numerically with other benchmarks like the ideal achievable rate computed by disregarding matching constraints and the achievable rate obtained by using sub-optimal matching strategies like conjugate matching and frequency-flat transmission. We also propose a practical methodology to approximate the achievable rate bound by using the optimal transmission coefficient to derive a physically realizable matching network through the ADS software.

With increasing frequencies, bandwidths, and array apertures, the phenomenon of beam squint arises as a serious impairment to beamforming. Fully digital arrays with true time delay per antenna element are a potential solution, but they require downconversion at each element. This paper shows that hybrid arrays can perform essentially as well as digital arrays once the number of radio-frequency chains exceeds a certain threshold that is far below the number of elements. This threshold is determined by only a few physical parameters—bandwidth, array size, and beamforming direction—and can be expressed in a remarkably simple closed form. The result is robust, holding also for suboptimum yet highly appealing beamspace architectures.

Beam codebooks are a recent feature to enable high dimension multiple-input multiple-output in 5G. Codebooks comprised of customizable beamforming weights can be used to transmit reference signals and aid the channel state information (CSI) acquisition process. Codebooks are also used for quantizing feedback following CSI measurement. In this paper, we unify the beam management stages–codebook design, beam sweeping, feedback, and data transmission–to characterize the impact of codebooks throughout the process. We then design a neural network to find codebooks that improve the overall system performance. The proposed neural network is built on translating codebook and feedback knowledge into a consistent beamspace basis similar to a virtual channel model to generate initial access codebooks. This beamspace codebook algorithm is designed to directly integrate with current 5G beam management standards without changing the feedback format or requiring additional side information. Our simulations show that the neural network codebooks improve over traditional codebooks, even in dispersive sub-6GHz environments. We further use our framework to evaluate CSI feedback formats with regard to multi-user spectral efficiency. Our results suggest that optimizing codebook performance can provide valuable performance improvements, but optimizing the feedback configuration is also important in sub-6GHz bands.

The use of one-bit analog-to-digital converter (ADC) has been considered as a viable alternative to high resolution counterparts in realizing and commercializing massive multiple-input multiple-output (MIMO) systems. However, the issue of discarding the amplitude information by one-bit quantizers has to be compensated. Thus, carefully tailored methods need to be developed for one-bit channel estimation and data detection as the conventional ones cannot be used. To address these issues, the problems of one-bit channel estimation and data detection for MIMO orthogonal frequency division multiplexing (OFDM) system that operates over uncorrelated frequency selective channels are investigated here. We first develop channel estimators that exploit Gaussian discriminant analysis (GDA) classifier and approximate versions of it as the so-called weak classifiers in an adaptive boosting (AdaBoost) approach. Particularly, the combination of the approximate GDA classifiers with AdaBoost offers the benefit of scalability with the linear order of computations, which is critical in massive MIMO-OFDM systems. We then take advantage of the same idea for proposing the data detectors. Numerical results validate the efficiency of the proposed channel estimators and data detectors compared to other methods. They show comparable/better performance to that of the state-of-the-art methods, but require dramatically lower computational complexities and run times.

In this paper, we propose a novel architecture for a lens antenna array (LAA) designed to work with a small number of antennas and enable angle-of-arrival (AoA) estimation for advanced 5G vehicle-to-everything (V2X) use cases that demand wider bandwidths and higher data rates. We derive a received signal in terms of optical analysis to consider the variability of the focal region for different carrier frequencies in a wideband multi-carrier system. By taking full advantage of the beam squint effect for multiple pilot signals with different frequencies, we propose a novel reconfiguration of antenna array (RAA) for the sparse LAA and a max-energy antenna selection (MS) algorithm for the AoA estimation. In addition, this paper presents an analysis of the received power at the single antenna with the maximum energy and compares it to simulation results. In contrast to previous studies on LAA that assumed a large number of antennas, which can require high complexity and hardware costs, the proposed RAA with MS estimation algorithm is shown that meets the requirements of 5G V2X in a vehicular environment while utilizing limited RF hardware and has low complexity.

Network slicing at the radio access network (RAN) domain, called RAN slicing, requires elasticity, efficient resource sharing, and customization. In this scenario, radio resource scheduling (RRS) is responsible for dealing with scarce and limited frequency spectrum resources available at the RAN domain while fulfilling the slice intents. The wide variety of scenarios supported in 5G and beyond 5G networks makes the RRS problem in RAN slicing scenario a significant challenge. This paper proposes an intent-aware reinforcement learning method to perform the RRS function in a RAN slicing scenario. The slice’s quality of service intents is described in a common intent model in a service-level agreement. The proposed method tries to prevent intent faults by making the management of radio resources available among slices. This method uses slices’ and user equipment network metrics in the observation space. The proposed method is evaluated under different network conditions and outperforms different baselines considering the slices’ intents fulfillment.

Sensor-aided beamforming reduces the overheads associated with beam training in millimeter-wave (mmWave) multi-input-multi-output (MIMO) communication systems. Most prior work, though, neglects the challenges associated with establishing multi-user (MU) communication links in mmWave MIMO systems. In this paper, we propose a new framework for sensor-aided beam training in MU mmWave MIMO system. We leverage the beamspace representation of the channel that contains only the angles-of-departure (AoDs) of the channel’s significant multipath components. We show that a deep neural network (DNN)-based multimodal sensor fusion framework can estimate the beamspace representation of the channel using sensor data. To aid the DNN training, we introduce a novel supervised soft-contrastive loss (SSCL) function that leverages the inherent similarity between channels to extract similar features from the sensor data for similar channels. Finally, we design an MU beamforming strategy that uses the estimated beamspaces of the channels to select analog precoders for all users in a way that prevents transmission to multiple users over the same directions. Compared to the baseline, our approach achieves more than 4 times improvement in the median sum-spectral efficiency (SE) at 42 dBm equivalent isotropic radiated power (EIRP) with 4 active users. This demonstrates that sensor data can provide more channel information than previously explored, with significant implications for machine learning-based communication and sensing systems.

Dynamic metasurface antennas (DMA) have been proposed for massive multiple-input multiple-output (MIMO) and millimeter wave applications due to their ability to create dense, energy-efficient arrays. In this paper, we integrate DMAs into a realistic wireless environment to compare their performance in spectral and energy efficiency with a conventional phased array. We implement a practical transmitter architecture for the DMA and phased array to account for the power consumption and hardware constraints of the radio frequency (RF) front end. Simulation results for a MISO scenario show that while the DMA performs worse in spectral efficiency than an active phased array, the power consumption savings from the reconfigurable component enable better performance in energy efficiency. Therefore, DMAs can provide an energy-efficient alternative to typical phased arrays.

Conventional achievable rate analysis using Shannon's theory does not assume practical constraints imposed by Bode-Fano wideband matching theory. This leads to an achievable rate bound that cannot be attained by practical matching networks. In this paper, we generalize the information-theoretic achievable rate of a single-input-single-output (SISO) system by incorporating wideband matching constraints at the transmitter. We express the solution to the achievable rate optimization problem in terms of the optimized transmission coefficient and the Lagrangian parameters corresponding to the Bode-Fano inequality constraints. We also propose a practical strategy to design a physically realizable matching network through the ADS software which attains the achievable rate bound with near-optimality. In simulations, we apply this framework to a Chu's antenna and compare the achievable rate performance with the conventional conjugate matching strategy.

Reconfigurable intelligent surfaces (RISs )-aided communication at millimeter wave frequencies will likely require a large size RIS to achieve the expected link budget in outdoor environments. This large size of the RIS will induce a near field (NF) operation mode. Previous approaches to operate in the NF consider RIS configurations that provide very focused beams, targeted toward a grid of user locations to build the corresponding codebook. In this paper, we design variable width beam codebooks for the NF using a technique that maps the RIS elements to a tunable spherical surface. By tuning the size and center of the surface, variable width beams and illuminated areas can be achieved. Simulation results show the effectiveness of the proposed method to maintain a high average rate while expanding the illumination region associated to beam focusing. Our configuration also outperforms the state-of-the-art in terms of maintaining the intended shape of the illuminated region even if not in the boresight directions of the RIS.

We present a unified model for connected antenna arrays with a large number of tightly integrated (i.e., coupled) antennas in a compact space within the context of massive multiple-input multiple-output (MIMO) communication. We refer to this system as tightly-coupled massive MIMO. From an information-theoretic perspective, scaling the design of tightly-coupled massive MIMO systems in terms of the number of antennas, the operational bandwidth, and form factor was not addressed in prior art. We investigate this open research problem using a physically consistent modeling approach for far-field (FF) MIMO communication based on multi-port circuit theory. In doing so, we turn mutual coupling (MC) from a foe to a friend of MIMO systems design, thereby challenging a basic percept in antenna systems engineering that promotes MC mitigation/compensation. We show that tight MC widens the operational bandwidth of antenna arrays thereby unleashing a missing MIMO gain that we coin “bandwidth gain”. Furthermore, we derive analytically the asymptotically optimum spacing-to-antenna-size ratio by establishing a condition for tight coupling in the limit of large-size antenna arrays with quasi-continuous apertures. We also optimize the antenna array size while maximizing the achievable rate under fixed transmit power and inter-element spacing. Then, we study the impact of MC on the achievable rate of MIMO systems under line-of-sight (LoS) and Rayleigh fading channels. These results reveal new insights into the design of tightly-coupled massive antenna arrays as opposed to the widely-adopted “disconnected” designs that disregard MC by putting faith in the half-wavelength spacing rule.

Low-power millimeter wave (mmWave) multi-input multi-output communication systems can be enabled with the use of one-bit analog-to-digital converters. Owing to the extreme quantization, conventional signal processing tasks such as channel estimation are challenging, making uplink (UL) multiuser receivers difficult to implement. To address this issue, we first reformulate the UL channel estimation problem, and then combine the idea of$\ell_{1}$regularized logistic regression classification and Toeplitz matrix reconstruction in a properly designed optimization problem. Our new method is referred to as$\ell_{1}$regularized logistic regression with Toeplitz matrix reconstruction (L1-RLR-TMR). In addition, we develop a computationally efficient alternating direction method of multi-pliers (ADMM)-based implementation for the L1-RLR-TMR method. Numerical results demonstrate the performance of the L1-RLR-TMR method in comparison with other existing methods.

We present a unified model for connected antenna arrays with a large number of tightly integrated (i.e., coupled) antennas in a compact space within the context of massive multiple-input multiple-output (MIMO) communication. We refer to this system as tightly-coupled massive MIMO. From an information-theoretic perspective, scaling the design of tightly-coupled massive MIMO systems in terms of the number of antennas, the operational bandwidth, and form factor was not addressed in prior art. We investigate this open research problem using a physically consistent modeling approach for far-field (FF) MIMO communication based on multi-port circuit theory. In doing so, we turn mutual coupling (MC) from a foe to a friend of MIMO systems design, thereby challenging a basic percept in antenna systems engineering that promotes MC mitigation/compensation. We show that tight MC widens the operational bandwidth of antenna arrays thereby unleashing a missing MIMO gain that we coin “bandwidth gain”. Furthermore, we derive analytically the asymptotically optimum spacing-to-antenna-size ratio by establishing a condition for tight coupling in the limit of large-size antenna arrays with quasi-continuous apertures. We also optimize the antenna array size while maximizing the achievable rate under fixed transmit power and inter-element spacing. Then, we study the impact of MC on the achievable rate of MIMO systems under line-of-sight (LoS) and Rayleigh fading channels. These results reveal new insights into the design of tightly-coupled massive antenna arrays as opposed to the widely-adopted “disconnected” designs that disregard MC by putting faith in the half-wavelength spacing rule.

Today, numerous machine learning (ML) applications offer continuous data processing and real-time data analytics at the edge of wireless networks. Distributed real-time ML solutions are highly susceptible to the so-called straggler effect caused by resource heterogeneity, which can be mitigated by various computation offloading mechanisms that severely impact communication efficiency, especially in large-scale scenarios. To reduce the communication overhead, we leverage device-to-device (D2D) connectivity, which enhances spectrum utilization and allows for efficient data exchange between proximate devices. In particular, we design a novel D2D-aided coded distributed learning method named D2D-CDL for efficient load balancing across devices. The proposed solution captures system dynamics, includingdata(time-varying learning model, irregular intensity of data arrivals),device(diverse computational resources and volume of training data), anddeployment(different locations and D2D graph connectivity). To decrease the number of communication rounds, we derive an optimal compression rate, which minimizes the processing time. The resulting optimization problem provides suboptimal compression parameters that improve the total training time. Our proposed method is particularly beneficial for real-time collaborative applications, where users continuously generate training data thus yielding a model drift.

This paper introduces a novel information-theoretic approach for studying the effects of mutual coupling (MC), between the transmit and receive antennas, on the overall performance of single-input-single-output (SISO) near-field communications (NFC). By incorporating the finite antenna size constraint using Chu’s theory and under the assumption of canonical-minimum scattering (CMS), we derive the MC between two radiating volumes of fixed sizes. Expressions for the self and mutual impedances are obtained by the use of the reciprocity theorem. Based on a circuit-theoretic two-port model for SISO radio communication systems, we first establish its input-output relationship where the noise depends on the self/mutual impedances of the antennas, unlike the conventional assumption of independent additive white Gaussian noise. We then characterise the achievable data rate for a given pair of transmit and receive antenna sizes, thereby providing an upper bound on the system performance under physical size constraints. Through the lens of these findings, we shed new light on the influence of MC on the information-theoretic limits of near-field communications using compact antennas.

High-resolution fully digital transceivers are infeasible at millimeter-wave (mmWave) due to their increased power consumption, cost, and hardware complexity. The use of low-resolution converters is one possible solution to realize fully digital architectures at mmWave. In this paper, we consider a setting in which a fully digital base station with constant envelope quantized (CEQ) digital-to-analog converters on each radio frequency chain communicates with multiple single antenna users with individual signal-to-quantization-plus-interference-plus-noise ratio (SQINR) constraints over frequency selective channels. We first establish uplink downlink duality for the system with CEQ hardware constraints and OFDM-based transmission considered in this paper. Based on the uplink downlink duality principle, we present a solution to the multi-user multi-carrier beamforming and power allocation problem that maximizes the minimum SQINR over all users and sub-carriers. We then present a per sub-carrier version of the originally proposed solution that decouples all sub-carriers of the OFDM waveform resulting in smaller sub-problems that can be solved in a parallel manner. Our numerical results based on 3GPP channel models generated from Quadriga demonstrate improvements in terms of ergodic sum rate and ergodic minimum rate over state-of-the-art linear solutions. We also show improved performance over non-linear solutions in terms of the coded bit error rate with the increased flexibility of assigning individual user SQINRs built into the proposed framework.

Millimeter wave (mmWave) technology can achieve high-speed communication due to the large available spectrum. Furthermore, the use of directional beams in mmWave system provides a natural defense against physical layer security attacks. In practice, however, the beams are imperfect due to mmWave hardware limitations such as the low-resolution of the phase shifters. These imperfections in the beam pattern introduce an energy leakage that can be exploited by an eavesdropper. To defend against such eavesdropping attacks, we propose a directional modulation-based defense technique where the transmitter applies random circulant shifts of a beamformer. We show that the use of random circulant shifts together with appropriate phase adjustment induces artificial phase noise (APN) in the directions different from that of the target receiver. Our method corrupts the phase at the eavesdropper without affecting the communication link of the target receiver. We also experimentally verify the APN induced due to circulant shifts, using channel measurements from a 2-bit mmWave phased array testbed. Using simulations, we study the performance of the proposed defense technique against a greedy eavesdropping strategy in a vehicle-to-infrastructure scenario. The proposed technique achieves better defense than the antenna subset modulation, without compromising on the communication link with the target receiver.

In this paper, we develop algorithms for joint user scheduling and three types of mmWave link configuration: relay selection, codebook optimization, and beam tracking in millimeter wave (mmWave) networks. Our goal is to design an online controller that dynamically schedules users and configures their links to minimize system delay. To solve this complex scheduling problem, we model it as a dynamic decision-making process and develop two reinforcement learning-based solutions. The first solution is based on deep reinforcement learning (DRL), which leverages the proximal policy optimization to train a neural network-based solution. Due to the potential high sample complexity of DRL, we also propose an empirical multi-armed bandit (MAB)-based solution, which decomposes the decision-making process into a sequential of sub-actions and exploits classic maxweight scheduling and Thompson sampling to decide those sub-actions. Our evaluation of the proposed solutions confirms their effectiveness in providing acceptable system delay. It also shows that the DRL-based solution has better delay performance while the MAB-based solution has a faster training process.

Millimeter wave (MmWave) systems are vulnerable to blockages, which cause signal drop and link outage. One solution is to deploy reconfigurable intelligent surfaces (RISs) to add a strong non-line-of-sight path from the transmitter to receiver. To achieve the best performance, the location of the deployed RIS should be optimized for a given site, considering the distribution of potential users and possible blockers. In this paper, we find the optimal location, height and downtilt of RIS working in a realistic vehicular scenario. Because of the proximity between the RIS and the vehicles, and the large electrical size of the RIS, we consider a 3D geometry including the elevation angle and near-field beamforming. We provide results on RIS configuration in terms of both coverage ratio and area-averaged rate. We find that the optimized RIS improves the area-averaged rate fifty percent over the case without a RIS, as well as further improvements in the coverage ratio.

This paper introduces a novel information-theoretic approach for studying the effects of mutual coupling (MC), between the transmit and receive antennas, on the overall performance of single-input-single-output (SISO) near-field communications. By incorporating the finite antenna size constraint using Chu’s theory and under the assumption of canonical-minimum scattering, we derive the MC between two radiating volumes of fixed sizes. Expressions for the self and mutual impedances are obtained by the use of the reciprocity theorem. Based on a circuit-theoretic two-port model for SISO radio communication systems, we establish the achievable rate for a given pair of transmit and receive antenna sizes, thereby providing an upper bound on the system performance under physical size constraints. Through the lens of these findings, we shed new light on the influence of MC on the information-theoretic limits of near-field communications using compact antennas.

Eavesdropping attacks are a severe threat to millimeter-wave (mmWave) networks that use low-resolution phased arrays. Although directional beamforming in mmWave phased arrays provides natural defense against eavesdropping, the use of low-resolution phase shifters induces energy leakage into unintended directions. This energy leakage can be exploited by the adversaries. In this paper, we propose a directional modulation (DM)-based defense against eavesdropping attacks on low-resolution phased arrays. Our defense technique applies random circulant shifts to the beamformer for every symbol transmission. By appropriately adjusting the phase of the transmitted symbol, the transmitter (TX) can maintain a high-quality link with the receiver while corrupting the symbols transmitted along unintended directions. We theoretically analyze the secrecy mutual information (SMI) achieved by the proposed defense mechanism and show that our defense induces artificial phase noise (APN) along unintended directions, which increases the SMI of the system. Finally, we numerically show the superiority of the proposed defense technique over the state-of-the-art defense techniques.

Beam codebooks are a new feature of massive multiple-input multiple-output (M-MIMO) in 5G new radio (NR). Codebooks comprised of beamforming vectors are used to transmit reference signals and obtain limited channel state information (CSI) from receivers via the codeword index. This enables large arrays that cannot otherwise obtain sufficient CSI. The performance, however, is limited by the codebook design. In this paper, we show that machine learning can be used to train site-specific codebooks for initial access. We design a neural network based on an autoencoder architecture that uses a beamspace observation in combination with RF environment characteristics to improve the synchronization signal (SS) burst codebook. We test our algorithm using a flexible dataset of channels generated from QuaDRiGa. The results show that our model outperforms the industry standard (DFT beams) and approaches the optimal performance (perfect CSI and singular value decomposition (SVD)-based beamforming), using only a few bits of feedback.

As larger bandwidths are used in multiple antenna wireless systems, the frequency selectivity of the antenna arrays starts to impact rate. Motivated by optimizing the achievable rate in compact antenna arrays, we present a system model that incorporates the effects of mutual coupling (MC) of wideband physically realizable single-input multiple-output (SIMO) and multiple-input single-output (MISO) antenna systems. For the SIMO system setup, we extract the noise correlation matrices for two different antenna array configurations (parallel and co-linear). We optimize the inter-element spacing in each alignment while maximizing the achievable rate and fixing the transmit power. Then, we compare the two compact antenna designs to a perfectly matched single omni-directional antenna while accounting for MC. Likewise, for the MISO antenna system, we derive the optimal beamformer that maximizes the achievable rate using the same antenna configurations as the SIMO system. Then, we study the impact of MC and develop a new single-port matching technique for wideband antenna arrays. Finally, we provide reciprocity plots to compare the performance of the SIMO-MISO systems using different channel models.

Using ideas from Chu and Bode/Fano theories, we characterize the maximum achievable rate over the single-input single-output wireless communication channels under a restriction on the antenna size at the receiver. By employing circuit-theoretic multiport models for radio communication systems, we derive the information-theoretic limits of compact antennas. We first describe an equivalent Chu’s antenna circuit under the physical realizability conditions of its reflection coefficient. Such a design allows us to subsequently compute the achievable rate for a given receive antenna size thereby providing a physical bound on the system performance that we compare to the standard size-unconstrained Shannon capacity. We also determine the effective signal-to-noise ratio (SNR) which strongly depends on the antenna size and experiences an apparent finite-size performance degradation where only a fraction of Shannon capacity can be achieved. We further determine the optimal signaling bandwidth which shows that impedance matching is essential in both narrowband and broadband scenarios. We also examine the achievable rate in presence of interference showing that the size constraint is immaterial in interference-limited scenarios. Finally, our numerical results of the derived achievable rate as function of the antenna size and the SNR reveal new insights for the physically consistent design of radio systems.

Nonlinear precoding and pulse shaping are jointly considered in multi-user massive multiple-input multiple-output (MIMO) systems with low resolution D/A-converters (DACs) in terms of algorithmic approach as well as large system performance. Two design criteria are investigated: the mean squared error (MSE) with active constellation extension (ACE) and the symbol error rate (SER). Both formulations are solved based on a modified version of the generalized approximate message passing (GAMP) algorithm. Furthermore, theoretical performance results are derived based on the state evolution analysis of the GAMP algorithm. The MSE based technique is extended to jointly perform over-the-air (OTA) spectral shaping and precoding for frequency-selective channels, in which the spectral performance is characterized at the transmitter and at the receiver. Simulation and analytical results demonstrate that the MSE based approach yields the same performance as the SER based formulation in terms of uncoded SER. The analytical results provide good performance predictions up to medium SNR. Substantial improvements in detection, as well as spectral performance, are obtained from the proposed combined pulse shaping and precoding approach compared to standard linear methods.

Phased arrays, commonly used in IEEE 802.11ad and 5G radios, are capable of focusing radio frequency signals in a specific direction or a spatial region. Beamforming achieves such directional or spatial concentration of signals and enables phased array-based radios to achieve high data rates. Designing beams for millimeter wave and terahertz communication using massive phased arrays, however, is challenging due to hardware constraints and the wide bandwidth in these systems. For example, beams which are optimal at the center frequency may perform poor in wideband communication systems where the radio frequencies differ substantially from the center frequency. The poor performance in such systems is due to differences in the optimal beamformers corresponding to distinct radio frequencies within the wide bandwidth. Such a mismatch leads to a misfocus effect in near-field systems and the beam squint effect in far-field systems. In this paper, we investigate the misfocus effect and propose InFocus, a low complexity technique to construct beams that are well suited for massive wideband phased arrays. The beams are constructed using a carefully designed frequency modulated waveform in the spatial dimension. InFocus mitigates beam misfocus and beam squint when applied to near-field and far-field systems.

Beamforming with high dimensional antenna arrays provides the gain needed to enable high bandwidth communication in the sub-terahertz (THz) band. The resulting narrow beams, however, come at the cost of increased sensitivity to beam alignment errors. A potential remedy to this problem is to introduce a form a macrodiversity through non-coherent joint transmission (NC-JT). We employ a stochastic geometry framework to analyze the performance of a THz network employing user-centric base station clustering and NC-JT. We derive semi-closed form lower bounds for the coverage probability and ergodic capacity experienced by a typical user in the network. Our model includes THz networks without joint transmission as a special case. Overall, our analysis indicates that joint transmission from a few base stations improves coverage and capacity by mitigating the impact of beam misalignment. Moreover, in certain settings, NC-JT is a more efficient usage a network access points compared to the non-cooperative case, and reduces the sensitivity of coverage and capacity to the choice of user beamwidth.

We develop a novel stochastic geometry framework to quantify the SINR coverage probability of a Terahertz (THz) cellular network. THz frequencies will require highly directional beams, leading to inevitably imperfect beam alignment. We derive a tractable and accurate semi-closed form lower bound for the coverage probability of a typical user in the network, introducing a novel approach of characterizing non line-of-sight (NLOS) links as equivalent LoS links using a non-homogeneous Poisson Point Process. We use the coverage bound to investigate the SINR scaling trends with base station density and array directivity at the base station and user. Dense base station deployments are required to achieve sufficient coverage and our analysis exposes a tradeoff between directivity (array gain) and loss in SINR due to misalignment.

Wireless cellular networks have many parameters that are normally tuned upon deployment and re-tuned as the network changes. Many operational parameters affect reference signal received power (RSRP), reference signal received quality (RSRQ), signal-to-interference-plus-noise-ratio (SINR), and, ultimately, throughput. In this paper, we develop and compare two approaches for maximizing coverage and minimizing interference by jointly optimizing the transmit power and downtilt (elevation tilt) settings across sectors. To evaluate different parameter configurations offline, we construct a realistic simulation model that captures geographic correlations. Using this model, we evaluate two optimization methods: deep deterministic policy gradient (DDPG), a reinforcement learning (RL) algorithm, and multi-objective Bayesian optimization (BO). Our simulations show that both approaches significantly outperform random search and converge to comparable Pareto frontiers, but that BO converges with two orders of magnitude fewer evaluations than DDPG. Our results suggest that data-driven techniques can effectively self-optimize coverage and capacity in cellular networks.

Millimeter-wave (mmWave) communications, using directional beams, is a key enabler for high-throughput mobile ad hoc networks. These directional beams are organized into multiple codebooks according to beam resolution, with each codebook consisting of a set of equal-width beams that cover the whole angular space. The code-book with narrow beams delivers high throughput, at the expense of scanning time. Therefore overall throughput maximization is achieved by selecting a mmWave codebook that balances between beamwidth (beamforming gain) and beam alignment overhead. Further, these codebooks have some potential natural structures such as the non-decreasing instantaneous rate or the unimodal throughput as one traverses from the codebook with wide beams to the one with narrow beams. We study the codebook selection problem through a multi-armed bandit (MAB) formulation in mmWave networks with rapidly-varying channels. We develop multiple novel Thompson Sampling-based algorithms for our setting given different codebook structures with theoretical guarantees on regret. We further collect real-world (60 GHz) measurements with 12-antenna phased arrays, and show the performance benefits of our approaches in an IEEE 802.11ad/ay emulation setting.

We abstract the core logical functions from applications that require ultra-low-latency wireless communications to provide a novel definition for reliability. Real-time applications - such as intelligent transportation, remote surgery, and industrial automation - involve a significant element of control and decision making. Such systems involve three logical components: observers (e.g. sensors) measuring the state of an environment or dynamical system, a centralized executive (e.g. controller) deciding on the state, and agents (e.g. actuators) that implement the executive's decisions. The executive harvests the observers' measurements and decides on the short-term trajectory of the system by instructing its agents to take appropriate actions. All observation packets (typically uplink) and action packets (typically downlink) must be delivered by hard deadlines to ensure the proper functioning of the controlled system. In-full on-time delivery cannot be guaranteed in wireless systems due to inherent uncertainties in the channel such as fading and unpredictable interference; accordingly, the executive will have to drop some packets. We develop a novel framework to formulate the Observer Selection Problem (OSP) through which the executive schedules a sequence of observations that maximize its knowledge about the current state of the system. To solve this problem efficiently yet optimally, we devise a branch-and-bound algorithm that systematically prunes the search space. Our work is different from existing work on real-time communications in that communication reliability is not conveyed by packet loss or error rate, but rather by the extent of the executive's knowledge about the state of the system it controls.

Frequency-hopping (FH) MIMO radar-based dual-function radar communication (FH-MIMO DFRC) enables communication symbol rate to exceed radar pulse repetition frequency, which requires accurate estimations of timing offset and channel parameters. The estimations, however, are challenging due to unknown, fast-changing hopping frequencies and the multiplicative coupling between timing offset and channel parameters. In this article, we develop accurate methods for a single-antenna communication receiver to estimate timing offset and channel for FH-MIMO DFRC. First, we design a novel FH-MIMO radar waveform, which enables a communication receiver to estimate the hopping frequency sequence (HFS) used by radar, instead of acquiring it from radar. Importantly, the novel waveform incurs no degradation to radar ranging performance. Then, via capturing distinct HFS features, we develop two estimators for timing offset and derive mean squared error lower bound of each estimator. Using the bounds, we design an HFS that renders both estimators applicable. Furthermore, we develop an accurate channel estimation method, reusing the single hop for timing offset estimation. Validated by simulations, the accurate channel estimates attained by the proposed methods enable the communication performance of DFRC to approach that achieved based on perfect timing and ideal knowledge of channel.

The network operators will soon be accommodating a new type of users: unmanned aerial vehicles (UAVs). 5G New Radio (NR) technology operating in the millimeter-wave (mmWave) frequency bands can support the emerging bandwidth-hungry applications facilitated by such aerial devices. To reliably integrate UAVs into the NR-based network infrastructure, new system models that capture the features of UAVs in urban environments are required. As city building blocks constitute one of the primary sources of blockage on the links from the UAV to its serving base station (BS), the corresponding line-of-sight (LoS) probability models are essential for accurate performance evaluation in realistic scenarios. We propose a LoS probability model in UAV communication setups over regular urban grid deployments, which is based on a Manhattan Poisson line process. Our approach captures different building height distributions as well as their dimensions and densities. Under certain characteristic distributions, closed-form expressions for the LoS probability are offered. Our numerical results demonstrate the importance of accounting for the building height distribution type as well as the orientation of the UAV with respect to its BS. By comparing our model with the standard ITU and 3GPP formulations, we establish that the latter provide an overly optimistic approximation for various deployments.

Millimeter wave (mmWave) communication is one viable solution to support Gbps sensor data sharing in vehicular networks. The use of large antenna arrays at mmWave and high mobility in vehicular communication make it challenging to design fast beam alignment solutions. In this paper, we propose a novel framework that learns the channel angle-of-departure (AoD) statistics at a base station (BS) and uses this information to efficiently acquire channel measurements. Our framework integrates online learning for compressive sensing (CS) codebook learning and the optimized codebook is used for CS-based beam alignment. We formulate a CS matrix optimization problem based on the AoD statistics available at the BS. Furthermore, based on the CS channel measurements, we develop techniques to update and learn such channel AoD statistics at the BS. We use the upper confidence bound (UCB) algorithm to learn the AoD statistics and the CS matrix. Numerical results show that the CS matrix in the proposed framework provides faster beam alignment than standard CS matrix designs. Simulation results indicate that the proposed beam training technique can reduce overhead by 80% compared to exhaustive beam search, and 70% compared to standard CS solutions that do not exploit any AoD statistics.

In this work, we develop DeepWiPHY, a deep learning-based architecture to replace the channel estimation, common phase error (CPE) correction, sampling rate offset (SRO) correction, and equalization modules of IEEE 802.11ax based orthogonal frequency division multiplexing (OFDM) receivers. We first train DeepWiPHY with a synthetic dataset, which is generated using representative indoor channel models and includes typical radio frequency (RF) impairments that are the source of nonlinearity in wireless systems. To further train and evaluate DeepWiPHY with real-world data, we develop a passive sniffing-based data collection testbed composed of Universal Software Radio Peripherals (USRPs) and commercially available IEEE 802.11ax products. The comprehensive evaluation of DeepWiPHY with synthetic and real-world datasets (110 million synthetic OFDM symbols and 14 million real-world OFDM symbols) confirms that, even without fine-tuning the neural network's architecture parameters, DeepWiPHY achieves comparable performance to or outperforms the conventional WLAN receivers, in terms of both bit error rate (BER) and packet error rate (PER), under a wide range of channel models, signal-to-noise (SNR) levels, and modulation schemes.

A fully-digital mmWave wideband JCR places difficult demands of power consumption and hardware complexity on the receivers' analog-to-digital converters (ADCs). To address these concerns, we present a low-complexity proof-of-concept (PoC) development for a wideband MIMO JCR that uses a mmWave communications waveform and low-resolution ADCs, while maintaining a separate radio-frequency chain per antenna. To accurately characterize the radar performance of our developed PoC tested, we conduct experiments using a trihedral corner reflector and apply traditional as well as advanced receiver processing algorithms. The results demonstrate that our MIMO PoC platform with a fully-digital JCR waveform at 73 GHz carrier frequency, 2 GHz bandwidth, and 1-bit ADCs achieves high range/direction estimation accuracy as well as high detection capability with a wide field of view.

Receivers with one-bit analog-to-digital converters (ADCs) are promising for high bandwidth millimeter wave (mmWave) systems as they consume less power than their full resolution counterparts. The extreme quantization in one-bit receivers and the use of large antenna arrays at mmWave make channel estimation challenging. In this paper, we develop channel estimation algorithms that exploit the low-rank property of mmWave channels. We also propose a novel training solution that results in a low complexity implementation of our algorithms. Simulation results indicate that the proposed methods achieve better channel reconstruction than compressed sensing-based techniques that exploit sparsity of mmWave channels.

Millimeter wave channels exhibit structure that allows beam alignment with fewer channel measurements than exhaustive beam search. From a compressed sensing (CS) perspective, the received channel measurements are usually obtained by multiplying a CS matrix with a sparse representation of the channel matrix. Due to the constraints imposed by analog frontends, designing CS matrices that efficiently exploit the channel structure is challenging. In this paper, we propose an end-to-end deep learning technique to design a structured CS matrix that is well suited to the underlying channel distribution, leveraging both sparsity and the particular spatial structure that appears in vehicular channels. The channel measurements acquired with the designed CS matrix are then used to predict the best beam for link configuration. Simulation results for vehicular communication channels indicate that our deep learning-based approach achieves better beam alignment than standard CS techniques that use the random phase shift-based design.

Millimeter wave (mmWave) has the potential to offer high data rates for vehicle-to-everything (V2X) communication. In this paper, we provide an introduction to important use cases of V2X as they pertain to fifth generation (5G) communication networks. As 5G technology is still evolving to support vehicles, and mmWave is reflected and blocked by vehicles, it remains unclear if the target rates for the use cases can be achieved. Motivated by the different data rate requirements, we introduce a methodology for evaluating rates in 5G mmWave V2X scenarios. Our approach leverages available city CAD models, realistic traffic simulators, and industry standard ray tracing tools to allow site-specific propagation evaluation. This approach may be used to devise insight into the role of traffic density, antenna array placement, and base station density in important urban propagation settings. Results are provided that highlight the application to develop a rate map for an urban intersection. They show that rate increases in dense deployments by more than ten percent per base station, but only decreases about one percent when going from light to heavy traffic.

Millimeter wave (mmWave) communication in typical wearable and data center settings is short range. As the distance between the transmitter and the receiver in short range scenarios can be comparable to the length of the antenna arrays, the common far field approximation for the channel may not be applicable. As a result, dictionaries that result in a sparse channel representation in the far field setting may not be appropriate for short distances. In this paper, we develop a novel framework to exploit the structure in short range mmWave channels. The proposed method splits the channel into several subchannels for which the far field approximation can be applied. Then, the structure within and across different subchannels is leveraged using message passing. We show how information about the antenna array geometry can be used to design message passing factors that incorporate structure across successive subchannels. Simulation results indicate that our framework can be used to achieve better beam alignment with fewer channel measurements when compared to standard compressed sensing-based techniques that do not exploit structure across subchannels.

