Reconfigurable antennas (RAs) utilize the electromagnetic (EM) domain to provide dynamic control over antenna radiation patterns, which offers an effective way to enhance power efficiency in wireless links. Unlike conventional arrays with fixed element patterns, RAs enable on-demand beampattern synthesis by directly controlling each antenna's EM characteristics. While existing research on RAs has primarily focused on improving spectral efficiency, this paper explores their application for downlink localization. Moreover, the majority of existing works focus on far-field scenarios with little attention on near-field (NF). Motivated by these gaps, we consider a synthesis model in which each antenna generates desired beampatterns from a finite set of EM basis functions. We then formulate a joint optimization problem for the baseband (BB) and EM precoders with the objective of minimizing the user equipment (UE) position error bound (PEB) in NF conditions. Our analytical derivations and extensive simulation results demonstrate that the proposed hybrid precoder design for RAs significantly improves UE positioning accuracy compared to traditional non-reconfigurable arrays.

Achieving reliable multidimensional Vehicle-to-Vehicle (V2V) channel state information (CSI) prediction is both challenging and crucial for optimizing downstream tasks that depend on instantaneous CSI. This work extends traditional prediction approaches by focusing on four-dimensional (4D) CSI, which includes predictions over time, bandwidth, and antenna (TX and RX) space. Such a comprehensive framework is essential for addressing the dynamic nature of mobility environments within intelligent transportation systems, necessitating the capture of both temporal and spatial dependencies across diverse domains. To address this complexity, we propose a novel context-conditioned spatiotemporal predictive learning method. This method leverages causal convolutional long short-term memory (CA-ConvLSTM) to effectively capture dependencies within 4D CSI data, and incorporates context-conditioned attention mechanisms to enhance the efficiency of spatiotemporal memory updates. Additionally, we introduce an adaptive meta-learning scheme tailored for recurrent networks to mitigate the issue of accumulative prediction errors. We validate the proposed method through empirical studies conducted across three different geometric configurations and mobility scenarios. Our results demonstrate that the proposed approach outperforms existing state-of-the-art predictive models, achieving superior performance across various geometries. Moreover, we show that the meta-learning framework significantly enhances the performance of recurrent-based predictive models in highly challenging cross-geometry settings, thus highlighting its robustness and adaptability.

In multi-cell wireless networks, providing intelligent services via federated learning (FL) becomes more challenging due to multi-level distribution shifts across clients and regions, as well as additional communication delays among edge and cloud servers. To address these issues, we propose SplitOMC, a split learning framework that integrates overlapping-area clients and a multi-exit neural architecture to jointly handle (i) client-preferred, (ii) out-of-preference, and (iii) out-of-region tasks. By strategically leveraging clients in overlapping regions, SplitOMC accelerates training without excessive backhaul communication, while maintaining both personalization and generalization. We theoretically analyze the convergence behavior of the proposed algorithm, ensuring performance stability under heterogeneous data and communication conditions. Extensive experiments on MNIST, CIFAR-10/100, and a real-world Jetson Nano testbed demonstrate that SplitOMC consistently achieves faster training and inference with improved accuracy compared to state-of-the-art methods. In particular, the framework shows robustness in resource-constrained and unstable network environments, highlighting its practical value for next-generation wireless intelligent services.

Timely delivery of delay-sensitive information over dynamic, heterogeneous networks is increasingly essential for a range of interactive applications, such as industrial automation, self-driving vehicles, and augmented reality. However, most existing network control solutions target onlyaveragedelay performance, falling short of providing strict End-to-End (E2E) peak latency guarantees. This paper addresses the challenge of reliably delivering packets within application-imposed deadlines by leveraging recent advancements in Multi-Agent Deep Reinforcement Learning (MA-DRL). After introducing the Delay-Constrained Maximum-Throughput (DCMT) dynamic network control problem, and highlighting the limitations of current solutions, we present a novel MA-DRL network control framework that leverages a centralized routing and distributed scheduling architecture. The proposed framework leverages critical networking domain knowledge for the design of effective MA-DRL strategies based on the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) technique, where centralized routing and distributed scheduling agents dynamically assign paths and schedule packet transmissions according to packet lifetimes, thereby maximizing on-time packet delivery. The generality of the proposed framework allows integrating both data-driven Deep Reinforcement Learning (DRL) agents and traditional rule-based policies in order to strike the right balance between performance and learning complexity. Our results confirm the superiority of the proposed framework with respect to traditional stochastic optimization-based approaches and provide key insights into the role and interplay between data-driven DRL agents and new rule-based policies for both efficient and high-performance control of latency-critical services.

Accurate path loss (PL) modeling is essential for evaluating and optimizing cell-free massive MIMO systems, especially in dense urban environments where traditional models fail to capture the complexity of real-world propagation. This paper introduces CUNEC (Cell-free massive MIMO for Urban Non-stationary Environments with Correlations), a novel PL model that accounts for spatial non-stationarity, inter-access point (AP)/user equipment (UE) correlations, and urban-specific propagation phenomena such as corner diffraction and street canyon waveguiding. CUNEC segments AP-UE paths by street order, models PL as a stochastic function of urban geometry, and integrates spatially correlated shadowing. The parameters are derived from large-scale ray tracing and validated against both additional ray tracing in New York, NY and real-world channel measurements in Los Angeles, CA. Compared to the conventional α–β model, CUNEC significantly improves accuracy in the considered urban propagation scenarios. An open-source dataset comprising over 30,000 AP locations and 128 UE positions is also released to support reproducible research and future system development.

Accurate modeling of line-of-sight (LOS) probability is crucial for wireless channel description and coverage planning. The presence of a LOS impacts other channel characteristics such as pathloss, fading depth, delay- and angular spread, etc. Existing models, although useful, are based on very limited datasets. In this paper, we establish a framework to produce high accuracy LOS models from geospatial data in different environments, and apply it to create a LOS model for macrocells, using datasets of the United States (US) on a national scale, using more than$13,000$locations of real-world macrocells. Based on this we create a new, fully parameterized model that better describes macrocell deployments in the US than the 3GPP model. We furthermore demonstrate that for improved accuracy the LOS probability should be modeled on a per cell basis, and the model parameters treated as random variables; we provide a full description and parameterization of this novel approach and by simulations show that it better predicts the inter-cell interference at the cell-edge than an average-based model.

Extreme resource constraints make large-scale machine learning (ML) with distributed clients challenging in wireless networks. On the one hand, large-scale ML requires massive information exchange between clients and server(s). On the other hand, these clients have limited battery and computation powers that are often dedicated to operational computations. Split federated learning (SFL) is emerging as a potential solution to mitigate these challenges, by splitting the ML model into clientside and server-side model blocks, where only the client-side block is trained on the client device. However, practical applications require personalized models that are suitable for the client's personal task. Motivated by this, we propose a personalized hierarchical split federated learning (PHSFL) algorithm that is specially designed to achieve better personalization performance. More specially, owing to the fact that regardless of the severity of the statistical data distributions across the clients, many of the features have similar attributes, we only train the body part of the federated learning (FL) model while keeping the (randomly initialized) classifier frozen during the training phase. We first perform extensive theoretical analysis to understand the impact of model splitting and hierarchical model aggregations on the global model. Once the global model is trained, we finetune each client classifier to obtain the personalized models. Our empirical findings suggest that while the globally trained model with the untrained classifier performs quite similarly to other existing solutions, the fine-tuned models show significantly improved personalized performance.

Channel models that represent various operating conditions a communication system might experience are important for design and standardization of any communication system. While statistical channel models have long dominated this space, machine learning (ML) is becoming a popular alternative approach. However, existing approaches have mostly focused on predictive solutions to match instantaneous channel realizations. Other solutions have focused on pathloss modeling, while double-directional (DD) channel representation is needed for a complete description. Motivated by this, we (a) develop a generative solution that uses a hybrid Transformer (hTransformer) model with a low-rank projected attention calculation mechanism and a bidirectional long short-term memory (BiLSTM) layer to generate complete DD channel information and (b) design a domain-knowledge-informed training method to match the generated and true channel realizations' statistics. Our extensive simulation results validate that the generated samples' statistics closely align with the true statistics while mostly outperforming the performance of existing predictive approaches.

This research paper delves into leveraging Machine Learning (ML) for precise localization in GPS-challenged environments like urban canyons, addressing the complexities of time-varying signal propagation types, where transient obstructions, such as vehicles, can modify the channel state information (CSI) over time. It presents a novel approach termed semantic localization, which recognizes signal propagation conditions as semantic elements, incorporating them into the localization framework to enhance both accuracy and resilience. To tackle the issue of diverse CSIs at each location and the extensive need for labeled data, the paper proposes a multi-task deep domain adaptation (DA) strategy. This approach trains neural networks using a limited set of labeled data complemented by a vast array of unlabeled samples, coupled with innovative scenario adaptive learning techniques for optimal representation learning and knowledge transfer. Employing Bayesian theory for the efficient management of task importance weights minimizes the necessity for laborious parameter tuning. By making certain assumptions, the study introduces a deep homoscedastic DA method for enhanced joint task efficacy. Through detailed simulations using a 3D ray tracing dataset, the paper evidences that the integration of environmental semantics and the advanced DA localization techniques markedly elevates the precision of localization in various demanding settings.

