Integrated sensing and communication (ISAC) and the Frequency Range 3 (FR3) (upper mid-band) spectrum are among the key enablers of future wireless systems. ISAC promises new sensing functionalities for networks historically designed for communications, while the FR3 spectrum, approximately from 7 to 24GHz, offers large bandwidths and diverse propagation characteristics that significantly extend deployment possibilities. Motivated by the potential synergy between these two paradigms, this work presents an experimental investigation of a multiband ISAC channel in the FR3 range under realistic conditions. Using the Pi-Radio software-defined radio (SDR) platform and superresolution parameter estimation methods, we design a multiband testbed that measures sensing metrics such as the probability of detection (PD), probability of false alarm (PFA), and localization root mean-squared error (RMSE) across sub-bands at 6.5, 8.75, 10, 15, and 21.7 GHz. To analyze how communication performance reacts to environmental dynamics, we introduce the channel update rate gain (CURG), a new metric that quantifies achievable data-rate gains induced by target-dependent channel variations.

Joint detection and localization of users and scatterers in multipath-rich channels on multiple bands is critical for integrated sensing and communication (ISAC) in 6G. Existing multiband sensing methods are limited by classical beamforming or computationally expensive approaches. This paper introduces alternating direction method of multipliers (ADMM)-assisted compressed multiband sensing (CMS), hereafter referred to as ADMM-CMS, which is a novel framework for multiband sensing using uplink quadrature amplitude modulation-modulated pilot symbols. To solve the CMS problem, we develop an adaptive ADMM algorithm that adjusts to noise and ensures automatic stopping if converged. ADMM combines the decomposability of dual ascent with the robustness of augmented Lagrangian methods, making it suitable for large-scale structured optimization. Simulations show that ADMM-CMS achieves higher spatial resolution and improved denoising compared to Bartlett-type beamforming, yielding a 34 dB gain in per-antenna transmit power for achieving a 0.9 successful recovery probability (SRP). Moreover, compared to performing compressed sensing separately on the constituent 7 GHz and 10 GHz sub-bands, ADMM-CMS achieves reductions in delay root mean squared error of 34.46% and 40.76%, respectively, at -41 dBm per-antenna transmit power, while also yielding improved SRP. Our findings demonstrate ADMM-CMS as an efficient enabler of ISAC in frequency range 3 (FR3, 7-24 GHz) for 6G systems.

The following paper presents a multi-band sensing channel quality analysis in the upper mid-band, also known as frequency range 3 (FR3). Measurements were conducted at 6.5 GHz, 8.75 GHz, 10 GHz, and 15 GHz, using a setup designed for integrated sensing and communication (ISAC). The sensing channel quality is evaluated using the estimation reliability metric, based on the iterative Levenberg–Marquardt (LM) algorithm. Given the static environment, we also validate a method to handle time-invariant dense multipath components (DMCs). Results show that lower bands enable the detection of more specular components due to lower path loss, but stronger DMC leads to lower estimation SNR. Higher bands provide cleaner estimates despite detecting fewer components. The trade-offs inherent to upper and lower FR3 bands highlight the potential of multi-band ISAC in the FR3 spectrum.

The upper mid-band FR3 spectrum (7–24GHz) has garnered significant interest for future cellular services. However, utilizing a large portion of this band requires careful interference coordination with incumbent satellite systems. This paper investigates interference from high-power terrestrial base stations (TN-BSs) to satellite downlink receivers. A central challenge is that the victim receivers, i.e., ground-based non-terrestrial user equipment (NTN-UEs), such as satellite customer premises equipment, must first be detected, and their channels estimated, before the TN-BS can effectively place nulls in their directions. We explore a potential solution where NTN-UEs periodically transmit preambles or beacon signals that TN-BSs can use for detection and channel estimatio. The performance of this nulling approach is analyzed in a simplified scenario with a single victim, revealing the interplay between path loss and estimation quality in determining nulling performance. To further validate the method, we conduct a detailed multi-user site-specific ray-tracing (RT) simulation in a rural environment. The results show that the proposed nulling approach is effective under realistic parameters, even with high densities of victim units, although TN-BS may require a substantial number of antennas.

This demonstration presents a prototyping platform for 6G cellular experimentation, leveraging the open-source Open Air Interface (OAI) 5G implementation and software-defined radios (SDRs) to operate in the candidate upper mid-band spectrum$(\mathbf{7 - 2 4} \mathbf{~ G H z})$for 6G, also known as Frequency Range 3 (FR3). The platform showcases two distinct FR3 end-to-end demonstrations: (1) a fully operational end-to-end communication link utilizing OAI, and (2) an open-radio unit (O-RU) implementation tailored for an O-RAN architecture. The O-RU integrates an Analog Devices O-RU with the Pi-Radio FR3 frontend radio, enabling flexible experimentation with radio hardware and software. The first demonstration highlights the potential of OAI to extend its open 5G framework for 6G prototyping in new frequency bands. The second demonstration focuses on the open O-RU's ability to serve as a testbed for hardwarespecific upper mid-band prototyping, with a focus on the physical layer. In addition to that, we present and demonstrate a Xilinx RFSoC-based open-source channel sounder implementation at FR3, which is key to assess RF propagation in frontier spectrum. Together, these demonstrations provide a comprehensive platform for researchers and practitioners to explore 6G innovations in the emerging upper mid-band spectrum, fostering open development and collaboration in next-generation cellular networks.

The growing demand for broader bandwidth in cellular networks has turned the upper mid-band (7–24 GHz) into a focal point for expansion. However, the integration of terrestrial cellular and incumbent satellite services, particularly in the 12 GHz band, poses significant interference challenges. This paper investigates the interference dynamics in terrestrial-satellite coexistence scenarios and introduces a novel beamforming approach that leverages available ephemeris data for dynamic interference mitigation. By establishing spatial radiation nulls directed towards visible satellites, our technique ensures the protection of satellite uplink communications without markedly compromising terrestrial downlink quality. Through a practical case study, we demonstrate that our approach maintains the satellite uplink signal-to-noise ratio (SNR) degradation under 0.1 dB and incurs only a negligible SNR penalty for the terrestrial downlink. Our findings offer a promising pathway for efficient spectrum sharing in the upper mid-band, fostering a concurrent enhancement in both terrestrial and satellite network capacity.

The growing focus on indoor robot navigation utilizing wireless signals has stemmed from the capability of these signals to capture high-resolution angular and temporal measurements. Prior heuristic-based methods, based on radio frequency (RF) propagation, are intuitive and generalizable across simple scenarios, yet fail to navigate in complex environments. On the other hand, end-to-end (e2e) deep reinforcement learning (RL) can explore a rich class of policies, delivering surprising performance when facing complex wireless environments. However, the price to pay is the astronomical amount of training samples, and the resulting policy, without fine-tuning (zero-shot), is unable to navigate efficiently in new scenarios unseen in the training phase. To equip the navigation agent with sample-efficient learning and zero-shot generalization, this work proposes a novel physics-informed RL (PIRL) where a distance-to-target-based cost (standard in e2e) is augmented with physics-informed reward shaping. The key intuition is that wireless environments vary, but physics laws persist. After learning to utilize the physics information, the agent can transfer this knowledge across different tasks and navigate in an unknown environment without fine-tuning. The proposed PIRL is evaluated using a wireless digital twin (WDT) built upon simulations of a large class of indoor environments from the AI Habitat dataset augmented with electromagnetic radiation simulation for wireless signals. It is shown that the PIRL significantly outperforms both e2e RL and heuristic-based solutions in terms of generalization and performance. Source code is available at https://github.com/Panshark/PIRL-WIN.

This work presents the results of measurements conducted on small drones and a bionic bird using a 60 GHz millimeter wave radar, analyzing their micro-Doppler characteristics in both time and frequency domains. In particular, we focus on their distinct nature of movement, i.e., rotating propellers and flapping wings, rather than relying on their materials. The time-series measurements show comparable differences in the phase of the samples as a result of micro-Doppler effects. Utilizing the collected measurement data, we develop neural network models to accurately differentiate between bionic birds and drones, having a significant potential for application in airports where precise object identification is essential. We adopt a convolutional neural network for detecting changes in the amplitude values and a convolutional long- and short-term memory for identifying the phase difference between the drone and bird signatures. The results reveal that distinguishing between small drones and birds can be done based on the phase difference of the scattered radar signals, even with a high noise variance.

In an increasingly visual world, people with blindness and low vision (pBLV) face substantial challenges in navigating their surroundings and interpreting visual information. From our previous work, VIS4ION is a smart wearable that helps pBLV in their daily challenges. It enables multiple microservices based on artificial intelligence (AI), such as visual scene processing, navigation, and vision-language inference. These microservices require powerful computational resources and, in some cases, stringent inference times, hence the need to offload computation to edge servers. This paper introduces a novel video streaming platform that improves the capabilities of VIS4ION by providing real-time support of the microservices at the network edge. When video is offloaded wirelessly to the edge, the time-varying nature of the wireless network requires adaptation strategies for a seamless video service. We demonstrate the performance of our adaptive real-time video streaming platform through experimentation with an open-source 5G deployment based on open air interface (OAI). The experiments demonstrate the ability to provide microservices robustly in time-varying network conditions.

High-rank line-of-sight (LOS) MIMO systems have attracted considerable attention for millimeter wave and THz communications. The small wavelengths in these frequencies enable spatial multiplexing with massive data rates at long distances. Such systems are also being considered for multi-path non-LOS (NLOS) environments. In these scenarios, standard channel models based on plane waves cannot capture the curvature of each wave front necessary to model spatial multiplexing. This work presents a novel and simple multi-path wireless channel parametrization where each path is replaced by a LOS path with a reflected image source. The model is fully valid for all paths with specular planar reflections, and captures the spherical nature of each wave front. Importantly, it is shown that the model uses only two additional parameters relative to the standard plane wave model. Moreover, the parameters can be easily captured in standard ray tracing. The accuracy of the approach is demonstrated on detailed ray tracing simulations at 28GHz and 140GHz in a dense urban area.

Wideband millimeter-wave communication systems can be extended to provide radar-like sensing capabilities on top of data communication, in a cost-effective manner. However, the development ofjoint communication and sensingtechnology is hindered by practical challenges, such as occlusions to the line-of-sight path and clock asynchrony between devices. The latter introducestime-varyingtiming and frequency offsets that prevent the estimation of sensing parameters and, in turn, the use of standard signal processing solutions. Existing approaches cannot be applied to commonly used phased-array receivers, as they build on stringent assumptions about the multipath environment, and are computationally complex. We present JUMP, the first system enablingpracticalbistatic and asynchronous joint communication and sensing, while achieving accurate target tracking and micro-Doppler extraction in realistic conditions. Our system compensates for the timing offset by exploiting the channel correlation across subsequent packets. Further, it tracks multipath reflections and eliminates frequency offsets by observing the phase of a dynamically-selected static reference path. JUMP has been implemented on a 60 GHz experimental platform, performing extensive evaluations of human motion sensing, including non-line-of-sight scenarios. In our results, JUMP attains comparable tracking performance to a full-duplex monostatic system and similar micro-Doppler quality with respect to a phase-locked bistatic receiver.