Spatial channel covariance information can replace full instantaneous channel state information for the analog precoder design in hybrid analog/digital architectures. Obtaining spatial channel covariance estimation, however, is challenging in the hybrid structure due to the use of fewer radio frequency (RF) chains than the number of antennas. In this paper, we propose a spatial channel covariance estimation method based on higher-order tensor decomposition for spatially sparse time-varying frequency-selective channels. The proposed method leverages the fact that the channel can be represented as a low-rank higher-order tensor. We also derive the Cramér-Rao lower bound on the estimation accuracy of the proposed method. Numerical results and theoretical analysis show that the proposed tensor-based approach achieves higher estimation accuracy in comparison with prior compressive-sensing-based approaches or conventional angle-of-arrival estimation approaches.

In this paper, we propose and evaluate a novel double-sequence low-resolution frequency synchronization method in millimeter-wave (mmWave) systems. In our system model, the base station uses analog beams to send the synchronization signal with infinite-resolution digital-to-analog converters. The user equipment employs a fully digital front end to detect the synchronization signal with low-resolution analog-to-digital converters (ADCs). The key ingredient of the proposed method is the custom designed synchronization sequence pairs, from which there exists an invertible function (a ratio metric) of the carrier frequency offset (CFO) to be estimated. We use numerical examples to show that the ratio metric is robust to the quantization distortion. To implement our proposed method in practice, we propose to optimize the double-sequence design parameters such that: (i) for each individual user, the impact of the quantization distortion on the CFO estimation accuracy is minimized, and (ii) the resulting frequency range of estimation can capture as many users' CFOs as possible. Numerical results reveal that our proposed algorithm can provide a flexible means to estimate CFO in a variety of low-resolution settings.

Simultaneous wireless information and power transfer (SWIPT) augments a wireless device's capability to transmit data by adding a harvesting component to the standard information processing receiver. With SWIPT, both information processing and energy scavenging are combined to allow a SWIPT device to become its own recurrent power source. This paper proposes, analyzes, and compares the functionality of a novel selective orthogonal frequency-division multiplexing (OFDM) receiver architecture and evaluates the rate-energy (R-E) tradeoff of the system. Additionally, the feasibility of harvesting sufficient energy to power the OFDM processing portion of the receiver is examined. To facilitate the evaluation, Monte Carlo simulations are run presenting system level R-E tradeoff and feasibility performance. Selective OFDM transmission is found to provide improved energy harvesting and sustainability over conventional OFDM transmission.

The capability of smarter networked devices to dynamically select appropriate radio connectivity options is especially important in the emerging millimeter-wave (mmWave) systems to mitigate abrupt link blockage in complex environments. To enrich the levels of diversity, mobile mmWave relays can be employed for improved connection reliability. These are considered by 3GPP for on-demand densification on top of the static mmWave infrastructure. However, performance dynamics of mobile mmWave relaying is not nearly well explored, especially in realistic conditions, such as urban vehicular scenarios. In this paper, we develop a mathematical framework for the performance evaluation of mmWave vehicular relaying in a typical street deployment. We analyze and compare alternative connectivity strategies by quantifying the performance gains made available to smart devices in the presence of mmWave relays. We identify situations where the use of mmWave vehicular relaying is particularly beneficial. Our methodology and results can support further standardization and deployment of mmWave relaying in more intelligent 5G+ "all-mmWave" cellular networks.

In this paper we present a novel capacity optimization methodology for a two-element multi-band single-input multiple-output (SIMO) antenna. We first introduce a system model that incorporates antenna mutual coupling in the wideband regime. Then, we extract the noise correlation matrices from two different antenna configurations (parallel and co-linear). We optimize the spacing in each scenario while maximizing the Shannon capacity under transmit power constraint. Consequently, we compare the two compact planar antenna designs to the AWGN capacity baseline at different inter- element spacing while accounting for mutual coupling. Finally, we introduce a matching network that maximizes the capacity of the wideband SIMO system.

Compressed sensing (CS)-based beam alignment is a promising solution for rapid link configuration in millimeter wave (mmWave) systems that use large arrays. Translating CS to practical mmWave radios, however, can be challenging due to carrier frequency offset (CFO). Standard sparse recovery techniques that use random sampling strategies to acquire channel measurements can fail even if there is a slight mismatch in carrier frequencies. In this paper, we show that restricting the randomness in compressive sampling to local sets can achieve robustness to structured errors due to CFO. The proposed approach requires fewer channel measurements than comparable algorithms and has the same complexity as standard CS.

Millimeter wave multiple-input-multiple-output (MIMO) achieves the best performance when reliable channel state information is used to design the beams. Most channel estimation methods proposed in the literature, however, ignore practical hardware impairments such as carrier frequency offset (CFO) and may fail under such impairment. In this paper, we present a joint CFO and channel estimation method based on tensor modeling and compressed sensing. Simulation results indicate that the proposed method yields better channel recovery performance than the benchmark and that it is more robust to a small number of channel measurements.

Accurate path gain models are critical for coverage prediction and radio frequency (RF) planning in wireless communications. In many settings irregular terrain induces blockages and scattering making it difficult to predict the path gain. Current solutions are either computationally expensive or slope-intercept fits that do not capture local deviations due to terrain variation, leading to large prediction errors. We propose to use machine learning to learn path gain based on terrain elevation as features. We implement different neural network architectures with dense and convolutional layers that could include effects difficult to describe with traditional models (e.g. back scatter). We test our framework on an extensive set of measured path gain data and consistently predict with 5 dB Root Mean Squared Error, an 8 dB improvement over traditional slope-intercept solutions.

Many control applications demand stringent latency and reliability requirements that cannot be met by existing wireless technologies. Prior work has modeled a pilot-assisted, variable-rate communication procedure that describes the transmission of commands from a centralized controller to a number of agents (e.g. actuators) through a number of access points (APs). This procedure comprises two phases with pre-allocated time budgets per transmission cycle. There is a training phase where channel states between AP-agent pairs are estimated, and a downlink phase where the commands are sequentially transmitted at adapted rates before the start of a new cycle - a hard deadline. System reliability is compromised, though, when the agents fail to receive the controller's commands before the start of a subsequent cycle. In this paper, we calculate a closed-form expression for the probability of hard-deadline violation when there are two agents. We find upper and lower bounds on this probability when there are more agents. We observe that these bounds approximate the probability of hard-deadline violation which reflects the reliability of the system.

Orthogonal time frequency space (OTFS) modulation outperforms orthogonal frequency division multiplexing (OFDM) in high-mobility scenarios. One challenge for OTFS massive MIMO is downlink channel estimation due to the required high pilot overhead. In this paper, we propose a 3D structured orthogonal matching pursuit (3D-SOMP) algorithm based channel estimation technique. First, we show that the OTFS MIMO channel exhibits 3D structured sparsity: normal sparsity along the delay dimension, block sparsity along the Doppler dimension, and burst sparsity along the angle dimension. Based on the 3D structured channel sparsity, we then formulate the downlink channel estimation problem as a sparse signal recovery problem. Simulation results show that the proposed 3D-SOMP algorithm can achieve accurate channel state information with low pilot overhead.

Designing efficient and robust beam alignment strategies for millimeter wave (mmWave) systems is important for overcoming training overheads and practical hardware impairments. In this work, we leverage side information in the form of prior knowledge of the angular support of the propagation channel (direction information) to design a compressive sensing (CS) based beam alignment algorithm. Existing CS based channel estimation approaches assume perfect phase information of the measurements, which is not the case with the low-cost off-the-shelf mmWave phased arrays. Instead, we develop a two-stage algorithm where we use the magnitude of measurements (aka non-coherent measurements); using phase retrieval (PR) followed by sparse recovery, we estimate the channel gain across various (quantized) spatial angles. To validate the proposed algorithm, we develop a fully reconfigurable mmWave testbed with custom-made 2-bit phased arrays. We perform a careful calibration to the phased arrays, thus enabling generations of precise desired beam patterns. Our implementation and real experiments validate both the proposed algorithm and calibration process by demonstrating consistency between the experimental results and the theoretical analysis.

Ultra-high reliability and low latency have not been in the mainstream in most wireless networks. Mobile networks have been driven so far by human-centric communications, delay-tolerant content, and non-critical services. The main target have been to boosting data rate and increasing coverage, adopting a rather best-effort networking approach. As wireless connectivity starts to get the status of a commodity, there is an increasing focus on support of services that rely critically on wireless links and therefore the reliability of the wireless connections. Next generation wireless systems, mainly 5G and beyond, are designed to provide wireless connectivity for massive machine-type communications (mMTC) and to support ultra-reliable, low latency communication (URLLC) for mission-critical services. URLLC scenarios impose stringent requirements in terms of latency (ranging from 1 ms and below to few milliseconds end-to-end latency depending on the use cases) and reliability (higher than 99.9999%). This does not mean that there are interesting applications where reliability is of paramount importance, while latency can be in the order of seconds, as in e.g. certain remote healthcare applications. Nevertheless, the coupling of low-latency networking and reliable communication is mainly driven by the need to push the technology boundaries and address a plethora of socially useful services and business domains that could benefit greatly from it. Some of the most challenging use cases are factory automation and industrial control, automated driving/flying, haptic communications, and real-time remote healthcare. URLLC is also expected to revolutionize processes in the areas of smart cities, smart farming, smart grid, remote manufacturing, and algorithmic trading. Although the URLLC constraints and the nature of real-time mission-critical applications imply the predominance of short packets and low-rate transmissions, future evolution of URLLC may also consider rate requirements. The emergence of immersive services, such as augmented and virtual reality (AR/VR), high-definition entertainment and gaming, and consumer robotics, calls for real-time, high-fidelity, broadband networks operating at latencies of few milliseconds.

Due to the low traffic congestion, high fuel efficiency, and comfortable travel experience, vehicular platoon systems (VPSs) become one of the most promising applications in millimeter wave (mmWave) vehicular networks. In this paper, an effective spatial framework for mmWave vehicle-to-everything (V2X) networks with VPSs is proposed by utilizing stochastic geometry approaches. Base stations (BSs) are modeled by a Poisson point process and vehicles are distributed according to multiple type II Matérn hard-core processes. To characterize the blockage process caused by vehicles, a closed-form expression is deduced to distinguish line-of-sight (LOS) and non-LOS transmission. This expression demonstrates that LOS links are independent of horizontal communication distances. Several closed-form probability density functions of the communication distance between a reference platoon and its serving transmitter (other platoons or BSs) are derived for analyzing the generated path loss. After designing three practical user association techniques, tractable expressions for coverage probabilities are figured out. Our work theoretically shows that the maximum density of VPSs exists and large antenna scales benefit the networks' coverage performance. The numerical results illustrate that platoons outperform individual vehicles in terms of road spectral efficiency and the considered system is LOS interference-limited.

We consider a downlink periodic wireless communications system, where multiple access points cooperatively transmit packets to a number of devices, e.g., actuators in an industrial control system. Each period consists of two phases: an uplink training phase and a downlink data transmission phase. Each actuator must successfully receive its unique packet within a single transmission phase; else, an outage is declared. Such an outage can be caused by two events: a transmission error due to transmission at a rate that the channel cannot actually support or time overflow, where the downlink data phase is too short, given the channel conditions to successfully communicate all the packets. We determine the closed-form expressions for the time overflow probability when there are just two field devices, as well as the transmission error probability for an arbitrary number of devices. In addition, we provide upper and lower bounds on the time overflow probability for an arbitrary number of devices. We propose a novel variable-rate transmission method that eliminates time overflow. Detailed system-level simulations are used to identify system design guidelines, such as the optimal amount of training time, as well as for benchmarking the proposed system design versus non-cooperative cellular, cooperative fixed-rate, and cooperative relaying.

Millimeter wave (mmWave) systems can enable high data rates if the link between the transmitting and receiving radios is configured properly. Fast configuration of mmWave links, however, is challenging due to the use of large antenna arrays and hardware constraints. For example, a large amount of training overhead is incurred by exhaustive search-based beam alignment in typical mmWave phased arrays. In this paper, we present a framework called FALP for Fast beam Alignment with Low-resolution Phase shifters. FALP uses an efficient set of antenna weight vectors to acquire channel measurements, and allows faster beam alignment when compared to exhaustive scan. The antenna weight vectors in FALP can be realized in ultra-low power phase shifters whose resolution can be as low as one-bit. From a compressed sensing (CS) perspective, the CS matrix designed in FALP satisfies the restricted isometry property and allows CS algorithms to exploit the fast Fourier transform. The proposed framework also establishes a new connection between channel acquisition in phased arrays and magnetic resonance imaging.

In high mobility applications of millimeter wave (mmWave) communications, e.g., vehicle-to-everything communication and next-generation cellular communication, frequent link configuration can be a source of significant overhead. We use the sub-6 GHz channel covariance as an out-of-band side information for mmWave link configuration. Assuming: 1) a fully digital architecture at sub-6 GHz and 2) a hybrid analog-digital architecture at mmWave, we propose an out-of-band covariance translation approach and an out-of-band aided compressed covariance estimation approach. For covariance translation, we estimate the parameters of sub-6 GHz covariance and use them in theoretical expressions of covariance matrices to predict the mmWave covariance. For out-of-band aided covariance estimation, we use weighted sparse signal recovery to incorporate out-of-band information in compressed covariance estimation. The out-of-band covariance translation eliminates the in-band training completely, whereas out-of-band aided covariance estimation relies on in-band as well as out-of-band training. We also analyze the loss in the signal-to-noise ratio due to an imperfect estimate of the covariance. The simulation results show that the proposed covariance estimation strategies can reduce the training overhead compared to the in-band only covariance estimation.

Channel estimation at millimeter wave (mmWave) carrier frequencies is challenging when large antenna arrays are used. Prior work has leveraged the sparse nature of mmWave channels via compressed sensing-based algorithms for channel estimation. Most of these algorithms, though, assume perfect synchronization and are vulnerable to phase errors that arise due to carrier frequency offset and phase noise. Recently, sparsity-aware, non-coherent beamforming algorithms that are robust to phase errors were proposed for narrowband phased array systems with full resolution analog-to-digital converters. Such energy-based algorithms, however, are not robust to heavy quantization at the receiver. In this paper, we develop a joint carrier frequency offset and wideband channel estimation algorithm that is scalable across different hardware architectures. Our method exploits the sparse nature of mmWave channels in the angle-delay domain, in addition to the compressibility of the phase error vector. We formulate the joint estimation as a quantized sparse bilinear optimization problem and then use message passing for recovery. We also give an efficient implementation of a generalized bilinear message passing algorithm for the joint estimation in one-bit receivers. Simulation results show that our method is able to estimate the frequency offset and the channel compressibility, even in the presence of phase noise.

The two key features of millimeter wave-(mmWave) based MIMO communication are the use of large antenna arrays at the transceivers and large bandwidth. The former complicates the design of optimal beamformers, while the latter makes the system frequency-selective and, thus, requires equalization. Conventionally, for wideband mmWave channels, choosing the precoders/combiners have involved frequency-selective designs that are based on channel state information. In this paper, we show that under some assumptions, semi-unitary frequency-flat precoding and combining are sufficient for low-scattering millimeter wave channels. To show this, we evaluate the conditions and practical settings under which the dominant subspaces of the frequency-domain channel matrices are similar. We model the frequency-dependence of uniform linear antenna arrays, which leads to what is known as beam-squint, for two different practical setting to analyze the optimality of frequency-flat beamforming designs under practical hardware impairments. For the cases when the optimality conditions hold, we propose novel techniques based on compressive subspace estimation to design the optimal frequency-flat, semi-unitary precoders and combiners. Simulation results show that the system achieves near-digital spectral efficiencies at very small implementation cost of beamformers and channel training overhead.

In this paper, we propose and evaluate a novel beamforming strategy for directional frame timing synchronization in wideband millimeter-wave (mmWave) systems operating with low-resolution analog-to-digital converters (ADCs). In the employed system model, we assume multiple radio frequency chains equipped at the base station to simultaneously form multiple synchronization beams in the analog domain. We formulate the corresponding directional frame timing synchronization problem as a max-min multicast beamforming problem under low-resolution quantization. We first show that the formulated problem cannot be effectively solved by conventional single-stream beamforming based approaches due to large quantization loss and limited beam codebook resolution. We then develop a new multi-beam probing based directional synchronization strategy, targeting at maximizing the minimum received synchronization signal-to-quantization-plus-noise ratio (SQNR) among all users. Leveraging a common synchronization signal structure design, the proposed approach synthesizes an effective composite beam from the simultaneously probed beams to better trade off the beamforming gain and the quantization distortion. Numerical results reveal that for wideband mmWave systems with low-resolution ADCs, the timing synchronization performance of our proposed method outperforms the existing approaches due to the improvement in the received synchronization SQNR.

The millimeter wave WLAN standard can be used for joint communication-radar by exploiting the waveform preamble as a radar pulse. The velocity estimation accuracy with this approach, however, is limited due to the short integration time. A physical increase in the radar pulse integration duration, however, leads to a decrease in the communication data rate. In this paper, a coprime-based pulse design approach for IEEE 802.11ad-based radar is proposed that uses only a few non-uniformly placed preambles to construct several virtual pulses for enhancing the velocity estimation accuracy/resolution as compared to the conventional approach without sacrificing the communication data rate. The simulation results demonstrate that the coprime-based virtual pulse design improves the velocity estimation resolution by a factor of about 60x at a vehicle separation distance of 10m and by a factor of about 20× at a distance of 100 m, while simultaneously achieving 7 Gbps data rate.

Millimeter wave (mmWave) multiple-input multiple-output (MIMO) transceivers employ narrow beams to obtain a large array-gain, rendering them sensitive to changes in the angles of arrival and departure of the paths. Since the singular vectors that span the channel subspace are used to design the precoder and combiner, we propose a method to track the receiver-side channel subspace during data transmission using a separate radio frequency (RF) chain dedicated for channel tracking. Under certain conditions on the transmit precoder, we show that the receiver-side channel subspace can be estimated during data transmission without knowing the structure of the precoder or the transmitted data. The performance of the proposed method is evaluated through simulations.

Competing cellular operators aggressively share infrastructure in many major US markets. If operators also share spectrum licenses, intra- cellular interference will become correlated with inter-cellular interference. We propose a mathematical framework to model a two-operator millimeter-wave (mmWave) cellular network with co- located base-stations (BSs). We then characterize the SINR distribution for an arbitrary network to understand the impact of varying the spatial correlation between the operators' networks. An interesting observation is that sharing spectrum and infrastructure yields a higher rate coverage probability for higher rate thresholds, but has a lower coverage for lower thresholds. This suggests that networks catering for low-rate, limited-QoS devices, are at a disadvantage when spectrum and infrastructure are shared.

Channel estimation at millimeter wave (mmWave) allows designing hybrid precoders and combiners to maximize performance metrics such as the spectral efficiency. A major source of difficulty when estimating the channel is the carrier frequency offset. In this paper, we propose the first joint compressive - Maximum Likelihood (ML) algorithm for joint carrier frequency offset and channel estimation at mmWave. Simulation results show that small estimation errors and near-optimal values of spectral efficiency can be achieved without incurring in significant overhead and/or computational complexity.

Autonomous vehicles (AVs) have the potential to transform road transportation by improving safety and increasing traffic/fuel efficiency. Currently, AVs rely primarily on line-of-sight sensing technologies to gather information about their surroundings. Some of the surrounding objects (e.g., vehicles, infrastructure, and pedestrians), however, can potentially communicate with the AV to help it create a better digital map of the world around it. In this work, we quantify the gains vehicle-to-vehicle (V2V) communication can provide for AVs. Specifically, we focus on the safety and maneuver planning of the AVs. Our preliminary findings indicate that V2V can reduce a significant fraction of AV collisions. Further, V2V can considerably reduce the maneuver completion time.

This paper presents a low complexity maximum likelihood detection (MLD) algorithm called one-bit-sphere-decoding for an uplink massive multiple-input multiple-output (MIMO) system with one-bit analog-to-digital converters (ADCs). The idea of the proposed algorithm is to estimate the transmitted symbol vector sent by uplink users (a codeword vector) by searching over a sphere, which contains a collection of codeword vectors close to the received signal vector at the base station in terms of a weighted Hamming distance. To reduce the computational complexity for the construction of the sphere, the proposed algorithm divides the received signal vector into multiple sub-vectors each with reduced dimension. Then, it generates multiple spheres in parallel, where each sphere is centered at the sub-vector and contains a list of sub-codeword vectors. Simulation results demonstrate that the proposed algorithm achieves near-MLD performance, while reducing the computational complexity compared to the existing MLD method.

Traffic monitoring is important in urban areas. Traffic sensing solutions based on static cameras, however, do not offer a flexible, inexpensive solution for short-term traffic studies. To overcome the limitations of static solutions, we propose an aerial traffic monitoring system. It uses unmanned aerial vehicles (UAV) and onboard cameras to capture traffic video, which is sent and processed in the cloud. To validate the proposed system, we implemented a prototype with a quadcopter, an onboard camera with video and data processing algorithms, and a web application. The video processing module includes a vehicle detection stage based on the Haar cascade model and a frame-by-frame tracking stage. After experimental testing based on videos collected by the UAV, we conclude that the designed system can monitor traffic with high accuracy and flexibility.

Demonstrating the feasibility of large antenna array beamforming is essential for realizing mmWave communication systems. This is due to the dependency of these systems on the large array beamforming gains to provide sufficient received signal power. In this paper, the design of a proof-of-concept prototype that demonstrates these gains in practice is explained in detail. We develop a mmWave system with digitally controlled analog front-end. The developed prototype uses 60 GHz phased arrays and universal software radio peripheral (USRP) controllers. The software interface of our design is easily reproducible and can be leveraged for future mmWave prototypes and demonstrations.

In this paper, we develop a framework for using the dedicated short range communication (DSRC) waveform for the purposes of automotive radar. Our approach operates on the frequency domain channel estimates generated by the OFDM physical layer used in DSRC. We consider a two path channel model, with the first cluster corresponding to direct signal interference and the second cluster corresponding to the signal reflected from the target. The target ranging and direction of arrival information is encoded in the parameters of the reflected path. We estimate the parameters of the direct and reflected path using the least squares matching pursuit algorithm by exploiting their relative power difference. The performance of the algorithm is evaluated through numerical simulations assuming low power omnidirectional 5 dBi antennas, Swerling type 0 and type 3 target models, 10 MHz transmission bandwidth and different analog-to-digital quantization resolutions. Simulations results show submeter accuracy in location estimation for a significant range of target distances.

We characterize the ergodic spectral efficiency of a cooperative type of K-tier heterogeneous networks (HetNets) with limited feedback. Specifically, a base station (BS) coordination set is formed by using dynamic clustering across the tiers, wherein the intra-cluster interference is mitigated by using multi-cell zero-forcing based on limited feedback. Modeling the network based on stochastic geometry, we derive analytical expressions for the ergodic spectral efficiency as a function of the system parameters. Leveraging the obtained expression, we formulate a feedback allocation problem and obtain a solution to improve the ergodic spectral efficiency. Simulations show the spectral efficiency improvement by using the proposed feedback allocation. One major finding in the obtained solution is that allocating more feedback to stronger intra-cluster BSs is efficient.

We characterize the detection probability of blockage sensing by radars deployed on towers in cellular networks. If the signal-to-interference ratio of the reflected pilot signal is larger than the predefined threshold and there is no other blockage between the radar and the corresponding blockage, the radar successfully detects the blockage. Modeling radar and blockage locations using stochastic geometry, we derive the detection probability as a function of the system parameters, chiefly the radar and blockage densities. Leveraging the obtained expression, we provide some guidelines for efficiently deploying radars to enhance the detection probability.

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.

Channel feedback is essential in frequency division duplexing (FDD) massive multiple-input multiple-output (MIMO) systems. Unfortunately, prior work on multiuser MIMO has shown that the feedback overhead scales linearly with the number of base station (BS) antennas, which is large in massive MIMO systems. To reduce the feedback overhead, we propose an angle-of-departure (AoD) adaptive subspace codebook for channel feedback in FDD massive MIMO systems. Our key insight is to leverage the observation that path AoDs vary more slowly than the path gains. Within the angle coherence time, by utilizing the constant AoD information, the proposed AoD-adaptive subspace codebook is able to quantize the channel vector in a more accurate way. From the performance analysis, we show that the feedback overhead of the proposed codebook only scales linearly with a small number of dominant (path) AoDs instead of the large number of BS antennas. Moreover, we compare the proposed quantized feedback technique using the AoD-adaptive subspace codebook with a comparable analog feedback method. Extensive simulations show that the proposed AoD-adaptive subspace codebook achieves good channel feedback quality, while requiring low overhead.

Millimeter wave (mmWave) communication is one feasible solution for high data-rate applications like vehicular-to-everything communication and next generation cellular communication. Configuring mmWave links, which can be done through channel estimation or beam-selection, however, is a source of significant overhead. In this paper, we propose using spatial information extracted at sub-6 GHz to help establish the mmWave link. Assuming a fully digital architecture at sub-6 GHz; and an analog architecture at mmWave, we outline a strategy to extract spatial information from sub-6 GHz and its use in mmWave compressed beam-selection. Specifically, we formulate compressed beam-selection as a weighted sparse signal recovery problem, and obtain the weighting information from sub-6 GHz channels. In addition, we outline a structured precoder/combiner design to tailor the training to out-of-band information. We also extend the proposed out-of-band aided compressed beam-selection approach to leverage information from all active subcarriers at mmWave. To simulate multi-band frequency dependent channels, we review the prior work on frequency dependent channel behavior and outline a multi-frequency channel model. The simulation results for achievable rate show that out-of-band aided beam-selection can considerably reduce the training overhead of in-band only beam-selection.

Blocking objects (blockages) between a transmitter and receiver cause wireless communication links to transition from line-of-sight (LOS) to non-LOS propagation, which can greatly reduce the received power, particularly at the higher frequencies such as millimeter wave. We consider a cellular network in which a mobile user attempts to connect to two or more base stations (BSs) simultaneously, to increase the probability of at least one LOS link, which is a form of macrodiversity. We develop a framework for determining the LOS probability as a function of the number of BSs, when taking into account the correlation between blockages: for example, a single blockage close to the device-including the user's own body-could block multiple BSs. We consider the impact of the size of blocking objects on the system's n th order LOS probability and show that macrodiversity gains are higher when the blocking objects are small. We also show that the BS density must scale as the square of the blockage density to maintain a given level of LOS probability.

This paper presents a low-complexity near-maximum-likelihood-detection (near-MLD) algorithm called one-bit sphere decoding for an uplink massive multiple-input multiple-output system with one-bit analog-to-digital converters. The idea of the proposed algorithm is to estimate the transmitted symbol vector sent by uplink users (a codeword vector) by searching over a sphere, which contains a collection of codeword vectors close to the received signal vector at the base station in terms of a weighted Hamming distance. To reduce the computational complexity for the construction of the sphere, the proposed algorithm divides the received signal vector into multiple subvectors each with a reduced dimension. Then, it generates multiple spheres in parallel, where each sphere is centered at the subvector and contains a list of subcodeword vectors. The detection performance of the proposed algorithm is also analyzed by characterizing the probability that the proposed algorithm performs worse than the MLD. The analysis shows how the dimension of each sphere and the size of the subcodeword list are related to the performance-complexity tradeoff achieved by the proposed algorithm. Simulation results demonstrate that the proposed algorithm achieves near-MLD performance, while reducing the computational complexity compared to the existing MLD method.

Massive MIMO is attractive for wireless information and energy transfer due to its ability to focus energy toward desired spatial locations. In this paper, the overall power transfer efficiency (PTE) and the energy efficiency (EE) of a wirelessly powered massive MIMO system are investigated, where a multi-antenna base-station (BS) uses wireless energy transfer to charge single-antenna energy harvesting users on the downlink. The users may exploit the harvested energy to transmit information to the BS on the uplink. The overall system performance is analyzed while accounting for the nonlinear nature of practical energy harvesters. First, for wireless energy transfer, the PTE is characterized using a scalable model for the BS circuit power consumption. The PTE-optimal values for the number of BS antennas and users are derived. Then, for wireless energy and information transfer, the EE performance is characterized. The EE-optimal BS transmit power is derived in terms of the key system parameters, such as the number of BS antennas and the number of users. As the number of antennas becomes large, increasing the transmit power improves the EE for moderate to large number of antennas. Simulation results suggest that it is energy efficient to operate the system in the massive MIMO regime.

Communication systems with low-resolution analog-to-digital-converters (ADCs) can exploit channel state information at the transmitter and receiver. This paper presents codebook designs and performance analyses for limited feedback MIMO systems with finite-bit ADCs. A point-to-point single-user channel is firstly considered. When the received signal is sliced by 1-bit ADCs, the absolute phase at the receiver is important to align the phase of the received signals. A new codebook design for beamforming, which separately quantizes the channel direction and the residual phase, is therefore proposed. For the multi-bit case where the optimal transmission method is unknown, suboptimal Gaussian signaling and eigenvector beamforming is assumed to obtain a lower bound of the achievable rate. It is found that to limit the rate loss, more feedback bits are needed in the medium SNR regime than the low and high SNR regimes, which is quite different from the conventional infinite-bit ADC case. Second, a multi-user system where a multiple-antenna transmitter sends signals to multiple single-antenna receivers with finite-bit ADCs is considered. Based on the derived performance loss due to finite-bit ADCs and finite-bit CSI feedback, the number of bits per feedback should increase linearly with the ADC resolution in order to restrict the rate loss.

Spatial channel covariance information can replace full knowledge of the entire channel matrix for designing analog precoders in hybrid multiple-input-multiple-output (MIMO) architecture. Spatial channel covariance estimation, however, is challenging for the hybrid MIMO architecture because the estimator operating at baseband can only obtain a lower dimensional pre-combined signal through fewer radio frequency chains than antennas. In this paper, we propose two approaches to covariance estimation based on compressive sensing techniques. One is to apply a time-varying sensing matrix, and the other is to exploit the prior knowledge that the covariance matrix is Hermitian. We present the rationale behind the two ideas and validate the superiority of the proposed methods by theoretical analysis and numerical simulations. We conclude the paper by extending the proposed algorithms from narrowband MIMO systems with a single receive antenna to wideband systems with multiple receive antennas.

We characterize the ergodic spectral efficiency of a non-cooperative and a cooperative type ofK-tier heterogeneous network with limited feedback. In the non-cooperative case, a multi-antenna base station (BS) serves a single-antenna user using maximum-ratio transmission based on limited feedback. In the cooperative case, a BS coordination set is formed by using dynamic clustering across the tiers, wherein the intra-cluster interference is mitigated by using multi cell zero-forcing also based on limited feedback. Modeling the network based on stochastic geometry, we derive analytical expressions for the ergodic spectral efficiency as a function of the system parameters. Leveraging the obtained expressions, we formulate feedback partition problems and obtain solutions to improve the ergodic spectral efficiency. Simulations show the spectral efficiency improvement by using the obtained feedback partitions. Our major findings are as follows: 1) in the non-cooperative case, the feedback is only useful in a particular tier if the mean interference is small enough; 2) in the cooperative case, allocating more feedback to stronger intra-cluster BSs is efficient; and 3) in both cases, the obtained solutions do not change depending on the instantaneous signal-to-interference ratio.

Channel estimation is useful in millimeter wave (mm-wave) MIMO communication systems. Channel state information allows optimized designs of precoders and combiners under different metrics, such as mutual information or signal-to-interference noise ratio. At mm-wave, MIMO precoders and combiners are usually hybrid, since this architecture provides a means to trade-off power consumption and achievable rate. Channel estimation is challenging when using these architectures, however, since there is no direct access to the outputs of the different antenna elements in the array. The MIMO channel can only be observed through the analog combining network, which acts as a compression stage of the received signal. Most of the prior work on channel estimation for hybrid architectures assumes a frequency-flat mm-wave channel model. In this paper, we consider a frequency-selective mm-wave channel and propose compressed sensing-based strategies to estimate the channel in the frequency domain. We evaluate different algorithms and compute their complexity to expose tradeoffs in complexity overhead performance as compared with those of previous approaches.

In this paper, we use a stochastic geometry approach to quantify the ergodic rate of each user in an outdoor mm-wave ad hoc network. For a variety of use cases, it is reasonable to assume users will be clustered around a central point (e.g., WiFi hotspot or clusterhead) rather than uniformly distributed. Our results indicate that, in contrast to sub-6-GHz networks, clustered mm-wave ad hoc networks tolerate the increased interference because directional antenna arrays reduce the interference. For certain antenna array configurations and user densities, uncoordinated users within a cluster outperform TDMA. Additionally, we derive a scaling law for uniform mm-wave ad hoc networks and propose a heuristic scaling for clustered networks. The per user ergodic rate remains constant if mm-wave antenna arrays scale sub-linearly with the number of users for uniform networks or linearly in clustered networks as users are added to the cluster. Last, we compute expressions that quantify the loss in ergodic rate per user when alignment error occurs at the receiver and transmitter. Our results show that even relatively small errors in alignment can lead to significant ergodic rate reduction.

Millimeter-wave (mmWave) systems use directional beams to support high-rate data communications. Small misalignment between the transmit and receive beams (e.g., due to the mobility) can result in a significant drop of the received signal quality, especially in line-of-sight communication channels. In this paper, we propose and evaluate high-resolution angle tracking strategies for wideband mmWave systems with mobility. We custom design pairs of auxiliary beams as the tracking beams, and use them to capture the angle variations, toward which the steering directions of the data beams are adjusted. Different from conventional beam tracking designs, the proposed framework neither depends on the angle variation model nor requires an on-grid assumption. For practical implementation of the proposed methods, we examine the impact of the array calibration errors on the auxiliary beam pair design. Numerical results reveal that by employing the proposed methods, good angle tracking performance can be achieved under various antenna array configurations, channel models, and mobility conditions.

Millimeter-wave systems are characterized by the use of highly directional antennas and the presence of blockages, which significantly alter the path-loss and small-scale fading parameters. The received power of each interferer depends on the direction it points and whether it is line-of- sight (LOS), non-LOS (i.e., partially blocked), or completely blocked. While interferers that are sufficiently far away will almost certainly be completely blocked, a finite number of interferers in close proximity will be subject to random partial blockages. Previous attempts to characterize mmWave networks have made the simplifying assumption that all interferers within some radius, called the LOS ball, are unblocked, while interferers beyond that radius are non-LOS. However, compared to simulation results, the LOS ball assumption tends to overestimate outage. In this paper, we present an accurate yet tractable analysis of finite mmWave networks that dispenses with the LOS ball assumption. In the analysis, each interferer has a distribution that is selected randomly from several possibilities, each representing different blockage and directivity states. First, the exact outage probability is found for a finite network with interferers in fixed locations. Then, the spatially averaged outage probability is found by averaging over the interferer locations. While the focus is on device-to-device networks, the analysis is general enough to find applications outside of the present mmWave framework.

Hybrid architectures reduce power consumption for MIMO systems at millimeter wave (mmWave) with respect to all-digital solutions. Fast configuration of hybrid arrays is one of the main challenges to be solved to unlock the potential of mmWave in high mobility scenarios. Channel estimation is one of the means of obtaining the information needed to configure these arrays. Prior work on channel estimation at mmWave considers a frequency-flat narrowband channel model, although the the mmWave channel is intrinsically wideband and frequency-selective. In this paper, we design a frequency-domain algorithm that leverages the sparsity in the channel to estimate the frequency-selective mmWave channel. We evaluate this technique by simulation and study its complexity, comparing the trade-offs complexity-overhead-performance to those of previous approaches. We conclude that the proposed approach outperforms previous algorithms, providing a better performance at a lower computational cost.