Next-generation wireless communication systems will incorporate millimeter wave (mmWave) as one of the key technologies to increase data rates. These forthcoming mmWave systems will integrate multiple-input multiple-output (MIMO) technology to ensure sufficient link margin and are expected to be deployed in conjunction with sub-6 GHz systems. Configuring a MIMO communication link usually relies on estimating channel state information (CSI), which is difficult to acquire at mmWave frequencies due to the low pre-beamforming signal-to-noise ratio (SNR). In this paper, we propose a novel approach to estimate mmWave MIMO channels by leveraging out-of-band information from a sub-6GHz band. Utilizing this approach, we develop three channel estimation methods and compare these methods with one using only in-band information. We investigate the influence of theK-factor, the SNR and the number of antennas on the performance of the proposed channel estimation methods through simulations. Additionally, we compare these methods in terms of their computational complexity. Finally, we validate the performance of the proposed methods through channel measurements conducted in an outdoor environment. The results demonstrate that the proposed methods outperform in-band mmWave channel estimation in terms of spectral efficiency, particularly in scenarios of low SNR and high K-factor.

Recent advances in deep learning have successfully replaced classical algorithms with machine learning models based on neural networks (NNs). This is particularly prevalent in proactive caching. As NNs grows more capable as their size in terms of storage and computation increases, NN-based proactive caching achieves performance improvement. Nonetheless, there remain challenges in implementing NN-based proactive caching in realistic environments with dynamic user movement. These are due to the fixed structure of NNs that should expand the input size to match the dimensions of the input with the dimensions of the NN’s input units. To address these challenges, this paper proposes a scalable proactive caching framework, named slimmable federated reinforcement learning (SlimFRL). By adopting slimmable neural networks (SNNs) in FRL, our SlimFRL easily adjusts the widths of the SNNs during training according to the number of users. Moreover, due to the scalability of SNNs, our SlimFRL can set the appropriate input dimension while not using imputation, leading to performance improvement. This paper also validates the performance and advantages of SlimFRL in terms of reward and additional cost functions. Additionally, this paper proposes several training algorithms for SlimFRL and corroborates their superiority with convergence analysis and various experiments.

Since the design of wireless MIMO systems requires knowledge of the double-directional (i.e., directionally resolved at both link ends) channel characteristics, and 5G/6G use higher frequency bands, there is the need for double-directional measurements in the mmWave spectrum, along with channel sounders that can accurately perform such measurements. This paper introduces a novel channel sounding approach based on a redirecting rotating mirror arrangement (ReRoMA). The method is low-cost and flexible as it requires only a single radio frequency chain at each link end and performs mechanical beamsteering. However, in contrast to existing rotating-horn systems, it physically separates the signal generation/transmission and the beam steering components, resulting in orders-of-magnitude faster measurements. The paper outlines the fundamental concept, describes details of the implementation, and demonstrates its application and accuracy using a 60GHz prototype for measurements in static reference scenarios, as well as dynamic measurements. We illustrate the detected propagation paths using dynamic angular and delay power spectra and correlate these findings with the surrounding environmental structure. Locations of environmental objects are detected within the Fourier resolution determined by bandwidth and horn width, with no noticeable degradation due to the faster measurements.

Backhaul traffic congestion caused by the video traffic of a few popular files can be alleviated by storing the to-be-requested content at various levels in wireless video caching networks. Typically, content service providers (CSPs) own the content, and the users request their preferred content from the CSPs using their (wireless) internet service providers (ISPs). As these parties do not reveal their private information and business secrets, traditional techniques may not be readily used to predict the dynamic changes in users’ future demands. Motivated by this, we propose a novelresource-awarehierarchicalfederatedlearning (RawHFL) solution for predicting user’s future content requests. A practical data acquisition technique is used that allows the user to update its local training dataset based on its requested content. Besides, since networking and other computational resources are limited, considering that only a subset of the users participate in the model training, we derive the convergence bound of the proposed algorithm. Based on this bound, we minimize a weighted utility function for jointly configuring the controllable parameters to train the RawHFL energy efficiently under practical resource constraints. Our extensive simulation results validate the proposed algorithm’s superiority, in terms of test accuracy and energy cost, over existing baselines.

Indoor localization is a challenging problem that - unlike outdoor localization - lacks a universal and robust solution. Machine Learning (ML), particularly Deep Learning (DL), methods have been investigated as a promising approach. Although such methods bring remarkable localization accuracy, they heavily depend on the training data collected from the environment. The data collection is usually a laborious and time-consuming task, but Data Augmentation (DA) can be used to alleviate this issue. In this paper, different from previously used DA, we propose methods that utilize the domain knowledge about wireless propagation channels and devices. The methods exploit the typical hardware component drift in the transceivers and/or the statistical behavior of the channel, in combination with the measured Power Delay Profile (PDP). We comprehensively evaluate the proposed methods to demonstrate their effectiveness. This investigation mainly focuses on the impact of factors such as the number of measurements, augmentation proportion, and the environment of interest impact the effectiveness of the different DA methods. We show that in the low-data regime (few actual measurements available), localization accuracy increases up to 50%, matching non-augmented results in the high-data regime. In addition, the proposed methods may outperform the measurement-only highdata performance by up to 33% using only 1/4 of the amount of measured data. We also exhibit the effect of different training data distribution and quality on the effectiveness of DA. Finally, we demonstrate the power of the proposed methods when employed along with Transfer Learning (TL) to address the data scarcity in target and/or source environments.

Cell-free massive MIMO (CF-mMIMO), where each user equipment (UE) is connected to multiple access points (APs), is emerging as an important component for fifth-generation (5G) and sixth-generation (6G) cellular systems. Accurate channel models based on measurements are required to optimize the design and deployment of such systems. This paper presents an extensive measurement campaign for CF-mMIMO in an urban environment. A new “virtual AP” technique measures channels between 80 UE locations and more than 20, 000 possible micro-cellular AP locations. Measurements are done at 3.5 GHz carrier frequency with 350 MHz bandwidth (BW). The paper describes the measurement setup and data processing, shows sample results and their physical interpretation, and provides statistics for key quantities such as pathloss, shadowing, delay spread (DS), and delay window. We find pathloss coefficients of 2.9 and 10.4 for line-of-sight (LOS) and non line-of-sight (NLOS), respectively, where the high LOS coefficient is mainly because larger distance leads to more grazing angle of incidence and thus lower antenna gain in our setup. Shadowing standard deviations are 5.1/16.6 dB, and root mean squared (RMS) DSs of -80.6/-72.6 dBs, where dBs is defined as$t_{\mathrm { dBs}}=10\log _{10}(t_{\mathrm { sec}})$. The measurements can also be used for parameterizing a certain type of channel model, namely Cell-free massive MIMO for Urban Non-stationary Environment with Correlations (CUNEC), which will be reported in future work.

With the move towards 6G and associated technology deployment in higher frequency bands, measurements of directionally-resolved channels and sounders capable of performing such measurements are a necessity. In this paper, we present a new concept of channel sounding based on a Redirecting Rotating Mirror Arrangement (ReRoMA), capable of performing double-directional channel measurements at millimeter wave frequencies by mechanical beam steering orders of magnitude faster than existing rotating-horn arrangements. We present this new concept, describe a prototype operating at 60 GHz, and use it to perform, as proof-of-principle, a dynamic cart-to-cart channel measurements at a T-intersection scenario. We show that this sounding principle works and allows the directional evaluation of the channel. We visualize the different resolvable propagation paths in terms of dynamic angular and delay power spectrum, and relate them to the environmental geometry.

Video caching can significantly improve backhaul traffic congestion by locally storing the popular content that users frequently request. A privacy-preserving method is desirable to learn how users' demands change over time. As such, this paper proposes a novel resource-aware hierarchical federated learning (RawHFL) solution to predict users' future content requests under the realistic assumptions that content requests are sporadic and users' datasets can only be updated based on the requested content's information. Considering a partial client participation case, we first derive the upper bound of the global gradient norm that depends on the clients' local training rounds and the successful reception of their accumulated gradients over the wireless links. Under delay, energy and radio resource constraints, we then optimize client selection and their local rounds and central processing unit (CPU) frequencies to minimize a weighted utility function that facilitates RawHFL's convergence in an energy-efficient way. Our simulation results show that the proposed solution significantly outperforms the considered baselines in terms of prediction accuracy and total energy expenditure.

Cooperative connected automated mobility depends on sensing and wireless communication functions. With increasing carrier frequency both functions can be realized with the same hardware, however, the attenuation of radio signals increases quadratically with the carrier frequency. Hence, link setup becomes challenging in vehicular scenarios due to the required beam finding process. In this paper we investigate the multipath components of the vehicle-to-infrastructure (V2I) radio channel in three frequency bands with center frequencies of 3.2 GHz, 34.3 GHz and 62.35 GHz using measurement data with 155.5 MHz bandwidth and a sounding repetition rate of $31.25 \mu \mathrm{~s}$. The channel impulse responses are collected simultaneously at all three carrier frequencies. Using the high temporal sampling rate we apply the CLEAN algorithm, enabling the estimation of the weight, delay and Doppler frequency of multipath components. By analyzing the collinearity of the Doppler normalized scattering function between the frequency bands we found that the collinearity between the 3.2 GHz and 34.3 GHz band as well as between the 3.2 GHz and 62.35 GHz is smaller in the non-line of sight (NLOS) region but increases for the line-of-sight (LOS).