Millimeter wave systems rely on narrow beams (beamforming) and dense cell deployments for reliable communication. Tracking these beams from multiple cells can increase power consumption and signaling overhead. Therefore, a mobile needs to selectively and smartly track beams under power/overhead constraints. In this paper, we propose a fully data-driven, long short-term memory (LSTM)-based, selective link tracking approach. These approaches are developed for both fixed and adaptive power/overhead constraints, which also predict the magnitude of the best performing beam. The algorithms are validated in simulations of a$\mathrm {28 GHz}$5G New Radio (NR)-like system in an urban area with realistic navigation routes utilizing detailed ray-tracing. The simulations demonstrate that the proposed methods outperform classic and deep reinforcement learning (RL) approaches in terms of tracking accuracy, power saving and overhead for both analog and digital beamforming architectures. We also argue that the prediction of the proposed method can be easily performed on a digital signal processor of a modern chipset with minimal resource consumption.

In this paper, we study a navigation problem where a mobile robot needs to locate a mmWave wireless signal. Using the directionality properties of the signal, we propose an estimation and path planning algorithm that can efficiently navigate in cluttered indoor environments. We formulate Extended Kalman filters for emitter location estimation in cases where the signal is received in line-of-sight or after reflections. We then propose to plan motion trajectories based on belief-space dynamics in order to minimize the uncertainty of the position estimates. The associated non-linear optimization problem is solved by a state-of-the-art constrained iLQR solver. In particular, we propose a method that can handle a large number of obstacles (∼ 300) with reasonable computation times. We validate the approach in an extensive set of simulations. We show that our estimators can help increase navigation success rate and that planning to reduce estimation uncertainty can improve the overall task completion speed.

Low-resolution digital-to-analog and analog-to-digital converters (DACs and ADCs) have attracted considerable attention in efforts to reduce power consumption in millimeter wave (mmWave) and massive MIMO systems. This paper presents an information-theoretic analysis with capacity bounds for classes of linear transceivers with finite quantization. The transmitter modulates symbols via a unitary transform followed by a DAC and the receiver employs an ADC followed by the inverse unitary transform. If the unitary transform is set to a discrete Fourier transform (DFT) matrix, the model naturally captures filtering and spectral constraints. In particular, this model allows studying the impact of quantization on out-of-band (OOB) emission constraints. The out-of-band emission constraints are defined using a “spectrum mask” in practical wireless systems. All transmissions need to meet the OOB constraint to allow other services and technologies to operate in adjacent bands.In the limit of a large random unitary transform, it is shown that the effect of quantization can be precisely described via an additive Gaussian noise model. This model in turn leads to simple and intuitive expressions for the power spectrum of the transmitted signal and a lower bound to the capacity with quantization. Comparison with non-quantized capacity and a capacity upper bound that does not make linearity assumptions suggests that while low resolution quantization has minimal impact on the achievable rate at typical parameters in 5G systems, satisfying OOB emissions is potentially much more of a challenge.

This paper provides a deterministic channel model for a scenario where wireless connectivity is established through a reflection off a smooth planar surface of an infinite extent. The developed model is rigorously built upon the physics of wave propagation and is as precise as tight are the unboundedness and smoothness assumptions on the surface. This model allows establishing how line-of-sight multiantenna communication is altered by a reflection off an electrically large surface, a situation of high interest for mmWave and terahertz frequencies.

Millimeter wave systems suffer from high power consumption and are constrained to use low resolution quantizers —digital to analog and analog to digital converters (DACs and ADCs). However, low resolution quantization leads to reduced data rate and increased out-of-band emission noise. In this paper, a multiple-input multiple-output (MIMO) system with linear transceivers using low resolution DACs and ADCs is considered. An information-theoretic analysis of the system to model the effect of quantization on spectrospatial power distribution and capacity of the system is provided. It is shown that the impact of quantization can be accurately described via a linear model with additive independent Gaussian noise. This model in turn leads to simple and intuitive expressions for spectrospatial power distribution of the transmitter and a lower bound on the achievable rate of the system. The derived model is validated through simulations and numerical evaluations, where it is shown to accurately predict both spectral and spatial power distributions.

Generative Adversarial Networks (GANs) are a widely-used tool for generative modeling of complex data. Despite their empirical success, the training of GANs is not fully understood due to the joint training of the generator and discriminator. This paper analyzes these joint dynamics when the true samples, as well as the generated samples, are discrete, finite sets, and the discriminator is kernel-based. A simple yet expressive framework for analyzing training called the $\textit{Isolated Points Model}$ is introduced. In the proposed model, the distance between true samples greatly exceeds the kernel width so that each generated point is influenced by at most one true point. The model enables precise characterization of the conditions for convergence both to good and bad minima. In particular, the analysis explains two common failure modes: (i) an approximate mode collapse and (ii) divergence. Numerical simulations are provided that predictably replicate these behaviors.

We provide a deterministic channel model for a scenario where wireless connectivity is established through a reflection from a planar smooth surface of an infinite extent. The developed model is rigorously built upon the physics of wave propagation, and is as precise as tight are the unboundedness and smoothness assumptions on the surface. This model allows establishing that line-of-sight spatial multiplexing can take place via reflection off an electrically large surface, a situation of high interest for mmWave and terahertz frequencies.

Millimeter wave (mmWave) systems rely on communication in narrow beams for directional and spatial multiplexing gains. A key challenge in realizing these systems is beam tracking, particularly in environments with high mobility and blockage. Additionally, in wide-area mmWave cellular systems, user equipment (UE) devices must often simultaneously track signals from multiple cells, since links to individual cells can be unreliable. Models of the channel dynamics across multiple cells and multiple beams are difficult to derive from first principles. In this work, we propose a fully data-driven approach based on a novel auto-encoder integrated long short term memory (LSTM) network, which predicts multiple beams from multiple cells, one time step in the future. The key innovation is to use an auto-encoder pre-processing step, which reduces the dimensionality of the input– the main challenge in multi-cell, multi-beam tracking. The prediction capability of the proposed network is verified and compared to common baseline predictors as well as popular machine learning (ML) based neural network predictors in realistic system-level simulations using a commercial ray-tracer. We observe that predictions from the proposed network, which utilizes auto-encoders for dimensionality reduction, offers significantly better best beam accuracy and lower beam misalignment loss than common baseline approaches. We also discuss outage prediction and proactive beam switching as applications of the multi-cell multi-beam prediction.

Blockage is a substantial problem at millimeter wave (mmWave) frequencies, much more so than sub-6 GHz. Blockage events caused by objects such as cars and humans occur quickly and have substantial attenuation; typical attenuations in this work range from 11 to 22 dB, with a maximum of 32 dB. This problem is overcome with spatial diversity. During blockage events, mmWave transceivers can beamform towards an unblocked path. In this paper we explore this idea in depth: if the primary path is blocked, which alternate paths will be available? To answer this question, we have built a measurement system that uses 60 GHz phased arrays to rapidly obtain spatially resolved measurements of the channel. Scans are repeated every 3.2 ms, allowing us to observe blockage events that occur on the different paths in the channel. The design of this system is presented, and a description is given of three human-body blockage measurement campaigns. Paths are assigned states of blocked/unblocked, and each measurement is expressed as a time series of states which specify the status of each path. The evolution between states is described with a Markov model, and the measurement results are used to derive transition probabilities between states.

The millimeter wave bands are being increasingly considered for wireless communication to unmanned aerial vehicles (UAVs). Critical to this undertaking are statistical channel models that describe the distribution of constituent parameters in scenarios of interest. This paper presents a general modeling methodology based on data-training a generative neural network. The proposed generative model has a two-stage structure that first predicts the link state (line-of-sight, non-line-of-sight, or outage), and subsequently feeds this state into a conditional variational autoencoder (VAE) that generates the path losses, delays, and angles of arrival and departure for all the propagation paths. The methodology is demonstrated for$\mathrm {28~GHz}$air-to-ground channels between UAVs and a cellular system in representative urban environments, with training datasets produced through ray tracing. The demonstration extends to both standard base stations (installed at street level and downtilted) as well as dedicated base stations (mounted on rooftops and uptilted). The proposed approach is able to capture complex statistical relations in the data and it significantly outperforms standard 3GPP models, even after refitting the parameters of those models to the data.

With growing interest in mmWave connectivity for unmanned aerial vehicles (UAVs), a basic question is whether networks intended for terrestrial service can provide sufficient aerial coverage as well. To assess this possibility in the context of urban environments, extensive system-level simulations are conducted using a generative channel model recently proposed by the authors. It is found that standard downtilted base stations at street level, deployed with typical microcellular densities, can indeed provide satisfactory UAV coverage. Interestingly, this coverage is made possible by a conjunction of antenna sidelobes and strong reflections. As the deployments become sparser, the coverage is only guaranteed at progressively higher UAV altitudes. The incorporation of base stations dedicated to UAV communication, rooftop-mounted and uptilted, would strengthen the coverage provided their density is comparable to that of the standard deployment, and would be instrumental for sparse deployments of the latter.

Contemporary wisdom based on empirical studies suggests that standard recurrent neural networks (RNNs) do not perform well on tasks requiring long-term memory. However, RNNs’ poor ability to capture long-term dependencies has not been fully understood. This paper provides a rigorous explanation of this property in the special case of linear RNNs. Although this work is limited to linear RNNs, even these systems have traditionally been difficult to analyze due to their non-linear parameterization. Using recently-developed kernel regime analysis, our main result shows that as the number of hidden units goes to infinity, linear RNNs learned from random initializations are functionally equivalent to a certain weighted 1D-convolutional network. Importantly, the weightings in the equivalent model cause an implicit bias to elements with smaller time lags in the convolution, and hence shorter memory. The degree of this bias depends on the variance of the transition matrix at initialization and is related to the classic exploding and vanishing gradients problem. The theory is validated with both synthetic and real data experiments.

Understanding generalization and estimation error of estimators for simple models such as linear and generalized linear models has attracted a lot of attention recently. This is in part due to an interesting observation made in machine learning community that highly over-parameterized neural networks achieve zero training error, and yet they are able to generalize well over the test samples. This phenomenon is captured by the so called double descent curve, where the generalization error starts decreasing again after the interpolation threshold. A series of recent works tried to explain such phenomenon for simple models. In this work, we analyze the asymptotics of estimation error in ridge estimators for convolutional linear models. These convolutional inverse problems, also known as deconvolution, naturally arise in different fields such as seismology, imaging, and acoustics among others. Our results hold for a large class of input distributions that include i.i.d. features as a special case. We derive exact formulae for estimation error of ridge estimators that hold in a certain high-dimensional regime. We show the double descent phenomenon in our experiments for convolutional models and show that our theoretical results match the experiments.

There is a growing interest in reusing cellular base stations on the ground to provide long range, high-speed wireless connectivity to UAVs. Towards this goal, we present SkyRoute – a novel and powerful simulation platform for rapid and realistic assessment of UAV cellular connectivity. SkyRoute combines real base station locations and antenna data with a lightweight version of the widely-used ns-3 simulation platform for full-stack wireless channel and cellular network simulation. As an exemplary application, we demonstrate realistic coverage and cell selection prediction in a large metropolitan area.

Wireless communication at millimeter wave frequencies is an attractive option for high-bit-rate connectivity to unmanned aerial vehicles (UAVs). However, conducting the channel measurements necessary to assess the communication performance at these frequencies has been challenging due to the severe payload and power restrictions in commercial UAVs. This work presents a novel lightweight (approximately 1.3kg) channel measurement system at 28GHz installed on a commercially available UAV. A ground transmitter equipped with a horn antenna conveys sounding signals to a UAV equipped with a lightweight spectrum analyzer. We demonstrate that the measurements can be highly influenced by the onboard antenna pattern as shaped by the UAV’s frame. A calibration procedure is presented to correct for the resulting angular variations in antenna gain. The measurement setup is then validated on real flights from an airstrip at distances in excess of 300m.