Compressed beam-selection (CBS) exploits the limited scattering of the millimeter wave (mmWave) channel using compressed sensing and finds the best beam-pair with limited overhead. The CBS procedure can further benefit from the knowledge of some additional structure in the channel. As mmWave systems are envisioned to be deployed in conjunction with sub-6 GHz systems, we use the spatial information extracted at sub-6 GHz as out-of-band side information about the mmWave channel. In particular, we formulate beam-selection as a weighted sparse signal recovery problem, and obtain the weights using sub-6 GHz angular information. Furthermore, we formalize the notion of spatial congruence between sub-6 GHz and mmWave, and numerically evaluate the degree of spatial congruence necessary for the success of the proposed approach. The simulation results illustrate that the proposed approach reduces the training overhead of the CBS approach by 3×.

The IEEE 802.11ad waveform can be used for automotive radar by exploiting the Golay complementary sequences in the preamble of a frame. The performance of radar, however, is limited by the preamble structure. In this paper, we propose an adaptive preamble design that permits a trade-off between radar parameters' estimation accuracy and communication rate. To quantify this trade-off, we propose a minimum mean square error (MMSE) metric based on rate distortion theory. The simulation results demonstrate that by adapting the preamble structure, we can achieve decimeter-level range mean square error (MSE) per symbol duration and gigabit per second (Gbps) data rates simultaneously for a distance upto 280 m.

The high hardware complexity of a massive MIMO base station, which requires hundreds of radio chains, makes it challenging to build commercially. One way to reduce the hardware complexity and power consumption of the receiver is to lower the resolution of the analog-to-digital converters (ADCs). We derive an achievable rate for a massive MIMO system with arbitrary quantization and use this rate to show that ADCs with as low as 3 bits can be used without significant performance loss at spectral efficiencies around 3.5 bpcu per user, also under interference from stronger transmitters and with some imperfections in the automatic gain control.

This paper investigates the processing of delay and Doppler information with IEEE 802.11p OFDM signaling for multi-target detection. We study the feasibility of extending IEEE 802.11p short-range communication (DSRC) in vehicles to automotive radio detection and ranging (radar) functionality. By exploiting the unique structure of 802.11p OFDM packets over multiple subcarriers and multiple time-slots, we apply the estimation of signal parameters via rotational invariance technique (ESPRIT) for concurrent multi-target detection and range/velocity estimation. Numerical results show sub-0.2m accuracy in range estimation and sub-0.02m/s accuracy in velocity estimation with high probability.

Hybrid analog/digital precoding in the downlink of multiuser massive MIMO systems can reduce the number of RF chains hence reducing total cost and improving power efficiency. Having few RF chains, however, makes it difficult for a base station to acquire instantaneous channel state information across all antennas. We develop a hybrid technique that uses only long-term (slowly changing) channel statistics in computing the analog precoding matrix. The proposed analog precoder is designed to maximize signal-to-leakage-plus-noise ratio (SLNR) when combined with a baseband precoder. We also propose a constrained precoder design that reduces the effect of a hardware constraint where the analog precoders are realized with phase shifters.

Millimeter wave (mmWave) systems will likely employ large antennas at both the transmitter and receiver for directional beamforming. Hybrid analog/digital MIMO architectures have been proposed previously for leveraging both array gain and multiplexing gain, while reducing the power consumption in analog-to-digital converters. Channel knowledge is needed to design the hybrid precoders/combiners, which is difficult to obtain due to the large antenna arrays and the frequency selective nature of the channel. In this paper, we propose a sparse recovery based time-domain channel estimation technique for hybrid architecture based frequency selective mmWave systems. The proposed compressed sensing channel estimation algorithm is shown to provide good estimation error performance, while requiring small training overhead. The simulation results show that using multiple RF chains at the receiver and the transmitter further reduces the training overhead.

Millimeter wave (mmWave) cellular systems will enable gigabit-per-second data rates due to the large bandwidth available at mmWave frequencies. Thanks to the small wavelength corresponding to the mmWave frequencies, mmWave systems can benefit from exploiting large antenna arrays at both transmitter and receiver. Although highly directional beamforming has been envisioned to play a key role to realize sufficient link margin, it is also possible to use these large arrays in other ways. For example, a hybrid array architecture can be exploited to either cancel or null an interferer. In this paper we analyse the effect of interference cancellation in downlink mmWave communications. We exploit partial zero forcing (PZF) at the user in order to cancel the interference from a set of interfering base stations (BSs) and derive closed form expression for the probability of coverage. Simulation results show that as the density of base stations increases, interference mitigation through partial zero forcing enhances the probability of coverage implying the necessity of interference mitigation in dense mmWave networks.

We propose a new mixed-analog-to-digital convertor (mixed-ADC) architecture for cloud-RAN (C-RAN) systems. The RRH is equipped with a mixed-ADC pool that includes multiple ADC units with various resolutions. In this pool, the RRH selects the appropriate ADCs and connects the selected ADCs to each antenna to quantize the received signals, thereby each antenna can have a different resolution ADC. The quantized signals are sent to a centralized baseband unit (BBU) via a capacity limited fronthaul pipe. To maximize the spectral efficiency of the considered system in a single-user uplink phase, we formulate an optimization problem for ADC selection by exploiting an approximation of the generalized mutual information (GMI). Subsequently we propose a solution. The simulations show the improvement in the GMI by using the proposed ADC selection. Our major findings are: i) In a C-RAN with limited fronthaul capacity, the proposed mixed-ADC makes more efficient use of the fronthaul capacity. ii) In selecting ADCs, assigning a high resolution ADC to a strong channel is beneficial.

Channel estimation allows millimeter wave (mmWave) MIMO communication systems to design pre-coders and combiners under different objective functions. Hybrid MIMO architectures provide a good trade-off power consumption-performance at mmWave frequencies, but most of the prior work on channel estimation for these structures assumes a narrowband channel model. In this paper, we propose a sparse approach for frequency selective channel estimation for mmWave channels, assuming a hybrid architecture. Simulation results show that the estimation error is small and the computational complexity is kept low. Moreover, the algorithm requires less training overhead than competing approaches based on beam training.

Channel feedback is essential for frequency division duplex (FDD) massive multiple-input multiple-output (MIMO) systems to realize precoding and power allocation. Traditional codebooks for channel feedback, where the required number of feedback bits is proportional to the number of base station (BS) antennas, can not scale up with massive MIMO due to the large number of BS antennas. To solve this problem, in this paper, we propose an angle-of-departure (AoD) adaptive subspace codebook to reduce the codebook size and feedback overhead. Specifically, by leveraging the concept of angle coherence time, which implies that the path AoDs vary much slower than path gains, we propose an AoD-adaptive subspace codebook to quantize the channel vector in a more accurate way. We also provide performance analysis of the proposed AoD-adaptive subspace codebook, where we prove that the required number of feedback bits only scales linearly with the number of resolvable AoDs, which is much smaller than the number of BS antennas. This quantitative result is also verified by simulations.

Millimeter wave (mmWave) communication offers Gbps data transmission, which can support massive data sharing in vehicle-to-infrastructure (V2I) networks. In this paper, we analyze the blockage effects among different vehicles and coverage probability of a typical receiver, considering cross street BSs near urban intersections in a multi-lane mmWave vehicular network. First, a three-dimensional model of blockage among vehicles on different lanes is considered. Second, we compute the coverage probability considering the interference of cross street base stations. Incorporating the blockage effects, we derive an exact and semi closed-form expression of the cumulative distribution density (CDF) of the association link path gain. Then, a tight approximation of the coverage probability is computed. We provide numerical results to verify the accuracy of the analytic results. We demonstrate the effects of blockage and the cross street interference. Also, we compare coverage probability with different BSs intensities under various street settings. It is shown that in multi-lane V2I networks, blockage among vehicles is not significant. Also, deploying more BSs does not increase coverage probability efficiently in ultra-dense streets.

Millimeter wave (mmWave) could be a key technology to support high data rate demands for automated vehicles. MmWave needs array gain for the best performance, but this requires correctly pointing the beam, known as beam alignment. Dynamic blockages make beam alignment challenging in the vehicular setting. This paper proposes to leverage a vehicle's position along with past beam measurements to rank desirable pointing directions that can reduce the required beam training to a small set of pointing directions. The ranking is conducted using a learning-to-rank approach, which is a popular machine learning method used in recommender systems. The learning uses a kernel based model, and a new metric for evaluating ranked lists of pointing directions tailored to beam alignment is proposed. The proposed method provides a scalable framework for exploiting context information.

Massive MIMO, a building block of future 5G systems, is attractive for wireless information and energy transfer. This is largely due to its ability to focus energy towards desired spatial locations. In this paper, the overall energy efficiency of a wirelessly powered massive MIMO system is investigated where a multi-antenna base-station uses wireless energy transfer to charge single- antenna energy harvesting users on the downlink. The users exploit the harvested energy to transmit information to the base-station on the uplink. Using a scalable model for the circuit power consumption at the base-station, the energy efficiency performance (measured in bits/joule) of the overall system is characterized. A closed-form expression is derived for the energy efficiency- optimal downlink transmit power in terms of the key system parameters such as the number of base- station antennas and the number of users. Simulation results suggest that it is energy efficient to operate the system in the massive MIMO regime. As the number of antennas becomes large, increasing the transmit power as well as serving more users help improve the energy efficiency for moderate to large number of antennas.

Wireless communication is now moving to automotive systems. In the near future, vehicles and road side units (RSUs) will be equipped with various sensors such as radars, LIDARs, cameras, and GPSs to generate enormous sensor information. To enhance the safety and efficiency, they will share this raw sensor information each other through Gbps data rates using millimeter-wave (mmWave) communications. To realize this vision, two unfavorable attributes of mmWave communications should be addressed: 1) Overcoming blockage and 2) fast link configuration. One viable approach to addressing these issues is to exploit RSU equipped with many sensors. At a higher position such as at the top of the RSU, LoS links between vehicles and the RSU are more likely to be guaranteed as well as it facilitates the extraction of useful information for the fast link configuration such as the distances or the angles between vehicles and the RSU using the sensors. In this paper, we assume that from the sensors (aside from the relevant technology) the angular support information for the LoS link of each vehicle is known to the RSU in prior and we propose a fast link configuration method using the angular support information.

In this paper, we develop a framework for an innovative perceptive mobile (i.e. cellular) network that integrates sensing with communication, and supports new applications widely in transportation, surveillance and environmental sensing. Three types of sensing methods implemented in the base-stations are proposed, using either uplink or downlink multiuser communication signals. The required changes to system hardware and major technical challenges are briefly discussed. We also demonstrate the feasibility of estimating sensing parameters via developing a compressive sensing based scheme and providing simulation results to validate its effectiveness.

Beam-steering has great potentials for joint communications and sensing, which is becoming a demanding feature on many emerging platforms such as unmanned aerial vehicles and smart cars. Although beam-steering has been extensively studied for communications and radar sensing respectively, its application in the joint system is not straightforward due to different beamforming requirements by communications and sensing. In this paper, we propose a low-cost system framework which allows seamless operation of communications and sensing, using two small- size steerable analog antenna arrays. We provide system architecture, high-level protocols, detailed signal model, novel beamforming design and advanced 1D compressive sensing algorithms for joint communications and sensing. We also provide preliminary simulation results which validate the effectiveness of the proposed technique in resolving closely located objects.

Hybrid analog and digital precoding allows millimeter wave (mmWave) systems to achieve both array and multiplexing gain. The design of the hybrid precoders and combiners, though, is usually based on the knowledge of the channel. Prior work on mmWave channel estimation with hybrid architectures focused on narrowband channels. Since mmWave systems will be wideband with frequency selectivity, it is vital to develop channel estimation solutions for hybrid architectures-based wideband mmWave systems. In this paper, we develop a sparse formulation and compressed sensing-based solutions for the wideband mmWave channel estimation problem for hybrid architectures. First, we leverage the sparse structure of the frequency-selective mmWave channels and formulate the channel estimation problem as a sparse recovery in both time and frequency domains. Then, we propose explicit channel estimation techniques for purely time or frequency domains and for combined time/frequency domains. Our solutions are suitable for both single carrier-frequency domain equalization and orthogonal frequency-division multiplexing systems. Simulation results show that the proposed solutions achieve good channel estimation quality, while requiring small training overhead. Leveraging the hybrid architecture at the transceivers gives further improvement in estimation error performance and achievable rates.

This paper focuses on wireless powered 5G dense cellular networks, where base station (BS) delivers energy to user equipment (UE) via the microwave radiation in sub-6 GHz or millimeter wave (mmWave) frequency, and UE uses the harvested energy for uplink information transmission. By addressing the impacts of employing different numbers of antennas and bandwidths at lower and higher frequencies, we evaluate the amount of harvested energy and throughput in such networks. Based on the derived results, we obtain the required small cell density to achieve an expected level of harvested energy or throughput. Also, we obtain that when the ratio of the number of sub-6-GHz BSs to that of the mmWave BSs is lower than a given threshold, UE harvests more energy from an mmWave BS than a sub-6-GHz BS. We find how many mmWave small cells are needed to perform better than the sub-6-GHz small cells from the perspectives of harvested energy and throughput. Our results reveal that the amount of harvested energy from the mmWave tier can be comparable to the sub-6-GHz counterpart in the dense scenarios. For the same tier scale, mmWave tier can achieve higher throughput. Furthermore, the throughput gap between different mmWave frequencies increases with the mmWave BS density.

Wireless power transfer may enable remotely powered operation of low-energy devices in challenging environments such as tactical networks. Millimeter wave (mmWave) is a possible candidate for wireless power transfer, thanks to the use of directional antenna arrays. This letter presents a feasibility study of using mmWave for wireless power transfer in a large-scale network consisting of power beacons and energy harvesters. Using stochastic geometry, system performance is characterized while treating the network nodes as potential blockages to mmWave signals. A link-level metric (energy coverage probability) and a network-level metric (overall success probability) are considered. The former captures whether a typical energy transfer link provides the requisite energy, while the latter also takes the network load into account. Numerical simulations suggest that network densification helps improve the performance, despite an increase in the blockage density. For a given density, deploying an optimal fraction of nodes as power beacons maximizes the number of successful energy harvesters. Finally, mmWave outperforms lower frequency solutions despite blockages.

We provide a comprehensive overview of mathematical models and analytical techniques for millimeter wave (mmWave) cellular systems. The two fundamental physical differences from conventional sub-6-GHz cellular systems are: 1) vulnerability to blocking and 2) the need for significant directionality at the transmitter and/or receiver, which is achieved through the use of large antenna arrays of small individual elements. We overview and compare models for both of these factors, and present a baseline analytical approach based on stochastic geometry that allows the computation of the statistical distributions of the downlink signal-to-interference-plus-noise ratio (SINR) and also the per link data rate, which depends on the SINR as well as the average load. There are many implications of the models and analysis: 1) mmWave systems are significantly more noise-limited than at sub-6 GHz for most parameter configurations; 2) initial access is much more difficult in mmWave; 3) self-backhauling is more viable than in sub-6-GHz systems, which makes ultra-dense deployments more viable, but this leads to increasingly interference-limited behavior; and 4) in sharp contrast to sub-6-GHz systems cellular operators can mutually benefit by sharing their spectrum licenses despite the uncontrolled interference that results from doing so. We conclude by outlining several important extensions of the baseline model, many of which are promising avenues for future research.

Wirelessly powered communications will entail short packets due to naturally small payloads, low-latency requirements, and/or insufficient energy resources to support longer transmissions. In this paper, a wireless-powered communication system is investigated, where an energy harvesting transmitter, charged by power beacons via wireless energy transfer, attempts to communicate with a receiver over a noisy channel. Under a save-then-transmit protocol, the system performance is characterized using metrics, such as the energy supply probability at the transmitter, and the achievable rate at the receiver for the case of short packets. The analytical treatment is provided for two cases: a three-node setup with a single power beacon and a large-scale network with multiple power beacons. Leveraging finite-length information theory, tractable analytical expressions are derived for the considered metrics in terms of the harvest blocklength, the transmit blocklength, the harvested power, the transmit power, and the network density. The analysis provides several useful design guidelines. Though using a small transmit power or a small transmit blocklength helps avoid energy outages, the consequently smaller signal-to-noise ratio or the fewer coding opportunities may cause a data decoding error. Scaling laws are derived to capture this inherent tradeoff between the harvest and transmit blocklengths. Numerical results reveal that power control is essential for improving the achievable rate of the considered system. The asymptotically optimal transmit power yields nearly optimal performance in the finite blocklength regime.

Energy harvesting is an emerging technology for enabling green, sustainable, and autonomous wireless networks. In this paper, a large-scale wireless network with energy harvesting transmitters is considered, where a group of transmitters forms a cluster to cooperatively serve a desired receiver amid interference and noise. To characterize the link-level performance, closed-form expressions are derived for the transmission success probability at a receiver in terms of key parameters such as node densities, energy harvesting parameters, channel parameters, and cluster size, for a given cluster geometry. The analysis is further extended to characterize a network-level performance metric, capturing the tradeoff between link quality and the fraction of receivers served. Numerical simulations validate the accuracy of the analytical model. Several useful insights are provided. For example, while more cooperation helps improve the link-level performance, the network-level performance might degrade with the cluster size. Numerical results show that a small cluster size (typically 3 or smaller) optimizes the network-level performance. Furthermore, substantial performance can be extracted with a relatively small energy buffer. Moreover, the utility of having a large energy buffer increases with the energy harvesting rate as well as with the cluster size in sufficiently dense networks.

We propose a mixed analog-to-digital converter ADC (mixed-ADC) structure for a cloud-RAN system, where a single-antenna user terminal communicates with a multi-antenna remote radio head (RRH). In the proposed structure, the RRH is equipped with a mixed-ADC pool that includes multiple ADC units with various resolutions. In this pool, the RRH selects the appropriate ADCs and connects the selected ADCs to each antenna to quantize the received signals; thereby each antenna can have a different resolution ADC. The fronthaul capacity is limited, so that the sum of the bits produced in the selected ADCs is also limited. To maximize the spectral efficiency or the energy efficiency of such a system, we propose algorithms for ADC resolution selection based on an approximation of the generalized mutual information in the low signal-to-noise regime. In the proposed algorithms, we show that for spectral efficiency, using high-resolution ADC on the strong channels is beneficial. The results for energy efficiency maximization are similar, though the largest resolutions are reduced to save power. The simulations show that the proposed method provides significant performance improvement.

Massive MIMO systems achieve high sum spectral efficiency by simultaneously serving large numbers of users. In time division duplexing systems, however, the reuse of uplink training pilots among cells results in channel estimation errors, which causes downlink inter-cell interference. Handling this interference is challenging due to the large channel dimensionality and the high complexity associated with implementing large precoding/combining matrices. In this paper, we propose multi-layer precoding to enable efficient and low-complexity operation in full-dimensional massive MIMO, where a large number of antennas are used in two dimensions. In multi-layer precoding, the precoding matrix of each base station is written as a product of a number of precoding matrices. Multi-layer precoding: 1) leverages the directional characteristics of large-scale MIMO channels to manage inter-cell interference with low channel knowledge requirements and 2) allows for an efficient implementation using hybrid analog/digital architectures. We present and analyze a specific multi-layer precoding design for full-dimensional massive MIMO systems. The asymptotic optimality of the proposed design is then proved for some special yet important channels. Numerical simulations verify the analytical results and illustrate the potential gains of multi-layer precoding compared with other multi-cell precoding solutions.

Enabling the high data rates of millimeter wave (mmWave) cellular systems requires deploying large antenna arrays at both the basestations and mobile users. Prior work on coverage and rate of mmWave cellular networks focused on the case when basestations and mobile beamforming vectors are predesigned for maximum beamforming gains. Designing beamforming/combining vectors, though, requires training, which may impact both the SINR coverage and rate of mmWave systems. This paper evaluates mmWave cellular network performance while accounting for the beam training/association overhead. First, a model for the initial beam association is developed based on beam sweeping and downlink control pilot reuse. To incorporate the impact of beam training, a new metric, called the effective reliable rate, is defined and adopted. Using stochastic geometry, the effective rate of mmWave cellular networks is derived for two special cases: near-orthogonal pilots and full pilot reuse. Analytical and simulation results provide insights into the answers of two important questions. First, what is the impact of beam association on mmWave network performance? Then, should orthogonal or reused pilots be employed? The results show that unless the employed beams are very wide, initial beam training with full pilot reuse is nearly as good as perfect beam alignment.

This paper investigates the feasibility of mmWave frequencies for personal networks of wireless wearable devices in enclosed settings (e.g., commuter trains, subways, airplanes, airports, or offices). At these frequencies, specular reflections off surfaces are expected to contribute to the capture of intended signal power and, simultaneously, to aggravate the interference at the receivers. Meanwhile, blockages by obstacles and people-including the individuals wearing the devices-are expected to shield receivers from interference. With the aid of stochastic geometry and random shape theory, we assess the interplay of surface reflections and blockages for dense deployments of wearable networks equipped with directional antenna arrays in relevant indoor settings.

Hybrid analog/digital architectures and receivers with low-resolution analog-to-digital converters (ADCs) are two low power solutions for wireless systems with large antenna arrays, such as millimeter wave and massive multiple-input multiple-output systems. Most prior work represents two extreme cases in which either a small number of radio frequency (RF) chains with full-resolution ADCs, or low-resolution ADC with a number of RF chains equal to the number of antennas is assumed. In this paper, a generalized hybrid architecture with a small number of RF chains and a finite number of ADC bits is proposed. For this architecture, achievable rates with channel inversion and singular value decomposition-based transmission methods are derived. Results show that the achievable rate is comparable to that obtained by full-precision ADC receivers at low and medium SNRs. A trade-off between the achievable rate and power consumption for the different numbers of bits and RF chains is devised. This enables us to draw some conclusions on the number of ADC bits needed to maximize the system energy efficiency. Numerical simulations show that coarse ADC quantization is optimal under various system configurations. This means that hybrid combining with coarse quantization achieves better energy-rate trade-off compared with both hybrid combining with full-resolutions ADCs and 1-bit ADC combining.

Analog-to-digital converters (ADCs) stand for a significant part of the total power consumption in a massive multiple-input multiple-output (MIMO) base station. One-bit ADCs are one way to reduce power consumption. This paper presents an analysis of the spectral efficiency of single-carrier and orthogonal-frequency-division-multiplexing (OFDM) transmission in massive MIMO systems that use one-bit ADCs. A closed-form achievable rate, i.e., a lower bound on capacity, is derived for a wideband system with a large number of channel taps that employ low-complexity linear channel estimation and symbol detection. Quantization results in two types of error in the symbol detection. The circularly symmetric error becomes Gaussian in massive MIMO and vanishes as the number of antennas grows. The amplitude distortion, which severely degrades the performance of OFDM, is caused by variations between symbol durations in received interference energy. As the number of channel taps grows, the amplitude distortion vanishes and OFDM has the same performance as single-carrier transmission. A main conclusion of this paper is that wideband massive MIMO systems work well with one-bit ADCs.

Hybrid analog/digital precoding architectures can address the tradeoff between achievable spectral efficiency and power consumption in large-scale MIMO systems. This makes them a promising candidate for millimeter wave systems, which deploy large antenna arrays at both the transmitter and the receiver to guarantee sufficient received signal power. Most prior work on hybrid precoding focused on narrowband channels and assumed fully connected hybrid architectures. Millimeter wave (mmWave) systems, though, are expected to be wideband with frequency selectivity. In this paper, a closed-form solution for fully connected OFDM-based hybrid analog/digital precoding is developed for frequency selective mmWave systems. This solution is then extended to partially connected but fixed architectures in which each RF chain is connected to a specific subset of the antennas. The derived solutions give insights into how the hybrid subarray structures should be designed. Based on this, a novel technique that dynamically constructs the hybrid subarrays knowing the long-term channel characteristics is developed. Simulation results show that the proposed hybrid precoding solutions achieve spectral efficiencies close to that obtained with fully digital architectures in wideband mmWave channels. Furthermore, the results indicate that the developed dynamic subarray solution outperforms the fixed hybrid subarray structures in various system and channel conditions.

We consider the problem of estimating the frame error rate (FER) of a given memoryless binary symmetric channel by observing the success or failure of transmitted packets. Whereas FER estimation is relatively straightforward if all observations correspond to packets with equal length, the problem becomes considerably more complex when this is not the case. We develop FER estimators when transmissions of different lengths are observed, together with the Cramer-Rao lower bound (CRLB). Although the main focus is on maximum likelihood (ML) estimation, we also obtain low-complexity schemes performing close to optimal in some scenarios. In a second stage, we consider the case in which FER estimation is performed at a node different from the receiver, and incorporate the impairment of unreliable observations by considering noisy ACK/NAK feedback links. The impact of unreliable feedback is analyzed by means of the corresponding CRLB. In this setting, the ML estimator is obtained by applying the expectation-maximization algorithm to jointly estimate the error probabilities of the data and feedback links. Simulation results illustrate the benefits of the proposed estimators.

Channel estimation is of critical importance in millimeter-wave (mmWave) multiple-input multiple-output (MIMO) systems. Due to the use of large antenna arrays, low-complexity mmWave specific channel estimation algorithms are required. In this paper, an auxiliary beam pair design is proposed to provide high-resolution estimates of the channel's angle-of-departure (AoD) and angle-of-arrival (AoA) for mmWave MIMO systems. By performing an amplitude comparison with respect to each auxiliary beam pair, a set of ratio measures that characterize the channel's AoD and AoA are obtained by the receiver. Either the best ratio measure or the estimated AoD is quantized and fed back to the transmitter via a feedback channel. The proposed technique can be incorporated into control channel design to minimize initial access delay. Though the design principles are derived assuming a high-power regime, evaluation under more realistic assumption shows that by employing the proposed method, good angle estimation performance is achieved under various signal-to-noise ratio levels and channel conditions.

In this paper, a novel two-dimensional super-resolution angle-of-departure (AoD) and angle-of-arrival (AoA) estimation technique is proposed for wideband millimeter-wave multiple-input multiple-output systems with dual-polarized antenna elements. The key ingredient of the proposed method is the custom designed beam pairs, from which there exists an invertible function of the AoD/AoA. A new multi-layer reference signal structure is developed for the proposed method to facilitate angle estimation for wideband channels with dual-polarized antenna elements. To reduce feedback in closed-loop frequency division duplexing systems, a novel differential feedback strategy is proposed to feedback the estimated angle pairs. Numerical results demonstrate that good azimuth/elevation AoD and AoA estimation performance can be achieved under different levels of signal-to-noise ratio, channel conditions, and antenna array configurations.

Wireless networks with directional antennas, like millimeter wave (mmWave) networks, have enhanced security. For a large-scale mmWave ad hoc network in which eavesdroppers are randomly located, however, eavesdroppers can still intercept the confidential messages, since they may reside in the signal beam. This paper explores the potential of physical layer security in mmWave ad hoc networks. Specifically, we characterize the impact of mmWave channel characteristics, random blockages, and antenna gains on the secrecy performance. For the special case of uniform linear array (ULA), a tractable approach is proposed to evaluate the average achievable secrecy rate. We also characterize the impact of artificial noise in such networks. Our results reveal that in the low transmit power regime, the use of low mmWave frequency achieves better secrecy performance, and when increasing transmit power, a transition from low mmWave frequency to high mmWave frequency is demanded for obtaining a higher secrecy rate. More antennas at the transmitting nodes are needed to decrease the antenna gain obtained by the eavesdroppers when using ULA. Eavesdroppers can intercept more information by using a wide beam pattern. Furthermore, the use of artificial noise may be ineffective for enhancing the secrecy rate.

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.

Sharing the spectrum among multiple operators seems promising in millimeter wave (mmWave) systems. One explanation is the highly directional transmission in mmWave, which reduces the interference caused by one network on the other networks sharing the same resources. In this paper, we model a mmWave cellular system where an operator that primarily owns an exclusive-use license of a certain band can sell a restricted secondary license of the same band to another operator. This secondary network has a restriction on the maximum interference it can cause to the original network. Using stochastic geometry, we derive expressions for the coverage and rate of both networks, and establish the feasibility of secondary licensing in licensed mmWave bands. Results show that the restricted secondary operator can achieve good coverage with a small impact on the original operator. Our results also illustrate that the spectrum sharing gains increase with narrow beams and when the network densifies.

This paper considers the design of hybrid precoders and combiners for mmWave MIMO systems with per-antenna power constraints and the additional limitations introduced by the phase-shifting network in the analog processing stage. Previous hybrid designs were obtained using a total power constraint, but in practical implementations per-antenna constraints are more realistic, specially at mmWave, given the large number of power amplifiers used in the transmit array. Assuming perfect channel knowledge, we obtain first an approximation to the all-digital solution for the precoder and the combiner given the per-antenna constraints. Then, we develop a new method for the design of the hybrid precoder and combiner which attempts to match such all- digital approximation. Simulation results show the effectiveness of the proposed approach, which performs close to the all-digital solution.

Recent advancements in manufacturing and favorable regulatory positions make millimeter wave (mmWave) a target for next-generation wireless networks. For a variety of use-cases, it is reasonable to assume users will be clustered around a central point (e.g. WiFi hotspot) rather than uniformly located. In this paper, we develop analytic expressions for the capacity of clustered mmWave ad hoc networks under two channel access assumptions: uncoordinated channel access (UCA) and TDMA. We show that with directional antenna arrays UCA can outperform TDMA in terms of per cluster capacity if the cluster density is not too large. We define and develop a method for characterizing the balance between the intra-cluster coverage with the inter-cluster coverage. For small cluster sizes, the required inter-cluster distance severely impacts the inter-cluster communication rate.

In this paper, auxiliary beam pair design is developed to provide high-resolution estimates of channel's elevation/azimuth angle-of-departure (AoD) and angle-of-arrival (AoA) for millimeter- wave full-dimension MIMO (FD-MIMO) systems. Pairs of custom designed analog beams are formed at both the transmitter and receiver to help acquire channel information. It is shown via simulation results that by employing the proposed method, promising elevation/azimuth AoD and AoA estimation performance can be achieved under various signal- to-noise ratio levels and channel conditions.

Wireless networks with directional antennas, like millimeter wave (mmWave) networks, have enhanced security. For a large scale mmWave ad hoc network in which eavesdroppers are randomly located, however, eavesdroppers can still intercept the confidential messages, since they may reside in the signal beam. This paper explores the potential of physical layer security in the mmWave ad hoc networks. Specifically, we characterize the impact of mmWave channel characteristics and large antenna arrays on the secrecy performance. We also characterize the impact of artificial noise in this networks. Our results reveal that in the low transmit power regime, the use of low mmWave frequency achieves better secrecy performance, when increasing transmit power, a transition from low mmWave frequency to high mmWave frequency is demanded for obtaining more secrecy rate. Eavesdroppers can intercept more information by using wide beam pattern. Furthermore, the use of artificial noise may be unable to enhance the secrecy rate for the case of low node density.

Hybrid analog/digital precoding allows millimeter wave MIMO systems to leverage large antenna array gains while permitting low cost and power consumption hardware. Most prior work has focused on hybrid precoding for narrow-band mmWave systems. MmWave systems, however, will likely operate on wideband channels with frequency selectivity. Therefore, this paper considers frequency selective hybrid precoding with RF beamforming vectors taken from a quantized codebook. For this system, a low-complexity yet near-optimal greedy algorithm is developed for the design of the hybrid analog/digital precoders. The proposed algorithm greedily selects the RF beamforming vectors using Gram-Schmidt orthogonalization. Simulation results show that the developed precoding design algorithm achieves very good performance compared with the unconstrained solutions while requiring less complexity.

We investigate the performance of wideband massive MIMO base stations that use one-bit ADCs for quantizing the uplink signal. Our main result is to show that the many taps of the frequency-selective channel make linear combiners asymptotically consistent and the quantization noise additive and Gaussian, which simplifies signal processing and enables the straightforward use of OFDM. We also find that single-carrier systems and OFDM systems are affected in the same way by one-bit quantizers in wideband systems because the distribution of the quantization noise becomes the same in both systems as the number of channel taps grows.

This paper addresses the problem of designing thinned arrays with minimized side lobe levels for antenna selection in millimeter wave MIMO systems. We propose a new optimization solution based on compressed sensing techniques and convex optimization relaxation which we show to be a heuristic that solves the original binary optimization problem of side lobe level minimization. We compare the proposed method with other approaches from the literature like simulated annealing and genetic algorithms showing the superiority of the method in terms of performance, running time and ease of parameter tuning. The simulation results cover a wide range of dimensions and situations.

In this paper we analyze the mean squared error (MSE) for one-bit compressed sensing schemes based on measurement matrices that correspond to unit norm tight frames. We show that, as in the unquantized case, sensing with unit norm tight frames improves the MSE in the reconstruction of sparse vectors from one-bit measurements using l\ and thresholding algorithms. From our analytical and experimental results we conclude that when implementing one-bit compressed sensing schemes with fixed measurement matrices unit norm tight frames are the measurements of choice.

Auxiliary beam pairs are proposed in millimeter-wave cellular systems for closed-loop hybrid precoding. Pairs of custom designed analog beams are formed to help acquire channel information. It is shown via simulations that auxiliary beam pairs have lower complexity and better achievable rates with a moderate amount of feedback, compared to conventional beam training methods.

In conventional applications of time division duplex (TDD) in cellular systems, the time resource split between uplink (UL) and downlink (DL) is fixed across all base stations (BSs) in the network. This leads to under utilization of BS resources when there is a mismatch between the expected and experienced UL/DL traffic in a given cell. A dynamic split that varies in each cell is desirable, but is challenging due to the high interference experienced by UL receivers in one cell from DL transmissions in adjacent cells. This paper analyzes the performance of UL users in dynamic TDD enabled next generation cellular networks using a stochastic geometry framework. The analysis highlights the trade-off between spectral efficiency and resource utilization for dynamic TDD. With appropriate interference mitigation, dynamic TDD offers a significant gain in data rates as compared to static TDD, which is higher when the BSs are lightly loaded and/or the fraction of UL users is low.

Beam alignment is a source of overhead in mobile millimeter wave communication systems due to the need for frequent repointing. Beam switching architectures can reduce the amount of repointing required by leveraging position prediction. This paper presents an optimization of beam design in terms of rate. We consider a non-congested two-lane highway scenario where road side units are installed on lighting poles. Under this scenario, line-of-sight to the road side unit is very likely and vehicle speed does not vary much. We formulate and solve numerically using a gradient descent method for an optimal beam design to maximize the data rate for non-overlap beams. The result shows close performance to the equal coverage beam design. We study the effect of the overlap on the average rate and outage and compare the equal coverage with the equal beamwidth design. Numerical examples show that the equal coverage design can achieve up to 1.5× the rate of the equal beamwidth design confirming the importance of the choice of beam design.

Simultaneous use of high-end wearable wireless devices like smart glasses is challenging in a dense indoor environment due to the high nature of interference. In this scenario, the millimeter wave (mmWave) band offers promising potential for achieving gigabits per second throughput. Here we propose a novel system model for analyzing system performance of mmWave based communication among wearables. The proposed model accounts for the non-isotropy of the indoor environment and the effects of reflections that are predominant for indoor mmWave signals. The effect of human body blockages are modeled and the system performance is shown to hugely vary depending on the user location, body orientation and the density of the network. Closed form expressions for spatially averaged signal to interference plus noise ratio distribution are also derived as a function of the location and orientation of a reference user.

We consider a multi-antenna downlink cellular network using either single-user maximal ratio transmission (MRT) or multi-user zero-forcing (ZF) transmission. The locations of the base stations are modeled by a Poisson point process to allow the inter-cell interference to be tractably analyzed. A tight lower bound on the optimum number of feedback bits maximizing the net spectral efficiency is derived, whereby the cost of feedback sent via uplink is subtracted from the corresponding gain in downlink spectral efficiency. When using MRT, the optimum number of feedback bits is shown to scale linearly with the number of antennas, and logarithmically with the channel coherence time. With ZF, the optimum amount of feedback scales the same as with MRT, but additionally also increases linearly with the pathloss exponent.