This paper investigates multi-user grouping (MUG) to improve energy efficiency (EE) in cell-free massive multiple-input multiple-output systems with access point (AP) switch on/off (ASO). ASO puts those APs into sleep mode which have little contribution with any spatially-multiplexed user equipments (UEs). However, in general, ASO can be less effective especially when spatially-multiplexed UEs are widely distributed in the area. Therefore, it is crucial to systematically integrate ASO and MUG, where UEs are divided into groups for spatial multiplexing. As a concrete procedure, we propose a practical MUG-ASO algorithm utilizing the criteria based on correlations of large-scale fading among UEs for effective EE improvement. The proposed algorithm can effectively select closely locating UEs into the same group for spatial multiplexing, and then it can put comparatively large number of APs into sleep mode at each time slot. Numerical example simulations show the effectiveness of the integration for EE improvement; in a typical case, the proposed algorithm can improve EE by 29.4% at 50-percentile compared to the conventional ASO algorithms with random MUG without severe degradation of spectral efficiency.

The design of cell-free massive MIMO (CF-mMIMO) systems requires accurate, measurement-based channel models. This paper provides the first results from the by far most extensive outdoor measurement campaign for CF-mMIMO channels in an urban environment. We measured impulse responses between over 20, 000 potential access point (AP) locations and 80 user equipments (sUEs) at 3.5 GHz with 350 MHz bandwidth (BW). Measurements use a “virtual array” approach at the AP and a hybrid switched/virtual approach at the UE. This paper describes the sounder design, measurement environment, data processing, and sample results, particularly the evolution of the power-delay profiles (sPDPs) as a function of the AP locations, and its relation to the propagation environment.

Data-intensive augmented information (AgI) services (e.g., metaverse applications such as virtual/augmented reality), designed to deliver highly interactive experiences resulting from the real-time combination of live data-streams and pre-stored digital content, are accelerating the need for distributed compute platforms with unprecedented storage, computation, and communication requirements. To this end, the integrated evolution of next-generation networks (5G/6G) and distributed cloud technologies (mobile/edge/cloud computing) have emerged as a promising paradigm to address the interaction- and resource-intensive nature of data-intensive AgI services. In this paper, we focus on the design of control policies for the joint orchestration of compute, caching, and communication (3C) resources in next-generation 3C networks for the delivery of data-intensive AgI services. We design the first throughput-optimal control policy that coordinates joint decisions around (i) routing paths and processing locations for live data streams, with (ii) cache selection and distribution paths for associated data objects. We then extend the proposed solution to include a max-throughput data placement policy and two efficient replacement policies. Numerical results demonstrate the superior performance obtained via the novel multi-pipeline flow control and 3C resource orchestration mechanisms of the proposed policy, compared with state-of-the-art algorithms that lack full 3C integrated control.

The THz band (0.1-10 THz) has attracted considerable attention for next-generation wireless communications due to the large amount of available bandwidth that may be key to meet the rapidly increasing data rate requirements. Before deploying a system in this band, a detailed wireless channel analysis is required as the basis for proper design and testing of system implementations. One of the most important deployment scenarios of this band is the outdoor microcellular environment, where the Transmitter (Tx) and the Receiver (Rx) have a significant height difference (typically ≥ 10 m). In this paper, we present double-directional (i.e., directionally resolved at both link ends) channel measurements in such a microcellular scenario encompassing street canyons and an open square. Measurements are done for a 1 GHz bandwidth between 145-146 GHz and an antenna beamwidth of 13 degree; distances between Tx and Rx are up to 85 m and the Tx is at a height of 11.5 m from the ground. The measurements are analyzed to estimate path loss, shadowing, delay spread, angular spread, and multipath component (MPC) power distribution. These results allow the development of more realistic and detailed THz system performance assessment.

Condensed channel parameters, such as Root Mean Square (RMS) delay spread and angular spread, are essential ways of representing results from the measurement of wireless propagation channels. However, real-world measurements are invariably affected by noise, which is not inherently reduced by the Fourier processing - in contrast to high-resolution parameter estimation. Even when the signal-to-noise ratio is relatively high, e.g., 20 dB, the impact of the noise on the condensed parameters can be significant and lead to erroneous estimates. This can be explained, e.g., for the computation of the RMS delay spread, where long delays (which often carry noise only) are "up-weighted" by the square of the delay. While techniques like noise thresholding are often applied, the reasons for choosing thresholds are usually not described in detail. This paper systematically analyzes noise thresholding for joint delay-angle measurements and derives equations for the impact of thresholds on the computation of delay and angle RMS spreads and window parameters under various measurement circumstances. The paper provides results based on closed-form equations, as well as MonteCarlo (MC) simulations, and application to real-world measurements in the sub-THz band.

Large-scale channel prediction, i.e., estimation of the pathloss from geographical/morphological/building maps, is an essential component of wireless network planning. Ray tracing (RT)-based methods have been widely used for many years, but they require significant computational effort that may become prohibitive with the increased network densification and/or use of higher frequencies in B5G/6G systems. In this paper, we propose a data-driven, model-free pathloss map prediction (PMP) method, called PMNet. PMNet uses a supervised learning approach: it is trained on a limited amount of RT data and map data. Once trained, PMNet can predict pathloss over location with high accuracy (an RMSE level of$10^{-2}$) in a few milliseconds. We further extend PMNet by employing transfer learning (TL). TL allows PMNet to learn a new network scenario quickly ($\times 5.6$faster training) and efficiently (using$\times 4.5$less data) by transferring knowledge from a pre-trained model, while retaining accuracy. Our results demonstrate that PMNet is a scalable and generalizable ML-based PMP method, showing its potential to be used in several network optimization applications.

This study presents a novel deep reinforcement learning (DRL)-based handover (HO) protocol, called DHO, specifically designed to address the persistent challenge of long propagation delays in low-Earth orbit (LEO) satellite networks’ HO procedures. DHO skips the Measurement Report (MR) in the HO procedure by leveraging its predictive capabilities after being trained with a pre-determined LEO satellite orbital pattern. This simplification eliminates the propagation delay incurred during the MR phase, while still providing effective HO decisions. The proposed DHO outperforms the legacy HO protocol across diverse network conditions in terms of access delay, collision rate, and handover success rate, demonstrating the practical applicability of DHO in real-world networks. Furthermore, the study examines the trade-off between access delay and collision rate and also evaluates the training performance and convergence of DHO using various DRL algorithms.

Terahertz (THz) communication signals are susceptible to severe degradation because of the molecular interaction with the atmosphere in the form of subsequent absorption and re-radiation. Recently, reconfigurable intelligent surface (RIS) has emerged as a potential technology to assist in THz communications by boosting signal power or providing virtual line-of-sight (LOS) paths. However, the re-radiated energy has either been modeled as a scattering component or as additive Gaussian noise in the literature. Since the precise characterization is still a work in progress, this paper presents the first comparative investigation of the performance of an RIS-aided THz system under these two extreme re-radiation models. In particular, we first develop a novel parametric channel model that encompasses both models of the re-radiation through a simple parameter change, and then utilize that to design a robust block-coordinate descent (BCD) algorithmic framework which maximizes a lower bound on channel capacity while accounting for imperfect channel state information (CSI). In this framework, the original problem is split into two sub-problems: a) receive beamformer optimization, and b) RIS phase-shift optimization. As the latter sub-problem (unlike the former) has no analytical solution, we propose three approaches for it: a) semi-definite relaxation (SDR) (high complexity), b) signal alignment (SA) (low complexity), and c) gradient descent (GD) (low complexity). The time complexities associated with the proposed approaches are explicitly derived. We analytically demonstrate the limited interference suppression capability of a passive RIS by deriving the stationary points of signal-to-interference and noise ratio (SINR) of a one-element RIS system with one interferer. Our numerical results also demonstrate that slightly better throughput is achieved when the re-radiation manifests as scattering.

Distributed antenna systems (DAS) have attracted significant attention for next-generation wireless systems. This largely motivated by the inherent macrodiversity, i.e., the fact that shadowing of the links to the different access points (APs) is different, which plays a major role in the reliability and capacity of such systems. However, shadowing correlation might reduce the benefits. In this paper, we provide the first stochastic geometry-based analysis of the impact of correlated shadowing on the uplink performance of DAS. Since the statistics of the SNR are determined by the second moment, we use Fenton-Wilkinson (F-W) moment matching and the second moment measure, to formulate it as triple integral. From this, we further construct two closed-form approximations with different degrees of accuracy and simplicity. Extensive Monte Carlo simulations validate the theoretical inferences and approximation accuracy. The results show that a large decorrelation distance will increase the variance of uplink SNR, and a small path-loss exponent leads to a stronger dependence of the second moment on the decorrelation distance. The results can also serve as the basis for future investigations of cell-free massive MIMO systems.