Discontinuous reception (DRX), wherein a user equipment (UE) temporarily disables its receiver, is a critical power saving feature in modern cellular systems. DRX is likely to be aggressively used at mmWave and sub-THz frequencies due to the high front-end power consumption. A key challenge for DRX at these frequencies is blockage-induced link outages: a UE will likely need to track many directional links to ensure reliable multi-connectivity, thereby increasing the power consumption. In this paper, we explore bandit algorithms for link tracking in connected mode DRX that reduce power consumption by tracking only a fraction of the available links, but without adversely affecting the outage and throughput performance. Through detailed, system level simulations at 28 GHz (5G) and 140 GHz (6G), we observe that even sub-optimal link tracking policies can achieve considerable power savings with relatively little degradation in outage and throughput performance, especially with digital beamforming at the UE. In particular, we show that it is feasible to reduce power consumption by 75% and still achieve up to 95% (80%) of the maximum throughput using digital beamforming at 28 GHz (140 GHz), subject to an outage probability of at most 1%.

Phylogenetics study the evolutionary history of a collection of organisms based on observed heritable molecular traits, finding practical application in a wide range of domains, from conservation biology and epidemiology, to forensics and drug development. A fundamental computational kernel to evaluate evolutionary histories, also referred to as phylogenies, is the Phylogenetic Likelihood Function (PLF), which dominates the total execution time (by up to 95%) of widely used maximum-likelihood phylogenetic methods. Numerous efforts to boost PLF performance over the years mostly focused on accelerating computation; since the PLF is a data-intensive, memory-bound operation, performance remains limited by data movement. In this work, we employ near-memory computation units (NMUs) within a FPGA-based computing environment with disaggregated memory to alleviate the data movement problem and improve performance and energy efficiency when inferring large-scale phylogenies. NMUs were deployed on a multi-FPGA emulation platform for the IBM dReDBox disaggregated datacenter prototype. We find that performance and power efficiency improves by an order of magnitude when NMUs compute on local data that reside on the same server tray. This is achieved through an efficient data-allocation scheme that minimizes inter-tray data transfers (remote-data movement) when computing the PLF. More specifically, we observe up to 22x better FLOPS performance and 13x higher power efficiency (FLOPS/Watt) over the more traditional, accelerator-as-a-coprocessor model, which requires explicit remote-data transfers between disaggregated memory modules and accelerator units.

Millimeter wave wireless systems rely heavily on directional communication in narrow steerable beams. Tools to measure the spatial and temporal nature of the channel are necessary to evaluate beamforming and related algorithms. This paper presents a novel 60 GHz phased-array based directional channel sounder and data analysis procedure that can accurately extract paths and their transmit and receive directions. The gains along each path can also be measured for analyzing blocking scenarios. The sounder is validated in an indoor office environment.

A key challenge in mmWave systems is the rapid variations in channel quality along different beam directions. MmWave links are highly susceptible to blockage and small changes in the orientation of the device or appearance of blockers can lead to dramatic changes in link quality along any given direction. Many low-latency applications need to accurately predict link quality from multiple directions and multiple cells. This paper presents a novel long short term memory (LSTM)-based method for predicting multi-directional link quality in mmWave systems. The method is validated on two problems: A realistic simulation of multi-cell link tracking in an environment with randomly moving human and vehicular blockers at 28 and 140 GHz, and beam prediction in a real indoor setting at 60 GHz.

At the heart of machine learning lies the question of generalizability of learned rules over previously unseen data. While over-parameterized models based on neural networks are now ubiquitous in machine learning applications, our understanding of their generalization capabilities is incomplete and this task is made harder by the non-convexity of the underlying learning problems. We provide a general framework to characterize the asymptotic generalization error for single-layer neural networks (i.e., generalized linear models) with arbitrary non-linearities, making it applicable to regression as well as classification problems. This framework enables analyzing the effect of (i) over-parameterization and non-linearity during modeling; (ii) choices of loss function, initialization, and regularizer during learning; and (iii) mismatch between training and test distributions. As examples, we analyze a few special cases, namely linear regression and logistic regression. We are also able to rigorously and analytically explain the \emph{double descent} phenomenon in generalized linear models.

Real-world applications require light-weight, energy-efficient, fully autonomous robots. Yet, increasing autonomy is oftentimes synonymous with escalating computational requirements. It might thus be desirable to offload intensive computation-not only sensing and planning, but also low-level whole-body control-to remote servers in order to reduce on-board computational needs. Fifth Generation (5G) wireless cellular technology, with its low latency and high bandwidth capabilities, has the potential to unlock cloud-based high performance control of complex robots. However, state-of-the-art control algorithms for legged robots can only tolerate very low control delays, which even ultra-low latency 5G edge computing can sometimes fail to achieve. In this work, we investigate the problem of cloud-based whole-body control of legged robots over a 5G link. We propose a novel approach that consists of a standard optimization-based controller on the network edge and a local linear, approximately optimal controller that significantly reduces on-board computational needs while increasing robustness to delay and possible loss of communication. Simulation experiments on humanoid balancing and walking tasks that includes a realistic 5G communication model demonstrate significant improvement of the reliability of robot locomotion under jitter and delays likely to be experienced in 5G wireless links.

Low-resolution digital-to-analog and analog-to-digital converters (DACs and ADCs) have attracted considerable attention in efforts to reduce power consumption in millimeter wave (mmWave) and massive MIMO systems. This paper presents an information-theoretic analysis with capacity bounds for classes of linear transceivers with quantization. The transmitter modulates symbols via a unitary transform followed by a DAC and the receiver employs an ADC followed by the inverse unitary transform. If the unitary transform is set to an FFT matrix, the model naturally captures filtering and spectral constraints which are essential to model in any practical transceiver. In particular, this model allows studying the impact of quantization on out-of-band emission constraints. In the limit of a large random unitary transform, it is shown that the effect of quantization can be precisely described via an additive Gaussian noise model. This model in turn leads to simple and intuitive expressions for the power spectrum of the transmitted signal and a lower bound to the capacity with quantization. Comparison with non-quantized capacity and a capacity upper bound that does not make linearity assumptions suggests that while low resolution quantization has minimal impact on the achievable rate at typical parameters in 5G systems today, satisfying out-of-band emissions are potentially much more of a challenge.

Pi-Radio's vision is to democratize wireless research by providing advanced mmWave Software Defined Radio (SDR) platforms to the community at plainly affordable price points. Pi-Radio's v1 SDR features a 4-channel fully-digital transceiver that operates in the 57-64 GHz band. Fully-digital (a.k.a. MIMO) transceiver architectures enable multiple simultaneous TX/RX beams, standing in stark contrast with phased arrays featuring analog beamformers that are capable of transmitting/receiving only one beam at a time. This opens up a whole set of research problems to work on, across virtually every layer of the protocol stack. In this demo, the team will: (1) prove the correct formation of different TX/RX beams by applying geometrically determined beamforming weights, and (2) prove the benefits of fully-digital beamforming by transmitting four independent streams of data with an OFDM-based physical layer.

We consider the problem of estimating the input and hidden variables of a stochastic multi-layer neural network from an observation of the output. The hidden variables in each layer are represented as matrices with statistical interactions along both rows as well as columns. This problem applies to matrix imputation, signal recovery via deep generative prior models, multi-task and mixed regression, and learning certain classes of two-layer neural networks. We extend a recently-developed algorithm -- Multi-Layer Vector Approximate Message Passing (ML-VAMP), for this matrix-valued inference problem. It is shown that the performance of the proposed Multi-Layer Matrix VAMP (ML-Mat-VAMP) algorithm can be exactly predicted in a certain random large-system limit, where the dimensions $N\times d$ of the unknown quantities grow as $N\rightarrow\infty$ with $d$ fixed. In the two-layer neural-network learning problem, this scaling corresponds to the case where the number of input features, as well as training samples, grow to infinity but the number of hidden nodes stays fixed. The analysis enables a precise prediction of the parameter and test error of the learning.

Channel measurements at millimeter wave (mmWave) frequencies are typically carried out using directional antennas to overcome high path loss at mmWave frequencies. Unlike traditional directional channel measurements using narrow beam horn antennas where ray paths can be uniquely mapped to a direction with sufficient high accuracy, phased arrays with irregular beam pattern offers additional complexity in determining the arrival statistics of ray paths. In this work, we propose a systematic method to infer the ray path characteristics with the knowledge of beam patterns used by the phased array. We leverage on the channel measurement data obtained from extensive 60 GHz measurement campaign performed for indoor living room scenario and extract the ray path information from the measured data with high reliability. We also verify our findings through ray tracing simulation.

Due to the heavy reliance of millimeter-wave (mmWave) wireless systems on directional links, beamforming (BF) with high-dimensional arrays is essential for cellular systems in these frequencies. Thus, performing the array processing in a power-efficient manner is a fundamental challenge. Analog and hybrid BF require few analog-to-digital and digital-to-analog converters (ADCs and DACs), but can only communicate in a small number of directions at a time, limiting directional search, spatial multiplexing, and control signaling. Digital BF enables flexible spatial processing but must be operated at a low quantization resolution to stay within reasonable power levels. This decrease in quantizer resolution distorts the received as well as the transmitted signal. To assess the effect of coarse quantization at the receiver, this paper presents a system level analytic framework based on a simple additive quantization noise model (AQNM). The analysis verified through extensive simulations reveals that at moderate resolutions (3-4 bits per ADC), there is negligible loss in downlink cellular capacity from quantization. In essence, the low resolution ADCs limit the high SNR, where cellular systems typically do not operate. For the transmitter, it is shown that DACs with 4 or more bits of resolution can support high order modulations, and do not violate the adjacent carrier leakage limit set by 3rdGeneration Partnership Project (3GPP) New Radio (NR) standards for cellular operations. In fact, our findings suggest that low resolution digital BF architectures can be a power-efficient alternative to analog or hybrid BF for both transmitters and receivers at millimeter-wave.

We consider the problem of estimating the parameters of a multivariate Bernoulli process with auto-regressive feedback in the high-dimensional setting where the number of samples available is much less than the number of parameters. This problem arises in learning interconnections of networks of dynamical systems with spiking or binary valued data. We also allow the process to depend on its past up to a lag p, for a general $p \geq 1$, allowing for more realistic modeling in many applications. We propose and analyze an $\ell_1$-regularized maximum likelihood (ML) estimator under the assumption that the parameter tensor is approximately sparse. Rigorous analysis of such estimators is made challenging by the dependent and non-Gaussian nature of the process as well as the presence of the nonlinearities and multi-level feedback. We derive precise upper bounds on the mean-squared estimation error in terms of the number of samples, dimensions of the process, the lag $p$ and other key statistical properties of the model. The ideas presented can be used in the rigorous high-dimensional analysis of regularized $M$-estimators for other sparse nonlinear and non-Gaussian processes with long-range dependence.

Deep generative priors are a powerful tool for reconstruction problems with complex data such as images and text. Inverse problems using such models require solving an inference problem of estimating the input and hidden units of the multi-layer network from its output. Maximum a priori (MAP) estimation is a widely-used inference method as it is straightforward to implement, and has been successful in practice. However, rigorous analysis of MAP inference in multi-layer networks is difficult. This work considers a recently-developed method, multilayer vector approximate message passing (ML-VAMP), to study MAP inference in deep networks. It is shown that the mean squared error of the ML-VAMP estimate can be exactly and rigorously characterized in a certain high-dimensional random limit. The proposed method thus provides a tractable method for MAP inference with exact performance guarantees.