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.

Beam alignment is an important component of millimeter wave communication systems, which use analog beam steering.We analyze the performance of beam alignment methods based on the hierarchical beam sweeping that are adopted in IEEE 802.11ad and IEEE 802.15.3c. Previous work studies misalignment probability due to thermal noise or antenna gain fluctuation alone. At the early stage of the alignment process, however, both effects are important. We incorporate both effects and derive probability of misalignment not only with respect to the best path but also to secondary ones. Our analysis can capture the misalignment severity in terms of power loss with respect to the perfect alignment case. Numerical examples show that line-of-sight channels are more constrained by the thermal noise, while the non- line-of-sight channels are more constrained by the antenna gain fluctuations.

Millimeter wave (mmWave) networks are sensitive to blockages due to buildings in urban areas. This is critical for vehicle-to-infrastructure networks which are cellular networks designed to support emerging vehicular applications. Motivated by measurement and ray tracing results in urban microcells, instead of characterizing the pathloss by Euclidean distance, we calculate it by the weighted sum of segment length along the propagation path, i.e., Manhattan distance, and a certain corner loss at the intersections along the path. We analyze network performance by modeling the urban microcell network by a Manhattan Poisson line process. Our results show significant differences between Manhattan and Euclidean distance- based pathloss models. Assuming the receiver is associated with the base station (BS) with the smallest pathloss, we derive closed-form expression of the distribution of the associated link pathloss. We obtain the coverage probability and reveal the impacts of interference from the LOS and NLOS BSs. It is shown that in this scenario the interference from a NLOS parallel street is negligible.

Millimeter wave (mmWave) MIMO will likely use hybrid analog and digital precoding, which uses a small number of RF chains to reduce the energy consumption associated with mixed signal components like analog-to-digital components not to mention baseband processing complexity. However, most hybrid precoding techniques consider a fully connected architecture requiring a large number of phase shifters, which is also energy-intensive. In this paper, we focus on the more energy-efficient hybrid precoding with subconnected architecture, and propose a successive interference cancelation (SIC)-based hybrid precoding with near-optimal performance and low complexity. Inspired by the idea of SIC for multiuser signal detection, we first propose to decompose the total achievable rate optimization problem with nonconvex constraints into a series of simple subrate optimization problems, each of which only considers one subantenna array. Then, we prove that maximizing the achievable subrate of each subantenna array is equivalent to simply seeking a precoding vector sufficiently close (in terms of Euclidean distance) to the unconstrained optimal solution. Finally, we propose a low-complexity algorithm to realize SIC-based hybrid precoding, which can avoid the need for the singular value decomposition (SVD) and matrix inversion. Complexity evaluation shows that the complexity of SIC-based hybrid precoding is only about 10% as complex as that of the recently proposed spatially sparse precoding in typical mmWave MIMO systems. Simulation results verify that SIC-based hybrid precoding is near-optimal and enjoys higher energy efficiency than the spatially sparse precoding and the fully digital precoding.

Sharing the spectrum among multiple operators seems promising in millimeter wave (mmWave) systems. One explanation is the highly directional transmission in mmWave, which reduces the interference caused by one network on the other networks sharing the same resources. In this paper, we model a mmWave cellular system, where an operator that primarily owns an exclusive-use license of a certain band can sell a restricted secondary license of the same band to another operator. This secondary network has a restriction on the maximum interference it can cause to the original network. Using stochastic geometry, we derive expressions for the coverage and the rate of both networks, and establish the feasibility of secondary licensing in licensed mmWave bands. To explain economic tradeoffs, we consider a revenue-pricing model for both operators in the presence of a central licensing authority. Our results show that the original operator and central network authority can benefit from secondary licensing when the maximum interference threshold is properly adjusted. This means that the original operator and central licensing authority have an incentive to permit a secondary network to restrictively share the spectrum. Our results also illustrate that the spectrum sharing gains increase with narrow beams and when the network densifies.

Hybrid analog/digital precoding offers a compromise between hardware complexity and system performance in millimeter wave (mmWave) systems. This type of precoding allows mmWave systems to leverage large antenna array gains that are necessary for sufficient link margin, while permitting low cost and power consumption hardware. Most prior work has focused on hybrid precoding for narrow-band mmWave systems, with perfect or estimated channel knowledge at the transmitter. MmWave systems, however, will likely operate on wideband channels with frequency selectivity. Therefore, this paper considers wideband mmWave systems with a limited feedback channel between the transmitter and receiver. First, the optimal hybrid precoding design for a given RF codebook is derived. This provides a benchmark for any other heuristic algorithm and gives useful insights into codebook designs. Second, efficient hybrid analog/digital codebooks are developed for spatial multiplexing in wideband mmWave systems. Finally, a low-complexity yet near-optimal greedy frequency selective hybrid precoding algorithm is proposed based on Gram-Schmidt orthogonalization. Simulation results show that the developed hybrid codebooks and precoder designs achieve very-good performance compared with the unconstrained solutions while requiring much less complexity.

This paper proposes a stochastic geometry framework to analyze the signal-to-noise-and-interference ratio (SINR) and rate performance in a large-scale uplink massive multiple-input and multiple-output (MIMO) network. Based on the model, expressions are derived for spatial average SINR distributions over user and base station distributions with maximum ratio combining (MRC) and zero-forcing (ZF) receivers. We show that, using massive MIMO, the uplink SINR in certain urban marcocell scenarios is limited by interference. In the interference-limited regime, the results reveal that for MRC receivers, a superlinear (polynomial) scaling law between the number of base station antennas and scheduled users per cell preserves the uplink signal-to-interference ratio (SIR) distribution, while a linear scaling applies to ZF receivers. ZF receivers are shown to outperform MRC receivers in the SIR coverage, and the performance gap is quantified in terms of the difference in the number of antennas to achieve the same SIR distribution. Numerical results verify the analysis. It is found that the optimal compensation fraction in fractional power control to optimize rate is generally different for MRC and ZF receivers. Besides, simulations show that the scaling results derived from the proposed framework apply to the networks, where base stations are distributed according to a hexagonal lattice.

In massive multiple-input multiple-output (MIMO) systems, it may not be power efficient to have a pair of high-resolution analog-to-digital converters (ADCs) for each antenna element. In this paper, a near maximum likelihood (nML) detector for uplink multiuser massive MIMO systems is proposed where each antenna is connected to a pair of one-bit ADCs, i.e., one for each real and imaginary component of the baseband signal. The exhaustive search over all the possible transmitted vectors required in the original maximum likelihood (ML) detection problem is relaxed to formulate an ML estimation problem. Then, the ML estimation problem is converted into a convex optimization problem which can be efficiently solved. Using the solution, the base station can perform simple symbol-by-symbol detection for the transmitted signals from multiple users. To further improve detection performance, we also develop a two-stage nML detector that exploits the structures of both the original ML and the proposed (one-stage) nML detectors. Numerical results show that the proposed nML detectors are efficient enough to simultaneously support multiple uplink users adopting higher-order constellations, e.g., 16 quadrature amplitude modulation. Since our detectors exploit the channel state information as part of the detection, an ML channel estimation technique with one-bit ADCs that shares the same structure with our proposed nML detector is also developed. The proposed detectors and channel estimator provide a complete low power solution for the uplink of a massive MIMO system.

The highly directional and adaptive antennas used in mmWave communication open up the possibility of uncoordinated sharing of spectrum licenses between commercial cellular operators. There are several advantages to sharing including a reduction in license costs and an increase in spectrum utilization. In this paper, we establish the theoretical feasibility of spectrum license sharing among mmWave cellular operators. We consider a heterogeneous multi-operator system containing multiple independent cellular networks, each owned by an operator. We then compute the signal-to-interference-and-noise ratio and rate distribution for downlink mobile users of each network. Using the analysis, we compare the systems with fully shared licenses and exclusive licenses for different access rules and explore the trade-offs between system performance and spectrum cost. We show that sharing spectrum licenses increases the per-user rate when antennas have narrow beams and is also favored when there is a low density of users. We also consider a multi-operator system where BSs of all the networks are co-located to show that the simultaneous sharing of spectrum and infrastructure is also feasible. We show that all networks can share licenses with less bandwidth and still achieve the same per-user median rate as if they each had an exclusive license to spectrum with more bandwidth.

This paper proposes a method for designing base station (BS) clusters and cluster patterns for pair-wise BS coordination. The key idea is that each BS cluster is formed by using the second-order Voronoi region, and the BS clusters are assigned to a specific cluster pattern by using edge-coloring for a graph drawn by Delaunay triangulation. The main advantage of the proposed method is that the BS selection conflict problem is prevented, while users are guaranteed to communicate with their two closest BSs in any irregular BS topology. With the proposed coordination method, analytical expressions for the rate distribution and the ergodic spectral efficiency are derived as a function of relevant system parameters in a fixed irregular network model. In a random network model with a homogeneous Poisson point process, a lower bound on the ergodic spectral efficiency is characterized. Through system level simulations, the performance of the proposed method is compared with that of conventional coordination methods: dynamic clustering and static clustering. Our major finding is that, when users are dense enough in a network, the proposed method provides the same level of coordination benefit with dynamic clustering to edge users.

Millimeter wave (mmWave) communication provides high data rates thanks to large arrays at the transmitter and receiver, coupled with large bandwidth channels. Exploiting the arrays is challenging due to the need to configure precoding at the transmitter based on the large frequency selective channel. In this paper, we exploit the power iteration principle and propose a robust analog precoding training algorithm that can be applied in both frequency division duplex transmission systems and time division duplex transmission systems with or without RF calibration. We further analyze the convergence of the proposed algorithm and show how it converges to the singular value decomposition optimality exponentially. We propose null space projection on top of the power iteration to form multiple orthogonal beams at the transmitter and receiver. Strongest tap selection with proper energy pruning is used to collect as much precoding gain as possible from a frequency selective fading channel. The exponential effective SINR mapping performance is evaluated and demonstrates that the overall approach works smoothly. Numerical simulation results demonstrate algorithm robustness and the algorithm works not only for the simplified mmWave directional channels, but also for more general rich scattering channels.

This paper considers large random wireless networks, where transmit-and-receive node pairs communicate within a certain range while sharing a common spectrum. By modeling the spatial locations of nodes as a Poisson point process, analytical expressions for the ergodic spectral efficiency of a typical node pair are derived as a function of the channel state information available at a receiver (CSIR) in terms of relevant system parameters: the density of communication links, the number of receive antennas, the path loss exponent, and the operating signal-to-noise ratio. One key finding is that when the receiver only exploits CSIR for the direct link, the sum spectral efficiency increases linearly with the density, provided the number of receive antennas increases as a certain superlinear function of the density. When each receiver exploits CSIR for a set of dominant interfering links in addition to that of the direct link, the sum spectral efficiency increases linearly with both the density and the path loss exponent if the number of antennas is a linear function of the density. This observation demonstrates that having CSIR for dominant interfering links provides an order gain in the scaling law. It is also shown that this linear scaling holds for direct CSIR when incorporating the effect of the receive antenna correlation, provided that the rank of the spatial correlation matrix scales superlinearly with the density. These scaling laws are derived from integral representations of the distribution of the signal to interference and noise ratio, which are of independent interest and which in turn derived from stochastic geometry and more precisely from the theory of shot noise fields. Simulation results back the scaling laws and the integral representations.

The millimeter wave (mmWave) band, a prime candidate for 5G cellular networks, seems attractive for wireless energy harvesting since it will feature large antenna arrays and extremely dense base station (BS) deployments. The viability of mmWave for energy harvesting though is unclear, due to the differences in propagation characteristics, such as extreme sensitivity to building blockages. This paper considers a scenario where low-power devices extract energy and/or information from the mmWave signals. Using stochastic geometry, analytical expressions are derived for the energy coverage probability, the average harvested power, and the overall (energy-and-information) coverage probability at a typical wireless-powered device in terms of the BS density, the antenna geometry parameters, and the channel parameters. Numerical results reveal several network and device level design insights. At the BSs, optimizing the antenna geometry parameters, such as beamwidth, can maximize the network-wide energy coverage for a given user population. At the device level, the performance can be substantially improved by optimally splitting the received signal for energy and information extraction, and by deploying multi-antenna arrays. For the latter, an efficient low-power multi-antenna mmWave receiver architecture is proposed for simultaneous energy and information transfer. Overall, simulation results suggest that mmWave energy harvesting generally outperforms lower frequency solutions.

Device-to-device (D2D) communication allows serving local wireless traffic bypassing the system's infrastructure. One way to control the interference in D2D networks is to carefully channelize transmissions. This paper presents an analytical characterization of ITLinQ, one of the principal D2D channelization schemes proposed to date. Recognizing that it captures well the spatial characteristics of D2D networks, a stochastic geometry setting is utilized for this analysis. The derived expressions enable gleaning insights on how ITLinQ avoids situations of excessive interference, and they facilitate optimizing the controllable parameters of ITLinQ so as to maximize the system spectral efficiency (bits/s/Hz per unit area). With the parameters thus optimized, the ultimate performance of ITLinQ can be evaluated with respect to other D2D channel allocation schemes. In particular, performance evaluation comparisons with the FlashLinQ scheme are provided, and the gains with respect to an unchannelized network are quantified.

We propose a low complexity antenna selection algorithm for low target rate users in cloud radio access networks. The algorithm consists of two phases. In the first phase, each remote radio head (RRH) determines whether to be included in a candidate set by using a predefined selection threshold. In the second phase, RRHs are randomly selected within the candidate set made in the first phase. To analyze the performance of the proposed algorithm, we model RRHs' and users' locations by a homogeneous Poisson point process, whereby the signal-to-interference ratio (SIR) complementary cumulative distribution function is derived. By approximating the derived expression, an approximate optimum selection threshold that maximizes the SIR coverage probability is obtained. Using the obtained threshold, we characterize the performance of the algorithm in an asymptotic regime, where the RRH density goes to infinity. The obtained threshold is then modified depending on various algorithm options. A distinguishable feature of the proposed algorithm is that the algorithm complexity keeps constant independent to the RRH density, so that a user is able to connect to a network without heavy computation at baseband units.

We consider a downlink cellular network where multi-antenna base stations (BSs) transmit data to single-antenna users by using one of two linear precoding methods with limited feedback: 1) maximum ratio transmission (MRT) for serving a single user or 2) zero forcing (ZF) for serving multiple users. The BS and user locations are drawn from a Poisson point process, allowing expressions for the signal-to-interference coverage probability and the ergodic spectral efficiency to be derived as a function of system parameters, such as the number of BS antennas and feedback bits, and the pathloss exponent. We find a tight lower bound on the optimum number of feedback bits to maximize the net spectral efficiency, which captures the overall system gain by considering both of downlink and uplink spectral efficiency using limited feedback. Our main finding is that, when using MRT, the optimum number of feedback bits scales linearly with the number of antennas, and logarithmically with the channel coherence time. When using ZF, the feedback scales in the same ways as MRT, but also linearly with the pathloss exponent. The derived results provide system-level insights into the preferred channel codebook size by averaging the effects of short-term fading and long-term pathloss.

Millimeter communication systems use large antenna arrays to provide good average received power and to take advantage of multi-stream MIMO communication. Unfortunately, due to power consumption in the analog front-end, it is impractical to perform beamforming and fully digital precoding at baseband. Hybrid precoding/combining architectures have been proposed to overcome this limitation. The hybrid structure splits the MIMO processing between the digital and analog domains, while keeping the performance close to that of the fully digital solution. In this paper, we introduce and analyze several algorithms that efficiently design hybrid precoders and combiners starting from the known optimum digital precoder/combiner, which can be computed when perfect channel state information is available. We propose several low complexity solutions which provide different trade-offs between performance and complexity. We show that the proposed iterative solutions perform better in terms of spectral efficiency and/or are faster than previous methods in the literature. All of them provide designs which perform close to the known optimal digital solution. Finally, we study the effects of quantizing the analog component of the hybrid design and show that even with coarse quantization, the average rate performance is good.

In this paper, we investigate the performance of typical indoor users in urban two-tier heterogeneous mobile networks with indoor-deployed small cell base stations (BSs) and outdoor BSs. The urban topology is modeled by a Boolean scheme of blockages, which is tied together with the deployment of outdoor and indoor BSs as well as the signal propagation characteristics. Each building is assumed to be served by an indoor small cell BS with a certain probability, which is denoted as occupation probability. Indoor and outdoor environments are partitioned by walls with a certain penetration loss. We separately account for outdoor BSs in line of sight (LOS) and non-line of sight (NLOS), as well as small cell BSs in neighboring and non-neighboring buildings. We derive expressions for the signal to interference ratio (SIR)-coverage probability and the average spectral efficiency of a typical indoor user. Numerical evaluations are performed with 3GPP compliant macro- and pico-BS scenarios. Our results exhibit that the indoor area coverage, which is a function of the building density and size, significantly affects the impact of the target building's wall penetration loss. The analysis is simplified by various assumptions, which are verified by extensive Monte Carlo simulations.

Emerging applications involving device-to-device communication among wearable electronics require gigabits per second throughput, which can be achieved by utilizing millimeter-wave (mmWave) frequency bands. When many such communicating devices are indoors in close proximity, such as in a train, car, or airplane cabin, interference can be a serious impairment. This paper uses stochastic geometry to analyze the performance of mmWave networks with a finite number of interferers in a finite network region. Prior work considered either lower carrier frequencies with different antenna and channel assumptions, or a network with an infinite spatial extent. In this paper, human users not only carry potentially interfering devices, but also act to block interfering signals. Using a sequence of simplifying assumptions, accurate expressions for coverage and rate are developed that capture the effects of key antenna characteristics, such as directivity and gain, and are a function of the finite area and number of users. The assumptions are validated through a combination of analysis and simulation. The main conclusions are that mmWave frequencies can provide gigabits per second throughput even with omni-directional transceiver antennas, and larger, more directive antenna arrays give better system performance.

Massive multiple-input multiple-output (MIMO) is a potential physical layer technology for 5G cellular networks. This paper leverages stochastic geometry to derive the uplink signal-to- interference (SIR) distribution in massive MIMO networks. Based on the derived expressions, a scaling law between the number of base station antennas and scheduled users per cell is provided to preserve the uplink SIR distribution, where the impacts of correlation in small-scale fading and power control in the form of fractional path loss compensation are taken account. Numerical results verify the analysis, and show that fractional power control with a compensation fraction of 0.5 is nearly optimal for the average achievable rate in certain cases.

Massive multiple-input multiple-output (MIMO) with uniform planar arrays (UPAs), which is often referred to as full-dimension (FD) MIMO, is being strongly considered for future wireless communication standards. FD-MIMO can control transmit and receive beams in both the horizontal and vertical domains and fully exploit the large number of antennas of massive MIMO. It is widely accepted that Kronecker-product codebooks using a discrete Fourier transform (DFT) structure are suitable to quantize the downlink channel at the user for FD-MIMO systems relying on frequency division duplexing (FDD). In this paper, we numerically study the characteristics of FD-MIMO channels using a three-dimensional (3D) channel model that captures realistic channel properties. Based on the study, we identify a limitation of conventional Kronecker-product codebooks and propose advanced channel quantization techniques that can further improve channel quantization quality.

Static base-station clustering allows clustered transmitters to jointly serve a group of users and thus eliminate the intra-cluster interference. The network performance is then bottlenecked by the cluster-edge users. Semi-static clustering can help improve the performance along the cluster edges by time-sharing between different clustering patterns. We propose a simple clustering and user scheduling algorithm to gauge the performance gain of semi-static clustering. Under a stochastic geometry framework, we derive analytical expressions for the coverage and rate of a user at a given location. As the cluster size goes to infinity, we show that the outage probability of semi-static clustering decays at the same order as that of static clustering. Thus, in the asymptotic regime, the performance gain provided by semi- static clustering can be characterized by a linear factor. Numerical results demonstrate the gain of semi-static clustering in the non-asymptotic regime.

Wireless link adaptation configures the physical layer of the transmitter in the presence of dynamic channel conditions to maximize link performance under error rate reliability constraints. Typically, error rate reliability targets are defined externally and remain fixed, regardless of the physical layer configuration. In this paper, we allow link adaptation to define its own error rate reliability target. We derive a closed form expression for the optimal target block error rate (BLER) that maximizes the throughput with hybrid automatic repeat request (HARQ). This expression shows that the optimal BLER target is inversely proportional to the SINR. We conduct LTE simulations with link adaptation that exploits the optimal BLER target to reinforce the utility of our derived BLER target expression and to showcase that a performance improvements of up to 30% in throughput can be obtained through dynamic BLER targets for link adaptation.

In this paper, we propose a threshold-based antenna selection algorithm for uplink dense cloud radio access networks (C- RANs), where a baseband processor unit (BBU) cloud is separately placed from densely deployed remote radio heads (RRHs). The proposed algorithm consists of two phases. In the first phase, each RRH determines whether to be included or not in a candidate set by using a predefined selection threshold. In the second phase, a RRH is randomly selected within the candidate set made in the first phase. By modeling the network with a homogeneous Poisson point process (PPP), the signal- to-interference ratio (SIR) complementary cumulative distribution function (CCDF) is derived when applying the proposed algorithm. Exploiting the derived expression, an approximate optimum selection threshold that maximizes the SIR coverage performance is obtained in terms of relevant system parameters, chiefly the SIR target, the pathloss exponent, and the RRH and user densities. Simulations demonstrate the performance of the obtained selection threshold. The key advantage of the proposed algorithm is its simplicity. Since the complexity caused by selecting a RRH is reasonable irrespective of the density of the RRH while guaranteeing the certain performance, the proposed algorithm is easily applied in a C-RAN where the RRHs are densely deployed.

Hybrid precoding, a combination of analog and digital precoding, is an attempt to reach a compromise between complexity and performance. By exploiting more than one RF chain, hybrid precoding enables a millimeter wave (mmWave) system to take advantage of both spatial multiplexing and beamforming gain. A major challenge with hybrid precoding is its configuration in wideband systems. The reason is that the analog beamforming weights are the same across the entire band. In this paper, we propose a frequency selective hybrid precoding technique for a mmWave orthogonal frequency division multiple access system. We propose an algorithm to jointly optimize the wideband analog beamformers and the per- subcarrier digital precoders. We compare an upper bound with our solution and show that our optimized approach achieves good performance in terms of beamforming gain in the sense that it minimizes the loss caused by allocating multiple users to different subcarriers with a limited number of radio frequency chains.

Multiple input multiple output (MIMO) systems employing large antenna arrays are the basic architecture for millimeter wave (mmWave) systems. Due to the higher bandwidths to be used at mmWave, the corresponding sampling rates of high-resolution analog-to-digital converters (ADCs) are also very high, so that ADCs become the most power hungry devices in the reception chain. One solution is to employ low resolution, i.e. one-bit, ADCs. We develop an adaptive one-bit compressed sensing scheme that can be used at low-resolution mmWave receivers for channel estimation. The simulation results show that the adaptive one-bit compressed sensing scheme outperforms the fixed one in the context of mmWave channel estimation.

Compared to the existing lower frequency wireless power transfer, millimeter wave (mmWave) power transfer takes advantage of the high-dimensional multi-antenna and narrow beam transmission. In this paper we introduce wireless power transfer for mmWave cellular networks. Here, we consider users with large energy storage that are recharged by the mmWave base stations prior to uplink information transmission, and analyze the average harvested energy and average achievable rate. Numerical results corroborate our analysis and show that the serving base station plays a dominant role in wireless power transfer, and the contribution of the interference power from the interfering base stations is negligible, even when the interfering base stations are dense. By examining the average achievable rate in the uplink, when increasing the base station density, a transition from a noise-limited regime to an interference-limited regime is observed.

A Manhattan Poisson line process divides the plane into an infinite number of rectangular rooms with walls extending infinitely along the axes. When the path loss is dominated by the penetration through each of the walls, a Poisson field of transmitters creates a heavy tailed interference at a randomly picked room, whose distribution is tractable in the Laplace domain. Interference correlation at different rooms is explicitly available. This model gives the first tractable mathematical abstraction to indoor physical environments where wireless signals are shadowed by (common) walls. Applying the analytical results leads to a formula for success probabilities of a transmission attempt between two given rooms.

Millimeter wave (mmWave) systems will likely employ directional beamforming with large antenna arrays at both the transmitters and receivers. Acquiring channel knowledge to design these beamformers, however, is challenging due to the large antenna arrays and small signal-to-noise ratio before beamforming. In this paper, we propose and evaluate a downlink system operation for multi-user mmWave systems based on compressed sensing channel estimation and conjugate analog beamforming. Adopting the achievable sum-rate as a performance metric, we show how many compressed sensing measurements are needed to approach the perfect channel knowledge performance. The results illustrate that the proposed algorithm requires an order of magnitude less training overhead compared with traditional lower-frequency solutions, while employing mmWave-suitable hardware. They also show that the number of measurements need to be optimized to handle the trade-off between the channel estimate quality and the training overhead.

High resolution direction-of-arrival (DOA) estimation is an important problem in many array signal processing applications. This paper proposes an augmented covariance estimator for DOA estimation. The new method exploits the periodicity of the covariance lags when the DOAs are assumed on a discrete grid with a certain resolution. Then, it achieves twice the resolution of typical methods such as the direct augmentable approach or forward backward spatial smoothing. When the sources are not on the discrete grid, an interpolated array manifold technique is proposed to mitigate the grid mismatch error.

Antenna subset modulation (ASM) is a physical layer security technique that is well suited for millimeter wave communication systems. The key idea is to vary the radiation pattern at the symbol rate by selecting one from a subset of patterns with a similar main lobe and different side lobes. This paper shows that ASM is not robust to an eavesdropper that makes multiple simultaneous measurements at multiple angles. The measurements are combined and used to formulate an estimation problem to undo the effects of the side lobe randomization. Simulations show the performance of the estimation algorithms and how the eavesdropper can effectively recover the information if the signal-to-noise ratio exceeds a certain threshold. Using fewer active radio frequency chains makes it harder for the attacker to recover the transmit symbol, at the expense of more grating lobes.

Wireless communication at millimeter wave (mmWave) frequencies is attractive for cellular, local area, and ad hoc networks due to the potential for channels with large bandwidths. As a byproduct of directional beamforming and propagation differences, some studies have claimed that mmWave networks will be noise rather than interference limited. This paper presents a derivation of the instantaneous interference-to-noise ratio (INR) distribution of a mmWave ad hoc network. Random network model of transmitters represented by a Poisson point process with a narrowband channel model is used to derive an approximation of the INR distribution. The analysis shows that the shape of the INR distribution is determined largely by the line-of-sight interferers, which depends on the overall network density and building blockage. A main conclusion drawn is that even with highly directional beamforming, interference can only sometimes be neglected in an ad hoc network. With a reasonable choice of system parameters, the interference is nearly always stronger than the noise power in dense networks.

This paper studies the interplay between massive MIMO and device-to-device (D2D) networking in a single cell setting, where cellular uplink resources are shared by D2D. The spatial positions of underlaid D2D transmitters are modeled by a Poisson point process. All the transmissions (both cellular and D2D) are SIMO (i.e., single-input multiple-output) with the base station (BS) having a very large antenna array. Assuming perfect channel state information at the receivers, we study cellular and D2D spectral efficiency. In the asymptotic regime where the number of BS antennas goes to infinity, we find that the received signal-to-interference-plus-noise ratio (SINR) of any cellular user increases unboundedly and the effects of noise, fast fading, and the interfering signals from the other co-channel cellular users and the infinite D2D transmitters vanish completely. In the non-asymptotic regime, we derive simple analytical lower bounds for both cellular and D2D spectral efficiency, which allow for efficient numerical evaluation.

This paper characterizes the performance of multiuser joint transmission (MU-JT) with pair-wise dynamic base station (BS) clustering. For analyzing the performance of such BS cooperation method, a tractable model is presented by means of stochastic geometry. Using tools of stochastic geometry, a tight lower bound of the instantaneous signal-to-interference ratio (SIR) distribution is derived in a closed form in terms of relevant system parameters: the path-loss exponent and the topologies of users in the BS cooperative region. Our key finding is that the pair-wise dynamic BS cooperation through MU-JT provides a better rate coverage performance than that of single-user joint transmission (SU-JT) over the entire range of the rate threshold when each user is close enough to the associated BS. Through simulations, the exactness of the derived analytical expression is verified.

Millimeter wave (mmWave) multiple-input multipleoutput (MIMO) communication with large antenna arrays has been proposed to enable gigabit per second communication for next generation cellular systems and local area networks. A key difference relative to lower frequency solutions is that in mmWave systems, precoding/combining can not be performed entirely at digital baseband, due to the high cost and power consumption of some components of the radio frequency (RF) chain. In this paper we develop a low complexity algorithm for finding hybrid precoders that split the precoding/combining process between the analog and digital domains. Our approach exploits sparsity in the received signal to formulate the design of the precoder/combiners as a compressed sensing optimization problem. We use the properties of the matrix containing the array response vectors to find first an orthonormal analog precoder, since sparse approximation algorithms applied to orthonormal sensing matrices are based on simple computations of correlations. Then, we propose to perform a local search to refine the analog precoder and compute the baseband precoder. We present numerical results demonstrate substantial improvements in complexity while maintaining good spectral efficiency.

In this paper, we propose a new retrospective interference alignment for two-cell multiple-input multiple-output (MIMO) interfering multiple access channels (IMAC) with the delayed channel state information at the transmitters (CSIT). It is shown that having delayed CSIT can strictly increase the sum-DoF compared to the case of no CSIT. The key idea is to align multiple interfering signals from adjacent cells onto a small dimensional subspace over time by fully exploiting the previously received signals as side information with outdated CSIT in a distributed manner. Remarkably, we show that the retrospective interference alignment can achieve the optimal sum-DoF in the context of two-cell two-user scenario by providing a new outer bound.

Ad hoc networks consistently under perform compared to cellular networks. Protocols either create residual interference that leads to poor signal-to-interference-plus-noise ratios or try to coordinate transmissions leading to fewer transmission opportunities. Communication with millimeter-wave (mmWave) devices offers the potential for higher bandwidth communication channels and reduced interference in ad hoc networks. This paper uses a stochastic geometry approach to characterize the coverage probability of a mmWave ad hoc network with directional antennas and random building blockages. The coverage probability in the presence of noise and both line-of-sight and non-line-of-sight interference is analyzed and used to derive the transmission capacity. Performance of mmWave is then analyzed in terms of area spectral efficiency and rate coverage. The results show that mmWave networks support larger densities, higher area spectral efficiencies, and better rate coverage compared to traditional, lower-frequency ad hoc networks.

Millimeter wave (mmWave) technology is widely used for automotive radar applications, like adaptive cruise control and obstacle detection. Unlike conventional radar waveforms which are usually propriety, this paper explores the use of a consumer wireless local area network (WLAN) waveform in the 60GHz unlicensed mmWave band for automotive radar applications. In particular, this paper develops a joint framework of long range automotive radar (LRR) and vehicle-to-vehicle communication (V2V) at 60 GHz by exploiting the special data-aided structure (repeated Golay complimentary sequences) of an IEEE 802.11ad single carrier physical layer (SCPHY) frame. This framework leverages the signal processing algorithms used in the typical WLAN receiver for time and frequency synchronization to perform radar parameter estimation. The initial simulation results show that it is possible to achieve the desired range accuracy of 0.1 m with a very high probability of detection (above 99%) using the preamble of a SCPHY frame. Furthermore, the velocity estimation algorithm achieves the desired accuracy of 0.1 m/s at high SNR using the preamble and pilot words of only a single frame.

The availability of large bandwidth at millimeter wave (mmWave) frequencies makes mmWave an ideal candidate to realize multi-Gbps data rates. MmWave has been applied to indoor applications (e.g. IEEE 802.11ad), and it is also being considered for cellular. There is not much work, however, in applying mmWave to vehicular channels. This is likely due to the misconception that the Doppler spread will be too high at small mmWave wavelengths, which is true only for omnidirectional communication. In this work, closed form approximate expressions relating the coherence time and beamwidth are derived taking directional communication into account. The result shows that the coherence time increases at least proportional to the inverse of the beamwidth. The derivation also takes the beam misalignment due to receiver movement into account and it can be shown that the optimal beamwidth is non-zero in contrast to models that assume perfect beam pointing.

This paper considers a millimeter wave (mmWave) system that enables multi-Gbps wireless service for high speed trains. MmWave systems require proper beam alignment to achieve good performance making it difficult to apply to high speed trains due to the need for frequent realignment. In this paper, using the IEEE 802.11ad system parameters, we first show that the channel coherence time will not be enough for communication with wide beams, and thus conventional approaches based on beam sweeping become inefficient if not impossible. Then we consider a beam switching approach that leverages the position information from the train control system for efficient beam alignment. Using this network architecture, we investigate the optimal choice of beamwidth and show that a properly optimized system can achieve multi-Gbps throughput.

A space-time physical-layer network coding (ST-PNC) method is presented for information exchange among multiple users over fully connected multiway relay networks. The method involves two steps: 1) side-information learning and 2) space-time relay transmission. In the first step, different sets of users are scheduled to send signals over networks, and the remaining users and relays overhear the transmitted signals, thereby learning the interference patterns. In the second step, multiple relays cooperatively send out linear combinations of signals received in the previous phase using space-time precoding so that all users efficiently exploit their side information in the form of 1) what they sent and 2) what they overheard in decoding. This coding concept is illustrated through two simple network examples. It is shown that ST-PNC improves the sum of degrees of freedom (sum-DoF) of the network compared to existing interference management methods. With ST-PNC, the sum-DoF of a general multiway relay network without channel knowledge at the users is characterized in terms of relevant system parameters, chiefly the number of users, the number of relays, and the number of antennas at relays. A major implication of the derived results is that efficiently harnessing both transmitted and overheard signals as side information brings significant performance improvements to fully connected multiway relay networks.

This paper proposes a random network model for a device-to-device (D2D) underlaid cellular system using stochastic geometry and develops centralized and distributed power control algorithms. The goal of centralized power control is twofold: ensure that the cellular users have sufficient coverage probability by limiting the interference created by underlaid D2D users, while scheduling as many D2D links as possible. For the distributed power control method, the optimal on-off power control strategy is proposed, which maximizes the sum rate of the D2D links. Expressions are derived for the coverage probabilities of cellular, D2D links, and the sum rate of the D2D links in terms of the density of D2D links and the path-loss exponent. The analysis reveals the impact of key system parameters on the network performance. For example, the bottleneck of D2D underlaid cellular networks is the cross-tier interference between D2D links and the cellular user, not the D2D intratier interference when the density of D2D links is sparse. Simulation results verify the exactness of the derived coverage probabilities and the sum rate of D2D links.

The structured nature of video data motivates introducing video-aware decisions that make use of this structure for improved video transmission over wireless networks . In this paper, we introduce an architecture for real-time video transmission over multiple-input multiple-output (MIMO) wireless communication systems using loss visibility side information . We quantify the perceptual importance of a packet through the packet loss visibility and use the loss visibility distribution to provide a notion of relative packet importance . To jointly achieve high video quality and low latency, we define the optimization objective function as the throughput weighted by the loss visibility of each packet, a proxy for the total perceptual value of successful packets per unit of time. We solve the problem of mapping video packets to MIMO subchannels and adapting per-stream rates to maximize the proposed objective . We show that the solution enables jointly reaping gains in terms of improved video quality and lower latency. Optimized packet-stream mapping enables transmission of more relevant packets over more reliable streams while unequal modulation opportunistically increases the transmission rate on the stronger streams to enable low latency delivery of high priority packets. Tested on H.264-encoded video sequences, for a 4 ×4 MIMO system with three spatial streams, the proposed architecture achieves 8 dB power reduction for the same video quality and supports 2.4× higher throughput due to unequal modulation. Furthermore, the gains are achieved at the expense of few bits of cross-layer overhead rather than a complex cross-layer design.