Pathloss prediction is an essential component of wireless network planning. While ray tracing based methods have been successfully used for many years, they require significant computational effort that may become prohibitive with the increased network densification and/or use of higher frequencies in 5G/BSG (beyond 5G) systems. In this paper, we propose and evaluate a data-driven and model-free pathloss prediction method, dubbed PMNet. This method uses a supervised learning approach: training a neural network (NN) with a limited amount of ray tracing (or channel measurement) data and map data and then predicting the pathloss over location with no ray tracing data with a high level of accuracy. Our proposed pathloss map prediction-oriented NN architecture, which is empowered by state-of-the-art computer vision techniques, outperforms other architectures that have been previously proposed (e.g., UNet, RadioUNet) in terms of accuracy while showing generalization capability. Moreover, PMNet trained on a 4-fold smaller dataset surpasses the other baselines (trained on a 4-fold larger dataset), corroborating the potential of PMNet.11The trained model and codes are publicly available on the Github page: https://github.com/abman23/PMNet

The ongoing large-scale deployment of 5G (3GPP New Radio) cellular systems makes it attractive to use their signals for “opportunistic” bistatic radar sensing, where the base stations serve as transmitters and user equipment (UEs) as receivers. Such 5G based radar sensing has the potential to complement and improve existing advanced driver assistance systems (ADAS) and future self-driving cars, e.g., in terms of increased sensing range. However, realizing a radar sensing system using strictly standards-compliant 5G-NR signals is a complicated task, mainly because of the signal inconsistency across time and frequency. This paper first analyzes the different reference signals (RSs) defined in the NR standard and which combinations thereof are suitable for vehicular radar applications. We then present several signal processing approaches to overcome difficulties arising from the standardized signals; in particular two-dimensional interpolation between non-regularly spaced locations in the time-frequency plane, where the classical Nyquist-Shannon sampling breaks down, as well as serial interference cancellation(SIC) for determination of the peaks in the delay-Doppler domain with better-than-Fourier resolution. We finally demonstrate the use of payload data for improving the radar estimates. Simulation results demonstrate the validity of our approach.

Indoor localization is a challenging task. Compared to outdoor environments where GPS is dominant, there is no robust and almost-universal approach. Recently, machine learning (ML) has emerged as the most promising approach for achieving accurate indoor localization. Nevertheless, its main challenge is requiring large datasets to train the neural networks. The data collection procedure is costly and laborious, requiring extensive measurements and labeling processes for different indoor environments. The situation can be improved by Data Augmentation (DA), a general framework to enlarge the datasets for ML, making ML systems more robust and increasing their generalization capabilities. This paper proposes two simple yet surprisingly effective DA algorithms for channel state information (CSI) based indoor localization motivated by physical considerations. We show that the number of measurements for a given accuracy requirement may be decreased by an order of magnitude. Specifically, we demonstrate the algorithms' effectiveness by experiments conducted with a measured indoor WiFi measurement dataset: As little as 10% of the original dataset size is enough to get the same performance as the original dataset. We also showed that if we further augment the dataset with the proposed techniques, test accuracy is improved more than three-fold.

This paper describes our pathloss prediction system submitted to the ICASSP 2023 First Pathloss Radio Map Prediction Challenge. We describe the architecture of PMNet, a neural network we specifically designed for pathloss prediction. Moreover, to enhance the prediction performance, we apply several machine learning techniques, including data augmentation, fine-tuning, and optimization of the network architecture. Our system achieves an RMSE of 0.02569 on the provided RadioMap3Dseer dataset, and 0.0383 on the challenge test set, placing it in the 1st rank of the challenge.

This paper investigates the use of machine learning to a particularly challenging wireless localization problem, namely localization in the presence of high environmental dynamics. We first introduce the semantic localization scheme to achieve this goal, adding time-varying environmental semantics recognition as a joint task with localization. The environmental semantics is characterized by the propagation conditions between the base station (BS) and the receiver(s). To reduce the negative impact of high dynamics, our method is based on the deep unsupervised domain adaptation (UDA) techniques, which jointly train the network with labeled and unlabeled samples. This also greatly reduces the effort during data collection since the acquisition of unlabeled data requires much smaller effort than labeled data. We further propose a deep multi-task UDA method with a novel scenario adaptive learning strategy, enhancing the optimization process of the neural network with the environmental semantics. Lastly, we evaluate the proposed method over the widely used deepMIMO dataset that is generated from detailed ray tracing. The experimental results demonstrate the superior performance of semantic localization over standalone localization; the proposed method outperforms the competing ones over multiple experimental conditions.

The availability of large bandwidths in the terahertz (THz) band will be a crucial enabler of high data rate applications in next-generation wireless communication systems. The urban microcellular scenario is an essential deployment scenario where the base station (BS) is significantly higher than the user equipment (UE). Under practical operating conditions, moving objects (i.e., blockers) can intermittently obstruct various parts of the BS- UE link. Therefore, in the current paper, we analyze the effect of such blockers. We assume a blockage of the strongest beam pair and investigate the availability and extent of angular diversity, i.e., alternative beampairs that can sustain communication when the strongest is blocked. The analysis uses double-directional channel measurements in urban microcellular scenarios for 145– 146 GHz with BS-UE distances between 18 to 83 m. We relate the communication-system quantities of beam diversity and capacity to the wireless propagation conditions. We show that the SNR loss due to blockage depends on the blocked angular range and the specific location, and we find mean blockage loss to be on the order of 10–20 dB in line-of-sight (LOS) and 5–12 dB in NLOS (non-LOS). This analysis can contribute to the design of intelligent algorithms or devices (e.g., beamforming, intelligent reflective surfaces) to overcome the impact of the blockage.

Since many new applications require a combination of caching, computing, and communications (3C), there is an increasing interest in the optimization of networks that jointly perform 3C. Although a number of studies for particular designs of such networks have been published, the characterization of fundamental performance limits has been rarely explored. Aiming to fill this gap, this paper provides an asymptotic outage analysis for downlink interference-limited wireless edge networks with joint 3C. In particular, we derive analytical closed-form expressions as functions of delay and 3C parameters for outage probability and optimal caching policy, respectively. Based on these, we then derive insightful outage probability expressions for the optimal caching policy and two important reference caching policies using asymptotic analysis. We provide insights and interpretations based on the derived expressions. Computer simulations validate our analytical results and insights.

The 60 GHz band plays an important role for wireless signalling with extremely high data rates, both for WiFi and cellular applications. For the design and performance analysis of this band, the impact of human interactions on the propagation from transmitter to receiver has to be taken into account. While the impact of a single human body blocking the line-of-sight (LOS) has been investigated as a deterministic effect, statistical models describing the effect of multiple human bodies, acting as reflectors, on received power and delay spread are still lacking. To close this gap, this paper analyzes measurements of 60 GHz channel impulse responses in static but “evolutionary” office scenarios that involve one and two people and uses them to calibrate a ray tracer that allows the generation of a larger number of channel realizations. Regression fits are applied to the resulting channel responses to obtain an accurate characterization of human-induced power and delay variations in proximity situations where humans give rise to additional multipath11The work of B. Abou Ali Modad and A.F. Molsich was supported in part by the National Science Foundation and the National Institute of Standards and Technology..

The geometry-based stochastic channel models (GSCM), which can describe realistic channel impulse responses, often rely on the existence of both local and far scatterers. However, their visibility from both the base station (BS) and mobile station (MS) depends on their relative heights and positions. For example, the condition of visibility of a scatterer from the perspective of a BS is different from that of an MS and depends on the height of the scatterer. To capture this, we propose a novel GSCM where each scatterer has dual disk visibility regions (VRs) centered on itself for both BS and MS, with their radii being our model parameters. Our model consists of short and tall scatterers, which are both modeled using independent inhomogeneous Poisson point processes (IPPPs) having distinct dual VRs. We also introduce a probability parameter to account for the varying visibility of tall scatterers from different MSs, effectively emulating their noncontiguous VRs. Using stochastic geometry, we derive the probability mass function (PMF) of the number of multipath components (MPCs), the marginal and joint distance distributions for an active scatterer, the mean time of arrival (ToA), and the mean received power through non-line-of-sight (NLoS) paths for our proposed model. By selecting appropriate model parameters, the propagation characteristics of our GSCM are demonstrated to closely emulate those of the COST-259 model.

Device-to-Device (D2D) communication systems have recently received great interest especially for use by Public Safety Organizations (PSOs); in these scenarios, communications involving infrastructure nodes (base stations) might not always be possible, or sufficiently reliable. LTE Sidelink, the 3GPP standard for D2D communication at sub-GHz frequencies, has been mandated by many PSOs for such situations. Of particular interest are Indoor-to-Outdoor (I2O) communications, where an emergency responder inside a building communicates with a command post at street level. In this paper, we present simulation results for LTE Sidelink performance over measured I2O channels, and provide comparison between these measurement results and the 3GPP channel models. We also provide an "applique" on top of the standard (i.e., requiring no changes in the standard) that uses multiple-antenna combining to provide SNR gains compared to the standard SISO D2D Sidelink channel. We observe on average around 10 dB SNR gain for applying Maximum Ratio Combining (MRC) compared to a best-case SISO channel.