Unitary recurrent neural networks (URNNs) have been proposed as a method to overcome the vanishing and exploding gradient problem in modeling data with long-term dependencies. A basic question is how restrictive is the unitary constraint on the possible input-output mappings of such a network? This works shows that for any contractive RNN with ReLU activations, there is a URNN with at most twice the number of hidden states and the identical input-output mapping. Hence, with ReLU activations, URNNs are as expressive as general RNNs. In contrast, for certain smooth activations, it is shown that the input-output mapping of an RNN cannot be matched with a URNN, even with an arbitrary number of states. The theoretical results are supported by experiments on modeling of slowly-varying dynamical systems.

Communication and video capture from unmanned aerial vehicles (UAVs) offer significant potential for assisting first responders in remote public safety settings. In such uses, millimeter wave (mmWave) wireless links can provide high throughput and low latency connectivity between the UAV and a remote command center. However, maintaining reliable aerial communication in the mmWave bands is challenging due to the need to support high speed beam tracking and overcome blockage. This paper provides a simulation study aimed at assessing the feasibility of public safety UAV connectivity through a 5G link at 28 GHz. Real flight motion traces are captured during maneuvers similar to those expected in public safety settings. The motions traces are then incorporated into a detailed mmWave network simulator that models the channel, blockage, beamforming and full 3GPP protocol stack. We show that 5G mmWave communications can deliver throughput up to 1 Gbps with consistent sub ms latency when the base station is located near the mission area, enabling remote offloading of the UAV control and perception algorithms.

This paper presents experiments to assess and understand the feasibility of millimeter-wave (mmWave) radios for aerial links at low altitude, namely drone communications. In this preliminary study, a Commercial Off-The-Shelf (COTS) mm Wave radio is attached to a DJI Matrice 600 Pro drone. The measurement campaign reveals that while the technology is promising, the range is extremely limited, i.e., approximately 30m. Our analysis shows that the poor range is likely due to low-directionality of today's COTS devices as well as poor rate adaptation in mobile environments. More antennas at both the transmitter and the receiver, along with better 3D beamforming, will be essential to boosting the communication range and thus making this a viable technology for use cases such as high definition real-time monitoring in disaster response scenarios or rapid deployment of multi-Gbps aerial links to expand connectivity in underserved areas.

Due to the increasing density of wireless devices, the ever-growing demands for extremely high data rates, and the spectrum scarcity at the sub-6 GHz bands, making use of the spectrum-rich millimeter-wave (mmWave) frequencies is among the most important technology trends for future wireless networks. The major commercial potential of mmWave networks has led to mmWave being considered a key element for 5G-and-beyond mobile cellular networks, as well as for emerging Gbps-speed Wi-Fi networks based on the IEEE 802.11ad and draft IEEE 802.11ay standards. Despite this intense interest in mmWave communications from both the research community and industry, much fundamental research is still needed, especially at the higher layers of the networking stack.

The standard linear regression (SLR) problem is to recover a vector x0from noisy linear observations y = Ax0+ w. The approximate message passing (AMP) algorithm proposed by Donoho, Maleki, and Montanari is a computationally efficient iterative approach to SLR that has a remarkable property: for large i.i.d. sub-Gaussian matrices A, its per-iteration behavior is rigorously characterized by a scalar state-evolution whose fixed points, when unique, are Bayes optimal. The AMP algorithm, however, is fragile in that even small deviations from the i.i.d. sub-Gaussian model can cause the algorithm to diverge. This paper considers a “vector AMP” (VAMP) algorithm and shows that VAMP has a rigorous scalar state-evolution that holds under a much broader class of large random matrices A: those that are right-orthogonally invariant. After performing an initial singular value decomposition (SVD) of A, the per-iteration complexity of VAMP is similar to that of AMP. In addition, the fixed points of VAMP's state evolution are consistent with the replica prediction of the minimum mean-squared error derived by Tulino, Caire, Verdú, and Shamai. Numerical experiments are used to confirm the effectiveness of VAMP and its consistency with state-evolution predictions.

Deep generative networks provide a powerful tool for modeling complex data in a wide range of applications. In inverse problems that use these networks as generative priors on data, one must often perform inference of the inputs of the networks from the outputs. Inference is also required for sampling during stochastic training of these generative models. This paper considers inference in a deep stochastic neural network where the parameters (e.g., weights, biases and activation functions) are known and the problem is to estimate the values of the input and hidden units from the output. A novel and computationally tractable inference method called Multi-Layer Vector Approximate Message Passing (ML-VAMP) is presented. Our main contribution shows that the mean-squared error (MSE) of ML-VAMP can be exactly predicted in a certain large system limit. In addition, the MSE achieved by ML-VAMP matches the Bayes optimal value recently postulated by Reeves when certain fixed point equations have unique solutions.

The advent of the next iteration of mobile and wireless communication standards, the so called 5G, is already a reality. 3GPP released in December 2017 the first set of specifications of the 5G New Radio (NR), which introduced important innovations with respect to legacy networks. One of the main novelties is the use of very-high frequencies in the radio access, which requires highly-directional transmissions or beams to overcome the severe propagation losses. Therefore, it is paramount to manage these beams in an efficient manner in order to always choose the optimum set of beams. In this work, we describe the first NR-compliant beam management framework for the ns-3 network simulator. We aim at providing an open-source and fully-customizable solution to let the scientific community implement their solutions and assess their impact on the end-to-end network performance. Additionally, we describe the necessary modifications in ns-3 to align the radio frame structure to what the 3GPP standards mandate. Finally, we validate our results by running a simple mobility scenario.

Estimating a vector $\mathbf{x}$ from noisy linear measurements $\mathbf{Ax+w}$ often requires use of prior knowledge or structural constraints on $\mathbf{x}$ for accurate reconstruction. Several recent works have considered combining linear least-squares estimation with a generic or plug-in ``denoiser" function that can be designed in a modular manner based on the prior knowledge about $\mathbf{x}$. While these methods have shown excellent performance, it has been difficult to obtain rigorous performance guarantees. This work considers plug-in denoising combined with the recently-developed Vector Approximate Message Passing (VAMP) algorithm, which is itself derived via Expectation Propagation techniques. It shown that the mean squared error of this ``plug-in" VAMP can be exactly predicted for a large class of high-dimensional random $\Abf$ and denoisers. The method is illustrated in image reconstruction and parametric bilinear estimation.

The millimeter-wave (mm-wave) frequencies offer the potential of orders of magnitude that increases in capacity for next-generation cellular systems. However, links in mm-wave networks are susceptible to blockage and may suffer from rapid variations in quality. Connectivity to multiple cells at mm-wave and/or traditional frequencies is considered essential for robust communication. One of the challenges in supporting multi-connectivity in mm-waves is the requirement for the network to track the direction of each link in addition to its power and timing. To address this challenge, we implement a novel uplink measurement system that, with the joint help of a local coordinator operating in the legacy band, guarantees continuous monitoring of the channel propagation conditions and allows for the design of efficient control plane applications, including handover, beam tracking, and initial access. We show that an uplink-based multi-connectivity approach enables less consuming, better performing, faster and more stable cell selection, and scheduling decisions with respect to a traditional downlink-based standalone scheme. Moreover, we argue that the presented framework guarantees: 1) efficient tracking of the user in the presence of the channel dynamics expected at mm-waves and 2) fast reaction to situations in which the primary propagation path is blocked or not available.

The availability of very wide spectrum in millimeter wave bands combined with large antenna arrays and ultra-dense networks raises two basic questions: What is the true value of overly abundant degrees of freedom and how can networks be designed to fully exploit them? This paper determines the capacity scaling of large cellular networks as a function of bandwidth, area, number of antennas, and base station density. It is found that the network capacity has a fundamental bandwidth scaling limit, beyond which the network becomes power-limited. An infrastructure multi-hop protocol achieves the optimal network capacity scaling for all network parameters. In contrast, current protocols that use only single-hop direct transmissions cannot achieve the capacity scaling in wideband regimes except in the special case when the density of base stations is taken to impractical extremes. This finding suggests that multi-hop communication will be important to fully realize the potential of next-generation cellular networks. Dedicated relays, if sufficiently dense, can also perform this task, relieving user nodes from the battery drain of cooperation. On the other hand, more sophisticated strategies such as hierarchical cooperation, that are essential for achieving capacity scaling in ad hoc networks, are unnecessary in the cellular context.

Rapidly fading channels caused by pedestrians in dense urban environments will have a significant impact on millimeter-wave (mmWave) communications systems that employ electrically-steerable and narrow beamwidth antenna arrays. A peer- to-peer (P2P) measurement campaign was conducted with 7o, 15o, and 60ohalf- power beamwidth (HPBW) antenna pairs at 73.5 GHz and with 1 GHz of RF null-to-null bandwidth in a heavily populated open square scenario in Brooklyn, New York, to study blockage events caused by typical pedestrian traffic. Antenna beamwidths that range approximately an order of magnitude were selected to gain knowledge of fading events for antennas with different beamwidths since antenna patterns for mmWave systems will be electronically-adjustable. Two simple modeling approaches in the literature are introduced to characterize the blockage events by either a two-state Markov model or a four-state piecewise linear modeling approach. Transition probability rates are determined from the measurements and it is shown that average fade durations with a -5 dB threshold are 299.0 ms for 7oHPBW antennas and 260.2 ms for 60oHPBW antennas. The four-state piecewise linear modeling approach shows that signal strength decay and rise times are asymmetric for blockage events and that mean signal attenuations (average fade depths) are inversely proportional to antenna HPBW, where 7oand 60oHPBW antennas resulted in mean signal fades of 15.8 dB and 11.5 dB, respectively. The models presented herein are valuable for extending statistical channel models at mmWave to accurately simulate real- world pedestrian blockage events when designing fifth-generation (5G) wireless systems.

With the increasing interest in the use of millimeter wave bands for 5G cellular systems comes renewed interest in resource sharing. Properties of millimeter wave bands such as massive bandwidth, highly directional antennas, high penetration loss, and susceptibility to shadowing, suggest technical advantages to spectrum and infrastructure sharing in millimeter wave cellular networks. However, technical advantages do not necessarily translate to increased profit for service providers, or increased consumer surplus. In this paper, detailed network simulations are used to better understand the economic implications of resource sharing in a vertically differentiated duopoly market for cellular service. The results suggest that resource sharing is less often profitable for millimeter wave service providers compared to microwave cellular service providers, and does not necessarily increase consumer surplus.

The standard linear regression (SLR) problem is to recover a vector x0from noisy linear observations y = Ax0+ w. The approximate message passing (AMP) algorithm recently proposed by Donoho, Maleki, and Montanari is a computationally efficient iterative approach to SLR that has a remarkable property: for large i.i.d. sub-Gaussian matrices A, its periteration behavior is rigorously characterized by a scalar stateevolution whose fixed points, when unique, are Bayes optimal. AMP, however, is fragile in that even small deviations from the i.i.d. sub-Gaussian model can cause the algorithm to diverge. This paper considers a “vector AMP” (VAMP) algorithm and shows that VAMP has a rigorous scalar state-evolution that holds under a much broader class of large random matrices A: those that are right-rotationally invariant. After performing an initial singular value decomposition (SVD) of A, the per-iteration complexity of VAMP is similar to that of AMP. In addition, the fixed points of VAMP's state evolution are consistent with the replica prediction of the minimum mean-squared error recently derived by Tulino, Caire, Verdú, and Shamai.