Antenna arrays will be an important ingredient in millimeter-wave (mmWave) cellular systems. A natural application of antenna arrays is simultaneous transmission to multiple users. Unfortunately, the hardware constraints in mmWave systems make it difficult to apply conventional lower frequency multiuser MIMO precoding techniques at mmWave. This paper develops low-complexity hybrid analog/digital precoding for downlink multiuser mmWave systems. Hybrid precoding involves a combination of analog and digital processing that is inspired by the power consumption of complete radio frequency and mixed signal hardware. The proposed algorithm configures hybrid precoders at the transmitter and analog combiners at multiple receivers with a small training and feedback overhead. The performance of the proposed algorithm is analyzed in the large dimensional regime and in single-path channels. When the analog and digital precoding vectors are selected from quantized codebooks, the rate loss due to the joint quantization is characterized, and insights are given into the performance of hybrid precoding compared with analog-only beamforming solutions. Analytical and simulation results show that the proposed techniques offer higher sum rates compared with analog-only beamforming solutions, and approach the performance of the unconstrained digital beamforming with relatively small codebooks.

Millimeter wave (mmWave) holds promise as a carrier frequency for fifth generation cellular networks. Because mmWave signals are sensitive to blockage, prior models for cellular networks operated in the ultra high frequency (UHF) band do not apply to analyze mmWave cellular networks directly. Leveraging concepts from stochastic geometry, this paper proposes a general framework to evaluate the coverage and rate performance in mmWave cellular networks. Using a distance-dependent line-of-site (LOS) probability function, the locations of the LOS and non-LOS base stations are modeled as two independent non-homogeneous Poisson point processes, to which different path loss laws are applied. Based on the proposed framework, expressions for the signal-to-noise-and-interference ratio (SINR) and rate coverage probability are derived. The mmWave coverage and rate performance are examined as a function of the antenna geometry and base station density. The case of dense networks is further analyzed by applying a simplified system model, in which the LOS region of a user is approximated as a fixed LOS ball. The results show that dense mmWave networks can achieve comparable coverage and much higher data rates than conventional UHF cellular systems, despite the presence of blockages. The results suggest that the cell size to achieve the optimal SINR scales with the average size of the area that is LOS to a user.

This paper characterizes the performance of coordinated beamforming with dynamic clustering. A downlink model based on stochastic geometry is put forth to analyze the performance of such a base station (BS) coordination strategy. Analytical expressions for the complementary cumulative distribution function (CCDF) of the instantaneous signal-to-interference ratio (SIR) are derived in terms of relevant system parameters, chiefly the number of BSs forming the coordination clusters, the number of antennas per BS, and the pathloss exponent. Utilizing this CCDF, with pilot overheads further incorporated into the analysis, we formulate the optimization of the BS coordination clusters for a given fading coherence. Our results indicate that: 1) coordinated beamforming is most beneficial to users that are in the outer part of their cells yet in the inner part of their coordination cluster and that 2) the optimal cluster cardinality for the typical user is small and it scales with the fading coherence. Simulation results verify the exactness of the SIR distributions derived for stochastic geometries, which are further compared with the corresponding distributions for deterministic grid networks.

This paper proposes an interference alignment method with distributed and delayed channel state information at the transmitter (CSIT) for a class of interference networks. The core idea of the proposed method is to align interference signals over time at the unintended receivers in a distributed manner. With the proposed method, achievable tradeoffs between the sum of degrees of freedom (sum-DoF) and feedback delay of CSI are characterized in both the X-channel and three-user interference channel to reveal the impact on how the CSI feedback delay affects the sum-DoF of the interference networks. A major implication of derived results is that distributed and moderately delayed CSIT is useful to strictly improve the sum-DoF over the case of no CSI at the transmitter in a certain class of interference networks. For a class of X-channels, the results show how to optimally use distributed and moderately delayed CSIT to yield the same sum-DoF as instantaneous and global CSIT. Furthermore, leveraging the proposed transmission method and the known outer bound results, the sum-capacity of the two-user X-channel with a particular set of channel coefficients is characterized within a constant number of bits.

In a device-to-device (D2D) underlaid cellular network, the uplink spectrum is reused by the D2D transmissions, causing mutual interference with the ongoing cellular transmissions. Massive MIMO is appealing in such a context as the base station's (BS's) large antenna array can nearly null the D2D-to-BS interference. The multi-user transmission in massive MIMO, however, may lead to increased cellular-to-D2D interference. This paper studies the interplay between massive MIMO and underlaid D2D networking in a multi-cell setting. We investigate cellular and D2D spectral efficiencies under both perfect and imperfect channel state information (CSI) at the receivers that employ partial zero-forcing. Compared to the case without D2D, there is a loss in cellular spectral efficiency due to D2D underlay. With perfect CSI, the loss can be completely overcome if the number of canceled D2D interfering signals is scaled with the number of BS antennas at an arbitrarily slow rate. With imperfect CSI, in addition to pilot contamination, a new asymptotic effect termed underlay contamination arises. In the non-asymptotic regime, simple analytical lower bounds are derived for both the cellular and D2D spectral efficiencies.

We present an optimized secure multi-antenna transmission approach based on artificial-noise-aided beamforming, with limited feedback from a desired single-antenna receiver. To deal with beamformer quantization errors as well as unknown eavesdropper channel characteristics, our approach is aimed at maximizing throughput under dual performance constraints-a connection outage constraint on the desired communication channel and a secrecy outage constraint to guard against eavesdropping. We propose an adaptive transmission strategy that judiciously selects the wiretap coding parameters, as well as the power allocation between the artificial noise and the information signal. This optimized solution reveals several important differences with respect to solutions designed previously under the assumption of perfect feedback. We also investigate the problem of how to most efficiently utilize the feedback bits. The simulation results indicate that a good design strategy is to use approximately 20% of these bits to quantize the channel gain information, with the remainder to quantize the channel direction, and this allocation is largely insensitive to the secrecy outage constraint imposed. In addition, we find that 8 feedback bits per transmit antenna is sufficient to achieve approximately 90% of the throughput attainable with perfect feedback.

Dual-polarized antennas provide a mechanism to achieve polarization diversity or multiplex non-interfering data streams over-the-air. Practical realization of dual-polarized transmission, however, must overcome challenges such as mobile misorientation with respect to the base station as well as non-ideal polarization isolation. With the advent of mmWave communications, dual-polarized large scale antenna arrays can be realized inexpensively. In this paper, we propose a beamforming algorithm that jointly designs the horizontal and vertical beamformers and combiners to maximize spectral efficiency in the presence of mobile misorientation and polarization leakage. First, we show that dual-polarized antenna arrays introduce channel structure that enables mobile orientation estimation without explicit channel knowledge. We derive the maximum likelihood estimate of the azimuth and elevation mobile rotation. Given these estimates, we design the horizontal and vertical beamformer and combiner with practical RF hardware constraints. We generalize the beamforming algorithm to enable multiplexing multiple data streams across different channel paths in the horizontal and vertical domain. Results show that mobile orientation estimation using dual-polarized antenna arrays achieves over 90% accuracy even below 0 dB. Using a realistic clustered channel model, spectral efficiency gains of the order of 5 dB are demonstrated over dual-polarized antennas with independent horizontal/vertical beam steering as well as single polarized antenna arrays.

In this paper, a space-time physical-layer network coding method is proposed in fully-connected multi-way relay networks. The proposed method includes two essential steps: side-information learning; and space-time relay transmission. In the phase of side-information learning, sets of users are scheduled to transmit messages over networks and the remaining users and relays overhear the transmitted messages to learn the interference patterns. In the phase of space-time relay transmission, multiple relays cooperatively transmit linear combinations of received messages in the previous phase using space-time precoding so that all users simultaneously harness their side-information in decoding. With this transmission technique, it is shown that the sum of degrees of freedom of the network is improved compared to existing interference management methods. Our key finding is that efficiently exploiting both transmitted and overheard messages as side-information brings substantial performance gains to fully-connected multi-way relay networks.

Frame error rate (FER) prediction in wireless communication systems is an important tool with applications to system level simulations and link adaptation, among others. Although in realistic communication scenarios it is expected to have codewords of different lengths, previous work on FER prediction marginally treated the dependency of the FER on the codeword length. In this paper, we present a method to estimate the FER using codewords of different length. We derive a low complexity FER estimator for frames of different length transmitted over a binary symmetric channel of unknown error probability. We extend this technique to coded systems by the use of effective SNR FER predictors. The proposed estimation scheme is shown to outperform other simpler estimation methods.

We study secure multi-antenna transmission with limited feedback from the intended receiver and no feedback from the malicious eavesdropper. Our system uses the artificial-noise-aided beamforming approach to enhance secrecy, considering slow fading channels with outage constraints on the reliability performance of legitimate communication and the secrecy performance against eavesdropping. Our analytical results provide conditions on the minimum number of feedback bits and the minimum strength of the intended channel for making secure transmission possible. We observe that strengthening the secrecy outage constraint puts higher requirements on the number of feedback bits and the strength of the intended channel. To maximize the achievable secrecy rate, the optimal transmit power allocation between the information signal and the artificial noise is also derived in closed form.

This paper characterizes the performance of coordinated beamforming with dynamic clustering. A downlink cellular model based on stochastic geometry is put forth to analyze the performance of such base station (BS) coordination strategy. Analytical expressions for the complementary cumulative distribution function (CCDF) of the instantaneous signal-to-interference ratio (SIR) are derived in terms of relevant system parameters, chiefly the number of coordinated BSs, the number of antennas, and the path-loss exponent. Utilizing this CCDF, with pilot overheads further incorporated into the analysis, we formulate the computation of the number of coordinated BSs that maximizes the spectral efficiency for a given fading coherence. The results make precise the intuition that the slower the fading, the more beneficial the coordination. Simulation results verify the exactness of the SIR distribution derived for stochastic geometries, which is further compared with the corresponding distribution for a deterministic grid model.

We conducted a subjective study wherein we found that viewers' Quality of Experience (QoE) was strongly correlated with the empirical cumulative distribution function (eCDF) of the predicted video quality. Based on this observation, we propose a rate-adaptation algorithm that can incorporate QoE constraints on the empirical cumulative quality distribution per user. Simulation results show that the proposed technique can reduce network resource consumption by 29% over conventional average-quality maximized rate-adaptation algorithms.

In this paper, we study the influence of 3-dimensional (3D) base station (BS) antenna patterns on the of downlink random heterogeneous cellular networks. To assess the impact of downtilt and vertical beamwidth, we propose a mathematical framework based on stochastic geometry (to model BS locations) and random shape theory (to model building geometry). Using this framework, we study the impact of different antenna parameters at the pico cell on the coverage and area spectral efficiency of the network. Through simulations we find that narrowing the vertical beamwidth of the pico cell must be accompanied by downtilt to result in improved coverage and capacity. We also find that high building density leads to improved network performance, motivating the deployment of heterogeneous networks in dense urban areas.

Newly developed hypertext transfer protocol (HTTP)-based video streaming technologies enable flexible rate-adaptation under varying channel conditions. Accurately predicting the users' quality of experience (QoE) for rate-adaptive HTTP video streams is thus critical to achieve efficiency. An important aspect of understanding and modeling QoE is predicting the up-to-the-moment subjective quality of a video as it is played, which is difficult due to hysteresis effects and nonlinearities in human behavioral responses. This paper presents a Hammerstein-Wiener model for predicting the time-varying subjective quality (TVSQ) of rate-adaptive videos. To collect data for model parameterization and validation, a database of longer duration videos with time-varying distortions was built and the TVSQs of the videos were measured in a large-scale subjective study. The proposed method is able to reliably predict the TVSQ of rate adaptive videos. Since the Hammerstein-Wiener model has a very simple structure, the proposed method is suitable for online TVSQ prediction in HTTP-based streaming.

This paper characterizes the degree-of-freedom (DoF) regions for the multiuser vector broadcast channel with periodic channel state information (CSI) feedback. As a part of the characterization, a new transmission method called space-time interference alignment is proposed, which exploits both the current and past CSI jointly. Using the proposed alignment technique, an inner bound of the sum-DoF region is characterized as a function of a normalized CSI feedback frequency, which measures CSI feedback speed compared to the speed of user's channel variations. One consequence of the result is that the achievable sum-DoF gain is improved significantly when a user sends back both current and outdated CSI compared to the case where the user sends back current CSI only. Then, a tradeoff between CSI feedback delay and the sum-DoF gain is characterized for the multiuser vector broadcast channel in terms of a normalized CSI feedback delay that measures CSI obsoleteness compared to channel coherence time. A crucial insight is that it is possible to achieve the optimal DoF gain if the feedback delay is less than a derived fraction of the channel coherence time. This precisely characterizes the intuition that a small delay should be negligible.

Millimeter wave (mmWave) signals experience orders-of-magnitude more pathloss than the microwave signals currently used in most wireless applications and all cellular systems. MmWave systems must therefore leverage large antenna arrays, made possible by the decrease in wavelength, to combat pathloss with beamforming gain. Beamforming with multiple data streams, known as precoding, can be used to further improve mmWave spectral efficiency. Both beamforming and precoding are done digitally at baseband in traditional multi-antenna systems. The high cost and power consumption of mixed-signal devices in mmWave systems, however, make analog processing in the RF domain more attractive. This hardware limitation restricts the feasible set of precoders and combiners that can be applied by practical mmWave transceivers. In this paper, we consider transmit precoding and receiver combining in mmWave systems with large antenna arrays. We exploit the spatial structure of mmWave channels to formulate the precoding/combining problem as a sparse reconstruction problem. Using the principle of basis pursuit, we develop algorithms that accurately approximate optimal unconstrained precoders and combiners such that they can be implemented in low-cost RF hardware. We present numerical results on the performance of the proposed algorithms and show that they allow mmWave systems to approach their unconstrained performance limits, even when transceiver hardware constraints are considered.

Large-scale blockages such as buildings affect the performance of urban cellular networks, especially at higher frequencies. Unfortunately, such blockage effects are either neglected or characterized by oversimplified models in the analysis of cellular networks. Leveraging concepts from random shape theory, this paper proposes a mathematical framework to model random blockages and analyze their impact on cellular network performance. Random buildings are modeled as a process of rectangles with random sizes and orientations whose centers form a Poisson point process on the plane. The distribution of the number of blockages in a link is proven to be a Poisson random variable with parameter dependent on the length of the link. Our analysis shows that the probability that a link is not intersected by any blockages decays exponentially with the link length. A path loss model that incorporates the blockage effects is also proposed, which matches experimental trends observed in prior work. The model is applied to analyze the performance of cellular networks in urban areas with the presence of buildings, in terms of connectivity, coverage probability, and average rate. Our results show that the base station density should scale superlinearly with the blockage density to maintain the network connectivity. Our analyses also show that while buildings may block the desired signal, they may still have a positive impact on the SIR coverage probability and achievable rate since they can block significantly more interference.

Performing link adaptation in a multiantenna and multiuser system is challenging because of the coupling between precoding, user selection, spatial mode selection and use of limited feedback about the channel. The problem is exacerbated by the difficulty of selecting the proper modulation and coding scheme when using orthogonal frequency division multiplexing (OFDM). This paper presents a data-driven approach to link adaptation for multiuser multiple input mulitple output (MIMO) OFDM systems. A machine learning classifier is used to select the modulation and coding scheme, taking as input the SNR values in the different subcarriers and spatial streams. A new approximation is developed to estimate the unknown interuser interference due to the use of limited feedback. This approximation allows to obtain SNR information at the transmitter with a minimum communication overhead. A greedy algorithm is used to perform spatial mode and user selection with affordable complexity, without resorting to an exhaustive search. The proposed adaptation is studied in the context of the IEEE 802.11ac standard, and is shown to schedule users and adjust the transmission parameters to the channel conditions as well as to the rate of the feedback channel.

We consider downlink transmission in a cellular network consisting of multi-antenna base stations (BSs), single-antenna mobile users, directional antennas each with a vertically adjustable beam, and control signaling delays in feedback and backhaul links. We propose a novel transmission technique in which intercell interference management is performed via coordinating the beamforming jointly in the horizontal and vertical planes of the wireless channel, denoted as coordinated 3D beamforming. In the horizontal plane, we focus on intercell interference cancelation (ICIC) and investigate its performance when the provided channel state information (CSI) is impaired due to delay/mobility. It is demonstrated that the superiority of ICIC over conventional intracell maximum ratio transmission is highly dependent on the CSI accuracy. In the vertical plane, we consider intercell interference control via coordinatively adapting the elevation angle of the BS antenna pattern, denoted as tilt, to the locations of the scheduled users. It is shown that with perfect CSI interference management should be performed in the horizontal plane using ICIC, while at high delay/mobility it should be done in the vertical plane via coordinated tilt adaptation. For intermediate values of delay/mobility, joint interference management in both planes is required.

Millimeter wave (mmWave) systems must use beamforming to overcome the heavy attenuation at mmWave frequencies and establish high-quality communication links with reasonably high spectral efficiency. When received signal power is sufficiently large and the propagation channel is sufficiently rich, beamforming with multiple data streams, known as precoding, could further increase data rates in mmWave systems. The high cost of digital devices in mmWave systems, however, implies that precoding is predominantly done in the analog domain, making mmWave precoding significantly more constrained than traditional multiple-input multiple-output (MIMO) solutions. In this paper, we propose an iterative precoding algorithm for a practical mmWave transmitter architecture in which all precoding is done in the analog domain. In addition to precoding, the proposed algorithm allows the mmWave system to adapt the rank of its transmission in response to varying propagation conditions. We present numerical results showing that the proposed multimode precoding algorithm allows systems to achieve large data rates, in some cases approaching channel capacity.

Real-time video demands quality-of-service (QoS) guarantees such as delay bounds for end-user satisfaction. Due to the stochastic nature of wireless fading channels, deterministic delay bounds are prohibitively difficult to guarantee. Instead, this paper proposes providing statistical delay guarantees using the concept of effective capacity. A multiuser setup is considered whereby different users have (possibly different) delay QoS constraints. The resource allocation policy that maximizes the sum video quality is derived and has a useful intuitive interpretation: The optimal operating point per user is such that the rate-distortion slope is the inverse of the supported video source rate per unit bandwidth, termed source spectral efficiency. Scheduling policies are also proposed to select a maximal user subset such that all selected users can meet their statistical delay requirement. Results show that QoS-aware scheduling and resource allocation enable supporting significantly more users under the same resource constraints.

Newly developed HTTP-based video streaming technology enables flexible rate-adaptation in varying channel conditions. The users' Quality of Experience (QoE) of rate-adaptive HTTP video streams, however, is not well understood. Therefore, designing QoE-optimized rate-adaptive video streaming algorithms remains a challenging task. An important aspect of understanding and modeling QoE is to be able to predict the up-to-the-moment subjective quality of video as it is played. We propose a dynamic system model to predict the time-varying subjective quality (TVSQ) of rate-adaptive videos that is transported over HTTP. For this purpose, we built a video database and measured TVSQ via a subjective study. A dynamic system model is developed using the database and the measured human data. We show that the proposed model can effectively predict the TVSQ of rate-adaptive videos in an online manner, which is necessary to be able to conduct QoE-optimized online rate-adaptation for HTTP-based video streaming.

We propose an adaptive quantization algorithm for subspace tracking on the Grassmann-manifold of p-dimensional subspaces in the n-dimensional Euclidean space. This quantization problem arises naturally in limited feedback based wireless communication systems, which apply precoding for interference cancellation and alignment. The proposed algorithm exploits the differential geometry associated with the Grassmann-manifold for efficient differential and predictive quantization. The algorithm is applied to channel state information quantization in a multi-user block-diagonalization based wireless communication system, demonstrating large throughput gains compared to memoryless quantization.

We investigate the impact of channel estimation on the performance of downlink random cellular networks. First, we derive a new closed-form expression for the coverage probability under certain practical conditions. We show that the coverage probability is dependent on the user and base station (BS) densities solely through their ratio for arbitrary pilot-training length. Next, we derive the optimal pilot-training length that maximizes the area spectral efficiency (ASE) in several asymptotic regimes, and capture the dependence of this optimal length on the ratio between the user and BS densities. The ASE loss due to training is shown to be less significant in small cell networks with a larger base station density.

This paper considers a fully-connected interference network with a relay in which multiple users equipped with a single antenna want to exchange multiple unicast messages with other users in the network by sharing the relay equipped with multiple antennas. For such a network, the optimal degrees of freedom (DoF) are derived by providing both converse and achievability. Further, considering single-antenna relays in the three-user fully-connected interference network, it is shown that three distributed relays with a single antenna is sufficient to achieve the optimal DoF. A major implication of the derived DoF results is that a relay with multiple antennas or distributed relays employing a single antenna increases the capacity scaling law of the multi-user interference network when multiple directional information flows are considered, even if the networks are fully-connected and all nodes operate in half-duplex. These results verify the intuition that the relay is useful in increasing DoF for the multi-way the interference network.

Cellular networks are taking on a more heterogeneous character with the introduction of low power nodes. Besides the aggregate performance over the entire network, it is also of interest to evaluate the coverage probability conditioning on a specific user location in a cell. This letter proposes a method to compute the location-specific coverage probability inside the inscribing ball of a weighted Voronoi cell in heterogeneous networks. Simulations show that the proposed closed-form approximation accurately characterizes the coverage probability at locations of interest inside the inscribing ball. Moreover the expression can also be applied to evaluate the performance at locations outside the inscribing ball with minor loss.

In this paper, we analyze a multiple-input multiple-output (MIMO) interference channel where nodes are randomly distributed on a plane as a spatial Poisson cluster point process. A Poisson cluster point process consists of clusters with fixed number of points randomly distributed as with the cluster centers distributed randomly on the plane. The nodes in each cluster use interference alignment (IA) to suppress intra-cluster interference but unlike most work on IA, we do not neglect inter-cluster interference. We also connect the accuracy of channel state information to the distance between the nodes, i.e., the quality of CSI degrades with increasing distance. Accounting for the training and feedback overhead, we derive the transmission capacity of this MIMO IA ad hoc network and then compare it to open-loop (interference-blind) spatial multiplexing. Finally, we present exemplary system setups where spatial multiplexing outperforms IA due to the imperfect channel state information or the non-aligned inter-cluster interference.

The small carrier wavelength at millimeter-wave (mm-Wave) frequencies enables featuring a large number of co-located antennas. This paper exploits the potential of large antenna arrays to develop a low-complexity directional modulation technique, Antenna Subset Modulation (ASM), for point-to-point secure wireless communication. The main idea in ASM is to modulate the radiation pattern at the symbol rate by driving only a subset of antennas in the array. This results in a directional radiation pattern that projects a sharply defined constellation in the desired direction and expanded further randomized constellation in other directions. Two techniques for implementing ASM are proposed. The first technique selects an antenna subset randomly for every symbol. While randomly switching antenna subsets does not affect the symbol modulation for a desired receiver along the main direction, it effectively randomizes the amplitude and phase of the received symbol for an eavesdropper along a sidelobe. Using a simplified statistical model, an expression for the average uncoded symbol error rate (SER) is derived as a function of the observation angle. To overcome the problem of large sidelobes in random antenna subset switching, the second technique uses an optimized antenna subset selection procedure based on simulated annealing to achieve superior performance compared with random selection. Numerical comparisons of the SER performance and secrecy capacity of the proposed techniques against those of conventional array transmission are presented to highlight the potential of ASM.

We propose two scheduling algorithms that seek to optimize the quality of scalably coded videos that have been stored at a video server before transmission. The first scheduling algorithm is derived from a Markov decision process (MDP) formulation developed here. We model the dynamics of the channel as a Markov chain and reduce the problem of dynamic video scheduling to a tractable Markov decision problem over a finite-state space. Based on the MDP formulation, a near-optimal scheduling policy is computed that minimizes the mean square error. Using insights taken from the development of the optimal MDP-based scheduling policy, the second proposed scheduling algorithm is an online scheduling method that only requires easily measurable knowledge of the channel dynamics, and is thus viable in practice. Simulation results show that the performance of both scheduling algorithms is close to a performance upper bound also derived in this paper.

This paper considers the two-cell two-hop multiple-input-multiple-output (MIMO) interference channel, where two source groups consisting of multiple users with a single antenna wish to communicate with two multiantenna destinations by sharing two multiantenna relays. For such a channel, an inner bound on the degrees of freedom is derived for different channel knowledge assumptions and relay operations. Assuming global channel knowledge at the relays and full-duplex relay operation, it is shown that two cascaded interfering links can be decomposed into two independent parallel relay channels while sharing the spectrum. The key to showing this result is a novel amplify-and-forward interference-free relay transmission method, which performs interference-shaping during reception and interference neutralization during transmission. Assuming that the relays have global channel knowledge only for the first hop, a spectrally efficient relaying protocol is proposed that overcomes the loss due to half-duplex relaying constraint. The proposed protocol improves performance compared to a trivial time-division-multiple access method for the two-cell two-hop MIMO interference channel.

Cooperation is viewed as a key ingredient for interference management in wireless networks. This paper shows that cooperation has fundamental limitations. First, it is established that in systems that rely on pilot-assisted channel estimation, the spectral efficiency is upper-bounded by a quantity that does not depend on the transmit powers; in this framework, cooperation is possible only within clusters of limited size, which are subject to out-of-cluster interference whose power scales with that of the in-cluster signals. Second, an upper bound is also shown to exist if the cooperation extends to an entire (large) system operating as a single cluster; here, pilot-assisted transmission is necessarily transcended. Altogether, it is concluded that cooperation cannot in general change an interference-limited network to a noise-limited one. Consequently, the existing literature that routinely assumes that the high-power spectral efficiency scales with the log-scale transmit power provides only a partial characterization. The complete characterization proposed in this paper subdivides the high-power regime into a degree-of-freedom regime, where the scaling with the log-scale transmit power holds approximately, and a saturation regime, where the spectral efficiency hits a ceiling that is independent of the power. Using a cellular system as an example, it is demonstrated that the spectral efficiency saturates at power levels of operational relevance.

Base station cooperation can use knowledge of the users' channel state information (CSI) at the transmitters to manage co-channel interference. A reasonable way to provide CSI to the base stations is through a finite rate limited feedback channel. Existing multicell limited feedback techniques require a large amount of feedback, which incurs an overhead penalty on the uplink. In this paper, a new feedback approach based on predictive vector quantization (PVQ) is proposed to reduce feedback requirements in multicell systems and provide high resolution CSI at base stations by exploiting temporal correlation in the channels. Transmitter and receiver structures are proposed to implement predictive limited feedback accounting for delay, for signals on the Grassmann manifold. Simulations show that the proposed PVQ framework yields higher sum-rates than memoryless quantization approaches for multicell limited feedback, in a cooperative system using intercell interference nulling.

We consider coordination in a multi-user multiple input single output cellular system. In contrast with existing base station cooperation methods that rely on sharing CSI with or without user data to manage interference, we propose to share user data only. We consider a system where blind interference alignment (BIA) is applied to serve multiple users in each cell. We apply interference coordination through data sharing to mitigate other-cell interference at the cell-edge users. While BIA mitigates intra-cell interference in MU-MISO systems, it does not address the problem of inter-cell interference. We apply interference coordination through data sharing to mitigate inter-cell interference at the cell-edge users. We propose a new cooperative BIA scheme that takes into account the users whose data is being shared between adjacent base stations. We derive the achievable sum rate with interference mitigation and we compare it to achievable rates with the original BIA strategy. Numerical results show that the achievable sum rate of the cell-edge users with data sharing decreases with increasing number of served users in each cell and increasing number of antennas at the base stations.

An alternating optimization algorithm was recently proposed for the K-user multiple-input multiple-output (MIMO) interference channel. For flat-fading channels and feasible problems, this algorithm successfully aligns the interfering signals exploiting the spatial dimensions. In this paper, we consider the case in which all pairwise MIMO channels are frequency-selective (convolutive), and the users transmit broadband signals using a single-carrier scheme. Unlike the flat-fading case, for frequency-selective channels it is necessary to add a spectral mask in the frequency response of the precoders and decoders to avoid trivial solutions. We show in the paper that each step of the alternating minimization algorithm can be reformulated as a convex optimization problem in which the autocorrelation function of the precoders or decoders is obtained. Upon convergence, a final spectral factorization stage must be applied to obtain the precoders and decoders from their autocorrelation functions. Simulation results are provided to illustrate the performance of the proposed algorithm.

Millimeter wave (mmWave) systems must overcome heavy signal attenuation to support high-throughput wireless communication links. The small wavelength in mmWave systems enables beamforming using large antenna arrays to combat path loss with directional transmission. Beamforming with multiple data streams, known as precoding, can be used to achieve even higher performance. Both beamforming and precoding are done at baseband in traditional microwave systems. In mmWave systems, however, the high cost of mixed-signal and radio frequency chains (RF) makes operating in the passband and analog domains attractive. This hardware limitation places additional constraints on precoder design. In this paper, we consider single user beamforming and precoding in mmWave systems with large arrays. We exploit the structure of mmWave channels to formulate the precoder design problem as a sparsity constrained least squares problem. Using the principle of basis pursuit, we develop a precoding algorithm that approximates the optimal unconstrained precoder using a low dimensional basis representation that can be efficiently implemented in RF hardware. We present numerical results on the performance of the proposed algorithm and show that it allows mmWave systems to approach waterfilling capacity.

Cooperation in a large wireless network with pilotassisted coherent communication is shown to have certain fundamental limitations, namely that even perfect cooperation cannot in general change an interference-limited network to a noise-limited one. Specifically, we demonstrate the existence of a spectral efficiency upper bound that does not grow with the transmit power, when channels are estimated via pilot signals. This is because pilot-assisted channel estimation is only possible within finite cooperation clusters, resulting in out-of-cluster interference that scales with the transmit power. Making the clusters excessively large can actually worsen this effect, nor does sidestepping the pilot-assisted channel estimation via noncoherent demodulation provide an escape. Using a cellular system as an example, it is demonstrated that the spectral efficiency saturates at power levels of operational relevance, indicating that the lackluster gains from cooperation observed in practice may be based on fundamental information-theoretic limitations, rather than current technology imperfections.

Delivering high perceptual quality video over wireless channels is challenging due to the changing channel quality and the variations in the importance of one source packet to the next for the end-user's perceptual experience. Leveraging perceptual metrics in concert with link adaptation to maximize perceptual quality and satisfy real-time delay constraints is largely unexplored. We introduce an APP/MAC/PHY cross-layer architecture that enables optimizing perceptual quality for delay-constrained scalable video transmission. We propose an online QoS-to-QoE mapping technique to quantify the loss visibility of packets from each video layer using the ACK history and perceptual metrics. At the PHY layer, we develop a link adaptation technique that uses the QoS-to-QoE mapping to provide perceptually-optimized unequal error protection per layer according to packet loss visibility. At the APP layer, the source rate is adapted by selecting the set of temporal and quality layers to be transmitted based on the channel statistics, source rates, and playback buffer state. The proposed cross-layer optimization framework allows the channel to adapt at a faster time scale than the video codec. Furthermore, it provides a tradeoff between playback buffer occupancy and perceptual quality. We show that the proposed architecture prevents playback buffer starvation, provides immunity against short-term channel fluctuations, regulates the buffer size, and achieves a 30% increase in video capacity versus throughput-optimal link adaptation.

Interference between nodes is a critical impairment in mobile ad hoc networks. This paper studies the role of multiple antennas in mitigating such interference. Specifically, a network is studied in which receivers apply zero-forcing beamforming to cancel the strongest interferers. Assuming a network with Poisson-distributed transmitters and independent Rayleigh fading channels, the transmission capacity is derived, which gives the maximum number of successful transmissions per unit area. Mathematical tools from stochastic geometry are applied to obtain the asymptotic transmission capacity scaling and characterize the impact of inaccurate channel state information (CSI). It is shown that, if each node cancels interferers, the transmission capacity decreases as as the outage probability vanishes. For fixed , as grows, the transmission capacity increases as where is the path-loss exponent. Moreover, CSI inaccuracy is shown to have no effect on the transmission capacity scaling as vanishes, provided that the CSI training sequence has an appropriate length, which we derive. Numerical results suggest that canceling merely one interferer by each node may increase the transmission capacity by an order of magnitude or more, even when the CSI is imperfect.

The transmission capacity of an ad-hoc network is the maximum density of active transmitters per unit area, given an outage constraint at each receiver for a fixed rate of transmission. Assuming that the transmitter locations are distributed as a Poisson point process, this paper derives upper and lower bounds on the transmission capacity of an ad-hoc network when each node is equipped with multiple antennas. The transmitter either uses eigen multi-mode beamforming or a subset of its antennas without channel information to transmit multiple data streams, while the receiver uses partial zero forcing to cancel certain interferers using some of its spatial receive degrees of freedom (SRDOF). The receiver either cancels the nearest interferers or those interferers that maximize the post-cancellation signal-to-interference ratio. Using the obtained bounds, the optimal number of data streams to transmit, and the optimal SRDOF to use for interference cancellation are derived that provide the best scaling of the transmission capacity with the number of antennas. With beamforming, single data stream transmission together with using all but one SRDOF for interference cancellation is optimal, while without beamforming, single data stream transmission together with using a fraction of the total SRDOF for interference cancellation is optimal.

Interference alignment (IA) is a multiplexing gain optimal transmission strategy for the interference channel. While the achieved sum rate with IA is much higher than previously thought possible, the improvement comes at the cost of requiring network channel state information at the transmitters. This can be achieved by explicit feedback, a flexible yet potentially costly approach that incurs large overhead. In this paper we propose analog feedback as an alternative to limited feedback or reciprocity based alignment. We show that the full multiplexing gain observed with perfect channel knowledge is preserved by analog feedback and that the mean loss in sum rate is bounded by a constant when signal-to-noise ratio is comparable in both forward and feedback channels. When signal-to-noise ratios are not quite symmetric, a fraction of the multiplexing gain is achieved. We consider the overhead of training and feedback and use this framework to numerically optimize the system's effective throughput. We present simulation results to demonstrate the performance of IA with analog feedback, verify our theoretical analysis, and extend our conclusions on optimal training and feedback length.

Interference alignment (IA) is a cooperative transmission strategy that, under some conditions, achieves the interference channel's maximum number of degrees of freedom. Realizing IA gains, however, is contingent upon providing transmitters with sufficiently accurate channel knowledge. In this paper, we study the performance of IA in multiple-input multiple-output systems where channel knowledge is acquired through training and analog feedback. We design the training and feedback system to maximize IA's effective sum-rate: a non-asymptotic performance metric that accounts for estimation error, training and feedback overhead, and channel selectivity. We characterize effective sum-rate with overhead in relation to various parameters such as signal-to-noise ratio, Doppler spread, and feedback channel quality. A main insight from our analysis is that, by properly designing the CSI acquisition process, IA can provide good sum-rate performance in a very wide range of fading scenarios. Another observation from our work is that such overhead-aware analysis can help solve a number of practical network design problems. To demonstrate the concept of overhead-aware network design, we consider the example problem of finding the optimal number of cooperative IA users based on signal power and mobility.

In this paper, we propose jointly optimized linear transceiver algorithms called interference aware-coordinated beamforming (IA-CBF) for a two-cell system where each base station is equipped with multiple transmit antennas. To generalize IA-CBF to more than two-cell scenarios, a new beam-switching mechanism combined with IA-CBF is proposed. For a two-cell system, we derive a minimum-mean-square-error-type IA-CBF algorithm based on a lower bound on the achievable sum rate. We propose optimal (under an assumption of zero other-cell interference) and suboptimal transmit/receive beamforming vectors through zero-forcing IA-CBF algorithms. We also investigate the optimality of the proposed IA-CBF algorithms with respect to the number of receive antennas. Numerical results confirm that the proposed system with two transmit/receive antennas achieves the full degrees of freedom (a.k.a. multiplexing gain) of the two-cell multiple-input multiple-output channel while showing a better sum rate performance than competitive solutions such as non-cooperative eigen-beamforming and interference nulling. A three-dimensional ray tracing tool is also used to evaluate the proposed multi-cell IA-CBF algorithm.