This paper suggests and analyzes adaptive bit allocation for the quantization of uplink signals of a cell-free massive MIMO (CF-mMIMO) system under limited fronthaul capacity. Specifically, we consider a CF-mMIMO system with hybrid processing, where at each access point (AP) a singular-value decomposition (SVD) reduces the number of streams that need to hauled, each stream is quantized with an adaptive number of bits, and a central processing unit (CPU) decodes the uplink signals. The hybrid processing, which the authors previously proposed, had been shown its potential to reduce fronthaul load without severe degradation of the spectral efficiency. However, as the bandwidths of the wireless system increases, the fronthaul capacity becomes comparatively tight, and the quantization noise would degrade the spectral efficiency severely. In order to improve the performance under such a scenario, this paper proposes algorithms for adaptive bit allocation of the output streams, based on the optimization of the average SNR, or the sum capacity. In addition, appropriate selection of the number of streams of the hybrid processing in each AP is also discussed. Computer simulations verify the effectiveness of these proposed methods.

Emerging distributed cloud architectures, e.g., fog and mobile edge computing, are playing an increasingly important role in the efficient delivery of real-time stream-processing applications (also referred to as augmented information services), such as industrial automation and metaverse experiences (e.g., extended reality, immersive gaming). While such applications require processed streams to be shared and simultaneously consumed by multiple users/devices, existing technologies lack efficient mechanisms to deal with their inherent multicast nature, leading to unnecessary traffic redundancy and network congestion. In this paper, we establish a unified framework for distributed cloud network control with generalized (mixed-cast) traffic flows that allows optimizing the distributed execution of the required packet processing, forwarding, and replication operations. We first characterize the enlarged multicast network stability region under the new control framework (with respect to its unicast counterpart). We then design a novel queuing system that allows scheduling data packets according to their current destination sets, and leverage Lyapunov drift-plus-penalty control theory to develop the first fully decentralized, throughput- and cost-optimal algorithm for multicast flow control. Numerical experiments validate analytical results and demonstrate the performance gain of the proposed design over existing network control policies.

THz band is envisioned to be used in 6G systems to meet the ever-increasing demand for data rate. However, before an eventual system design and deployment can proceed, detailed channel sounding measurements are required to understand key channel characteristics. In this paper, we present a first extensive set of channel measurements for urban outdoor environments that are ultra-wideband, and double-directional where both transmitter and receiver are at the same height. In all, we present measurements at 38 locations, consisting of nearly 50,000 impulse responses, representing both line-of-sight (LoS) and non-line-of-sight (NLoS) cases in the 1-100 m range between 145-146 GHz. We provide modeling for path loss, shadowing, delay spread, angular spread, multipath component (MPC) power distribution and Qtapnumber. We find, among other things, that outdoor communication over tens of meters is feasible in this frequency range even in NLoS scenarios, that omni-directional delay spreads of up to 100 ns, and directional delay spreads of up to 10 ns are observed, while angular spreads are also quite significant, and a surprisingly large number of MPCs are observed for 1 GHz bandwidth and 13° beamwidth. These results constitute an important first step towards better understanding the wireless channel in the THz band.

Performance assessment and optimization for networks jointly performing caching, computing, and communication (3C) has recently drawn significant attention because many emerging applications require 3C functionality. However, studies in the literature mostly focus on the particular algorithms and setups of such networks, while their theoretical understanding and characterization has been less explored. To fill this gap, this paper conducts the asymptotic (scaling-law) analysis for the delay-outage tradeoff of noise-limited wireless edge networks with joint 3C. In particular, assuming the user requests for different tasks following a Zipf distribution, we derive the analytical expression for the optimal caching policy. Based on this, we next derive the closed-form expression for the optimum outage probability as a function of delay and other network parameters for the case that the Zipf parameter is smaller than 1. Then, for the case that the Zipf parameter is larger than 1, we derive the closed-form expressions for upper and lower bounds of the optimum outage probability. We provide insights and interpretations based on the derived expressions. Computer simulations validate our analytical results and insights.

Next-generation distributed computing networks (e.g., edge and fog computing) enable the efficient delivery of delay-sensitive, compute-intensive applications by facilitating access to computation resources in close proximity to end users. Many of these applications (e.g., augmented/virtual reality) are also data-intensive: in addition to user-specific (live) data streams, they require access to shared (static) digital objects (e.g., image database) to complete the required processing tasks. When required objects are not available at the servers hosting the associated service functions, they must be fetched from other edge locations, incurring additional communication cost and latency. In such settings, overall service delivery performance shall benefit from jointly optimized decisions around (i) routing paths and processing locations for live data streams, together with (ii) cache selection and distribution paths for associated digital objects. In this paper, we address the problem of dynamic control of data-intensive services over edge cloud networks. We characterize the network stability region and design the first throughput-optimal control policy that coordinates processing and routing decisions for both live and static data-streams. Numerical results demonstrate the superior performance (e.g., throughput, delay, and resource consumption) obtained via the novel multi-pipeline flow control mechanism of the proposed policy, compared with state-of-the-art algorithms that lack integrated stream processing and data distribution control.

Cell-free massive multi-input multi-output (CF-mMIMO) systems are one of the key technologies for 6G. Currently, performance assessment of such systems is hampered by the fact that there are no specific path loss (PL) models for CF-mMIMO. Conventional PL models based on Euclidean distance and log-normal shadowing assuming spatial stationarity across coverage area are usually employed for simplicity but show significant deviations from reality particularly in urban environments, which are the main deployment scenario for CF-mMIMO. In this work, we provide the first realistic channel model for CF-mMIMO systems in urban environments, introducing non-isotropic, non-stationary behavior in different parts of street canyon locations and incorporating both correlations between access points, and between user equipments. Simulation results demonstrate the superior reproduction of typical PL values in urban street canyons.

Knowing the wireless channel characteristics, particularly the existence of a line-of-sight (LOS) connection from the user equipment (UE) to the base station (BS), is key in wireless system development and deployments. The LOS becomes even more important in high frequency systems such as fifth generation (5G) mm-wave and terahertz (THz) systems where the difference in channel characteristics varies greatly from LOS to non-LOS (NLOS) situations. While existing LOS models such as the 3GPP model and others in the literature are valuable, they lack cell-by-cell specificity and/or do not generalize well to different environments. In this paper, we aim to close these gaps and propose generalized models derived from extensive real-world analysis in diverse environments. We also study the performance of our models in system-level simulations in terms of signal-to-interference-ratio (SIR) and coverage.

THz communication has attracted a great deal of attention due to the large bandwidth available in this band. However, system development and deployment requires detailed knowledge of the wireless channel, which must be provided by measurements (channel sounding) and subsequent modeling in key scenarios of interest. One important scenario in this regard is the urban outdoor microcellular scenario where the Transmitter (Tx) and the Receiver (Rx) have a significant height difference. In this paper, we present ultra-wideband double-directional channel measurements in a microcellular scenario for the band between 145-146 GHz in a street canyon with Tx-Rx of distances up to 67 m. The results are analyzed in the delay and angular domain, and the impact of the street canyon on the power delay profiles and angular power spectra is detailed. These measurements thus contribute towards the creation of a more realistic and detailed THz channel model in these scenarios.

Virtual reality (VR) setups often can be characterized as Body Area Networks (BANs) where one of the transceivers is mounted on the head of the user. In order to design and assess such systems, suitable channel models need to be established. This paper presents a novel model of the propagation channel covering both attenuation and delay dispersion for such BANs. The modeling approach separates the on-body propagation from the contributions of the environment, and is thus easy to generalize to other environments as well. The model is parameterized based on an existing measurement campaign, and can thus be directly used for BAN network simulations.

Estimating vehicle locations is important for realizing Intelligent Transportation Systems (ITS). This paper considers utilizing vehicle-to-vehicle (V2V) communication for relative vehicular localization. In particular, we develop a machine learning (ML) solution that uses the Channel State Information (CSI) from multiple-antenna transceivers for vehicular localization. We develop suitable pre-processing to obtain a compact CSI representation as an input feature to the ML solution. The proposed solution is then based on feed-forward neural networks. Training and evaluation are done on measured real-world data in the 5.9 GHz band. The performance on two routes shows that the proposed feature may improve the performance while reducing the number of trainable parameters. Furthermore, the paper raises a number of interesting observations regarding the learnability in V2V ML-based localization solutions.

Performance assessment and optimization for networks jointly performing caching, computing, and communication (3C) has recently drawn significant attention because many emerging applications require 3C functionality. However, studies in the literature mostly focus on the particular algorithms and setups of such networks, while the theoretical understanding and characterization of such networks has been less explored. To fill this gap, this paper conducts the asymptotic (scaling-law) analysis for the delay-outage tradeoff of noise-limited wireless edge networks with joint 3C. In particular, we derive closed-form expressions for the optimum outage probability as function of delay and other network parameters via first obtaining the outage probability expression and then deriving the optimal caching policy. We provide insights and interpretations based on the derived expressions. Computer simulations validate our analytical results and insights.