The problem of estimating a random vector x from noisy linear measurements y=Ax+w with unknown parameters on the distributions of x and w, which must also be learned, arises in a wide range of statistical learning and linear inverse problems. We show that a computationally simple iterative message-passing algorithm can provably obtain asymptotically consistent estimates in a certain high-dimensional large-system limit (LSL) under very general parameterizations. Previous message passing techniques have required i.i.d. sub-Gaussian A matrices and often fail when the matrix is ill-conditioned. The proposed algorithm, called adaptive vector approximate message passing (Adaptive VAMP) with auto-tuning, applies to all right-rotationally random A. Importantly, this class includes matrices with arbitrarily bad conditioning. We show that the parameter estimates and mean squared error (MSE) of x in each iteration converge to deterministic limits that can be precisely predicted by a simple set of state evolution (SE) equations. In addition, a simple testable condition is provided in which the MSE matches the Bayes-optimal value predicted by the replica method. The paper thus provides a computationally simple method with provable guarantees of optimality and consistency over a large class of linear inverse problems.

A critical issue facing millimeter wave (mmWave) cellular systems is channel dynamics. MmWave signals are extremely susceptible to blocking and thus may vary rapidly with motion, orientation of the handset and local blockages. In this work, we derive a Finite State Markov Channel (FSMC) model of the mmWave channel from the statistics of ray tracing simulation data, which are based on channel measurements in an urban environment. The FSMC tracks the mutual information effective SINR (MI-ESNR), which can then be used in network simulations. The FSMC model is applied to analyze queue behavior and obtain the latency, throughput, packet error and droppage statistics for mmWave links at the MAC layer, making it useful for higher-layer analysis, as well. We evaluate the accuracy of the channel model for various model complexities and packet arrival rates and show that the model performance closely matches the empirical ray tracing data.

The approaching 5G era of cellular communications is posing stringent performance requirements. New groundbreaking applications can be enabled only by means of multi-Gbps data rates and ultra-low latencies. The spectrum scarcity at frequencies below 6 GHz stimulated a new wave of wireless research that focuses on higher bands, namely mmWave. Directionality and high penetration loss represent the key challenges when operating with such carriers. The resulting intermittent connectivity makes the user association problem even more complex and critical than in previous generations of cellular systems, where the channel was better behaved. In this paper, we aim at deriving an optimal and fair cell selection policy that encapsulates the reallocation cost of potential handovers, and captures the erratic nature of the mmWave channel. An important conclusion is that (i) if there is no, or minimal, reallocation cost, each user associates with a single BS, while (ii) for higher handover cost values, users tend to connect to multiple base stations simultaneously.

The millimeter wave (mmWave) bands offer the possibility of orders of magnitude greater throughput for fifth-generation (5G) cellular systems. However, since mmWave signals are highly susceptible to blockage, channel quality on any one mmWave link can be extremely intermittent. This paper implements a novel dual connectivity protocol that enables mobile user equipment devices to maintain physical layer connections to 4G and 5G cells simultaneously. A novel uplink control signaling system combined with a local coordinator enables rapid path switching in the event of failures on any one link. This paper provides the first comprehensive end-to-end evaluation of handover mechanisms in mmWave cellular systems. The simulation framework includes detailed measurement-based channel models to realistically capture spatial dynamics of blocking events, as well as the full details of Medium Access Control, Radio Link Control, and transport protocols. Compared with conventional handover mechanisms, this paper reveals significant benefits of the proposed method under several metrics.

Generalized linear models, where a random vector x is observed through a noisy, possibly nonlinear, function of a linear transform z = Ax, arise in a range of applications in nonlinear filtering and regression. Approximate message passing (AMP) methods, based on loopy belief propagation, are a promising class of approaches for approximate inference in these models. AMP methods are computationally simple, general, and admit precise analyses with testable conditions for optimality for large i.i.d. transforms A. However, the algorithms can diverge for general A. This paper presents a convergent approach to the generalized AMP (GAMP) algorithm based on direct minimization of a large-system limit approximation of the Bethe free energy (LSL-BFE). The proposed method uses a double-loop procedure, where the outer loop successively linearizes the LSL-BFE and the inner loop minimizes the linearized LSL-BFE using the alternating direction method of multipliers (ADMM). The proposed method, called ADMM-GAMP, is similar in structure to the original GAMP method, but with an additional least-squares minimization. It is shown that for strictly convex, smooth penalties, ADMM-GAMP is guaranteed to converge to a local minimum of the LSL-BFE, thus providing a convergent alternative to GAMP that is stable under arbitrary transforms. Simulations are also presented that demonstrate the robustness of the method for non-convex penalties as well.

The millimeter-wave (mmWave) frequencies have attracted considerable attention for fifth generation (5G) cellular communication as they offer orders of magnitude greater bandwidth than current systems. However, the medium access control (MAC) layer may need to be significantly redesigned to support the highly directional transmissions, and the demand for ultra-low latencies and high peak rates expected in mmWave communication. To address these challenges, we present a novel mmWave MAC layer frame structure with a number of enhancements, including flexible, highly granular transmission times, dynamic control signal locations, extended messaging, and the ability to efficiently multiplex directional control signals. Analytic formulas are derived for the utilization and control overhead as a function of control periodicity, number of users, traffic statistics, signal-to-noise ratio, and antenna gains. Importantly, the analysis can incorporate various front-end MIMO capability assumptions-a critical feature of mmWave. Under realistic system and traffic assumptions, the analysis reveals that the proposed flexible frame structure design offers significant benefits over designs with fixed frame structures similar to current 4G long-term evolution. It is also shown that the fully digital beamforming architectures offer significantly lower overhead compared with analog and hybrid beamforming under equivalent power budgets.

The tremendous bandwidth available in the millimeter wave frequencies above 10 GHz have made these bands an attractive candidate for next-generation cellular systems. However, reliable communication at these frequencies depends critically on beamforming with very high-dimensional antenna arrays. Estimating the channel sufficiently accurately to perform beamforming can be challenging due to both low coherence time and a large number of antennas. Also, the measurements used for channel estimation may need to be made with analog beamforming, where the receiver can “look” in only one direction at a time. This paper presents a novel method for estimation of the receive-side spatial covariance matrix of a channel from a sequence of power measurements made in different angular directions. It is shown that maximum likelihood estimation of the covariance matrix reduces to a non-negative matrix completion problem. We show that the non-negative nature of the covariance matrix reduces the number of measurements required when the matrix is low-rank. The fast iterative methods are presented to solve the problem. Simulations are presented for both single-path and multi-path channels using models derived from real measurements in New York City at 28 GHz.

Approximations of loopy belief propagation, including expectation propagation and approximate message passing, have attracted considerable attention for probabilistic inference problems. This paper proposes and analyzes a generalization of Opper and Winther's expectation consistent (EC) approximate inference method. The proposed method, called Generalized Expectation Consistency (GEC), can be applied to both maximum a posteriori (MAP) and minimum mean squared error (MMSE) estimation. Here we characterize its fixed points, convergence, and performance relative to the replica prediction of optimality.

The resource usage model for millimeter wave bands has been the subject of considerable debate. The massive bandwidth, highly directional antennas, high penetration loss and susceptibility to shadowing in these bands suggest certain advantages to spectrum and infrastructure sharing. In particular, resources that are "open", such as unlicensed spectrum or a deployment of base stations open to all service providers, may offer greater gains in mmWave bands than at conventional cellular frequencies. However, even when sharing is technically beneficial (as recent research in this area suggests that it is), it may not be profitable. In this paper, both the technical and economic implications of resource sharing in millimeter wave networks are studied. Millimeter wave service is considered in the economic framework of a network good, and detailed network simulations are used to understand data rates, profit, and demand for millimeter wave service, with and without open resources. The results suggest that "open" deployments of neutral small cells that serve subscribers of any service provider encourage market entry by making it easier for networks to reach critical mass, more than "open" (unlicensed) spectrum would.

This paper presents and compares two candidate large-scale propagation path loss models, the alpha-beta-gamma (ABG) model and the close-in (CI) free space reference distance model, for the design of fifth generation (5G) wireless communication systems in urban micro- and macro-cellular scenarios. Comparisons are made using the data obtained from 20 propagation measurement campaigns or ray- tracing studies from 2 GHz to 73.5 GHz over distances ranging from 5 m to 1429 m. The results show that the one-parameter CI model has a very similar goodness of fit (i.e., the shadow fading standard deviation) in both line-of-sight and non-line-of-sight environments, while offering substantial simplicity and more stable behavior across frequencies and distances, as compared to the three-parameter ABG model. Additionally, the CI model needs only one very subtle and simple modification to the existing 3GPP floating-intercept path loss model (replacing a constant with a close-in free space reference value) in order to provide greater simulation accuracy, more simplicity, better repeatability across experiments, and higher stability across a vast range of frequencies.

The estimation of a random vector with independent components passed through a linear transform followed by a componentwise (possibly nonlinear) output map arises in a range of applications. Approximate message passing (AMP) methods, based on Gaussian approximations of loopy belief propagation, have recently attracted considerable attention for such problems. For large random transforms, these methods exhibit fast convergence and admit precise analytic characterizations with testable conditions for optimality, even for certain non-convex problem instances. However, the behavior of AMP under general transforms is not fully understood. In this paper, we consider the generalized AMP (GAMP) algorithm and relate the method to more common optimization techniques. This analysis enables a precise characterization of the GAMP algorithm fixed points that applies to arbitrary transforms. In particular, we show that the fixed points of the so-called max-sum GAMP algorithm for MAP estimation are critical points of a constrained maximization of the posterior density. The fixed points of the sum-product GAMP algorithm for estimation of the posterior marginals can be interpreted as critical points of a certain free energy.

Millimeter wave (mmWave) bands have attracted considerable recent interest for next-generation cellular systems due to the massive available spectrum at these frequencies. However, a key challenge in designing mmWave cellular systems is initial access-the procedure by which a mobile device establishes an initial link-layer connection to a cell. MmWave communication relies on highly directional transmissions and the initial access procedure must thus provide a mechanism by which initial transmission directions can be searched in a potentially large angular space. Design options are compared considering different scanning and signaling procedures to evaluate access delay and system overhead. The channel structure and multiple access issues are also considered. The results of our analysis demonstrate significant benefits of low-resolution fully digital architectures in comparison with single stream analog beamforming.

In cosparse analysis compressive sensing (CS), one seeks to estimate a non-sparse signal vector from noisy sub-Nyquist linear measurements by exploiting the knowledge that a given linear transform of the signal is cosparse, i.e., has sufficiently many zeros. We propose a novel approach to cosparse analysis CS based on the generalized approximate message passing (GAMP) algorithm. Unlike other AMP-based approaches to this problem, ours works with a wide range of analysis operators and regularizers. In addition, we propose a novel ℓ0-like soft-thresholder based on MMSE denoising for a spike-and-slab distribution with an infinite-variance slab. Numerical demonstrations on synthetic and practical datasets demonstrate advantages over existing AMP-based, greedy, and reweighted-ℓ1approaches.

The generalized approximate message passing (GAMP) algorithm is an efficient method of MAP or approximate-MMSE estimation of x observed from a noisy version of the transform coefficients z = Ax. In fact, for large zero-mean i.i.d sub-Gaussian A, GAMP is characterized by a state evolution whose fixed points, when unique, are optimal. For generic A, however, GAMP may diverge. In this paper, we propose adaptive-damping and mean-removal strategies that aim to prevent divergence. Numerical results demonstrate significantly enhanced robustness to non-zero-mean, rank-deficient, column-correlated, and ill-conditioned A.