Wireless communication networks use link adaptation to select physical layer parameters that optimize the transmission strategy as a function of the wireless channel realization. In the vehicle-to-vehicle (V2V) networks considered in this letter, the short coherence time of the wireless channel makes link adaptation based on the impulse response challenging. Consequently, link adaptation in V2V wireless networks may instead exploit the large-scale characteristics of the wireless channel (i.e. path loss) since they evolve slowly and enable less frequent feedback. Large-scale channel information may be captured through channel or position/motion measurements. We show, through the definition of new large-scale coherence expressions, that channel measurements render large-scale coherence as a function of time-change while the position/motion measurements render coherence as a function of velocity-change. This letter is concluded with highway simulations of modeled and measured channels to demonstrate the advantage of position/motion information for feedback reduction in V2V link adaptation.

In this paper we analyze a constant multiple-input multiple-output interference channel where a set of active users are cooperating through interference alignment while a set of secondary users desire access to the channel. We find the minimum number of secondary transmit antennas required so that a secondary user can use the channel without affecting the sum rate of the active users, under a zero-forcing equalization assumption. When the secondary users have enough antennas, we derive several secondary user precoders that approximately maximize the secondary users' sum rate without changing the sum rate of the active users. When the secondary users do not have enough antennas, we perform numerical optimization to find secondary user precoders that cause minimum degradation to the sum rate of the active users. Through simulations, we confirm that i) with enough antennas at the secondary users, gains equivalent to the case of all the users cooperating through interference alignment is obtainable, and ii) when the secondary users do not have enough antennas, large rate losses at the active users can be avoided.channels

This paper presents a study on multiple-antenna interference channels, accounting for general overhead as a function of the number of users and antennas in the network. The model includes both perfect and imperfect channel state information based on channel estimation in the presence of noise. Three low-complexity methods are proposed for reducing the impact of overhead in the sum network throughput by partitioning users into orthogonal groups. The first method allocates spectrum to the groups equally, creating an imbalance in the sum rate of each group. The second proposed method allocates spectrum unequally among the groups to provide rate fairness. Finally, geographic grouping is proposed for cases where some receivers do not observe significant interference from other transmitters. For each partitioning method, the optimal solution not only requires a brute force search over all possible partitions, but also requires full channel state information, thereby defeating the purpose of partitioning. We therefore propose greedy methods to solve the problems, requiring no instantaneous channel knowledge. Simulations show that the proposed greedy methods switch from time-division to interference alignment as the coherence time of the channel increases, and have a small loss relative to optimal partitioning only at moderate coherence times.

Providing perceptual quality guarantees for video transmission over wireless channels is important for the end-user experience. The paper proposes an algorithm for perceptual quality optimization of scalable H.264 video with temporal and quality scalability. The algorithm supports adaptive unequal error protection so that different video layers are protected according to their relevance to video quality. For each video layer, a target packet error rate (PER) is selected such that a perceptual quality guarantee, measured using the multi-scale structural similarity (MS-SSIM) index, is satisfied. Given the target PER per video layer, the algorithm selects the modulation and coding scheme and the number of temporal and quality layers to transmit adaptively based on the channel state information (CSI) and source rates per video layer. Results show that the algorithm provides immunity against short term channel fluctuations, balances reliability and perceptual quality, reduces playback buffer starvation probability, and provides a convenient buffer management policy.

Single carrier frequency domain equalization (SC-FDE) uses cyclically prefixed quadrature amplitude modulation to permit simple frequency domain equalization at the receiver. Link adaptation for SC-FDE systems, where the modulation and coding rate are adapted based on the current channel state, is straightforward with perfect channel state information due to the simple analytical form of the post-processing signal-to-noise ratio (SNR). Imperfect channel state information, however, introduces adaptation errors. This paper proposes a machine learning-based approach for link adaptation in bit interleaved convolutionally encoded SC-FDE systems. To improve performance in the presence of channel uncertainty, principal component analysis is used to reduce the feature space dimensionality consisting of the channel coefficients, noise variance, and post-processing SNR. The reduced dimension feature set improves performance of the link adaptation classifier and leads to higher performance versus just the post-processing SNR estimate. Simulation results indicate that the proposed algorithm increases the goodput while maintaining the target packet error rate, achieving optimal adaptation in 95% of the tested cases.

Relays in cellular systems are sensitive to interference. A good relay design will mitigate received interference and will transmit in a way that avoids excess out-of-cell interference. This paper proposes a distributed relay beamforming algorithm for the two-hop interference channel, which models a relay-based cellular system. Interference pricing is used to provide the relays with information on how the interference impacts the end-to-end achievable rates. A new method is proposed to compute the interference prices via an approximation of the end-to-end achievable rate, which accounts for the relationship of the parameters in two hops. This alleviates the rate mismatch experienced by the naive application of single- hop interference pricing algorithms. Simulations show that the proposed algorithm outperforms single-hop interference pricing, zero forcing and maximal ratio transmission beamforming.

Grassmannian beamforming is an efficient way to quantize channel state information in multiple-input multiple-output wireless systems. Unfortunately, multiuser systems require larger codebooks since the quantization error creates residual interference that limits the sum rate performance. To reduce the feedback requirements in multiuser systems, we propose Grassmannian predictive coding to exploit temporal channel correlation. The proposed algorithm exploits the differential geometric structure of the Grassmann manifold. The difference between points, prediction, and quantization are defined using the tangent space of the Grassmann manifold. We show that with practical feedback rates, a significant sum rate improvement can be obtained as a function of the channel correlation.

In this paper we consider an interference channel where a set of primary active users are cooperating through interference alignment over a constant multiple-input-multiple-output channel while a set of secondary users desire access to the channel. We present the conditions under which a secondary user can be admitted to the network. For the admitted users, we derive several beamforming designs maximizing approximately the secondary users' sum-rate based on the number of the secondary users, the number of antennas at the secondary users and the total number of streams in the network of active users.

Users in a multicell cooperative system can feedback channel state information (CSI) of desired and interfering channels to the base station(s) using finite-bandwidth links to manage co-channel interference. Hence, the available feedback resources need to be partitioned efficiently between the multiple channels. In this paper, a feedback-bit allocation strategy is proposed for intercell interference nulling. The approach takes into account the received signal strengths and delays in the feedback and backhaul links. Closed-form expressions for bit partitions are derived, to allocate more bits to quantize the strong interferers with small delays and assign fewer bits to weak channels with large delays. Simulations show how the proposed algorithm yields higher sum-rates than equal bit allocation.

We study the problem of adaptive video data scheduling over wireless channels. We prove that, under certain assumptions, adaptive video scheduling can be reduced to a Markov decision process over a finite state space. Therefore, the scheduling policy can be optimized via standard stochastic control techniques using a Markov decision formulation. Simulation results show that significant performance improvement can be achieved over heuristic transmission schemes.

In this paper, we consider a hierarchical two-tier cellular network where a macrocell is overlaid with a tier of randomly distributed femtocells. We evaluate the combined effect of uncoordinated cross-tier interference, feedback delay, and quantization errors on the achievable rate of transmit beamforming with imperfect channel state information (CSI). We model the femtocell spatial distribution as a Poisson point process (PPP) and the temporal correlation of the channel according to a Gauss-Markov model. Using stochastic geometry tools, we derive the probability of outage at the macrocell users as a function of the temporal correlation, the femtocell density, and the feedback rate. We compute the maximum average achievable rate on the downlink of the macrocell network using a properly designed rate backoff scheme. We show that transmit beamforming with imperfect CSI is a viable option for the downlink of a two-tier cellular network, and that rate backoff recovers the loss in rate due to packet outage.

Interference alignment is a technique for designing transmit precoders that is known to achieve the maximum multiplexing gain in the interference channel. Prior work on interference alignment has assumed sum-power constraints in designing the transmit precoders. In practice, however, each transmit antenna has its own power amplifier which constrains the power radiated from each antenna. This paper proposes and analyzes two algorithms for performing interference alignment with per-antenna equality and inequality power constraints. It is proven that incorporating inequality constraints can be done arbitrarily and does not affect the feasibility of interference alignment. Further, we show that incorporating equality constraints is more difficult and requires more antennas for interference alignment to be feasible, especially in the case of single-stream systems. It is shown through analysis and numerical sum-rate calculations that per-antenna inequality constraints result in a systematic power loss versus the cases of per-antenna equality constraints or no constraints at all.

In this paper, we consider a two-tier network consisting of a macrocell and several closed-access femtocells sharing bandwidth with the macrocell. With multiple transmit antennas at both the macrocell and femtocell base stations, we propose a downlink power control and beamforming algorithm in two steps. In the first step, the beamformer of the macrocell base station is chosen to be the maximal ratio transmission beamformer, and we determine the power allocation at the macrocell and all femtocell base stations in a centralized manner by constraining that the signal power and leakage power associated with the femtocell beamformers satisfied certain predetermined thresholds. In the second step, the femtocell beamformers are obtained in a distributed manner by taking into account these thresholds. In the presence of imperfect channel state information, we further extend our proposed algorithm in a robust manner using robust optimization methodology.

This letter proposes three methods to jointly calculate beamforming and combining vectors for the downlink of TDD multiuser multiple-input multiple-output systems. The algorithms use quantized information of the beamformers to get the corresponding combiners, maximizing the signal-to-interference-plus-noise-ratio. Two of the proposed methods exceed in performance those already published. The third method reduces the feedforward overhead with a light degradation in performance. Our three proposals are based on a perfect interference cancellation at the transmitter before calculating the quantized beamformers.

This paper proposes an adaptive multi-mode transmission strategy to improve the spectral efficiency achieved in the multiple-input multiple-output (MIMO) broadcast channel with delayed and quantized channel state information. The adaptive strategy adjusts the number of active users, denoted as the transmission mode, to balance transmit array gain, spatial division multiplexing gain, and residual inter-user interference. Accurate closed-form approximations are derived for the achievable rates for different modes, which help identify the active mode that maximizes the average sum throughput for given feedback delay and channel quantization error. The proposed transmission strategy can be easily combined with round-robin scheduling to serve a large number of users. As instantaneous channel information is not exploited, the proposed algorithm cannot provide multiuser diversity gain, but it is still able to provide throughput gain over single-user MIMO at moderate signal-to-noise ratio. In addition, it has a light feedback overhead and only requires feedback of instantaneous channel state information from a small number of users. In the system with a feedback load constraint, it is shown that the proposed algorithm provides performance close to that achieved by opportunistic scheduling with instantaneous feedback from a large number of users.

In a two-way relay channel, two sources use one or more relay nodes to exchange data with each other. This paper considers a multiple input multiple output (MIMO) two-way relay channel, where each relay node has one or more antennas. Optimal relay transmission strategies for the two-way relay channel are derived to maximize the achievable rate with amplify and forward (AF) at each relay and to achieve the optimal diversity-multiplexing tradeoff (DM-tradeoff). To maximize the achievable rate with AF, an iterative algorithm is proposed which solves a power minimization problem subject to minimum signal-to-interference-and-noise ratio constraints at every step. The power minimization problem is nonconvex. The Karush Kuhn Tucker conditions, however, are shown to be sufficient for optimality. Capacity scaling law of the two-way relay channel with increasing number of relays is also established by deriving a lower and upper bound on the capacity region of the two-way relay channel. To achieve the optimal DM-tradeoff, a compress and forward strategy is proposed and its DM-tradeoff is derived. For the full-duplex two-way relay channel, the proposed strategy achieves the optimal DM-tradeoff, while for the half-duplex case the proposed strategy is shown to achieve the optimal DM-tradeoff under some conditions.

Interference is a performance limiting factor in dense cellular networks with aggressive frequency reuse. Cooperation among base stations (BSs) is a promising approach for improving data rates by eliminating or mitigating interference. For the downlink, the highest spectral efficiency gains are achieved through precoding with full coordination, which requires complete channel state information (CSI) and data be shared among BSs at the cost of significant utilization of the backhaul. In this paper, we propose distributed precoding techniques for the multicell MIMO downlink. Unlike prior work, our proposed precoders are both decentralized with respect to the BSs as well as capable of enabling multiple users to share the same frequency carrier spatially within each cell. Specifically, each BS designs its own precoder without requiring data or downlink CSI of links from other BSs. Since CSI is unlikely to be perfect, we study the effect of imperfect CSI on our proposed precoders and propose a robust precoder in the presence of CSI uncertainty. Simulations show that our proposed methods enjoy a rate increase with SNR similar to multicell joint dirty paper coding in the high SNR regime due to effective interference mitigation. Numerical results reflect the sensitivity of each proposed precoder with respect to the imperfectness in the available CSI.

We propose transceiver algorithms in cognitive radio networks where the cognitive users are equipped with multiple antennas. Prior work has focused on the design of precoding matrices to suppress interference to the primary receivers. This work considers designs of precoding and decoding matrices for spatial sensing to achieve two objectives: (i) to prevent interference to the primary receivers and (ii) to remove the interference, due to primary transmissions, at the secondary receiver. With single antenna primary terminals and two antenna cognitive terminals, a linear transceiver design has been introduced under a global channel state information (CSI) assumption . In this letter, multiple antenna primary and cognitive terminals and three different CSI scenarios depending upon the amount of CSI are studied: (i) local CSI, (ii) global CSI, and (iii) local CSI with side information. When local CSI is available, we leverage prior work and employ the projected-channel singular value decomposition (P-SVD). In the global CSI scenario, we propose a joint transmitter-receiver design under the assumption of full CSI of all the users at the secondary transceiver. To reduce the feedback overhead, we also propose a new iterative algorithm that exploits only local CSI with side information. In this algorithm, the secondary transmitter and receiver iteratively update precoding and decoding matrices based on the local CSI and side information (precoding/decoding matrices at the previous iteration step) to maximize the rate of the secondary link while maintaining the zero-interference constraint. Convergence is established in the special case of single stream beamforming. Numerical results confirm that the proposed joint design and the iterative algorithm show better achievable rate performance than the P-SVD technique at the expense, respectively, of CSI knowledge and side information.

The transmission capacity of an ad-hoc network is the maximum density of active transmitters per unit area, given an outage constraint at each receiver for a fixed rate of transmission. Most prior work on finding the transmission capacity of ad-hoc networks has focused only on one-way communication where a source communicates with a destination and no data is sent from the destination to the source. In practice, however, two-way or bidirectional data transmission is required to support control functions like packet acknowledgements and channel feedback. This paper extends the concept of transmission capacity to two-way wireless ad-hoc networks by incorporating the concept of a two-way outage with different rate requirements in both directions. Tight upper and lower bounds on the two-way transmission capacity are derived for frequency division duplexing. The obtained bounds are used to derive the optimal solution for bidirectional bandwidth allocation that maximizes the two-way transmission capacity, which is shown to perform better than allocating bandwidth proportional to the desired rate in both directions. Using the proposed two-way transmission capacity framework, a lower bound on the two-way transmission capacity with transmit beamforming using limited feedback is derived as a function of bandwidth and the number of bits allocated for feedback.

Alternating minimization algorithms are typically used to find interference alignment (IA) solutions for multiple-input multiple-output (MIMO) interference channels with more than K=3 users. For these scenarios many IA solutions exit, and the initial point determines which one is obtained upon convergence. In this paper, we propose a new iterative algorithm that aims at finding the IA solution that maximizes the average sum-rate. At each step of the alternating minimization algorithm, either the precoders or the decoders are moved along the direction given by the gradient of the sum-rate. Since IA solutions are defined by a set of subspaces, the gradient optimization is performed on the Grassmann manifold. The step size of the gradient ascent algorithm is annealed to zero over the iterations in such a way that during the last iterations only the interference leakage is being minimized and a perfect alignment solution is finally reached. Simulation examples are provided showing that the proposed algorithm obtains IA solutions with significant higher throughputs than the conventional IA algorithms.

Limited feedback beamforming improves link reliability with a small amount of feedback from the receiver to the transmitter. The performance of such a closed loop MIMO system is unknown in interference limited cellular environments, when the base stations have limited or no coordination. This paper establishes the degradation in throughput due to uncoordinated other cell interference and delay on the feedback channel. Under a Markov channel assumption, the paper shows that the throughput gain of cell edge users decays doubly exponentially as the delay increases. Numerical results illustrate how the decay rate decreases when the codebook size increases.

Existing work on limited feedback for cooperative multicell beamforming quantizes the desired and interfering channel state information (CSI) using separate codebooks. In this paper, it is shown that comparatively higher sum-rates can be obtained by jointly quantizing the desired and interfering CSI using a single codebook. A selection criterion is developed for random vector quantization (RVQ) to show that joint quantization with RVQ yields higher sum-rates than those obtained using separate codebooks. The generalized Lloyd algorithm is then used to generate codebooks using the codeword design strategy proposed in this paper. Simulations are used to show that the proposed joint quantization approaches perform almost as well as the full CSI case.

Supervised learning has been used to develop practical link adaptation algorithms for MIMO-OFDM under an equal rate per stream assumption. In this paper we develop supervised learning algorithms that select from non-uniform rates per stream. We show that the straightforward application of existing supervised learning link adaptation algorithms exhibits complexity that scales with the number of spatial streams. Therefore, we propose a decoupled stream link adaptation algorithm which reduces the complexity below the original supervised learning algorithm with uniform spatial streams. We further show that the performance loss of decoupled link adaptation is reduced in systems with non-uniform constellations per spatial stream. IEEE 802.11n and uncoded MIMO-OFDM simulations are used to validate the proposed algorithms.

In this paper we analyze pilot-aided channel estimation for the multiple-input multiple-output amplify-and-forward two-way relay channel. We use a linear minimum mean-square estimator for estimating the composite channel at each communicating terminal and by using the notion of “worst-case noise” we derive lower-bounds for the transmission sum-rate in both directions of the established bi-directional relay link.

Obtaining the most from multiple-antenna relay systems requires adapting the number of substreams based on channel conditions. In this paper, we develop algorithms for selecting dynamically the number of data streams and the transmit antenna subsets at both the source and relay to minimize the vector symbol error rate at a fixed overall rate. An antenna selection mode of operation is defined by the number of transmit antennas and the substream-to-antenna mapping at the source and relay. We propose three suboptimal multimode algorithms that aim to select the best mode based on the knowledge of channel conditions. The algorithms are shown by Monte Carlo simulations to achieve the full diversity order and to provide considerable gains over the existing designs.

This paper provides a simple closed-form SINR expression for interference alignment over MIMO channels with channel uncertainty. Assuming linear processing (specifically, zero-forcing) at the transmitters and receivers and a complex Gaussian interference channel, we show that random matrix theory can be successfully applied to find the SINR distribution of each stream for each user with channel uncertainty. Perfect channel knowledge constitutes a special case. This SINR distribution allows easy calculation of useful performance metrics like symbol error-rate and achievable sum rate.

The transmission capacity of an ad-hoc network is the maximum density of active transmitters in a unit area, given an outage constraint at each receiver for a fixed rate of transmission. Most prior work on finding the transmission capacity of ad-hoc networks has focused only on one-way communication where a source communicates with destination and no data is sent from destination to the source. In practice, however, two-way or bidirectional data transmission is required to support control functions like packet acknowledgements and channel feedback. This paper develops the concept of transmission capacity for two-way wireless ad-hoc networks, by incorporating the concept of a two-way outage with different rate requirements in both directions. Upper and lower bounds on the two-way transmission capacity are derived for frequency division duplexing, under the assumption that the channel coefficients are independent on different carrier frequencies. The derived bounds are used to derive the optimal solution for bidirectional bandwidth allocation that maximizes the two-way transmission capacity, which is shown to perform better than allocating bandwidth proportional to the desired rate in both directions.

With the introduction of multiple transmit and receive antennas in next generation wireless systems, real-time image and video communication are expected to become quite common, since very high data rates will become available along with improved data reliability. New joint transmission and coding schemes that explore advantages of multiple antenna systems matched with source statistics are expected to be developed. Based on this idea, we present an unequal power allocation scheme for transmission of JPEG compressed images over multiple-input multiple-output systems employing spatial multiplexing. The JPEG-compressed image is divided into different quality layers, and different layers are transmitted simultaneously from different transmit antennas using unequal transmit power, with a constraint on the total transmit power during any symbol period. Results show that our unequal power allocation scheme provides significant image quality improvement as compared to different equal power allocations schemes, with the peak-signal-to-noise-ratio gain as high as 14 dB at low signal-to-noise-ratios.

This paper investigates the impact of delayed channel state information at the transmitter (CSIT) on the MIMO broadcast channel with block diagonalization (BD) preceding. First, an upper bound for the achievable throughput is provided, which shows that BD is more robust to imperfect CSIT than zero-forcing precoding as it has fewer inter-user interfering streams. Due to residual inter-user interference, the throughput of BD still saturates at high SNR, which motivates switching between single-user and multi-user precoding. An accurate closed-form approximation is derived for the achievable throughput of the BD system, which provides guidance on the preferred transmission technique for a given scenario.

Using interference alignment, it has been shown that the number of degrees of freedom in the interference channel scales linearly with the number of users. Unfortunately, closed-form solutions for interference alignment over constant-coefficient channels with more than 3 users are difficult to derive. This paper proposes an algorithm for interference alignment in the MIMO interference channel with an arbitrary number of users, antennas, or spatial streams. The algorithm is an alternating minimization over the precoding matrices at the transmitters and the interference subspaces at the receivers, and is proven to converge. Numerical results show how the algorithm is useful for simulation and can give insight into the limitations of interference alignment.

Wireless ad hoc networks require bidirectional data transmission to support two-way traffic and control functions like packet acknowledgement. Most prior work on the capacity of wireless ad hoc networks, however, has focused only on one-way communication. In this paper we develop the concept of transmission capacity of two-way communication in wireless ad hoc networks. The transmission capacity has been used extensively to analyze one-way ad hoc networks - in this paper we provide a generalization that incorporates the concept of a two- way outage. We derive an upper bound and an approximation for the two-way transmission capacity, which are shown to be relatively tight for small outage probability constraints. We also quantify how the two-way success requirement reduces network capacity. Our numerical and simulation results show that for certain two-way networks the capacity loss is considerable.

Increasingly wireless networks use multi-antenna nodes as in IEEE 802.11n and 802.16. The physical layer (PHY) in such systems may use the antennas to provide multiple streams of data (spatial multiplexing) or to increase the robustness of fewer streams. These physical layers also provide support for sending packets at different rates by changing the modulation and coding of transmissions. Rate adaptation is the problem of choosing the best transmission mode for the current channel and in these systems requires choosing both the level of spatial multiplexing and the modulation and coding. Hydra is an experimental wireless network node prototype in which both the MAC and PHY are highly programmable. Hydra's PHY is essentially the 802.11n PHY, and currently supports two antennas and the same modulations and codings as 802.11n. Because of limitations of our hardware platform, the actual rates are a factor of 10 smaller than 802.11n. The MAC is essentially the 802.11 MAC with extensions, including the ability to feedback channel state or rate information from the receiver. Hydra was designed to allow experimentation with real radios, PHYs, and network stacks over real-world channels and it is well suited to studying rate adaptation in multi-antenna systems. To allow controlled experimentation, we also have the ability to perform experiments over emulated channels using exactly the same MAC and PHY used for RF transmissions. We present rate control experiments based on transmission over both real and emulated channels. Our experiments include measurements for single antenna systems and two antenna systems using a single or multiple spatial streams. We study rate adaptation algorithms using both explicit and implicit feedback from the receiver. A novel aspect of our results is the first experimental study of adaptation between single and multiple spatial streams for 802.11n style systems.

In the multiple-input multiple-output (MIMO) broadcast channel with imperfect channel state information (CSI), neither the capacity nor the optimal transmission technique have been fully discovered. In this paper, we derive achievable ergodic rates for a multi-antenna fading broadcast channel when CSI at the transmitter (CSIT) is delayed and quantized. It is shown that not all possible users should be supported with spatial division multiplexing due to the residual inter-user interference caused by imperfect CSIT. Based on the derived achievable rates, we propose a multi-mode transmission strategy to maximize the throughput, which adaptively adjusts the number of active users based on the channel statistics information.

An iterative algorithm is proposed to achieve the optimal rate region in a two-way relay channel, where two nodes want to exchange data with each other using multiple relays, and each relay employs an amplify and forward strategy. The iterative algorithm solves a power minimization problem at every step, subject to minimum signal-to-interference-and-noise ratio constraints, which is non-convex, however, for which the Karush Kuhn Tucker conditions are sufficient for optimality. Using simulations, the achievable rate region of the iterative algorithm is compared with the cut-set upper bound; the gap is shown to be quite small for most cases.

In conventional MIMO cellular systems, the spatial transmission mode, e.g. spatial multiplexing or transmit diversity, is chosen independently in each cell. Unfortunately, the optimum transmission mode for users that are interference limited depends to a large extent on the transmission mode used by interfering base stations. This paper proposes interference-aware link adaptation where base stations exchange their transmission plans with neighboring base stations and broadcast this information to the active users. Each user measures its long term susceptibility to spatial interference and returns this information to the base station via a spatial mode table. The base stations then schedule active users according to the decisions made in interfering base stations and the preferred transmission strategies of its own users. Simulations illustrate the ergodic sum rate the performance of switching between spatial multiplexing and statistical beamforming in different spatially correlated channels.

The asymptotic optimality of a jointly optimized linear multiuser beamforming system with two transmit and many receive antennas for the fading multiple-input multiple-output (MIMO) Gaussian broadcast channel is investigated. We derive the asymptotic sum capacity of the independent and identically-distributed (i.i.d.) MIMO broadcast channel and the asymptotic sum rate of a linear multiuser beamforming system with respect to the number of receive antennas per user, where two users are served with a zero inter-user interference constraint. Based on this result, we show that linear multiuser beamforming with many receive antennas can asymptotically achieve the sum capacity of the two-user i.i.d. MIMO broadcast channel even without opportunistic scheduling gain. Numerical results confirm that as the number of receive antennas increases the sum rate of linear multiuser beamforming system converges to the sum capacity.

A method for reducing feedback in a downlink multiuser MIMO-OFDM cellular system, with zero forcing beamforming at the base (BS) and antenna combining at each mobile (MS) is proposed. The method consists of partitioning the subcarriers into groups, choosing the quantized channel vector that better represents each group in terms of average quantization error. Numerical results show that savings up to 93% in overhead bits are obtained for SNR equal to 15 dB, with reductions in channel capacity that goes from 30% to 50% depending on the codebook size used for quantization.

Space-time block codes (STBCs) from coordinate interleaved orthogonal designs (CIODs) offer several advantages including full-diversity and single-symbol decodability. In an effort to assess their performance in quasi-static frequency nonselective i.i.d. Nakagami-m fading channels, we analyze the error rate, outage capacity, and information outage probability. First, based on an accurate closed-form formula for the average symbol pairwise error rate (SPER), we derive tight union upper and lower bounds on the symbol-error rate (SER). Second, we apply Gaussian and Gamma approximations to provide closed form expressions for the outage capacity. Third, using high signal-to-noise ratio (SNR) and moment-matching approximation techniques, we also derive accurate closed-form approximations for the information outage probability (IOP). Finally, we show that STBCs from CIODs provide full-diversity by deriving SER based and IOP-based asymptotic and instantaneous diversity orders. Monte-Carlo simulations show that the analytical results agree with simulation experiments.

This paper considers the multiuser multiple-input multiple-output (MIMO) Rayleigh fading broadcast channel. We consider the case where the multiple transmit antennas are used to deliver independent data streams to multiple users via a multiuser technique known as vector perturbation. We propose lattice-theoretic and rate-distortion based approaches to analyze the performance of these systems, taking into account the practical restrictions imposed by limited feedback and training. We show that performance is primarily determined by the ratio between the number of users and the number of transmit antennas. We then propose a new practical low-complexity low-rate feedback scheme, and show that the performance approaches the ideal rate-distortion based scheme.

Multimedia communication has become one of the main applications in commercial wireless systems. Multimedia sources, mainly consisting of digital images and videos, have high bandwidth requirements. Since bandwidth is a valuable resource, it is important that its use should be optimized for image and video communication. Therefore, interest in developing new joint source-channel coding (JSCC) methods for image and video communication is increasing. Design of any JSCC scheme requires an estimate of the distortion at different source coding rates and under different channel conditions. The common approach to obtain this estimate is via simulations or operational rate-distortion curves. These approaches, however, are computationally intensive and, hence, not feasible for real-time coding and transmission applications. A more feasible approach to estimate distortion is to develop models that predict distortion at different source coding rates and under different channel conditions. Based on this idea, we present a distortion model for estimating the distortion due to quantization and channel errors in MPEG-4 compressed video streams at different source coding rates and channel bit error rates. This model takes into account important aspects of video compression such as transform coding, motion compensation, and variable length coding. Results show that our model estimates distortion within 1.5 dB of actual simulation values in terms of peak-signal-to-noise ratio.

In this paper, we analyze the performance of maximum ratio combining (MRC) systems with imperfect channel estimation in the presence of cochannel interference (CCI) with an arbitrary power interference-to-noise ratio (INR). The maximum combining weights are the imperfect estimates of the desired user's fading channel coefficients and are assumed to be complex Gaussian distributed. The quantified measure for estimation error is the correlation coefficient between the true fading channel coefficients and their estimates. Exact closedform expressions are derived for the probability density function(pdf) of the signal-to-interference-plus-noise ratio (SINR), as well as performance metrics including outage probability and the average symbol error probability (ASEP) for some modulation formats. Simulation results demonstrate the accuracy of our theoretic analysis.

In this paper, we propose a mechanism to switch between multiple-input multiple-output (MIMO) with two transmit antennas and single-input multiple-output (SIMO) to conserve mobile terminals' energy. We focus on saving uplink RF transmission energy of mobile terminals in cellular systems supporting best effort traffic. The key idea is to judiciously slow down transmission rates when a base station is underutilized. We show that there exists a crossover point on the transmission rate below which SIMO consumes less power than MIMO when circuit power is included. The crossover point is an increasing function of the circuit power, the number of receive antennas and channel correlation, all of which increase the potential energy savings resulting from mode switching. We propose an adaptive mode switching algorithm combined with rate selection to maintain a user's target throughput while achieving energy efficiency. Extensive flow-level simulations under dynamic loads confirm that the proposed technique can reduce the transmission energy by more than 50% and enables an effective tradeoff between file transfer delay and energy conservation.

A clustered base transceiver station (BTS) coordination strategy is proposed for a large cellular MIMO network, which includes full intra-cluster coordination-to enhance the sum rate-and limited inter-cluster coordination-to reduce interference for the cluster edge users. Multi-cell block diagonalization is used to coordinate the transmissions across multiple BTSs in the same cluster. To satisfy per-BTS power constraints, three combined precoder and power allocation algorithms are proposed with different performance and complexity tradeoffs. For inter-cluster coordination, the coordination area is chosen to balance fairness for edge users and the achievable sum rate. It is shown that a small cluster size (about 7 cells) is sufficient to obtain most of the sum rate benefits from clustered coordination while greatly relieving channel feedback requirement. Simulations show that the proposed coordination strategy efficiently reduces interference and provides a considerable sum rate gain for cellular MIMO networks.

In this paper, we derive bounds on the structural similarity (SSIM) index as a function of quantization rate for fixed-rate uniform quantization of image discrete cosine transform (DCT) coefficients under the high rate assumption. The space domain SSIM index is first expressed in terms of the DCT coefficients of the space domain vectors. The transform domain SSIM Index is then used to derive bounds on the average SSIM index as a function of quantization rate for Gaussian and Laplacian sources. As an illustrative example, uniform quantization of the DCT coefficients of natural images is considered. We show that the SSIM index between the reference and quantized images fall within the bounds for a large set of natural images. Further, we show using a simple example that the proposed bounds could be very useful for rate allocation problems in practical image and video coding applications.

Spatial correlation among received signals has a significant impact on the performance of a multiple- input multiple-output (MIMO) system. In prior research, expressions were derived for the spatial correlation between the signals received at a uniform linear array (ULA) by assuming that the propagation environment has a single cluster, low angular spread or that the received rays arrive only along the broadside of the array. In this paper, analytical expressions are derived for the spatial correlation between the received signals of a ULA and uniform circular array without making any of the above mentioned assumptions. The derivation can be extended to suit most planar antenna array configurations. This makes the derived expressions valid for most practical propagation channels. Finally, we show using simulations that the derived expression evaluates spatial correlation more accurately than the existing models.

MIMO-OFDM wireless systems require adaptive modulation and coding based on channel state information (CSI) to maximize throughput in changing wireless channels. Traditional adaptive modulation and coding attempts to predict the best rate available by estimating the packet error rate for each modulation and coding scheme (MCS) by using CSI, which has shown to be challenging. This paper considers supervised learning with the k-nearest neighbor (k-NN) algorithm as a new framework for adaptive modulation and coding. Practical k-NN operation is enabled through feature space dimensionality reduction using subcarrier ordering techniques based on postprocessing SNR. Simulation results of an IEEE 802.11n draft-compatible physical layer in flat and frequency selective wireless channels shows the k-NN with an ordered subcarrier feature space performs near ideal adaptation under packet error rate constraints.

This paper investigates the tradeoff between throughput and feedback delay of limited feedback beamforming in indoor wireless channels with a practical MIMO-OFDM prototype. Past descriptions of this tradeoff are largely based on simplified models of the wireless channel. Unfortunately, wireless channel models may not accurately represent the complexities of a real wireless channel. Furthermore, system impairments, including channel estimation error, only exacerbate the problem of modeling a real wireless system. One such analytical result predicts the performance of limited feedback beamforming as an exponential function of feedback delay. This analytical result has been confirmed through Monte Carlo simulation under Rayleigh fading channel models. Through rigorous measurements and experimentation this paper both evaluates the performance of limited feedback beamforming under feedback delay and confirms the accuracy of the analytical results.

Research in cognitive radios has renewed interest in tools, such as spectrum estimation and modulation identification, to characterize the radio frequency (RF) environment. The use of multiple antennas for multiple-input multiple-output (MIMO) communications presents a new challenge in detecting and classifying signals. In this paper, we propose a cyclostationarity-based statistical test to detect space-time block codes, focusing on the two transmitter Alamouti space-time block code (STBC). Our test exploits a new characterization of the Alamouti code using fourth order cyclic frequencies. The test requires only a single receive antenna, and does not require any symbol synchronization.

Multi-input multi-output (MIMO) receivers have been designed and their communication performance analyzed under the assumption of additive Gaussian noise. Wireless transceivers, however, may be affected by radio frequency interference (RFI) that is well modeled using non-Gaussian impulsive statistics. In this paper, we consider the problem of receiver design for a two transmit, two receive antenna MIMO system in the presence of RFI. First, we show that RFI is well modeled using a bivariate Middleton Class A model and validate the model with measured data. Using this RFI model, we demonstrate that conventional MIMO receivers experience significant degradation in communication performance. Then we derive the maximum likelihood (ML) receiver assuming bivariate Middleton Class A noise. Furthermore, we develop a parameter estimation method for this noise model and propose two sub-optimal ML receivers with reduced computational complexity. Simulations show significant improvement in symbol error rate performance of the proposed techniques over receivers designed assuming additive Gaussian noise.