Cell-free massive multiple-input multiple-output systems are expected to provide faster and more robust connections to user equipments (UEs) by cooperation of a massive number of distributed access points (APs). Energy efficiency (EE) is becoming an important indicator to design and operate networks; to improve EE, use of sleep-mode of APs (SMA), also called AP switch on/off, for selected APs has been investigated. Although previous works analyze the performance of SMA in the presence of user-centric clustering (UCC), these two techniques are assumed to not affect each other. In this paper, we propose a new greedy combining algorithm (GCA), where SMA and UCC work alternately to obtain better performance, and show its superiority over a conventional algorithm. Example simulations show that GCA can achieve 44% higher total EE for 8 UEs and 59% for 16 UEs with 64 APs. Additionally, GCA also provides higher minimum spectral efficiency thanks to its structure of the algorithm.

This paper proposes hybrid signal processing schemes for the uplink cell-free massive MIMO; these schemes serve to reduce fronthaul loads to obtain a scalable centralized processing architecture. In this architecture, received signals of multiple receive antennas at the access points (APs) are compressed into fewer streams by local spatial signal processing and then the streams are forwarded to a central processing unit (CPU) via fronthaul, and the CPU performs scalable processing for channel estimation and signal detection based on partial minimum mean squared error (PMMSE). We propose two kinds of concrete local signal processing methods for this hybrid processing architecture: one is based on MMSE, and the other is based on principal component analysis (PCA) with eigenvalue decomposition (EVD). For the EVD, a local vector selection based EVD (LVS-EVD) that selects uniform number of eigenvectors for each AP in a standalone way, and a global vector selection based EVD (GVS-EVD) that determines the dimensions of the weight vector of each AP in the CPU, are further considered. Computer simulations verify the approaches and compare their effectiveness. In addition, we show that the GVS-EVD scheme can be operated with significantly reduced fronthaul loads without severe performance degradation.

Measurements of the propagation channels in real-world environments form the basis of all realistic system performance evaluations, as foundation of statistical channel models or to verify ray tracing. This is also true for the analysis of cell-free massive multi-input multi-output (CF-mMIMO) systems. However, such experimental data are difficult to obtain, due to the complexity and expense of deploying tens or hundreds of channel sounder nodes across the wide area a CF-mMIMO system is expected to cover, especially when different configurations and number of antennas are to be explored. In this paper, we provide a novel method to obtain channel data for CF-mMIMO systems using a channel sounder based on a drone, also known as a small unmanned aerial vehicle (UAV). Such a method is efficient, flexible, simple, and low-cost, capturing channel data from thousands of different access point (AP) locations within minutes. In addition, we provide sample 3.5 GHz measurement results analyzing deployment strategies for APs and make the data open source, so they may be used for various other studies. To our knowledge, our data are the first large-scale, real-world CF-mMIMO channel data.

Vehicle-to-vehicle (V2V) wireless communication systems are fundamental in many intelligent transportation applications, e.g., traffic load control, driverless vehicle, and collision avoidance. Hence, developing appropriate V2V communication systems and standardization require realistic V2V propagation channel models. However, most existing V2V channel modeling studies focus on car-to-car channels; only a few investigate truck-to-car (T2C) or truck-to-truck (T2T) channels. In this paper, a hybrid geometry-based stochastic model (GBSM) is proposed for T2X (T2C or T2T) channels in freeway environments. Next, we parameterize this GBSM from the extensive channel measurements. We extract the multipath components (MPCs) by using a joint maximum likelihood estimation (RiMAX) and then determine the cluster types based on their evolution patterns. We classify the determined clusters into line-of-sight, single-bounce reflections from static interaction objects (IOs), single-bounce reflections from mobile IOs, multiple-bounce reflections, and density multipath components (DMCs). Particularly, we model multiple-bounce reflections as double clusters following the COST 273/COST2100 method. This paper presents the complete parameterization of the channel model. We validate this model by comparing the delay spread and the angular spreads of arrival/departure obtained from the proposed model with the measurement data.

We are entering a rapidly unfolding future driven by the delivery of real-time computation services, such as industrial automation and augmented reality, collectively referred to as augmented information (AgI) services, over highly distributed cloud/edge computing networks. The interaction intensive nature of AgI services is accelerating the need for networking solutions that provide strict latency guarantees. In contrast to most existing studies that can only characterize average delay performance, we focus on the critical goal of delivering AgI services ahead of corresponding deadlines on a per-packet basis, while minimizing overall cloud network operational cost. To this end, we design a novel queuing system able to track data packets’ lifetime and formalize thedelay-constrained least-cost dynamic network control problem. To address this challenging problem, we first study the setting with average capacity (or resource budget) constraints, for which we characterize the delay-constrained stability region and design a throughput-optimal control policy leveraging Lyapunov optimization theory on an equivalent virtual network. Guided by the same principle, we tackle the peak capacity constrained scenario by developing thereliable cloud network control(RCNC) algorithm, which employs a two-way optimization method to make actual and virtual network flow solutions converge in an iterative manner. Extensive numerical results show the superior performance of the proposed control policy compared with the state-of-the-art cloud network control algorithm, and the value of guaranteeing strict end-to-end deadlines for the delivery of next-generation AgI services.

Many wireless networks, including 5G NR (New Radio) and future beyond 5G cellular systems, are expected to operate on multiple frequency bands. This paper considers the band assignment (BA) problem in dual-band systems, where the basestation (BS) chooses one of the two available frequency bands (centimeter-wave and millimeter-wave bands) to communicate with the user equipment (UE). While the millimeter-wave band might offer higher data rate, there is a significant probability of outage during which the communication should be carried on the (more reliable) centimeter-wave band. With mobility, the BA can be perceived as a sequential problem, where the BS uses previously observed information to predict the best band for a future time step. We formulate the BA as a binary classification problem and propose supervised Machine Learning (ML) solutions. We study the problem when both the BS and the UE use (i) omnidirectional antennas and (ii) both use directional antennas. In the omnidirectional case, we derive analytical benchmark solutions based on the Gaussian Process (GP) assumption for the inter-band shadow fading. In the directional case, where the labeling is shown to be complex, we propose an efficient labeling approach based on the Viterbi Algorithm (VA). We compare the performances for two channel models: (i) a stochastic channel and (ii) a ray-tracing based channel.

Throughput-Outage scaling laws for single-hop cache-aided device-to-device (D2D) communications have been extensively investigated under the assumption of the protocol model. However, the corresponding performance under physical models has not been explored; in particular it remains unclear whether link-level power control and scheduling can improve the asymptotic performance. This paper thus investigates the throughput-outage scaling laws of cache-aided single-hop D2D networks considering a general physical channel model. By considering the networks with and without the equal-throughput assumption, we analyze the corresponding outer bounds and provide the achievable performance analysis. Results show that when the equal-throughput assumption is considered, using link-level power control and scheduling cannot improve the scaling laws. On the other hand, when the equal-throughput assumption is not considered, we show that the proposed double time-slot framework with appropriate link-level power control and scheduling can significantly improve the throughput-outage scaling laws, where the fundamental concept is to first distinguish links according to their communication distances, and then enhance the throughput for links with small communication distances.

The operation of Terahertz (THz) communication can be significantly impacted by the interaction between the transmitted wave and the molecules in the atmosphere. In particular, it has been observed experimentally that the signal undergoes not only molecular absorption, but also molecular re-radiation. Two extreme modeling assumptions are prevalent in the literature, where the re-radiated energy is modeled in the first as additive Gaussian noise and in the second as a scattered component strongly correlated to the actual signal. Since the exact characterization is still an open problem, we provide in this paper the first comparative study of the performance of a reconfigurable intelligent surface (RIS) assisted THz system under these two extreme models of re-radiation. In particular, we employ an RIS to overcome the large pathloss by creating a virtual line-of-sight (LOS) path. We then develop an optimization framework for this setup and utilize the block-coordinate descent (BCD) method to iteratively optimize both RIS configuration vector and receive beamforming weight resulting in significant throughput gains for the user of interest compared to random RIS configurations. Our results reveal that a slightly better throughput is achieved under the scattering assumption for the molecular re-radiation than the noise assumption.

Wireless communications in the THz frequency range can allow data rates of hundreds of Gbit/s, and will thus be an important part of 6G. A first important step for system design is an understanding of the underlying propagation channel. In this paper, we present results from one of the first measurement campaigns for medium-distance (up to 35 m) outdoor channels in urban environments where both the directions and delays of multipath are measured with good resolution. The results show a surprisingly rich multipath environment, leading to significant dispersion in both delay and angular domains. We also find that metallic-covered surfaces lead to a considerable enhancement of multipath, which indicates an important impact of building materials. Overall, the results indicate that relying on pronounced sparsity for THz system design might not always be valid in this type of environment.

The timely delivery of resource-intensive and latency-sensitive services (e.g., industrial automation, augmented reality) over distributed computing networks (e.g., mobile edge computing) is drawing increasing attention. Motivated by the insufficiency of average delay performance guarantees provided by existing studies, we focus on the critical goal of delivering next generation real-time services ahead of corresponding deadlines on a per-packet basis, while minimizing overall cloud network resource cost. We introduce a novel queuing system that is able to track data packets’ lifetime and formalize the optimal cloud network control problem with strict deadline constraints. After illustrating the main challenges in delivering packets to their destinations before getting dropped due to lifetime expiry, we construct an equivalent formulation, where relaxed flow conservation allows leveraging Lyapunov optimization to derive a provably near-optimal fully distributed algorithm for the original problem. Numerical results validate the theoretical analysis and show the superior performance of the proposed control policy compared with state-of-the-art cloud network control.