A key practical constraint on the design of Hybrid automatic repeat request (HARQ) schemes is the modem chip area that needs to be allocated to store previously received packets. The fact that, in modern wireless standards, this area can amount to a large fraction of the overall chip has recently highlighted the importance of HARQ buffer management, that is, of the use of advanced compression policies for storage of received data. This work tackles the analysis of the throughput of standard HARQ schemes, namely Type-I, Chase Combining and Incremental Redundancy, under the assumption of a finite-capacity HARQ buffer by taking an information-theoretic standpoint based on random coding. Both coded modulation, via Gaussian signaling, and Bit Interleaved Coded Modulation (BICM) are considered. The analysis sheds light on questions of practical relevance for HARQ buffer management such as on the type of information to be extracted from the received packets and on how to store it.

Generalized Linear Models (GLMs), where a random vector x is observed through a noisy, possibly nonlinear, function of a linear transform z = Ax arise in a range of applications in nonlinear filtering and regression. Approximate Message Passing (AMP) methods, based on loopy belief propagation, are a promising class of approaches for approximate inference in these models. AMP methods are computationally simple, general, and admit precise analyses with testable conditions for optimality for large i.i.d. transforms A. However, the algorithms can easily diverge for general transforms. This paper presents a convergent approach to the generalized AMP (GAMP) algorithm based on direct minimization of a large-system limit approximation of the Bethe Free Energy (LSL-BFE). The proposed method uses a double-loop procedure, where the outer loop successively linearizes the LSL-BFE and the inner loop minimizes the linearized LSL-BFE using the Alternating Direction Method of Multipliers (ADMM). The proposed method, called ADMM-GAMP, is similar in structure to the original GAMP method, but with an additional least-squares minimization. It is shown that for strictly convex, smooth penalties, ADMM-GAMP is guaranteed to converge to a local minima of the LSL-BFE, thus providing a convergent alternative to GAMP that is stable under arbitrary transforms. Simulations are also presented that demonstrate the robustness of the method for non-convex penalties as well.

The increasing demand of data, along with the spectrum scarcity, are motivating a urgent shift towards exploiting new bands. This is the main reason behind identifying mmWaves as the key disruptive enabling technology for 5G cellular networks. Indeed, utilizing new bands means facing new challenges; in this context, they are mainly related to the radio propagation, which is shorter in range and more sensitive to obstacles. The resulting key aspects that need to be taken into account when designing mmWave cellular systems are directionality and link intermittency. The lack of network level results motivated this work, which aims at providing the first of a kind open source mmWave framework, based on the network simulator ns-3. The main focus of this work is the modeling of customizable channel, physical (PHY) and medium access control (MAC) layers for mmWave systems. The overall design and architecture of the model are discussed in details. Finally, the validity of our proposed framework is corroborated through the simulation of a simple scenario.

A key practical constraint on the design of hybrid automatic repeat request (HARQ) schemes is the size of the on-chip buffer that is available at the receiver to store previously received packets. In fact, in modern wireless standards such as LTE and LTE-A, the HARQ buffer size is one of the main drivers of the modem area and power consumption. This has recently highlighted the importance of HARQ buffer management, that is, of the use of buffer-aware transmission schemes and of advanced compression policies for the storage of received data. This work investigates HARQ buffer management by leveraging information-theoretic achievability arguments based on random coding. Specifically, standard HARQ schemes, namely Type-I, Chase Combining, and Incremental Redundancy, are first studied under the assumption of a finite-capacity HARQ buffer by considering both coded modulation, via Gaussian signaling, and Bit Interleaved Coded Modulation (BICM). The analysis sheds light on the impact of different compression strategies, namely the conventional compression log-likelihood ratios and the direct digitization of baseband signals, on the throughput. The optimization of coding blocklength is also investigated, highlighting the benefits of HARQ buffer-aware transmission scheme.

The acute disparity between increasing bandwidth demand and available spectrum has brought millimeter wave (mmWave) bands to the forefront of candidate solutions for the next-generation cellular networks. Highly directional transmissions are essential for cellular communication in these frequencies to compensate for higher isotropic path loss. This reliance on directional beamforming, however, complicates initial cell search since mobiles and base stations must jointly search over a potentially large angular directional space to locate a suitable path to initiate communication. To address this problem, this paper proposes a directional cell discovery procedure where base stations periodically transmit synchronization signals, potentially in time-varying random directions, to scan the angular space. Detectors for these signals are derived based on a Generalized Likelihood Ratio Test (GLRT) under various signal and receiver assumptions. The detectors are then simulated under realistic design parameters and channels based on actual experimental measurements at 28 GHz in New York City. The study reveals two key findings: 1) digital beamforming can significantly outperform analog beamforming even when digital beamforming uses very low quantization to compensate for the additional power requirements and 2) omnidirectional transmissions of the synchronization signals from the base station generally outperform random directional scanning.

With the shortage of spectrum in conventional cellular frequencies, millimeter-wave (mmWave) bands are being widely considered for use in next-generation networks. Multihop relaying is likely to play a significant role in mmWave cellular systems for self backhauling, range extension and improved robustness from path diversity. However, designing scheduling policies for these systems is challenging due to the need to account for both adaptive directional transmissions and dynamic time-division duplexing schedules, which are key enabling features of mmWave systems. This paper considers the problem of joint scheduling and congestion control in a multihop mmWave network using a Network Utility Maximization (NUM) framework. Interference is modeled with an exact model and two auxiliar simplified models: actual interference (AI), with a graph-based calculation of the Signal to Interference plus Noise Ratio (SINR) depending on dynamic link activity and directivity, as well as upper and lower bounds computed from worst-case interference (WI) and interference free (IF) approximations. Throughput and utility optimal policies are derived for all interference models (AI, WI and IF) with both deterministic Maximum Weighted and randomized Pick and Compare scheduling algorithms, jointly with decentralized Dual Congestion Control. Results are evaluated with numerical simulations, using accurate mmWave channel and beamforming gain approximations based on measurement campaigns.

With millimeter wave bands emerging as a strong candidate for 5G cellular networks, next-generation systems may be in a unique position where spectrum is plentiful. To assess the potential value of this spectrum, this paper derives scaling laws on the per mobile downlink feasible rate with large bandwidth and number of nodes, for both Infrastructure Single Hop (ISH) and Infrastructure Multi-Hop (IMH) architectures. It is shown that, for both cases, there exist critical bandwidth scalings above which increasing the bandwidth no longer increases the feasible rate per node. These critical thresholds coincide exactly with the bandwidths where, for each architecture, the network transitions from being degrees-of-freedom-limited to power-limited. For ISH, this critical bandwidth threshold is lower than IMH when the number of users per base station grows with network size. This result suggests that multi-hop transmissions may be necessary to fully exploit large bandwidth degrees of freedom in deployments with growing number of users per cell.

Approximate message passing (AMP) methods and their variants have attracted considerable recent attention for the problem of estimating a random vector x observed through a linear transform A. In the case of large i.i.d. A, the methods exhibit fast convergence with precise analytic characterizations on the algorithm behavior. However, the convergence of AMP under general transforms is not fully understood. In this paper, we provide sufficient conditions for the convergence of a damped version of the generalized AMP (GAMP) algorithm in the case of Gaussian distributions. It is shown that, with sufficient damping the algorithm can be guaranteed to converge, but the amount of damping grows with peak-to-average ratio of the squared singular values of A. This condition explains the good performance of AMP methods on i.i.d. matrices, but also their difficulties with other classes of transforms. A related sufficient condition is then derived for the local stability of the damped GAMP method under more general (possibly non-Gaussian) distributions, assuming certain strict convexity conditions.

With the severe spectrum shortage in conventional cellular bands, millimeter wave (mmW) frequencies between 30 and 300 GHz have been attracting growing attention as a possible candidate for next-generation micro- and picocellular wireless networks. The mmW bands offer orders of magnitude greater spectrum than current cellular allocations and enable very high-dimensional antenna arrays for further gains via beamforming and spatial multiplexing. This paper uses recent real-world measurements at 28 and 73 GHz in New York, NY, USA, to derive detailed spatial statistical models of the channels and uses these models to provide a realistic assessment of mmW micro- and picocellular networks in a dense urban deployment. Statistical models are derived for key channel parameters, including the path loss, number of spatial clusters, angular dispersion, and outage. It is found that, even in highly non-line-of-sight environments, strong signals can be detected 100-200 m from potential cell sites, potentially with multiple clusters to support spatial multiplexing. Moreover, a system simulation based on the models predicts that mmW systems can offer an order of magnitude increase in capacity over current state-of-the-art 4G cellular networks with no increase in cell density from current urban deployments.

Millimeter-wave (mmW) frequencies between 30 and 300 GHz are a new frontier for cellular communication that offers the promise of orders of magnitude greater bandwidths combined with further gains via beamforming and spatial multiplexing from multielement antenna arrays. This paper surveys measurements and capacity studies to assess this technology with a focus on small cell deployments in urban environments. The conclusions are extremely encouraging; measurements in New York City at 28 and 73 GHz demonstrate that, even in an urban canyon environment, significant non-line-of-sight (NLOS) outdoor, street-level coverage is possible up to approximately 200 m from a potential low-power microcell or picocell base station. In addition, based on statistical channel models from these measurements, it is shown that mmW systems can offer more than an order of magnitude increase in capacity over current state-of-the-art 4G cellular networks at current cell densities. Cellular systems, however, will need to be significantly redesigned to fully achieve these gains. Specifically, the requirement of highly directional and adaptive transmissions, directional isolation between links, and significant possibilities of outage have strong implications on multiple access, channel structure, synchronization, and receiver design. To address these challenges, the paper discusses how various technologies including adaptive beamforming, multihop relaying, heterogeneous network architectures, and carrier aggregation can be leveraged in the mmW context.

We consider the estimation of an independent and identically distributed (i.i.d.) (possibly non-Gaussian) vector x ∈ Rnfrom measurements y ∈ Rmobtained by a general cascade model consisting of a known linear transform followed by a probabilistic componentwise (possibly nonlinear) measurement channel. A novel method, called adaptive generalized approximate message passing (adaptive GAMP) is presented. It enables the joint learning of the statistics of the prior and measurement channel along with estimation of the unknown vector x. We prove that, for large i.i.d. Gaussian transform matrices, the asymptotic componentwise behavior of the adaptive GAMP is predicted by a simple set of scalar state evolution equations. In addition, we show that the adaptive GAMP yields asymptotically consistent parameter estimates, when a certain maximum-likelihood estimation can be performed in each step. This implies that the algorithm achieves a reconstruction quality equivalent to the oracle algorithm that knows the correct parameter values. Remarkably, this result applies to essentially arbitrary parametrizations of the unknown distributions, including nonlinear and non-Gaussian ones. The adaptive GAMP methodology thus provides a systematic, general and computationally efficient method applicable to a large range of linear-nonlinear models with provable guarantees.

Interference cancelation (IC) can provide significant gains in wireless networks with strong interference, that arise, for example, in emerging femto- and picocellular deployments. This paper consider the problem of optimal downlink rate selection in networks where each mobile can perform IC on up to one interferer. When mobiles are capable of IC, it is argued that rate selection can play an analogous roles as power control by permitting a tradeoff between rates on the desired link with “cancellability” on interfering links. A utility maximizing scheduler based on loopy belief propagation is presented that enables computationally-efficient local processing and low communication overhead. It is shown that the fixed points of the method are provably globally optimal for arbitrary (potentially non-convex) rate and utility functions. In addition, the result applies to an arbitrary networks where the interference is determined by a single dominant interferer, for which the IC problem is a special case. Simulations are presented in industry standard femtocellular network models.