Interference between nodes directly limits the capacity of mobile ad hoc networks. This paper focuses on spatial interference cancellation with perfect channel state information (CSI), and analyzes the corresponding network capacity. Specifically, by using multiple antennas, zero-forcing beamforming is applied at each receiver for canceling the strongest interferers. Given spatial interference cancellation, the network transmission capacity is analyzed in this paper, which is defined as the maximum transmitting node density under constraints on outage and the signal-to-interference-plus-noise ratio. Assuming that the locations of network nodes are Poisson distributed and spatially i.i.d. Rayleigh fading channels, mathematical tools from stochastic geometry are applied for deriving scaling laws for transmission capacity. Specifically, for a large number of antennas per node, the transmission capacity scales with the number of antennas raised to a fractional power, which depends only on the path-loss exponent. Moreover, for small target outage probability, transmission capacity is proved to increase following a power law, where the exponent is the inverse of the size of antenna array or larger depending on the pass-loss exponent. As shown by simulations, spatial interference cancellation increases transmission capacity by an order of magnitude or more even if only one extra antenna is added to each node.

This paper proposes non-iterative coordinated beamforming algorithms for a multiuser MIMO (multiple input multiple output) system with multiple antennas at the transmitter and multiple users, each with multiple receive antennas. The transmitter uses linear beamforming to convey information to each user, while each receiver uses a quantized combining vector, sent from the transmitter via a low-rate feedforward control channel. Two different algorithms for optimizing transmit beamformers and receive combining vectors are proposed: a joint optimization and a greedy search. Simulations show that the proposed methods using quantized codebooks approach the sum capacity of the MIMO broadcast channel.

In this paper, we present an algorithm for designing a linear equalizer that is optimal with respect to the structural similarity (SSIM) index. The optimization problem is shown to be non-convex, thereby making it non-trivial. The non-convex problem is first converted to a quasi-convex problem and then solved using a combination of first order necessary conditions and bisection search. To demonstrate the usefulness of this solution, it is applied to image denoising and image restoration examples. We show using these examples that optimizing equalizers for the SSIM index does indeed result in higher perceptual image quality compared to equalizers optimized for the ubiquitous mean squared error (MSE).

A codebook based limited feedback strategy is a practical way to obtain partial channel state information at the transmitter in a precoded multiple-input multiple-output (MIMO) wireless systems. Construction of conventional codebooks, such as Grassmannian and Fourier codebooks, relies on nonlinear search and iterative algorithms which often does not exhibit structure to ease storage or online search computation. Furthermore, multiple codebooks are needed to support beamforming and spatial multiplexing. In this paper, we propose a new codebook design based on Kerdock codes and mutually unbiased bases which enjoys performance similar to previously known codebooks. The proposed Kerdock codebook with quaternary alphabet has systematic construction, reduced storage, and reduced online search computation. Special structure in the codebook is used to derive a spatial multiplexing codebook from multiple columns of the beamforming codebook resulting in further storage reduction.

The multiuser multiple-input multiple-output (MIMO) broadcast channel uses multiple antennas at the transmitter to simultaneously deliver information to multiple users. Despite the theoretical capacity gains, the actual throughput of the system with limited feedback of channel state information suffers due to interspatial stream interference caused by quantization errors. This paper introduces a new method to mitigate the interference: transmit-beam matching. Using multiple receive antennas, transmit-beam matching minimizes the inter-spatial stream interference by each user's effective channel to the transmit-beam. It is evaluated based on a recent MIMO broadcast channel technique with limited feedback, per-user unitary rate control (PU2RC) and shown to effectively mitigate the interference.

This paper considers the use of multiple transmit antennas to deliver independent data streams to multiple users. In particular, we examine a multi-user technique known as vector perturbation. We provide a new lattice-theoretic approach to analyze its performance in the presence of Rayleigh fading. Vector perturbation is based on performing a channel inversion, with the additional step of perturbing the data signal prior to linear preceding to significantly reduce the required transmit power. To analyze such systems it is necessary to calculate the resulting average energy of the sphere-encoded signal vector, as this determines the signal-to-noise ratio (SNR) at the output of the demodulator. Previous results presented in the literature were partially analytic, requiring further numerical evaluation. Here, we derive a concise approximation to the output SNR. We also provide tight upper and lower bounds on the bit error rate for the reception of QAM symbols using the required modulo demodulator, as a function of the average energy of the sphere- encoded signal vector.

In this paper, we present a novel algorithm for wavelet domain image denoising using the soft thresholding function. The thresholds are designed to be locally optimal with respect to the structural similarity (SSIM) index. The SSIM Index is first expressed in terms of wavelet transform coefficients of orthogonal wavelet transforms. The wavelet domain representation of the SSIM Index, along with the assumption of a Gaussian prior for the wavelet coefficients is used to formulate the soft thresholding optimization problem. A locally optimal solution is found using a quasi-Newton approach. This solution is applied to denoise images in the wavelet domain. The visual quality of the images denoised using the proposed algorithm is shown to be higher compared to the MSE-optimal soft thresholding denoising solution, as measured by the SSIM Index.

Distributed space-time block coding is a diversity technique to mitigate the effects of fading in multi-hop wireless networks, where multiple relay stages are used by a source to communicate with its destination. This paper proposes a new distributed space-time block code called the cascaded orthogonal space-time block code (COSTBC) for the case where the source and destination are equipped with multiple antennas and each relay stage has one or more single antenna relays. Each relay stage is assumed to have receive channel state information (CSI) for all the channels from the source to itself, while the destination is assumed to have receive CSI for all the channels. To construct COSTBC, multiple orthogonal space-time block codes are used in cascade by the source and each relay stage. COSTBC is shown to achieve the maximum diversity gain in a multi-hop wireless network with flat Rayleigh fading channels. An explicit construction of COSTBCs is also provided. It is also shown that COSTBC requires minimum decoding complexity thanks to the connection to orthogonal space-time block codes.

This paper considers the problem of selecting a set of relay nodes to assist a transmitting node in a two-hop wireless network. Throughput-maximizing relay subset selection is a difficult problem that depends on variables such as node locations and power constraints. It is proposed that all relays employ partial decode-and-forward operations to improve the tractability of the relay selection problem. This allows relay selection to be transformed into a simpler relay placement problem which motivates two proximity-based relay selection algorithms. These algorithms are compared with a greedy algorithm based on relay channel gains to the sink and an algorithm that randomly selects relays. The diversity gain achieved by employing multiple relay nodes is derived. The proposed proximity-based algorithms offer good performance in terms of the expected achieved rate.

In this paper, we propose a new joint optimization of linear transmit beamforming and receive combining vectors for the multiple-input multiple-output (MIMO) broadcast channel. We consider the transmission of a single information stream to two users with two or more receive antennas. Unlike past work in which iterative computation is required to design the beamformers, we derive specific formulations for the transmit beamformers for two active users via a power iteration and a generalized eigen analysis. To enable practical implementation, a new limited feedback algorithm is proposed that exploits the structure of the algorithm to avoid full channel quantization. The feedback overhead of the proposed algorithm is independent of the number of receive antennas. Monte Carlo simulations are used to evaluate the bit error rate and the sum rate performances of the proposed algorithm. Simulation results show that the proposed method performs close to the sum capacity of the MIMO broadcast channel even with limited feedback.

Recent research has demonstrated that by utilizing channel state information at the transmitter, the physical layer can be optimized to provide higher link capacity and throughput, more efficiently share the channel with multiple users, increase range by exploiting diversity due to spatial and frequency selectivity, and simplify multi-user receivers through known interference cancellation. Unfortunately, acquiring channel state information at the transmitter is difficult. In most systems, the only opportunity for the transmitter to learn about the channel is through a feedback control channel. Because feedback information is control overhead, the rate of the feedback channel is limited. This motivates the study of limited feedback techniques where only partial or quantized information from the receiver is conveyed back to the transmitter.

It is now well known that employing channel adaptive signaling in wireless communication systems can yield large improvements in almost any performance metric. Unfortunately, many kinds of channel adaptive techniques have been deemed impractical in the past because of the problem of obtaining channel knowledge at the transmitter. The transmitter in many systems (such as those using frequency division duplexing) can not leverage techniques such as training to obtain channel state information. Over the last few years, research has repeatedly shown that allowing the receiver to send a small number of information bits about the channel conditions to the transmitter can allow near optimal channel adaptation. These practical systems, which are commonly referred to as limited or finite-rate feedback systems, supply benefits nearly identical to unrealizable perfect transmitter channel knowledge systems when they are judiciously designed. In this tutorial, we provide a broad look at the field of limited feedback wireless communications. We review work in systems using various combinations of single antenna, multiple antenna, narrowband, broadband, single-user, and multiuser technology. We also provide a synopsis of the role of limited feedback in the standardization of next generation wireless systems.

We derive optimal SNR-based transmit antenna selection rules at the source and relay for the nonregenerative half-duplex MIMO relay channel. While antenna selection is a suboptimal form of beamforming, it has the advantage that the optimization is tractable and can be implemented with only a few bits of feedback from the destination to the source and relay. We compare the bit error rate of optimal antenna selection at both the source and relay to other proposed beamforming techniques and propose methods for performing the necessary limited feedback.

Arrays designed for multiple-input multiple-output (MIMO) communication systems must provide good performance in terms of information theoretic and microwave theoretic performance measures for a large number of channel scenarios. Consequently, the general MIMO array design problem is challenging. This paper proposes a novel optimization methodology for designing circular patch arrays (CPAs), under a clustered MIMO channel model assumption. Based on the ergodic capacity, it is found that the single-cluster scenario can approximate CPA performance in clustered channels, reducing the number of channel scenarios that need to be considered. Using a new definition of channel spatial correlation that allows for pattern diversity, metrics are derived for evaluating average and outage capacity performance of CPAs, which can be expressed as a function of only the antenna parameters. With this expression, an optimization is formulated and solved numerically for optimizing the parameters of a 2-CPA on a capacity based objective function and microwave theory performance constraints. An example design is given corresponding to a 2-CPA in the 2.4 GHz unlicensed frequency band.

We propose an algorithm for designing linear equalizers that maximize the structural similarity (SSIM) index between the reference and restored signals. The SSIM index has enjoyed considerable application in the evaluation of image processing algorithms. Algorithms, however, have not been designed yet to explicitly optimize for this measure. The design of such an algorithm is nontrivial due to the nonconvex nature of the distortion measure. In this paper, we reformulate the nonconvex problem as a quasi-convex optimization problem, which admits a tractable solution. We compute the optimal solution in near closed form, with complexity of the resulting algorithm comparable to complexity of the linear minimum mean squared error (MMSE) solution, independent of the number of filter taps. To demonstrate the usefulness of the proposed algorithm, it is applied to restore images that have been blurred and corrupted with additive white gaussian noise. As a special case, we consider blur-free image denoising. In each case, its performance is compared to a locally adaptive linear MSE-optimal filter. We show that the images denoised and restored using the SSIM-optimal filter have higher SSIM index, and superior perceptual quality than those restored using the MSE-optimal adaptive linear filter. Through these results, we demonstrate that a) designing image processing algorithms, and, in particular, denoising and restoration-type algorithms, can yield significant gains over existing (in particular, linear MMSE-based) algorithms by optimizing them for perceptual distortion measures, and b) these gains may be obtained without significant increase in the computational complexity of the algorithm.

In this paper, we derive bounds on the structural similarity (SSIM) index as a function of quantization rate for fixed-rate uniform quantization of image discrete cosine transform (DCT) coefficients under the high-rate assumption. The space domain SSIM index is first expressed in terms of the DCT coefficients of the space domain vectors. The transform domain SSIM index is then used to derive bounds on the average SSIM index as a function of quantization rate for uniform, Gaussian, and Laplacian sources. As an illustrative example, uniform quantization of the DCT coefficients of natural images is considered. We show that the SSIM index between the reference and quantized images fall within the bounds for a large set of natural images. Further, we show using a simple example that the proposed bounds could be very useful for rate allocation problems in practical image and video coding applications.

Space-time block coding (STBC) is an attractive solution for improving quality in wireless links. In this paper, we analyze the impact of channel estimation error on the ergodic capacity and symbol error rate (SER) for space-time block coded multiple-input multiple-output (MIMO) systems. We derive a closed-form capacity expression over MIMO Rayleigh channels with channel estimation error. Moreover, we derive an exact closed-form SER for general PAM/PSK/QAM with channel estimation error. We show that, as expected, channel estimation error introduces the capacity loss and diversity gain loss. Furthermore, as the number of transmit and receive antennas increases, the sensitivity of the STBC system to channel estimation error increases. Simulation results demonstrate the accuracy of our analysis.

Precoding with block diagonalization (BD) is an attractive technique for approaching the sum capacity in the multiuser multiple-input multiple-output (MIMO) broadcast channel. Unfortunately, BD requires either global channel state information at every receiver or an additional training phase, which demands additional control overhead and additional system planning. In this paper we propose a new multiuser MIMO algorithm that combines BD with vector perturbation (VP). The proposed algorithm avoids the second training phase, reduces each user is receiver complexity thanks to pre-equalization with VP at the transmitter, and has comparable diversity performance to BD with maximum likelihood decoding algorithm. A bound on the achievable sum rate for the proposed technique is derived and used to show that BD with VP approaches the achievable sum rate of BD with water-filling. Numerical simulations confirm that the proposed technique provides better bit error rate and diversity performance than BD with a zero-forcing receiver as well as BD with zero-forcing precoding.

Block diagonalization is one approach for linear preceding in the multiple-input multiple-output broadcast channel that sends multiple interference free data streams to different users in the same cell. Unfortunately, block diagonalization neglects other-cell interference (OCI), which limits the performance of users at the edge of the cell. This paper presents an OCI-aware enhancement to block diagonalization that uses a whitening filter for interference suppression at the receiver and a novel precoder using the interference-plus-noise covariance matrix for each user at the transmitter. For complex Gaussian matrix channels, the asymptotic sum rate of the proposed system is analyzed under a large antenna assumption for isotropic inputs and compared to conventional block diagonalization. The capacity loss due to OCI is quantified in terms of results from single-user MIMO capacity. Several numerical examples compare achievable sum rates, the proposed asymptotic rates, and the capacity loss, in low and high interference regimes.

Block diagonalization is a preceding technique for multiuser MIMO systems that pre-cancels inter-user interference at the transmitter side. When there are a large number of base station antennas but a limited number of RF amplifiers, the system performance can be significantly improved by switching a subset of antennas to the RF chains and exploiting antenna selection diversity. The optimal antenna subset can be obtained by exhaustively searching over all possible antenna combinations. This brute-force search, however, is prohibitively complicated and impractical. To reduce the complexity, in this paper we propose several low-complexity suboptimal transmit selection algorithms that minimize a symbol error rate (SER) upper bound or maximize a capacity lower bound. Simulation results show that our proposed algorithms perform very close to the optimal exhaustive search, while the complexity is much lower.

Block diagonalization is one approach for linear preceding in the multiple-input multiple-output broadcast channel that sends multiple interference free data streams to different users in the same cell. Unfortunately, block diagonalization neglects other cell interference, which limits the performance of users at the edge of the cell. This paper presents an OCI-aware enhancement to block diagonalization that uses a whitening filter for interference suppression at the receiver and a novel precoder using the interference-plus-noise covariance matrix for each user at the transmitter. For complex Gaussian matrix channels, the asymptotic sum rate of the proposed system is analyzed under a large antenna assumption for isotropic inputs and compared to conventional block diagonalization.

Block diagonalization is a downlink preceding technique that pre-cancels inter-user interference in multiuser MIMO systems. When there are a large number of users, the system throughput can be significantly increased by selecting a subset of users and a subset of receive antennas for each user. The optimal user and antenna subset can be obtained by exhaustively searching over all possible user and antenna combinations to find the one with the highest sum throughput. This brute-force solution, however, is prohibitively complicated. To reduce the complexity, in this paper we propose a low-complexity suboptimal user and antenna selection algorithm. For most system configurations, we show that our proposed algorithm achieves up to 98% of the optimal sum throughput of the exhaustive search, where the complexity is orders of magnitude lower than the exhaustive search method.

On a multi-antenna downlink channel, separation of multiple users by transmit beamforming enables simultaneous transmission from the base station to the users, resulting in high sum throughput. This paper proposes and analyzes a practical algorithm for joint scheduling and orthogonal beamforming, which is enabled by feedback of quantized channel state information (CSI). In this approach, each user quantizes CSI using a codebook comprised of multiple orthonormal vector sets and sends back quantized CSI. Using feedback CSI, the base station jointly selects a set of orthogonal beamforming vectors and schedules a subset of feedback users for downlink transmission such that the throughput is maximized. For moderate to large numbers of users, the proposed algorithm achieves higher sum capacities than the conventional ones.

Space division multiple access (SDMA) is capable of achieving sum capacity that grows double logarithmically with the number of users. The sum rate for channel state information (CSI) feedback, however, increases linearly with the number of users, reducing the effective uplink capacity. To address this problem, a novel SDMA design is proposed, where the sum feedback rate is upper-bounded by a constant. This design consists of algorithms for CSI quantization, threshold based CSI feedback, and joint beamforming and scheduling. The key feature of the proposed approach is the use of feedback thresholds to select feedback users with large channel gains and small CSI quantization errors such that the sum feedback rate constraint is satisfied. Despite this constraint, the proposed SDMA design is shown to achieve a sum capacity growth rate close to the optimal one. Numerical results show that the proposed SDMA design is capable of attaining higher sum capacities than existing ones, even though the sum feedback rate is bounded.

This paper develops a contention-based opportunistic feedback technique towards relay selection in a dense wireless network. This technique enables the forwarding of additional parity information from the selected relay to the destination. For a given network, the effects of varying key parameters such as the feedback probability are presented and discussed. A primary advantage of the proposed technique is that relay selection can be performed in a distributed way. Simulation results find its performance to closely match that of centralized schemes that utilize GPS information, unlike the proposed method. The proposed relay selection method is also found to achieve throughput gains over a point-to-point transmission strategy.

For a wireless multi-antenna network with a large number of users, the sum capacity scales at most linearly with the number of antennas and double logarithmically the number of users. Achieving this optimal capacity scaling potentially requires feedback of channel state information (CSI) from all users, leading to overflow of the feedback channel. This paper proposes a limited feedback strategy that provides feedback control such that a sum CSI feedback rate constraint is satisfied. It is proved that a wireless multi-antenna network using the proposed limited feedback strategy achieves the optimal capacity scaling for the broadcast channel.

The optimal input covariance matrix Q that achieves the ergodic capacity under the coherent assumption in a point-to-point, multi-antenna setting is a function of the spatial correlation and the transmit SNR. While the eigenvectors of Q can be characterized in closed-form for many realistic correlation models, the eigenvalues have to be determined numerically. However, it is well-known that the rank of Q is a non-decreasing function of SNR. Motivated by this fact, in this work, we study communication with a low-complexity family of input covariance matrices that are characterized by their rank, assuming uniform power allocation over the smaller-dimensional eigen-space. We quantify the impact of spatial correlation on the M-th transition-SNR which is defined as the smallest SNR at which rank-M transmission becomes optimal.

This paper considers capacity scaling in the recently proposed two-way MIMO (multiple input multiple output) relay channel. In the two-way relay channel, two nodes use a relay for exchanging data with each other. Under the assumption that each node has perfect receive channel state information and all nodes work only in half duplex mode, this paper shows that the sum capacity scales linearly with the number of transmit antennas and logarithmically with the number of relays, as the number of relays grows large. This result shows that with two- way relay channels it is possible to asymptotically (in the number of relays) obtain full-duplex performance while using only half-duplex nodes.

This paper considers the joint optimization of the transmitter beamforming filters and receiver combining filters for the multiple-input multiple-output (MIMO) broadcast channel. A low-complexity iterative algorithm to compute the filters is proposed and then a closed-form expression of the filters for the downlink with two transmit antennas is derived, avoiding the need for iterative computation in this case. To enable practical implementation, a new limited feedback algorithm is proposed that exploits the channel structure in the closed form solution, and is independent of the number of receive antennas. Performance is evaluated by Monte Carlo simulations as a function of the amount of feedback. With two receive antennas, the proposed method performs close to the sum capacity of the MIMO broadcast channel, without the need for multiuser diversity and with only limited feedback.

This paper presents a derivation of an expression for the ergodic capacity of spatial multiplexing multiple-input-multiple-output (MIMO) systems with zero-forcing (ZF) receivers and independent substream detection. Assuming that the channel is unknown at the transmitter but known at the receiver, the ergodic capacity is formulated as a function of log-normal shadowing and Rayleigh fading. Gauss-Hermite quadrature integration is used to approximate the ergodic capacity expression in a concise form. The proposed analytical approach allows investigation of the effects of the shadowing standard deviation and the transmit correlation. Numerical and simulation results confirm that under various composite channel scenarios, the analytical results match well with the simulation results.

Previous research has shown that adaptive switching between multiple-input multiple-output (MIMO) transmission strategies like spatial multiplexing and beamforming increases link reliability and capacity gains, as compared to fixed transmission strategies. To get the full benefit of adaptive switching it is necessary to obtain accurate estimates of the SNR values when we switch between the transmission strategies. In this paper, it is shown that (relatively more) accurate switching point estimates can be obtained by taking into account real-life effects like mutual coupling and antenna efficiencies, for switching between statistical beamforming and spatial multiplexing. Using simulations, it is shown that accounting for these effects can make the switching point estimate more accurate by as much as 12 dB, compared to the case when the practical effects are not considered.

In this paper, the feasibility of multiple-input multiple-output (MIMO) systems in aircraft is examined for the specific application of seatback entertainment, using an approach built around a site-specific capacity analysis methodology. The average capacity is evaluated as a function of the seat location, using the proposed methodology that relies on inputs from measurement results or ray-tracing simulations. The extension of the methodology to optimize the access point and client antenna placement locations and array configurations is also described in the paper. While the specific results are for the deployment of MIMO communication in aircraft for seatback entertainment, the same methodology can be applied to other deployment scenarios as well.

It has been shown that a decentralized relay selection protocol based on opportunistic feedback from the relays yields good throughput performance in dense wireless networks. This selection strategy supports a hybrid-ARQ transmission approach where relays forward parity information to the destination in the event of a decoding error. Such an approach, however, suffers a loss compared to centralized strategies that select relays with the best channel gain to the destination. This paper closes the performance gap by adding another level of channel feedback to the decentralized relay selection problem. It is demonstrated that only one additional bit of feedback is necessary for good throughput performance. The performance impact of varying key parameters such as the number of relays and the channel feedback threshold is discussed. An accompanying bit error rate analysis demonstrates the importance of relay selection

Hydra is a flexible wireless network testbed being developed at UT Austin. Our focus is networks that support multiple wireless hops and where the network, especially the MAC, takes advantage of sophisticated PHY techniques, such as OFDM and MIMO. We argue that for this domain simulation alone is not adequate and that working prototypes are needed to validate algorithms and protocols. Hydra nodes consist of a flexible RF front-end and a general purpose machine with a software based MAC and PHY. Using the frameworks of the Click modular router and GNU radio and coding in C++ makes it relatively easy to implement working prototypes of cross-layer designs that require custom MACs and PHYs. We present the architecture and implementation of Hydra, as well as a preliminary cross-layer design experiment for a rate-adaptive MAC. These early results show Hydra is a capable prototyping tool for wireless network research.

The full diversity gain provided by a multi-antenna channel can be achieved by transmit beamforming and receive combining. This requires the knowledge of channel state information (CSI) at the transmitter which is difficult to obtain in practice. Quantized beamforming where fixed codebooks known at both the transmitter and the receiver are used to quantize the CSI has been proposed to solve this problem. Most recent works focus attention on limited feedback codebook design for the uncorrelated Rayleigh fading channel. Such designs are sub-optimal when used in correlated channels. In this paper, we propose systematic codebook design for correlated channels when channel statistical information is known at the transmitter. This design is motivated by studying the performance of pure statistical beamforming in correlated channels and is implemented by maps that can rotate and scale spherical caps on the Grassmannian manifold. Based on this study, we show that even statistical beamforming is near-optimal if the transmitter covariance matrix is ill-conditioned and receiver covariance matrix is well-conditioned. This leads to a partitioning of the transmit and receive covariance spaces based on their conditioning with variable feedback requirements to achieve an operational performance level in the different partitions. When channel statistics are difficult to obtain at the transmitter, we propose a universal codebook design (also implemented by the rotation-scaling maps) that is robust to channel statistics. Numerical studies show that even few bits of feedback, when applied with our designs, lead to near perfect CSI performance in a variety of correlated channel conditions.

In this paper, a novel transmission technique for the multiple-input multiple-output (MIMO) broadcast channel is proposed that allows simultaneous transmission to multiple users with limited feedback from each user. During a training phase, the base station modulates a training sequence on multiple sets of randomly chosen orthogonal beamforming vectors. Each user sends the index of the best beamforming vector and the corresponding signal-to-interference-plus-noise ratio for that set of orthogonal vectors back to the base station. The base station opportunistically determines the users and corresponding orthogonal vectors that maximize the sum capacity. Based on the capacity expressions, the optimal amount of training to maximize the sum capacity is derived as a function of the system parameters. The main advantage of the proposed system is that it provides throughput gains for the MIMO broadcast channel with a small feedback overhead, and provides these gains even with a small number of active users. Numerical simulations show that a 20% gain in sum capacity is achieved (for a small number of users) over conventional opportunistic space division multiple access, and a 100% gain (for a large number of users) over conventional opportunistic beamforming when the number of transmit antennas is four.

A novel multiuser scheduling and feedback strategy for the multiple-input multiple-output (MIMO) downlink is proposed in this paper. It achieves multiuser diversity gain without substantial feedback requirements. The proposed strategy uses per-antenna scheduling at the base station, which maps each transmit antenna at the base station (equivalently, a spatial channel) to a user. Each user has a number of receive antennas that is greater than or equal to the number of transmit antennas at the base station. Zero-forcing receivers are deployed by each user to decode the transmitted data streams. In this system, the base station requires users' channel quality on each spatial channel for scheduling. An opportunistic feedback protocol is proposed to reduce the feedback requirements. The proposed protocol uses a contention channel that consists of a fixed number of feedback minislots to convey channel state information. Feedback control parameters including the channel quality threshold and the random access feedback probability are jointly adjusted to maximize the average throughput performance of this system. Multiple receive antennas at the base station are used on the feedback channel to allow decoding multiple feedback messages sent simultaneously by different users. This further reduces the bandwidth of the feedback channel. Iterative search algorithms are proposed to solve the optimization for selection of these parameters under both scenarios that the cumulative distribution functions of users are known or unknown to the base station

This paper proposes a power control strategy for the uplink of cellular MIMO spatial multiplexing systems, with a linear MMSE receiver applied at the base station and a single active user per time instant. A fixed per-stream SINR target is employed that allows guaranteed QoS for delay-sensitive applications. A straightforward application of single antenna power control is not possible in the MIMO context due to coordination between receive antennas and nonlinear dependence between interference and eigenspaces of the channel matrices. Two schemes are proposed to solve the problem. The first equally allocates power to all transmit antennas. Deriving an SINR lower bound based on an eigenvalue approximation of the composite interference, allows application of the conventional single antenna power control framework to solve this problem. To improve the feasibility performance, a second scheme is proposed that adaptively allocates power on the transmit antennas, where an iterative algorithm based on game theory is used to sequentially update each user's power distribution. The optimal solution with full channel knowledge, and a practical near-optimal solution requiring only partial channel knowledge, are both derived. Numerical results show that power control, compared to supposedly optimal waterfilling strategies, actually achieves higher throughput at the low SINRs typical in cellular systems, with significantly lower overhead and complexity. Due to its better exploitation of spatial diversity and reduced transmit power (and hence reduced interference), adaptive power allocation increases the achievable SINR by an order of magnitude over equal power allocation, resulting in far better coverage.

Spatial multiplexing with multi-mode precoding provides a means to achieve both high capacity and high reliability in multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) systems. Multi-mode precoding uses linear transmit precoding that adapts the number of spatial multiplexing data streams or modes, according to the transmit channel state information (CSI). As such, it typically requires complete knowledge of the multi-mode precoding matrices for each subcarrier at the transmitter. In practical scenarios where the CSI is acquired at the receiver and fed back to the transmitter through a low-rate feedback link, this requirement may entail a prohibitive feedback overhead. In this paper, we propose to reduce the feedback requirement by combining codebook-based precoder quantization, to efficiently quantize and represent the optimal precoder on each subcarrier, and multi-mode precoder frequency down-sampling and interpolation, to efficiently reconstruct the precoding matrices on all subcarriers based on the feedback of the indexes of the quantized precoders only on a fraction of the subcarriers. To enable this efficient interpolation-based quantized multimode precoding solution, we introduce (1) a novel precoder codebook design that lends itself to precoder interpolation, across subcarriers, followed by mode selection, (2) a new precoder interpolator and, finally, (3) a clustered mode selection approach that significantly reduces the feedback overhead related to the mode information on each subcarrier. Monte-Carlo bit-error rate (BER) performance simulations demonstrate the effectiveness of the proposed quantized multi-mode precoding solution, at reasonable feedback overhead

The sum capacity of a Gaussian broadcast MIMO channel can be achieved with dirty paper coding (DPC). However, algorithms that approach the DPC sum capacity do not appear viable in the forseeable future, which motivates lower complexity interference suppression techniques. Block diagonalization (BD) is a linear preceding technique for downlink multiuser MIMO systems. With perfect channel knowledge at the transmitter, BD can eliminate other users' interference at each receiver. In this paper, we study the sum capacity of BD with and without receive antenna selection. We analytically compare BD without receive antenna selection to DPC for a set of given channels. It is shown that (1) if the user channels are orthogonal to each other, then BD achieves the same sum capacity as DPC; (2) if the user channels lie in the same subspace, then the gain of DPC over BD can be upper bounded by the minimum of the number of transmit and receive antennas. These observations also hold for BD with receive antenna selection. Further, we study the ergodic sum capacity of BD with and without receive antenna selection in a Rayleigh fading channel. Simulations show that BD can achieve a significant part of the total throughput of DPC. An upper bound on the ergodic sum capacity gain of DPC over BD is proposed for easy estimation of the gap between the sum capacity of DPC and BD without receive antenna selection.

This paper derives bounds on the asymptotic expected distortion (high rate) for quantization on the complex projective space denoted as /spl Copf/P/sup n-1/. In essence the problem of quantization in an Euclidean space with constraints can be posed as an unconstrained problem on an appropriate manifold. /spl Copf/P/sup n-1/ is a non-linear manifold that represents the constraints that arise in areas such as communication with multiple antennas at the transmitter and receiver. Due to the constraints, the distortion analysis developed for Euclidean spaces cannot be applied directly. The special structure of /spl Copf/P/sup n-1/ and the distortion measures that are defined on it differentiate this problem from traditional vector quantization in Euclidean spaces.

Power control is an important technique for interference management in interference-limited multiuser communication systems. In this paper, we propose a novel multidimensional power control technique for the uplink of cellular MIMO systems. Similar to prior single antenna power control work, we employ a fixed SINR target that allows guaranteed quality-of-service for delay-sensitive data applications. The proposed power control scheme is a more general technique because it incorporates single antenna, orthogonal multi-carrier, and utility-based power control schemes as special cases. Two approaches are proposed, the first where each user's power is equally allocated to its antenna array, in the second the allocation is adaptive. The optimal solution with full channel knowledge, and a practical near-optimal solution requiring only partial channel knowledge, are both derived. Numerical results show that power control actually achieves higher throughput a.t the low SINRs typical in cellular systems, compared to supposedly optimal water-filling strategies, with lower overhead and complexity. In the higher SINR ranges, iterative water-filling indeed slightly outperforms adaptive power allocation. Due to its better exploitation of spatial diversity and reduced transmit power (and hence reduced other-cell interference), adaptive power allocation increases the achievable SINR by an order of magnitude over equal power allocation, resulting in far better coverage.

In this paper, a novel transmission technique for the multiple-input multiple-output (MIMO) broadcast channel is proposed that allows simultaneous transmission to multiple users under a limited feedback requirement. During a training phase, the base station modulates a training sequence on multiple sets of randomly generated orthogonal beamforming vectors. Then, based on the users' feedback, the base station opportunistically selects the users and corresponding orthogonal vectors that maximize the sum capacity. From theoretical analysis, the optimal amount of training to maximize the sum capacity is derived as a function of the system parameters. The main advantage of the proposed system is that it provides throughput gains for the MIMO broadcast channel with a small feedback overhead, and provides these gains even with a small number of active users. Numerical simulations show that a 20% gain in sum capacity is achieved (for a small number of users) over conventional opportunistic space division multiple access, and a 100% gain (for a large number of users) over conventional opportunistic beamforming.

A novel method based on Markov chain theory is proposed for analyzing the effect of feedback delay on a transmit beamforming system with limited feedback. Using this method, the ergodic capacity with delayed feedback of channel state information is derived. The capacity gain with respect to the case of no feedback is shown to decrease at least exponentially with the feedback delay. From these results, useful design guidelines can be derived for choosing system parameters including the vehicular speed and the tolerable feedback delay.

A novel method is proposed for reducing the feedback rate of a transmit beamforming system with feedback of quantized channel state information. Specifically, the channel is modeled as a Markov chain and the feedback bits are compressed by truncating the Markov chain transition probabilities. Using the proposed method, the feedback rate can be compressed by more than 100% without degrading the system performance.

Preceding with block diagonalization is an attractive approach for approaching sum capacity in multiuser MIMO (multiple input multiple output) broadcast channels. This method though requires either global channel state information at every receiver or an additional training phase, which requires additional system planning. This paper proposes a lattice based multi-user precoder that uses block diagonalization combined with pre-equalization and perturbation for the multiuser MIMO broadcast channel. Achievable rates are computed and used to show that the proposed technique approaches the capacity with block diagonalization and water-filling but does not require the additional channel state information at the receiver. Monte Carlo simulations under the case of equal power allocation show that the proposed method provides better diversity and BER (bit error rate) performance than block diagonalization with a zero-forcing receiver.

Multiple antenna wireless systems with feedback of quantized channel information, called "limited feedback” systems, are attractive choices for improving the quality of downlink (DL) transmission. Most work in this area use the block-fading channel model where the DL channel is assumed constant in each block and different blocks uncorrelated. In this paper, we consider limited feedback for a temporally correlated DL channel. Markov models are introduced for characterizing the temporal correlation and probability distribution of the DL channel. Using the Markov models, average feedback rates are derived. Numerical results show that the feedback rates are proportional to the Doppler frequency.

Joint source-channel coding is becoming more important for wireless multimedia transmission due to high bandwidth requirements of these multimedia sources. Design of all joint source-channel coding schemes require an estimate of distortion at different source coding rates and under different channel conditions. In this paper, we present one such distortion model for estimating distortion due to quantization and channel errors in a joint manner for MPEG-4 compressed video streams. This model takes into account important aspects of video compression such as transform coding, motion compensation, and variable length coding. Results show that our model estimates distortion within 1 dB of actual simulation values in terms of peak-signal-to-noise-ratio

This paper considers low complexity transmission for MIMO bit-interleaved coded modulation (BICM) in spatially-correlated Rayleigh channels. We consider both statistical beam-forming (SB) and spatial-multiplexing with a zero-forcing (ZF) receiver. We derive tight closed-form bit error rate (BER) expressions based on a saddlepoint approximation. We then propose a practical adaptive algorithm which selects, based on the analytical results, the combination of code-rate, modulation format, and MIMO transmission scheme (SB or ZF) that maximizes throughput whilst maintaining a pre-defined BER.

We use a perceptual distortion metric-the structural similarity (SSIM) index, to derive a new linear estimator for estimating zero-mean Gaussian sources distorted by additive white Gaussian noise (AWGN). We use this estimator in an image denoising application and compare its performance with the traditional linear least squared error (LLSE) estimator. Although images denoised using the SSIM-optimized estimator have a lower peak signal-to-noise ratio (PSNR) compared to their LLSE counterparts, the SSIM-optimized estimator clearly outperforms the LLSE estimator in terms of the visual quality of the denoised images.