Distributed computing (cloud) networks, e.g., mobile edge computing (MEC), are playing an increasingly important role in the efficient hosting, running, and delivery of real-time stream-processing applications such as industrial automation, immersive video, and augmented reality. While such applications require timely processing of real-time streams that are simultaneously useful for multiple users/devices, existing technologies lack efficient mechanisms to handle their increasingly multicast nature, leading to unnecessary traffic redundancy and associated network congestion. In this paper, we address the design of distributed packet processing, routing, and duplication policies for optimal control of multicast stream-processing services. We present a characterization of the enlarged capacity region that results from efficient packet duplication, and design the first fully distributed multicast traffic management policy that stabilizes any input rate in the interior of the capacity region while minimizing overall operational cost. Numerical results demonstrate the effectiveness of the proposed policy to achieve throughput- and cost-optimal delivery of stream-processing services over distributed computing networks.

Throughput-Outage scaling laws for single-hop cache-aided device-to-device (D2D) communications have been extensively investigated under the assumption of the protocol model. However, the corresponding performance under physical models has not been explored; in particular it remains unclear whether link-level power control and scheduling can improve the asymptotic performance. This paper thus investigates the asymptotic throughput-outage tradeoff and derives its outer bound for cache-aided D2D networks under two common physical models. The results show that the asymptotic performance of the network under physical models is identical to that under the protocol model when requests are served with equal quality. This indicates that the throughput-outage performance cannot be improved asymptotically by using link-level power control and scheduling.

In this paper we present a software defined radio (SDR) based measurement framework that allows for simultaneous measurements of wireless communication channels over multiple bands. We present and discuss results of a wireless vehicular-to-infrastructure multiband channel measurement campaign at center frequencies of 3.2 GHz, 34.3 GHz and 62.35 GHz in a street crossing scenario. The measurement is conducted using a bandwidth of 155.5 MHz and a sounding repetition rate of 31.25 μs. We compare the measurements using the time-variant power delay profile (PDP) and the Doppler spectral density (DSD).

Cell-free massive multiple-input multiple-output systems are expected to provide faster and more robust connections to user equipments by cooperation of a massive number of distributed access points (APs), and to be one of the key technologies for beyond 5G. Recently, a measurement-based evaluation revealed that the performance of a semi-distributed deployment, where each AP has multiple antennas, is comparable to that of a fully-distributed deployment in terms of coverage in an indoor environment while reducing the number of APs. In this paper, we analyze the performance of various antenna distribution configurations, and show that semi-distributed deployments outperform fully-distributed deployment remarkably from both spectral and energy efficiency points of view. These characteristics of semi-distributed deployments enable us to construct more cost-effective networks, which is an important indicator to deploy the systems in real environment.

Smart manufacturing based on artificial intelligence and information communication technology will become the main contributor to the digital economy of the upcoming decades. In order to execute flexible production, smart manufacturing must holistically integrate wireless networking, computing, and automatic control technologies. This article discusses the challenges of this complex system engineering from a wireless networking perspective. Starting from enabling flexible reconfiguration of a smart factory, we discuss existing wireless technology and the trends of wireless networking evolution to facilitate multirobot smart factories. Furthermore, the special sequential decision-making of a multirobot manufacturing system is examined. Social learning can be used to extend the resilience of precision operation in a multirobot system by taking network topology into consideration, which also introduces a new vision for the cybersecurity of smart factories. A summary of highlights of technological opportunities for holistic facilitation of wireless networked multirobot smart factories rounds off this article.

Mobile communications have been undergoing a generational change every ten years or so. However, the time difference between the so-called “G's” is also decreasing. While fifth-generation (5G) systems are becoming a commercial reality, there is already significant interest in systems beyond 5G, which we refer to as the sixth generation (6G) of wireless systems. In contrast to the already published papers on the topic, we take a top-down approach to 6G. More precisely, we present a holistic discussion of 6G systems beginning with lifestyle and societal changes driving the need for next-generation networks. This is followed by a discussion into the technical requirements needed to enable 6G applications, based on which we dissect key challenges and possibilities for practically realizable system solutions across all layers of the Open Systems Interconnection stack (i.e., from applications to the physical layer). Since many of the 6G applications will need access to an order-of-magnitude more spectrum, utilization of frequencies between 100 GHz and 1 THz becomes of paramount importance. As such, the 6G ecosystem will feature a diverse range of frequency bands, ranging from below 6 GHz up to 1 THz. We comprehensively characterize the limitations that must be overcome to realize working systems in these bands and provide a unique perspective on the physical and higher layer challenges relating to the design of next-generation core networks, new modulation and coding methods, novel multiple-access techniques, antenna arrays, wave propagation, radio frequency transceiver design, and real-time signal processing. We rigorously discuss the fundamental changes required in the core networks of the future, such as the redesign or significant reduction of the transport architecture that serves as a major source of latency for time-sensitive applications. This is in sharp contrast to the present hierarchical network architectures that are not suitable to realize many of the anticipated 6G services. While evaluating the strengths and weaknesses of key candidate 6G technologies, we differentiate what may be practically achievable over the next decade, relative to what is possible in theory. Keeping this in mind, we present concrete research challenges for each of the discussed system aspects, providing inspiration for what follows.

Cache-aided wireless device-to-device (D2D) networks have demonstrated more promising performance improvement for video distribution than conventional distribution methods; thus, understanding the fundamental scaling behavior of such networks is highly important. However, the existing scaling laws for multi-hop networks are not optimal even in the case of Zipf popularity distributions (gaps between upper and lower bounds are not constants); furthermore, there are no scaling law results for such networks for the more practical case of a Mandelbrot-Zipf (MZipf) popularity distribution. We thus in this work investigate the throughput-outage performance for cache-aided wireless D2D networks adopting multi-hop communications, with the MZipf popularity distribution for file requests and users distributed according to Poisson point process. We propose an achievable content caching and delivery scheme, and then analyze its performance. We obtain the optimal scaling law by showing that the achievable performance is tight to the proposed outer bound. Since the Zipf distribution is a special case of the MZipf distribution, the optimal scaling law for the networks considering the Zipf popularity distribution is also obtained, which closes the gap in literature.

Wireless caching networks have been extensively researched as a promising technique for supporting the massive data traffic of multimedia services. Many of the existing studies on real-data traffic have shown that users of a multimedia service consecutively request multiple contents and this sequence is strongly dependent on the related list of the first content and/or the top referrer in the category. This paper thus introduces the notion of “temporary preference”, characterizing the behavior of users who are highly likely to request the next content from a certain target category (i.e., related content list). Based on this observation, this paper proposes both probabilistic caching and dynamic delivery policies for categorized contents and consecutive user demands. The proposed caching scheme maximizes the minimum of the cache hit rates for all users. In the delivery phase, a dynamic helper association policy for receiving multiple contents in a row is designed to reduce the delivery latency. By comparing with the content placement optimized for one-shot requests, numerical results verify the effects of categorized contents and consecutive user demands on the proposed caching and delivery policies.

Estimating downlink (DL) channel state information (CSI) in frequency division duplex (FDD) massive multi-input multi-output (MIMO) systems generally requires downlink pilots and feedback overheads. Accordingly, this paper investigates the feasibility of zero-feedback FDD massive MIMO systems based on channel extrapolation. We use the high-resolution parameter estimation (HRPE), specifically the space-alternating generalized expectation-maximization (SAGE) algorithm, to extrapolate the DL CSI based on the extracted parameters of multipath components in the uplink channel. We apply the HRPE to two different channel models: the vector spatial signature (VSS) model and the direction of arrival (DOA) model. We verify these methods through real-world channel data acquired from channel measurement campaigns with two different types of channel sounders: a) a switched array-based, real-time, time-domain, outdoors setup at 3.5 GHz, and b) a virtual array-based, high-accuracy, frequency-domain, indoors setup at 2.4 and 5-7 GHz. The performance metrics of the extrapolated channels that we evaluate include the mean squared error, beamforming efficiency, and spectral efficiency in multiuser MIMO scenarios. The results show that the HRPE-based channel extrapolation performs best under the simple VSS model, which does not require array calibration, and if the BS is in an open outdoor environment having line-of-sight (LOS) paths to well-separated users.

Vehicle-to-vehicle (V2V) wireless communications have many envisioned applications for ensuring traffic safety and for addressing traffic congestion. However, developing suitable communication systems and standards for this purpose requires developers to have accurate models for the V2V propagation channel. Likewise, the dynamic evolution of multipath components (MPCs) in V2V channels has not been well modeled in existing models. In this paper, we propose a geometry-based stochastic channel model for a lightly built-up urban environment and then parametrize the model from measurements. The MPCs are extracted based on a high-resolution parameter estimation; they are tracked and clustered through a joint algorithm. The identified clusters are classified as line-of-sight, reflections from static scatterers, reflections from mobile scatterers, multiple-bounce reflections, and diffuse scattering. Specifically, the multiple-bounce reflections are modeled as twin clusters that follow the COST 273/COST2100 approach. The paper gives a full parameterization of the channel model and supplies a step-by-step implementation recipe. We verify the model by comparing two second-order statistics, i.e., the root-mean-square (RMS) delay spread and the angular spreads of arrival/departure derived from the channel model, to the results obtained directly from the measurements. Furthermore, we also identify several key factors that strongly impact the synthetic channel performance.