The estimation of a random vector with independent components passed through a linear transform followed by a componentwise (possibly nonlinear) output map arises in a range of applications. Approximate message passing (AMP) methods, based on Gaussian approximations of loopy belief propagation, have recently attracted considerable attention for such problems. For large random transforms, these methods exhibit fast convergence and admit precise analytic characterizations with testable conditions for optimality, even for certain non-convex problem instances. However, the behavior of AMP under general transforms is not fully understood. In this paper, we consider the generalized AMP (GAMP) algorithm and relate the method to more common optimization techniques. This analysis enables a precise characterization of the GAMP algorithm fixed-points that applies to arbitrary transforms. In particular, we show that the fixed points of the so-called max-sum GAMP algorithm for MAP estimation are critical points of a constrained maximization of the posterior density. The fixed-points of the sum-product GAMP algorithm for estimation of the posterior marginals can be interpreted as critical points of a certain mean-field variational optimization.

We consider the problem of estimating a rank-one matrix in Gaussian noise under a probabilistic model for the left and right factors of the matrix. The probabilistic model can impose constraints on the factors including sparsity and positivity that arise commonly in learning problems. We propose a simple iterative procedure that reduces the problem to a sequence of scalar estimation computations. The method is similar to approximate message passing techniques based on Gaussian approximations of loopy belief propagation that have been used recently in compressed sensing. Leveraging analysis methods by Bayati and Montanari, we show that the asymptotic behavior of the estimates from the proposed iterative procedure is described by a simple scalar equivalent model, where the distribution of the estimates is identical to certain scalar estimates of the variables in Gaussian noise. Moreover, the effective Gaussian noise level is described by a set of state evolution equations. The proposed method thus provides a computationally simple and general method for rank-one estimation problems with a precise analysis in certain high-dimensional settings.

Gaussian and quadratic approximations of message passing algorithms on graphs have attracted considerable attention due to their computational simplicity, analytic tractability, and wide applicability in optimization and statistical inference problems. This paper summarizes a systematic framework for incorporating such approximate message passing (AMP) methods in general graphical models. The key concept is a partition of dependencies of a general graphical model into strong and weak edges, with each weak edge representing a small, linearizable coupling of variables. AMP approximations based on the central limit theorem can be applied to the weak edges and integrated with standard message passing updates on the strong edges. The resulting algorithm, which we call hybrid generalized approximate message passing (Hybrid-GAMP), can yield significantly simpler implementations of sum-product and max-sum loopy belief propagation. By varying the partition between strong and weak edges, a performance-complexity trade-off can be achieved. Structured sparsity problems are studied as an example of this general methodology where there is a natural partition of edges.

We consider the estimation of an i.i.d.\ vector $\xbf \in \R^n$ from measurements $\ybf \in \R^m$ obtained by a general cascade model consisting of a known linear transform followed by a probabilistic componentwise (possibly nonlinear) measurement channel. We present a method, called adaptive generalized approximate message passing (Adaptive GAMP), that enables joint learning of the statistics of the prior and measurement channel along with estimation of the unknown vector $\xbf$. The proposed algorithm is a generalization of a recently-developed method by Vila and Schniter that uses expectation-maximization (EM) iterations where the posteriors in the E-steps are computed via approximate message passing. The techniques can be applied to a large class of learning problems including the learning of sparse priors in compressed sensing or identification of linear-nonlinear cascade models in dynamical systems and neural spiking processes. We prove that for large i.i.d.\ Gaussian transform matrices the asymptotic componentwise behavior of the adaptive GAMP algorithm is predicted by a simple set of scalar state evolution equations. This analysis shows that the adaptive GAMP method can yield asymptotically consistent parameter estimates, which implies that the algorithm achieves a reconstruction quality equivalent to the oracle algorithm that knows the correct parameter values. The adaptive GAMP methodology thus provides a systematic, general and computationally efficient method applicable to a large range of complex linear-nonlinear models with provable guarantees.

Femtocells, despite their name, pose a potentially large disruption to the carefully planned cellular networks that now connect a majority of the planet's citizens to the Internet and with each other. Femtocells - which by the end of 2010 already outnumbered traditional base stations and at the time of publication are being deployed at a rate of about five million a year - both enhance and interfere with this network in ways that are not yet well understood. Will femtocells be crucial for offloading data and video from the creaking traditional network? Or will femtocells prove more trouble than they are worth, undermining decades of careful base station deployment with unpredictable interference while delivering only limited gains? Or possibly neither: are femtocells just a "flash in the pan"; an exciting but short-lived stage of network evolution that will be rendered obsolete by improved WiFi offloading, new backhaul regulations and/or pricing, or other unforeseen technological developments? This tutorial article overviews the history of femtocells, demystifies their key aspects, and provides a preview of the next few years, which the authors believe will see a rapid acceleration towards small cell technology. In the course of the article, we also position and introduce the articles that headline this special issue.

Interference coordination is a fundamental challenge in emerging femtocellular deployments. This paper considers a broad class of interference coordination and resource allocation problems for wireless links based on utility maximization with a general linear mixing interference model suitable for complex femtocellular systems. The resulting optimization problems are typically hard to solve optimally even using centralized algorithms but are an essential computational step in implementing rate-fair and queue stabilizing scheduling policies in wireless networks. We consider a belief propagation framework to solve such problems approximately. In particular, we construct approximations to the belief propagation iterations to obtain computationally simple and distributed algorithms with low communication overhead. Notably, our methods are very general and apply to, semi-static and dynamic interference coordination problems including the optimization of transmit powers, transmit beamforming vectors, fractional frequency reuse (FFR) and sub-band allocations to maximize the above objective. Numerical results for femtocell deployments demonstrate that such algorithms compute a very good operating point in typically just a couple of iterations.

We consider the problem of estimating sparse communication channels in the MIMO context. In small to medium bandwidth communications, as in the current standards for OFDM and CDMA communication systems (with bandwidth up to 20 MHz), such channels are individually sparse and at the same time share a common support set. Since the underlying physical channels are inherently continuous-time, we propose a parametric sparse estimation technique based on finite rate of innovation (FRI) principles. Parametric estimation is especially relevant to MIMO communications as it allows for a robust estimation and concise description of the channels. The core of the algorithm is a generalization of conventional spectral estimation methods to multiple input signals with common support. We show the application of our technique for channel estimation in OFDM (uniformly/contiguous DFT pilots) and CDMA downlink (Walsh-Hadamard coded schemes). In the presence of additive white Gaussian noise, theoretical lower bounds on the estimation of sparse common support (SCS) channel parameters in Rayleigh fading conditions are derived. Finally, an analytical spatial channel model is derived, and simulations on this model in the OFDM setting show the symbol error rate (SER) is reduced by a factor 2 (0 dB of SNR) to 5 (high SNR) compared to standard non-parametric methods - e.g. lowpass interpolation.

The replica method is a nonrigorous but well-known technique from statistical physics used in the asymptotic analysis of large, random, nonlinear problems. This paper applies the replica method, under the assumption of replica symmetry, to study estimators that are maximum a posteriori (MAP) under a postulated prior distribution. It is shown that with random linear measurements and Gaussian noise, the replica-symmetric prediction of the asymptotic behavior of the postulated MAP estimate of an -dimensional vector “decouples” as scalar postulated MAP estimators. The result is based on applying a hardening argument to the replica analysis of postulated posterior mean estimators of Tanaka and of Guo and Verdú. The replica-symmetric postulated MAP analysis can be readily applied to many estimators used in compressed sensing, including basis pursuit, least absolute shrinkage and selection operator (LASSO), linear estimation with thresholding, and zero norm-regularized estimation. In the case of LASSO estimation, the scalar estimator reduces to a soft-thresholding operator, and for zero norm-regularized estimation, it reduces to a hard threshold. Among other benefits, the replica method provides a computationally tractable method for precisely predicting various performance metrics including mean-squared error and sparsity pattern recovery probability.

This paper proposes a novel relaying-based approach for managing mobility in dense cellular networks. Dense deployment of base stations will have small cells, necessitating frequent handovers. Moreover, small cell solutions such as femtocells may lack low-delay backhaul connections to the operator core's network where handover procedures are coordinated. As a result, handover delays can be large, impacting signal quality severely. The proposed relay-based hierarchical mobility scheme forwards data over-the-air to other base stations close to mobile as the mobile moves but before the network point of attachment can be switched. Hybrid ARQ and randomized beamforming are used to opportunistically gain the benefits of relays with no explicit coordination between the relay base stations and the source or mobile. Thus, the protocol is attractive for high-speed dynamic environments and can be implemented with minimal messaging overhead. A simulation of the protocol in a 3GPP Long-Term Evolution (LTE) cellular system with dense cells and high-speed mobiles shows the ability of the protocol to mitigate short-term rate outages during handover with significant improvements for delay sensitive applications.

We propose an algorithm (SCS-FRI) to estimate multipath channels with Sparse Common Support (SCS) based on Finite Rate of Innovation (FRI) sampling. In this setup, theoretical lower-bounds are derived, and simulation in a Rayleigh fading environment shows that SCS-FRI gets very close to these bounds. We show how to apply SCS-FRI to OFDM and CDMA downlinks. Recovery of a sparse common support is, among other, especially relevant for channel estimation in a multiple output system or beam-forming from multiple input. The present algorithm is based on a multi-output extension of the Cadzow denoising/annihilating filter method.

We consider a broad class of interference coordination and resource allocation problems for wireless links where the goal is to maximize the sum of functions of individual link rates. Such problems arise in the context of, for example, fractional frequency reuse (FFR) for macro-cellular networks and dynamic interference management in femtocells. The resulting optimization problems are typically hard to solve optimally even using centralized algorithms but are an essential computational step in implementing rate-fair and queue stabilizing scheduling policies in wireless networks. We consider a belief propagation framework to solve such problems approximately. In particular, we construct approximations to the belief propagation iterations to obtain computationally simple and distributed algorithms with low communication overhead. Notably, our methods are very general and apply to, for example, the optimization of transmit powers, transmit beamforming vectors, and sub-band allocation to maximize the above objective. Numerical results for femtocell deployments demonstrate that such algorithms compute a very good operating point in typically just a couple of iterations.

We consider the optimal quantization of compressive sensing measurements along with estimation from quantized samples using generalized approximate message passing (GAMP). GAMP is an iterative reconstruction scheme inspired by the belief propagation algorithm on bipartite graphs which generalizes approximate message passing (AMP) for arbitrary measurement channels. Its asymptotic error performance can be accurately predicted and tracked through the state evolution formalism. We utilize these results to design mean-square optimal scalar quantizers for GAMP signal reconstruction and empirically demonstrate the superior error performance of the resulting quantizers.

We consider the estimation of a random vector observed through a linear transform followed by a componentwise probabilistic measurement channel. Although such linear mixing estimation problems are generally highly non-convex, Gaussian approximations of belief propagation (BP) have proven to be computationally attractive and highly effective in a range of applications. Recently, Bayati and Montanari have provided a rigorous and extremely general analysis of a large class of approximate message passing (AMP) algorithms that includes many Gaussian approximate BP methods. This paper extends their analysis to a larger class of algorithms to include what we call generalized AMP (G-AMP). G-AMP incorporates general (possibly non-AWGN) measurement channels. Similar to the AWGN output channel case, we show that the asymptotic behavior of the G-AMP algorithm under large i.i.d. Gaussian transform matrices is described by a simple set of state evolution (SE) equations. The general SE equations recover and extend several earlier results, including SE equations for approximate BP on general output channels by Guo and Wang.