The sum capacity of a Gaussian broadcast MIMO channel can be achieved with Dirty Paper Coding (DPC). Deploying DPC in real-time systems is, however, impractical. Block Diagonalization (BD) is an alternative precoding technique for downlink multiuser MIMO systems, which can eliminate inter-user interference at each receiver, at the expense of suboptimal sum capacity vs. DPC. In this paper, we study the sum capacity loss of BD for a fixed channel. We show that 1) if the user channels are orthogonal to each other, then BD achieves the complete sum capacity; and 2) if the user channels lie in a common row vector space, then the gain of DPC over BD can be bounded by the minimum of the number of transmit and receive antennas and the number of users. We also compare the ergodic sum capacity of DPC with that of BD in a Rayleigh fading channel. Simulations show that BD can achieve a significant part of the total throughput of DPC. An upper bound on the ergodic sum capacity gain of DPC over BD is derived, which can be evaluated with a few numerical integrations. With this bound, we can easily estimate how far away BD is from being optimal in terms of ergodic sum capacity, which is useful in directing practical system designs.

In this paper, we investigate a multiple input multiple output (MIMO) multiuser relay channel, where a source with multiple antennas sends data to multiple users via a relay with multiple antennas. The relay applies linear processing to the received signal and forwards the processed signal to multiple users. In our system model, the direct links from the source to the users are neglected. We propose algorithms to compute achievable sum rates of this system based on dirty paper coding. An achievable sum rate defines a sum rate that can be achieved in the MIMO multiuser relay channel with zero error probability for any user, hence it is also a lower bound of the capacity of this channel. These algorithms also produce coefficients of the precoder at the source node and the coefficients of the linear processing unit at the relay. Simulations show that the proposed system architecture and algorithms achieve sum rate performance that is close to the derived performance upper bound

We propose a multichannel feedback protocol to enable local scheduling with channel state information for wireless networks with orthogonal frequency division multiplexing (OFDM). In our proposed protocol, the frequency subcarrier domain is shared by multiple control channels, on which request-to-send (RTS) and clear-to-send (CTS) are exchanged. These control channels are created using random spreading signatures. Channel state information of the simultaneous transmissions, which defines the gains of these channels, is exchanged on these control channels and channel state information tables that contain the channel information of both the desired link and the interfering links are created at the transmit nodes, then scheduling decisions are made based on the channel information tables. We show that the proposed protocol improves the network throughput compared to IEEE 802.11 style protocols for a wireless local area network topology

In this paper we compare the following two methods of transmit preceding for the multiple antenna broadcast channel: vector perturbation applied to channel inversion (also termed zero forcing or ZF) precoding and scalar Tomlinson-Harashima (TH) precoding applied to sum-rate achieving transmit precoding. Our results indicate that vector perturbation applied to channel inversion preceding can significantly reduce power enhancement and yields the full diversity afforded by the channel to each user. Scalar TH-modulo reduction significantly reduces the power enhancement for precoding based on sum-rate criterion. The solution to vector perturbation applied to ZF precoding requires the solution to an integer optimization problem which is exponentially complex, or an approximation to the integer optimization problem which requires the Lenstra-Lenstra-Lovasz algorithm of polynomial complexity. Instead we propose a simpler solution (an approximation) to the vector perturbation problem based on the Rayleigh-Ritz theorem (R.A. Horn and C.R. Johnson, 1985). This approximate solution achieves the same diversity order as the optimal vector perturbation technique, but suffers a small coding loss. This solution is of polynomial complexity order. Further, a small increase in complexity with a "sphere"-based search around this solution yields significantly better performance. Since this vector perturbation is required to be done at the symbol rate, the lower complexity of the proposed algorithm is valuable in practice

We present a low complexity adaptive transmission approach for spatially correlated MIMO channels. The proposed scheme adaptively switches between orthogonal space-time block codes (OSTBC) and spatial multiplexing (SM), depending of the channel correlation and SNR. We derive an exact closed-form expression and tight upper bound on the OSTBC capacity in double-correlated channels, and examine the relative capacity of OSTBC and SM in terms of the spatial correlation. We then derive efficient closed-form BER expressions for practical OSTBC transmission employing bit-interleaved coded modulation (BICM). Based on these results, we propose a practical adaptive algorithm which selects the combination of MIMO transmission scheme (OSTBC or SM), and BICM mode, which achieves the highest spectral efficiency whilst satisfying a pre-defined BER

In this paper, we analyze a multiple-input multiple-output (MIMO) array consisting of two circular microstrip antennas, designed to exploit pattern diversity. We analytically derive the spatial correlation coefficients of this array as a function of the mode excited, for realistic clustered MIMO channel models. We compare the performance of the circular patch array (CPA) against an array of two spaced dipoles. In particular, we compute a theoretical tradeoff to predict when the pattern diversity provided by the CPA is more effective than space diversity from the uniform linear array (ULA), based on the eigenvalues of the spatial correlation matrix. Through simulations, we show that CPAs yield better performance or satisfy more restrictive size constraints than ULAs in clustered MIMO channels, depending on the element spacing of the ULA. These results make the CPA an attractive solution for miniaturized MIMO arrays for portable devices or access points.

The need for efficient joint source-channel coding (JSCC) is growing as new multimedia services are introduced in commercial wireless communication systems. An important component of practical JSCC schemes is a distortion model that can predict the quality of compressed digital multimedia such as images and videos. The usual approach in the JSCC literature for quantifying the distortion due to quantization and channel errors is to estimate it for each image using the statistics of the image for a given signal-to-noise ratio (SNR). This is not an efficient approach in the design of real-time systems because of the computational complexity. A more useful and practical approach would be to design JSCC techniques that minimize average distortion for a large set of images based on some distortion model rather than carrying out per-image optimizations. However, models for estimating average distortion due to quantization and channel bit errors in a combined fashion for a large set of images are not available for practical image or video coding standards employing entropy coding and differential coding. This paper presents a statistical model for estimating the distortion introduced in progressive JPEG compressed images due to quantization and channel bit errors in a joint manner. Statistical modeling of important compression techniques such as Huffman coding, differential pulse-coding modulation, and run-length coding are included in the model. Examples show that the distortion in terms of peak signal-to-noise ratio (PSNR) can be predicted within a 2-dB maximum error over a variety of compression ratios and bit-error rates. To illustrate the utility of the proposed model, we present an unequal power allocation scheme as a simple application of our model. Results show that it gives a PSNR gain of around 6.5 dB at low SNRs, as compared to equal power allocation.

Sum capacity optimal signatures in synchronous code-division multiple-access (CDMA) systems are functions of the codebook length as well as the number of active users. A new signature set must be assigned every time the number of active users changes. This correspondence considers signature sets that are less sensitive to changes in the number of active users. Equiangular signature sequences are proven to solve a certain max-min signal-to-interference-plus-noise problem, which results from their interference invariance. Unions of orthonormal bases have subsets that come close to satisfying the Welch bound. Bounds on the maximum number of bases with minimum maximum correlation are derived and a new construction algorithm is provided. Connections are made between these signature design problems, Grassmannian line packing, frame theory, and algebraic geometry

Improving downlink CDMA capacity has been an area of intensive research for the past decade, especially as the downlink has become the capacity limiting link. Multi-antenna technologies are an obvious candidate for increasing the downlink capacity, but successfully decoding spatially multiplexed signals is very challenging in an interference-limited environment, such as that of CDMA cellular systems. In this paper, a simple and novel MIMO-CDMA system design is developed, in which users are assigned to a transmit antenna either without regard to channel knowledge (static) or based on antenna selection feedback bits (dynamic). These proposed antenna partitioning techniques have a minimal increase in complexity and require only small changes to existing CDMA standards. The outage probability and capacity of the proposed systems are derived and it is shown that they outperform conventional CDMA systems regardless of the number of antennas or antenna partitioning technique.

Relay channels plays a central role in next-generation multihop wireless systems. This paper considers the MIMO relay channel where multiple antennas are employed by each terminal. New lower bounds on the capacity of a Gaussian MIMO relay channel are derived under the assumption that the transmitter employs either superposition coding or dirty-paper coding. The proposed lower bounds improve on a previously proposed lower bound that arises from a simple transmit strategy.

The statistics of natural scenes in the wavelet domain are accurately characterized by the Gaussian scale mixture (GSM) model. The model lends itself easily to analysis and many applications that use this model are emerging (e.g., denoising, watermark detection). We present an error-resilient image communications application that uses the GSM model and multiple description coding (MDC) to provide error-resilience. We derive a rate-distortion bound for GSM random variables, derive the redundancy rate-distortion function, and finally implement an MD image communication system.

This paper studies the problem of quantization of a source that lives on the complex Grassmann manifold. The special structure of the Grassmann manifold and the distortion measures that are defined on it differentiates this problem from the traditional problem of vector quantization in Euclidean spaces. Assuming a uniform source distribution along with a distortion based on chordal distance, codebook design algorithms are mentioned and rate distortion tradeoffs are studied. The expected distortion for such a quantizer is approximately characterized. These results are then applied to the performance analysis of a multiple antenna wireless communication system.

Multiple description codes generated by quantized frame expansions have been shown to perform well on erasure channels when compared to traditional channel codes. In this paper we propose a multiple description image coding scheme in the wavelet domain using quantized frame expansions. We form zerotrees from wavelet coefficients and apply a tight frame operator to the zerotrees. We then group appropriate expansions to form packets and evaluate the performance of the scheme over an erasure channel. We compare the performance of the proposed scheme with a conventional channel coding scheme.

We consider a low-complexity adaptive MIMO transmission approach for spatially correlated channels. The proposed scheme adaptively switches between different transmission modes depending on the changing channel conditions, as a means to enhance system capacity. Each mode is a combination of a transmission technique (i.e. statistical beamforming, double space-time transmit diversity and spatial multiplexing) and a modulation/coding scheme. We first motivate our adaptive algorithm by deriving new closed-form capacity expressions, and demonstrating significant information theoretic improvements over non-adaptive transmission. We then present a practical method to switch between different modes, based on the channel statistics. Our approach is shown to yield significant improvements in spectral efficiency for typical channel scenarios.

Multiple-input multiple-output (MIMO) wireless communication systems can offer high data rates through spatial multiplexing or substantial diversity using transmit diversity. In this letter, switching between spatial multiplexing and transmit diversity is proposed as a simple way to improve the diversity performance of spatial multiplexing. In the proposed approach, for a fixed rate, either multiplexing or diversity is chosen based on the instantaneous channel state and the decision is conveyed to the transmitter via a low-rate feedback channel. The minimum Euclidean distance at the receiver is computed for spatial multiplexing and transmit diversity and is used to derive the selection criterion. Additionally, the Demmel condition number of the matrix channel is shown to provide a sufficient condition for multiplexing to outperform diversity. Monte Carlo simulations demonstrate improvement over either multiplexing or diversity individually in terms of bit error rate.

Multiple-input multiple-output (MIMO) wireless systems use antenna arrays at both the transmitter and receiver to provide communication links with substantial diversity and capacity. Spatial multiplexing is a common space-time modulation technique for MIMO communication systems where independent information streams are sent over different transmit antennas. Unfortunately, spatial multiplexing is sensitive to ill-conditioning of the channel matrix. Precoding can improve the resilience of spatial multiplexing at the expense of full channel knowledge at the transmitter-which is often not realistic. This correspondence proposes a quantized precoding system where the optimal precoder is chosen from a finite codebook known to both receiver and transmitter. The index of the optimal precoder is conveyed from the receiver to the transmitter over a low-delay feedback link. Criteria are presented for selecting the optimal precoding matrix based on the error rate and mutual information for different receiver designs. Codebook design criteria are proposed for each selection criterion by minimizing a bound on the average distortion assuming a Rayleigh-fading matrix channel. The design criteria are shown to be equivalent to packing subspaces in the Grassmann manifold using the projection two-norm and Fubini-Study distances. Simulation results show that the proposed system outperforms antenna subset selection and performs close to optimal unitary precoding with a minimal amount of feedback.

Tight frames, also known as general Welch-bound- equality sequences, generalize orthonormal systems. Numerous applications - including communications, coding, and sparse approximation- require finite-dimensional tight frames that possess additional structural properties. This paper proposes an alternating projection method that is versatile enough to solve a huge class of inverse eigenvalue problems (IEPs), which includes the frame design problem. To apply this method, one needs only to solve a matrix nearness problem that arises naturally from the design specifications. Therefore, it is the fast and easy to develop versions of the algorithm that target new design problems. Alternating projection will often succeed even if algebraic constructions are unavailable. To demonstrate that alternating projection is an effective tool for frame design, the paper studies some important structural properties in detail. First, it addresses the most basic design problem: constructing tight frames with prescribed vector norms. Then, it discusses equiangular tight frames, which are natural dictionaries for sparse approximation. Finally, it examines tight frames whose individual vectors have low peak-to-average-power ratio (PAR), which is a valuable property for code-division multiple-access (CDMA) applications. Numerical experiments show that the proposed algorithm succeeds in each of these three cases. The appendices investigate the convergence properties of the algorithm.

In this paper, receiver designs for orthogonal frequency-division multiplexing (OFDM) systems that exploit the Alamouti transmit diversity technique are addressed. In Alamouti space-time coded OFDM systems, the simple Alamouti decoding at the receiver relies on the assumption that the channels do not change over an Alamouti codeword period (two consecutive OFDM symbol periods). Unfortunately, when the channel is fast fading, the assumption is not met, resulting in severe performance degradation. In this paper, a sequential decision feedback sequence estimation (SDFSE) scheme with an adaptive threshold (AT), a traditionally single-carrier equalization technique, is used to mitigate the performance degradation. A new method to set the threshold value is proposed. For small signal constellations like BPSK and QPSK, the SDFSE with an AT requires much lower complexity than a previous minimum mean square error approach at the cost of a small performance degradation. Furthermore, we show that the performance difference becomes smaller when channel estimation error is included. An adaptive effort sequence estimation (AESE) scheme is also proposed to furthur reduce the average complexity of the SDFSE scheme with an AT. The AESE scheme is based on the observation that a high Doppler frequency does not necessarily mean significant instantaneous channel variation. Simulations show the efficacy of the proposed SDFSE with an AT and the AESE.

Transmit beamforming and receive combining are low complexity, linear techniques that make use of the spatial diversity advantage provided by transmitters and/or receivers employing multiple antennas. There has been a growing interest in designing beamforming schemes for frequency division duplexing systems that use a limited amount of feedback from the receiver to the transmitter. This limited feedback conveys a beamforming vector chosen from a finite set known to both the transmitter and receiver. These techniques often use a set of beamforming vectors where the probability of error expression can not be easily formulated or bounded. It is of utmost importance to guarantee that the sets of beamforming and combining vectors are chosen such that full diversity order is achieved. For this reason, necessary and sufficient conditions on the sets of possible beamformers and combiners are derived that guarantee full diversity order in correlated Rayleigh fading.

Multiple-antenna wireless systems are of interest because they provide increased capacity over single-antenna systems. Several space-time signaling schemes have been proposed to make use of this increased capacity. Space-time techniques, such as space-time block coding and spatial multiplexing, can all be viewed as signaling with a multidimensional constellation. Because of the large capacity of multiple-input multiple-output (MIMO) channels, these multidimensional constellations often have large cardinalities. For this reason, it is impractical to perform optimal maximum-likelihood (ML) decoding for space-time systems, even for a moderate number of transmit antennas. In this paper, we propose a modified version of the classic Chase decoder for multiple-antenna systems. The decoder applies successive detection to yield an initial estimate of the transmitted bit sequence, constructs a list of candidate symbol vectors using this initial estimate, and then computes bit likelihood information over this list. Three algorithms are presented for constructing the candidate vector list. This decoder can be adjusted to have a fixed or variable complexity, while maintaining performance close to that of an ML decoder.

For a multiple antenna broadcast channel, the sum-rate capacity achieving transmit strategy requires the centralized transmitter to simultaneously communicate with multiple receivers. The objective of this paper is to design an implementable sum-rate capacity achieving transmit strategy that uses a combination of beamforming and coding for known interference. For a Gaussian broadcast channel with two transmit antennas and two receivers with one antenna each, results indicate that with QAM constellations there is significant gain in sum-rate capacity over an approach that uses only beamforming.

Multiuser spatial multiplexing uses precoding to support multiple users in multi-antenna wireless channels. In this paper, two transmit diversity techniques are proposed that use extra transmit antennas to obtain additional diversity. In one solution, an eigenmode selection technique is proposed to achieve higher diversity gain by optimally matching the data streams to eigenmodes with superior channel conditions. In the other solution, a multiuser antenna selection algorithm is proposed that achieves good performance with fewer RF chains and lower system cost. Selection criteria for single-user spatial multiplexing are extended to the multiuser scenario in the context of unitary downlink precoding, and a novel selection algorithm that is near optimal in terms of symbol error rate (SER) is proposed. Simulation results show dramatic SNR reductions of 6-10 dB at an uncoded SER of 10/sup -3/ with only one extra antenna.

Spatial multiplexing with linear precoding is a simple technique to take advantage of the high spectral efficiency provided by multiple-input multiple-output (MIMO) systems, and it can be simply implemented over frequency selective channels by using orthogonal frequency division multiplexing (OFDM). This requires channel state information (CSI) at the transmitter in the form of the precoding matrices for all OFDM subcarriers, which can be achieved using feedback. To reduce the feedback requirements, this paper proposes a new precoding method incorporating quantization of a fraction of precoding matrices followed by a special interpolation at the transmitter. Since the precoding matrices are unitary, a new interpolator is proposed on the space of unitary matrices for which the outputs are also unitary. The parameters of the interpolator are optimized based on a mean square error (MSE) or mutual information criterion. Simulations show that performance near that of complete CSI can be achieved with a reasonable amount of feedback.

The diversity gains available from multiple-input multiple-output (MIMO) wireless systems are well documented. These gains are realizable through the use of transmit beamforming and receive combining. Transmit beamforming relies on the assumption of channel knowledge at the transmitter, an assumption that is often unrealistic. Limited feedback beamformers have been designed over the past few years, but they concentrate primarily on the assumption of a spatially uncorrelated Rayleigh fading channel matrix. The paper addresses the design of limited feedback beamformers for transmit and receive correlated MIMO channels. In particular, we use a technique where the receiver chooses the beamforming vector from a codebook of possible vectors and conveys this vector over a limited feedback channel. We show how this method obtains full diversity order. Monte Carlo simulations show performance close to optimal beamforming.

Orthogonal space-time block codes (OSTBCs) are an efficient solution for obtaining full diversity order in Rayleigh fading channels. Unfortunately, OSTBCs exist for only certain numbers of transmit antennas and do not provide array gain as diversity techniques that exploit transmit channel information do. When channel state information is available at the transmitter, linearly precoded space-time codes can he used to support different numbers of transmit antennas and to improve array gain. Precoding generally requires complete channel knowledge, thus motivating limited feedback methods such as channel quantization or antenna subset selection. The paper investigates limited feedback precoding that uses a codebook of matrices known a priori to both the transmitter and receiver. Using the codebook, the receiver chooses a matrix based on current channel conditions and conveys the optimal codebook matrix to the transmitter over an error-free, zero-delay feedback channel A precoder matrix selection criterion is proposed that relates directly to minimizing the probability of symbol error of the precoded system. Low average distortion codebooks based on Grassmannian subspace packing are derived for the optimal codeword selection criterion. It is shown that codebooks designed by this method provide full diversity order in Rayleigh fading channels.

This paper presents a new MAC protocol, mitigating interference using multiple antennas with antenna selection (MIMA/AS-MAC), which uses multiple antennas to mitigate both interference from neighboring transmitters and fading. The proposed MIMA/AS-MAC protocol employs spatial multiplexing with antenna subset selection for data packet transmission, while using the Alamouti space-time code for control of packet transmission. To study its performance, the MIMA/AS-MAC was implemented in the ns-2 network simulator for a system with two antennas. The total network throughput and fairness between two traffic flows was measured. The results show that the MIMA/AS-MAC system outperforms the previously proposed MIMA-MAC scheme, which does not use an antenna selection scheme, as well as a hybrid of the IEEE 802.11 MAC protocol with Alamouti encoding.

We compare the capacities achieved by space-time water-filling and spatial water-filling for MIMO fading channels. Both the effects of fast fading and shadowing are considered. It is found that for Rayleigh fast fading MIMO channels, the spectral efficiency per antenna achieved by one-dimensional spatial water-filling is close to two-dimensional space-time water-filling. However, with log-normal shadowing, space-time water-filling achieves significantly higher capacity per antenna than spatial water-filling at low to moderate SNR regimes. Furthermore, space-time water-filling has lower computational complexity than spatial water-filling. It is also shown that space-time water-filling requires a priori knowledge of the channel gain distribution, and for Rayleigh channels with log-normal shadowing, the spectral efficiency advantage over spatial water-filling comes with an increased channel outage probability.

Transmit beamforming with receive combining is a simple method for exploiting the significant diversity provided by multiple-input multiple-output (MIMO) systems, and the use of orthogonal frequency division multiplexing (OFDM) enables low complexity implementation of this scheme over frequency selective MIMO channels. This paper proposes a beamforming technique that reduces feedback requirements for optimal beamforming. In the proposed architecture, the receiver sends back quantized versions of select beamforming vectors and the transmitter reconstructs the missing beamforming vectors through interpolation. Since a beamforming vector is phase invariant and has unit norm, a new spherical linear interpolator is proposed that exploits additional parameters for phase rotation. These parameters are determined at the receiver in the sense of maximizing the minimum channel gain or capacity, and sent back to the transmitter along with the quantized beamforming vectors. Simulation results show that the proposed beamforming method requires much less feedback information than optimal beamforming with only slight diversity loss.

Multiple-input multiple-output (MIMO) wireless systems obtain large diversity and capacity gains by employing multi-element antenna arrays at both transmitter and receiver. The theoretical performance benefits, however, are irrelevant unless low error rate, high spectral efficiency spatio-temporal signaling techniques are found. Most work in space-time coding concentrates on either designing low error rate codes or high data-rate codes but not both simultaneously. This paper proposes a new method for designing high data-rate spatio-temporal signals with low error rates. The basic idea is to use transmitter channel information in the form of limited feedback to adaptively vary the transmission scheme for a fixed data-rate. This adaptation is done by varying the number of substreams and the rate of each substream in a precoded spatial multiplexing system. We show how this method can be implemented in a limited feedback scenario where only finite sets, or codebooks, of possible precoding configurations are known to both the transmitter and receiver. Monte Carlo simulations show substantial performance gains over beamforming and spatial multiplexing.

The need for efficient joint source-channel coding is growing as new multimedia services are introduced in commercial wireless communication systems. An important component of practical joint source-channel coding schemes is a distortion model to measure the quality of compressed digital multimedia such as images and videos. Unfortunately, models for estimating the distortion due to quantization and channel bit errors in a combined fashion do not appear to be available for practical image or video coding standards. This paper presents a statistical model for estimating the distortion introduced in progressive JPEG compressed images due to both quantization and channel bit errors. Important compression techniques such as Huffman coding, DPCM coding, and run-length coding are included in the model. Examples show that the distortion in terms of peak signal to noise ratio can be predicted within a 2 dB maximum error.

Multiple-input multiple-output (MIMO) wireless systems can achieve significant diversity and array gain by using transmit beamforming and receive combining. In the absence of full channel knowledge at the transmitter, a quantized beamforming vector can be made available to the transmitter using a low-rate feedback channel, called limited feedback. A codebook based quantization approach is considered. Based on metrics such as signal-to-noise ratio and outage probability, maximization of the minimum distance between any pair of codewords has already been proposed as a codebook design criterion. An upper bound on the average SNR achieved by an optimal codebook designed using such a criterion is derived as a function of the feedback rate. This analysis quantifies the impact of the feedback rate and the number of transmit and receive antennas on the performance of the system.

A description of optimal sequences for direct-spread code-division multiple access (DS-CDMA) is a byproduct of recent characterizations of the sum capacity. This paper restates the sequence design problem as an inverse singular value problem and shows that the problem can be solved with finite-step algorithms from matrix theory. It proposes a new one-sided algorithm that is numerically stable and faster than previous methods.

Codebooks constructed from Welch bound equality (WBE) sequences have been show to be optimal in terms of sum capacity in synchronous CDMA systems. Unfortunately, these codebooks are a function of the number of active signatures and need to be reassigned as the number of active users changes to maintain optimality. To mitigate the problems caused by the loss of the Welch bound equality property, in this paper we propose a special subclass of WBE sequences for which the interference power experienced by each user depends only on the number of active users and the dimensions of the code. In deference to the relationship with Grassmannian line packing, we refer to this as a Grassmannian signature set. We study the interference properties of this set, comment on the sequence design problem, and illustrate improvements over arbitrary WBE sequence sets via simulation.

Spatial multiplexing multiple-input multiple-output (MIMO) wireless systems are of both theoretical and practical importance because they can achieve high spectral efficiencies by demultiplexing the incoming bit stream into multiple substreams. It has been shown that sending fewer substreams than the number of transmit antennas by linear precoding can provide improved error rate performance. Methods for designing linear precoders using perfect channel knowledge have previously been proposed. In many wireless systems, the assumption of complete channel knowledge is unrealistic because of the lack of forward and reverse channel reciprocity. To overcome this difficulty, we propose a precoding scheme that does not require transmit channel knowledge. The precoder is designed at the receiver and conveyed to the transmitter using a limited number of bits. The limited feedback represents an index within a finite set, or codebook, of precoding matrices. The receiver selects one of these codebook matrices using a modified version of a previously proposed full channel knowledge precoder selection criterion. A precoder codebook design method for maximizing the average effective channel power is shown to relate to chordal distance Grassmannian subspace packing. Simulation results show this technique outperforms antenna subset selection spatial multiplexing.

Multiple-input multiple-output (MIMO) wireless systems provides capacity much larger than that provided by traditional single-input single-output (SISO) wireless systems. Beamforming is a low complexity technique that increases the receive signal-to-noise ratio (SNR), however, it requires channel knowledge. Since in practice channel knowledge at the transmitter is difficult to realize, we propose a technique where the receiver designs the beamforming vector and sends it to the transmitter by transmitting a label in a finite set, or codebook, of beamforming vectors. A codebook design method for quantized versions of maximum ratio transmission, equal gain transmission, and generalized selection diversity with maximum ratio combining at the receiver is presented. The codebook design criterion exploits the quantization problem's relationship with Grassmannian line packing. Systems using the beamforming codebooks are shown to have a diversity order of the product of the number of transmit and the number of receive antennas. Monte Carlo simulations compare the performance of systems using this new codebook method with the performance of systems using previously proposed quantized and unquantized systems.

Spatial multiplexing techniques send independent data streams on different transmit antennas to maximally exploit the capacity of multiple-input multiple-output (MIMO) fading channels. Most existing multiplexing techniques are based on an idealized MIMO channel model representing a rich scattering environment. Realistic channels corresponding to scattering clusters exhibit correlated fading and can significantly compromise the performance of such techniques. In this paper, we study the design and performance of spatial multiplexing techniques based on a virtual representation of realistic MIMO fading channels. Since the nonvanishing elements of the virtual channel matrix are uncorrelated, they capture the essential degrees of freedom in the channel and provide a simple characterization of channel statistics. In particular, the pairwise-error probability (PEP) analysis for correlated channels is greatly simplified in the virtual representation. Using the PEP analysis, various precoding schemes are introduced to improve performance in virtual channels. Unitary precoding is proposed to provide robustness to unknown channel statistics. Nonunitary precoding techniques are proposed to exploit channel structure when channel statistics are known at the transmitter. Numerical results are presented to illustrate the attractive performance of the precoding techniques.

Multiple-input multiple-output (MIMO) wireless systems are of interest due to their ability to provide substantial gains in capacity and quality. The paper proposes equal gain transmission (EGT) to provide diversity advantage in MIMO systems experiencing Rayleigh fading. The applications of EGT with selection diversity combining, equal gain combining, and maximum ratio combining are addressed. It is proven that systems using EGT with any of these combining schemes achieve full diversity order when transmitting over a memoryless, flat-fading Rayleigh matrix channel with independent entries. Since, in practice, full channel knowledge at the transmitter is difficult to realize, a quantized version of EGT is proposed. An algorithm to construct a beamforming vector codebook that guarantees full diversity order is presented. Monte-Carlo simulation comparisons with various beamforming and combining systems illustrate the performance as a function of quantization.

Transmit beamforming and receive combining are simple methods for exploiting the significant diversity that is available in multiple-input multiple-output (MIMO) wireless systems. Unfortunately, optimal performance requires either complete channel knowledge or knowledge of the optimal beamforming vector; both are hard to realize. In this article, a quantized maximum signal-to-noise ratio (SNR) beamforming technique is proposed where the receiver only sends the label of the best beamforming vector in a predetermined codebook to the transmitter. By using the distribution of the optimal beamforming vector in independent and identically distributed Rayleigh fading matrix channels, the codebook design problem is solved and related to the problem of Grassmannian line packing. The proposed design criterion is flexible enough to allow for side constraints on the codebook vectors. Bounds on the codebook size are derived to guarantee full diversity order. Results on the density of Grassmannian line packings are derived and used to develop bounds on the codebook size given a capacity or SNR loss. Monte Carlo simulations are presented that compare the probability of error for different quantization strategies.

Multiple-input multiple-output (MIMO) wireless communication systems employ multiple transmit and multiple receive antennas to obtain significant improvement in channel capacity. However, the capacity is limited by the correlation of subchannels in non-ideal scattering environments. In this paper, we investigate MIMO systems that use antennas with dissimilar radiation patterns to introduce decorrelation, hence increasing channel capacity. We develop a ray tracing model that takes into account both the propagation channel and the transmit and receive antenna patterns. Using a computational electromagnetic simulator, we show that: (1) MIMO systems that exploit antenna pattern diversity allow for improvement over dual-polarized antenna systems; and (2) the capacity increase of such MIMO systems depends on the characteristics of the scattering environment.

Wireless systems with multiple-transmit and multiple-receive antennas (MIMO systems) are of interest due to their ability to provide substantial gains in capacity and quality. In this paper we propose equal gain transmission (EGT) as a technique to provide diversity advantage in MIMO systems. Using bounds on the error rate we show that EGT obtains a diversity gain on the order of the product of the number of transmit and receive antennas. Since in practice full channel knowledge at the transmitter is difficult to realize, we propose a technique using quantized phase information at the transmitter that achieves a full diversity advantage given enough bits of feedback. Monte Carlo simulation comparisons with other systems show the performance as a function of quantization.

Spatial multiplexing is a signaling strategy for achieving high spectral efficiencies in communication links that employ multiple transmit and multiple receive antennas. In such systems, it is desirable to use only a subset of the available transmit and/or receive antennas to reduce cost and complexity. We address the problem of optimal antenna subset selection in spatial multiplexing systems when only the second-order statistics of the channel are available. We derive selection criteria for both the maximum likelihood and zero forcing receivers, motivated by minimizing the average symbol error rate. We characterize the antenna selection gain and show that both coding gain and diversity gain is possible. We then use Monte Carlo simulations to validate our analysis.

Wireless systems with multiple transmit and receive antennas (MIMO systems) provide high capacity due to the plurality of modes available in the channel. Previous code designs for MIMO systems have focused primarily on multiplexed signaling for high data rate or diversity signaling for high link reliability. Based on Ganesan and Stoica (2000) and Hassibi and Hochwald (2000), and using results from frame theory, we present a MIMO space-time code design which bridges the gap between multiplexing and diversity and performs well both in terms of ergodic capacity as well as error-probability. In particular, we demonstrate that designs performing well from an ergodic capacity point of view do not necessarily perform well from an error probability point of view. Simulations illustrate performance of the proposed codes in narrowband MIMO Rayleigh fading channels.

Future wireless systems will employ multiple antennas at both transmitter and receiver to take advantage of large capacity gains. Two competing spatial modulation techniques for such systems are multiplexing, for high spectrum efficiency; and diversity, for high reliability. In this paper we show that the Demmel (1988) condition number of a MIMO (multiple-input multiple-output) channel characterizes its suitability for multiplexed transmission, over diversity transmission, based on a minimum Euclidean distance comparison. We examine the probability of obtaining channels suitable for multiplexing as a function of constellation, rate, and number of antennas, for i.i.d. flat-fading Rayleigh matrix channels.

Future cellular systems will employ spatial multiplexing with multiple antennas at both transmitter and receiver to take advantage of large capacity gains. In such systems it will be desirable to select a subset of available transmit antennas for link initialization, link maintenance, or handoff. In this paper we present a criteria for selecting the optimal antenna subset in terms of minimum error rate, when coherent receivers, either linear or maximum likelihood (ML), are used over a slowly varying channel. For the ML receiver we propose to pick the subset whose output constellation has the largest minimum Euclidean distance. For the linear receiver we propose use of the post-processing SNRs (signal to noise ratios) of the multiplexed streams whereby the antenna subset that induces the largest minimum SNR is chosen. Simulations demonstrate that our selection algorithms also provides diversity advantage thus making subset selection useful over fading channels.

Two outstanding examples of transmit diversity schemes for the multiple-antenna flat-fading channel are space-time block coding (STBC) and space-time trellis coding (STTC). We compare the performance of STBC and STTC in terms of the frame error rate keeping the transmit power, spectral efficiency and number of trellis states fixed. We discover that a simple concatenation of space-time block codes with traditional AWGN (additive white Gaussian noise) trellis codes outperforms some of the best known space-time trellis codes at SNRs (signal to noise ratios) of interest. Our result holds for a small number of trellis states with one or two receive antennas, and is useful for the design and implementation of multiple-antenna wireless systems.

Despite its practical importance in image processing and computer vision, blind blur identification and blind image restoration have so far been addressed under restrictive assumptions such as all-pole stationary image models blurred by zero or minimum-phase point-spread functions. Relying upon diversity (availability of a sufficient number of multiple blurred images), we develop blind FIR blur identification and order determination schemes. Apart from a minimal persistence of the excitation condition (also present with nonblind setups), the inaccessible input image is allowed to be deterministic or random and of unknown color of distribution. With the blurs satisfying a certain co-primeness condition in addition, we establish existence and uniqueness results which guarantee that single input/multiple-output FIR blurred images can be restored blindly, though perfectly in the absence of noise, using linear FIR filters. Results of simulations employing the blind order determination, blind blur identification, and blind image restoration algorithms are presented. When the SNR is high, direct image restoration is found to yield better results than indirect image restoration which employs the estimated blurs. In low SNR, indirect image restoration performs well while the direct restoration results vary with the delay but improve with larger equalizer orders.

Communicating reliably over the wireless fading channel is a significant challenge. Previous work has focused on exploiting the plurality of antennas available at the basestation for transmit diversity. As the forward channel is not typically known, transmit diversity schemes couple energy into the spatial channel without regard to the interference created towards cochannel users in other cells. To alleviate these problems we present a transmit diversity technique which uses strategically placed sets of antenna arrays and partial channel knowledge to direct multiple beams from different spatial locations to each user. Beamforming on each antenna array is used to achieve lower crosstalk among users while the presence of multiple arrays provides diversity benefit. Analysis of the proposed system using the pairwise error probability is presented along with simulations assuming perfect channel knowledge at the receiver.

Despite its practical importance in image processing and computer vision, blind blur identification and restoration has so far been addressed under restrictive assumptions such as all-pole stationary image models blurred by zero-or minimum-phase PSFs. Relying upon diversity (sufficient number of multiple blurred images), we develop blind FIR blur identification and order determination schemes. Apart from a minimal persistence of excitation condition (also present with non-blind setups), the inaccessible input is allowed to be deterministic or random and of unknown color or distribution. With the blurs also satisfying a certain co-primeness condition, we establish existence and uniqueness results which guarantee that single-input/multiple-output FIR blurred images can be restored perfectly but blindly using linear FIR filters. The resulting direct blind restoration filters by-pass the blur identification step and work well in higher SNRs. In lower SNRs better results are achieved by first identifying the blurs and then constructing the MMSE solution.