Many functional descriptions of spiking neurons assume a cascade structure where inputs are passed through an initial linear filtering stage that produces a low-dimensional signal that drives subsequent nonlinear stages. This paper presents a novel and systematic parameter estimation procedure for such models and applies the method to two neural estimation problems: (i) compressed-sensing based neural mapping from multi-neuron excitation, and (ii) estimation of neural receptive yields in sensory neurons. The proposed estimation algorithm models the neurons via a graphical model and then estimates the parameters in the model using a recently-developed generalized approximate message passing (GAMP) method. The GAMP method is based on Gaussian approximations of loopy belief propagation. In the neural connectivity problem, the GAMP-based method is shown to be computational efficient, provides a more exact modeling of the sparsity, can incorporate nonlinearities in the output and significantly outperforms previous compressed-sensing methods. For the receptive field estimation, the GAMP method can also exploit inherent structured sparsity in the linear weights. The method is validated on estimation of linear nonlinear Poisson (LNP) cascade models for receptive fields of salamander retinal ganglion cells.

The replica method is a non-rigorous but widely-accepted technique from statistical physics used in the asymptotic analysis of large, random, nonlinear problems. This paper applies the replica method to analyze non-Gaussian maximum a posteriori (MAP) estimation. The main result is a counterpart to Guo and Verdú's replica analysis of minimum mean-squared error estimation. The replica MAP analysis can be readily applied to many estimators used in compressed sensing, including basis pursuit, lasso, linear estimation with thresholding, and zero norm-regularized estimation. Among other benefits, the replica method provides a computationally-tractable method for exactly computing various performance metrics including mean-squared error and sparsity pattern recovery probability.

Pricing content-providers for connectivity to end- users and setting connection parameters based on the price is an evolving model on the Internet. The implications are heavily debated in telecom policy circles, and some advocates of "Network Neutrality" have opposed price based differentiation in connectivity. However, pricing content providers can possibly subsidize the end-user's cost of connectivity, and the consequent increase in end-user demand can benefit ISPs and content providers. This paper provides a framework to quantify the precise trade-off in the distribution of benefits among ISPs, content-providers, and end-users. The framework generalizes the well-known utility maximization based rate allocation model, which has been extensively studied as an interplay between the ISP and the end-users, to incorporate pricing of content-providers. We derive the resulting equilibrium prices and data rates in two different ISP market conditions: competition and monopoly. Network neutrality based restriction on content-provider pricing is then modeled as a constraint on the maximum price that can be charged to content-providers. We demonstrate that, in addition to gains in total and end- user surplus, content-provider experiences a net surplus from participation in rate allocation under low cost of connectivity. The surplus gains are, however, limited under monopoly conditions in comparison to competition in the ISP market.

This paper considers a simple on-off random multiple access channel (MAC), where n users communicate simultaneously to a single receiver. Each user is assigned a single codeword which it transmits with some probability lambda over m degrees of freedom. The receiver must detect which users transmitted. We show that detection for this random MAC is mathematically equivalent to a standard sparsity detection problem. Using new results in sparse estimation we are able to estimate the capacity of these channels and compare the achieved performance of various detection algorithms. The analysis provides insight into the roles of power control and multi-user detection.

Orthogonal matching pursuit (OMP) is a widely used greedy algorithm for recovering sparse vectors from linear measurements. A well-known analysis of Tropp and Gilbert shows that OMP can recover a k-sparse n-dimensional real vector from m = 4k log(n) noise-free random linear measurements with a probability that goes to one as n goes to infinity. This work shows strengthens this result by showing that a lower number of measurements, m = 2k log(n-k), is in fact sufficient for asymptotic recovery. Moreover, this number of measurements is also sufficient for detection of the sparsity pattern (support) of the vector with measurement errors provided the signal-to-noise ratio (SNR) scales to infinity. The scaling m = 2k log(n-k) exactly matches the number of measurements required by the more complex lasso for signal recovery.

The replica method is a non-rigorous but widely-used technique from statistical physics used in the asymptotic analysis of many large random nonlinear problems. This paper applies the replica method to non-Gaussian MAP estimation. It is shown that with large random linear measurements and Gaussian noise, the asymptotic behavior of the MAP estimate of an n-dimensional vector ``decouples as n scalar MAP estimators. The result is a counterpart to Guo and Verdus replica analysis on MMSE estimation. The replica MAP analysis can be readily applied to many estimators used in compressed sensing, including basis pursuit, lasso, linear estimation with thresholding and zero-norm estimation. In the case of lasso estimation, the scalar estimator reduces to a soft-thresholding operator and for zero-norm estimation it reduces to a hard-threshold. Among other benefits, the replica method provides a computationally tractable method for exactly computing various performance metrics including MSE and sparsity recovery.

The paper considers the problem of detecting the sparsity pattern of a$k$-sparse vector in${\BBR }^{n}$from$m$random noisy measurements. A new necessary condition on the number of measurements for asymptotically reliable detection with maximum-likelihood (ML) estimation and Gaussian measurement matrices is derived. This necessary condition for ML detection is compared against a sufficient condition for simple maximum correlation (MC) or thresholding algorithms. The analysis shows that the gap between thresholding and ML can be described by a simple expression in terms of the total signal-to-noise ratio (SNR), with the gap growing with increasing SNR. Thresholding is also compared against the more sophisticated Lasso and orthogonal matching pursuit (OMP) methods. At high SNRs, it is shown that the gap between Lasso and OMP over thresholding is described by the range of powers of the nonzero component values of the unknown signals. Specifically, the key benefit of Lasso and OMP over thresholding is the ability of Lasso and OMP to detect signals with relatively small components.

The use of subspace structure in source and channel coding is studied. We show that for source coding of an i.i.d. Gaussian source, restriction of the codebook to a union of subspaces need not induce any performance penalty. In fact, in N-dimensional space, a two-stage quantization of first projecting to the nearest of J subspaces of dimension K in a random first-stage codebook of subspaces, followed by quantizing to the nearest of codewords in a second-stage codebook within the K-dimensional subspace induces no performance loss. This structure allows the rate-distortion bound to be approached asymptotically with block length N. The dual results for channel coding are explicitly described: for an additive white Gaussian noise channel, we introduce a particular subspace-based codebook that induces no rate loss, and the Shannon capacity is achieved. While this has complexity exponential in N, it is reduced from an unstructured search.

Recent research suggests that neural systems employ sparse coding. However, there is limited theoretical understanding of fundamental resolution limits in such sparse coding. This paper considers a general sparse estimation problem of detecting the sparsity pattern of a $k$-sparse vector in $\R^n$ from $m$ random noisy measurements. Our main results provide necessary and sufficient conditions on the problem dimensions, $m$, $n$ and $k$, and the signal-to-noise ratio (SNR) for asymptotically-reliable detection. We show a necessary condition for perfect recovery at any given SNR for all algorithms, regardless of complexity, is $m = \Omega(k\log(n-k))$ measurements. This is considerably stronger than all previous necessary conditions. We also show that the scaling of $\Omega(k\log(n-k))$ measurements is sufficient for a trivial ``maximum correlation'' estimator to succeed. Hence this scaling is optimal and does not require lasso, matching pursuit, or more sophisticated methods, and the optimal scaling can thus be biologically plausible.

Encouraging recent results in compressed sensing or compressive sampling suggest that a set of inner products with random measurement vectors forms a good representation of a source vector that is known to be sparse in some fixed basis. With quantization of these inner products, the encoding can be considered universal for sparse signals with known sparsity level. We analyze the operational rate-distortion performance of such source coding both with genie-aided knowledge of the sparsity pattern and maximum likelihood estimation of the sparsity pattern. We show that random measurements induce an additive logarithmic rate penalty, i.e., at high rates the performance with rate R + O(log R) and random measurements is equal to the performance with rate R and deterministic measurements matched to the source.

If a signal x is known to have a sparse representation with respect to a frame, the signal can be estimated from a noise-corrupted observation y by finding the best sparse approximation to y. This paper analyzes the mean squared error (MSE) of this denoising scheme and the probability that the estimate has the same sparsity pattern as the original signal. The first main result is an MSE bound that depends on a new bound on approximating a Gaussian signal as a linear combination of elements of an overcomplete dictionary. This bound may be of independent interest for source coding. Further analyses are for dictionaries generated randomly according to a spherically-symmetric distribution and signals expressible with single dictionary elements. Easily-computed approximations for the probability of selecting the correct dictionary element and the MSE are given. In the limit of large dimension, these approximations have simple forms. The asymptotic expressions reveal a critical input signal-to-noise ratio (SNR) for signal recovery

Consider a communication system in which a filtered and quantized signal is sent over a channel with erasures and (potentially) additive noise. Linear MMSE estimation is achieved in such a system by Kalman filtering. Allowing any Markov erasure process and any Markov-state jump linear signal generation model, it is shown that the estimation performance at the receiver can be computed as a deterministic optimization with linear matrix inequality (LMl) constraints rather than a pseudorandom simulation. Furthermore, in contrast to the case without erasures, the filtering in the transmitter should not necessarily be MMSE prediction (whitening); a procedure is given to find a locally optimal prefilter. The main tools are recent LMI characterizations of asymptotic state estimation error covariance and output estimation error variance for discrete-time jump linear systems in which the discrete portion of the system state is a Markov chain. As another application of this framework, a novel analysis and optimization of a "streaming" version of multiple description coding based on subsampling is outlined.

Due to constraints in cost, power, and communication, losses often arise in large sensor networks. The sensor can be modeled as an output of a linear stochastic system with random losses of the sensor output samples. This paper considers the general problem of state estimation for jump linear systems where the discrete transitions are modeled as a Markov chain. Among other applications, this rich model can be used to analyze sensor networks. The sensor loss events are then modeled as Markov processes. Under the jump linear system model, many types of underlying losses can be easily considered, and the optimal estimator to be performed at the receiver in the presence of missing sensor data samples is given by a standard time-varying Kalman filter.We show that the asymptotic average estimation error variance converges and is given by a Linear Matrix Inequality, which can be easily solved. Under this framework, any arbitrary Markov loss process can be modeled, and its average asymptotic error variance can be directly computed. We include a few illustrative examples including .xed-length burst errors, a two-state model,and partial losses due to multiple SNR states. Our analysis encompasses modeling discrete changes not only in the received data as stated above, but also in the underlying system. In the context of the lossy sensor model, the former allows for variation in sensor positioning, power control, and loss of data communications; the latter could allow for discrete changes in the dynamics of the variable monitored by the sensor. This freedom in modeling yields a tool that is potentially valuable in various scenarios in which entities that share information are subjected to challenging and time-varying network conditions.

This work is focused on computing-via a deterministic optimization with linear matrix inequality (LMI) constraints, rather than a pseudorandom simulation-the performance of predictive quantization schemes under various scenarios for loss and degradation of encoded prediction error samples. The ability to make this computation then allows for the optimization of prediction filters with the aim of minimizing overall mean squared error (including the effects of losses) rather than to minimize the variance of the unquantized prediction error sequence. The main tools are recent characterizations of asymptotic state estimation error covariance and output estimation error variance in terms of LMIs. These characterizations apply to discrete-time jump linear systems in which the discrete portion of the system state is a Markov chain. Translating to the signal processing terminology, this means that the signal model is "piecewise ARMA," as is standard in many forms of speech processing.

Estimation problems with bounded, uniformly distributed noise arise naturally in reconstruction problems from over complete linear expansions with subtractive dithered quantization. We present a simple recursive algorithm for such bounded-noise estimation problems. The mean-square error (MSE) of the algorithm is "almost" O(1/n/sup 2/), where n is the number of samples. This rate is faster than the O(1/n) MSE obtained by standard recursive least squares estimation and is optimal to within a constant factor.