As mobile edge computing (MEC) finds widespread use for relieving the computational burden of compute- and interaction-intensive applications on end user devices, understanding the resulting delay and cost performance is drawing significant attention. While most existing works focus on single-task offloading in single-hop MEC networks, next generation applications (e.g., industrial automation, augmented/virtual reality) require advance models and algorithms for dynamic configuration of multi-task services over multi-hop MEC networks. In this work, we leverage recent advances in dynamic cloud network control to provide a comprehensive study of the performance of multi-hop MEC networks, addressing the key problems of multi-task offloading, timely packet scheduling, and joint computation and communication resource allocation. We present a fully distributed algorithm based on Lyapunov control theory that achieves throughput-optimal performance with delay and cost guarantees. Simulation results validate our theoretical analysis and provide insightful guidelines on the interplay between communication and computation resources in MEC networks.

Delay dispersion is one of the key propagation channel characteristics that impact system design and performance. In particular, for OFDM-based systems such as Wi-Fi, it determines both the amount of available frequency diversity and the minimum required cyclic prefix, which in turn impacts the spectral efficiency. For this reason, delay spread and power delay profiles have been analyzed for a long time. However, recent upgrades in Wi-Fi, in particular the addition of a new frequency range (6-7.1 GHz), and the introduction of adaptive beamforming, require a re-assessment based on new measurements. This paper presents extensive measurement results of RMS delay spread and interference quotient that take these developments into account. Results were measured in the 2.4-2.5, 5-6, and 6-7 GHz bands. Furthermore, we compare the “omni-directional” delay spread and interference quotient (i.e., when measured with omni-antennas at transmitter and receiver), to those that occur when beamformed antennas are used. We found that for both outdoor and indoor environments, beamforming typically improves the interference quotient (for a given window size) by about 3-5 dB. The 2.4 and 5-7 GHz bands show a significant difference, while we could not observe statistically significant differences between the 5-6 and 6-7 GHz bands.

Cache-aided wireless device-to-device (D2D) networks have demonstrated promising performance improvement for video distribution compared to conventional distribution methods. Understanding the fundamental scaling behavior of such networks is thus importance. Recently, based on real-world data, it has been observed that the popularity distribution should be modeled by a Mandelbrot-Zipf (MZipf) distribution, instead of the common Zipf distribution. We thus in this work investigate the throughput-outage performance for cache-aided wireless D2D network adopting multi-hop communications, with the MZipf popularity distribution for file requests and Poisson point process for user distribution. Considering the case that Zipf factor is larger than one, we first propose an achievable content caching and delivery scheme and analyze its performance. Then, by showing that the achievable performance is tight to the proposed outer bound, we show that an optimal scaling law for cache-aided wireless multi-hop D2D networks is obtained.

We explore and find the fundamental systemdegrading effects when using multiple orbital-angular-momentum (OAM) beams in a THz communications link under atmospheric turbulence in simulation. Unlike optical links with relatively small divergence effects, the crosstalk performance of THz OAM links is dependent on divergence-related parameters, including OAM mode order, frequency, and beam waist. Simulation results show: (i) for the cases with the same ratio of beam diameter to the Fried parameter (D/r0), the signal power increases and the crosstalk (XT) decreases when increasing the divergence-related parameters; and (ii) for the cases with the same atmospheric structure constant Cn2, the signal power decreases and the XT increases when increasing the divergence-related parameters. Moreover, for building a link where OAM +4 is transmitted with the parameters: (i) beam waist of 0.1 m and link distance of 200 m, and (ii) beam waist of 1 m and link distance of 1 km, the XT from neighbouring mode remains less than -15 dB when carrier wave frequency is <; 1 THz and 0.1 THz, respectively. In addition, simulation results also show that: (i) limited aperture size of the system has high influence on the XT performance under both weak and strong turbulence; and (ii) displacement of the system has high influence on the XT performance under no and weak turbulence.

While mm-wave systems are a mainstay for 5G communications, the inexorable increase of data rate requirements and user densities will soon require the exploration of next-generation technologies. Among these, Terahertz (THz) band communication seems to be a promising direction due to availability of large bandwidth in the electromagnetic spectrum in this frequency range, and the ability to exploit its directional nature by directive antennas with small form factors. The first step in the analysis of any communication system is the analysis of the propagation channel, since it determines the fundamental limitations it faces. While THz channels have been explored for indoor, short-distance communications, the channels for wireless access links in outdoor environments are largely unexplored. In this paper, we present the - to our knowledge - first set of double-directional outdoor propagation channel measurements for the THz band. Specifically, the measurements are done in the 141 - 148.5 GHz range, which is one of the frequency bands recently allocated for THz research by the Federal Communication Commission (FCC). We employ double directional channel sounding using a frequency domain sounding setup based on RF-over-Fiber (RFoF) extensions for measurements over 100 m distance in urban scenarios. An important result is the surprisingly large number of directions (i.e., direction-of-arrival and direction-of-departure pairs) that carry significant energy. More generally, our results suggest fundamental parameters that can be used in future THz Band analysis and implementations.

This paper proposes a user scheduling and power allocation method for content delivery in wireless caching helper networks without any stringent constraint on the interference model. For supporting delay-sensitive and time-varying user demands, the actual delivery quantity of the requested content should be dynamically controlled by advanced scheduling and power allocation. In addition, it is difficult for a central unit to control the content delivery due to a lack of knowledge of the entire time-varying network; therefore, a belief-propagation (BP)-based algorithm that facilitates distributed decisions on user scheduling and power allocation at every caching helper is presented. The proposed delivery scheme maximizes power efficiency while limiting the average delay of user request satisfactions by managing interference among users well. Simulation results show that the proposed scheme provides almost the same delay performance as the exhaustively found optimal one at the expense of little power consumption.

Recent investigations showed that cache-aided device-to-device (D2D) networks can be improved by properly exploiting the individual preferences of users. Since in practice it might be difficult to make centralized decisions about the caching distributions, this paper investigates the individual preference aware caching policy that can be implemented distributedly by users without coordination. The proposed policy is based on categorizing different users into different reference groups associated with different caching policies according to their preferences. To construct reference groups, learning-based approaches are used. To design caching policies that maximize throughput and hit-rate, optimization problems are formulated and solved. Numerical results based on measured individual preferences show that our design is effective and exploiting individual preferences is beneficial.

Fifth-generation (5G) new radio (NR) deployments are being rolled out in both the C-band (3.3 - 5.0 GHz) and millimeter-wave (mmWave) band (24.5 - 29.5 GHz). For outdoor scenarios, the C-band is expected to provide wide area coverage and throughput uniformity, whereas the mmWave band is expected to provide ultra-high throughput to dedicated areas within the C-band coverage. Due to the differences in the frequency bands, both systems are expected to be designed with different transmit and receive parameters, naturally resulting in performance variations proportional to the chosen parameters. Unlike many previous works, this paper presents measurement evaluations in central Auckland, New Zealand, from a pre-commercial deployment of a single-user, single-cell 5G-NR system operating in both bands. The net throughput, coverage reliability, and channel rank are analyzed across the two bands with baseband and analog beamforming. Our results show that the C-band coverage is considerably better than mmWave, with a consistently higher channel rank. Furthermore, the spatial stationarity region (SSR) for the azimuth angles-of-departure (AODs) is characterized, and a model derived from the measured beam identities is presented. The SSR of azimuth AODs is seen to closely follow a gamma distribution.

We examine the favorable propagation (FP) behavior of a massive multi-user multiple-input-multiple-output (MU-MIMO) system equipped with a uniform linear array (ULA), horizontal uniform rectangular array (HURA) or uniform circular array (UCA) using a ray-based channel model with user cluster sharing. We demonstrate FP for these systems and provide analytical expressions for the mean-squared distance (MSD) of the FP metric from its large-system limit for each of the aforementioned topologies. We use these results to examine the detrimental effects of user cluster sharing on FP behavior, and demonstrate the superior performance of the ULA as compared to the UCA and the HURA with equal inter-element spacing. Although cluster sharing has a negative impact on FP for finite arrays, we additionally examine the asymptotic rate of convergence to FP as a function of array size and show that this rate is unchanged with or without user cluster sharing.

Designing beamforming techniques for the downlink (DL) of frequency division duplex (FDD) massive MIMO is known to be a challenging problem due to the difficulty of obtaining channel state information (CSI). Indeed, since the uplink-downlink bands are disjoint, the system cannot rely on channel reciprocity to estimate the channel from uplink (UL) pilots as in time division duplexing (TDD) system. Still, in this paper, we propose original designs for robust beamformers that do not require any feedback from the users and only rely on the transmission of UL pilots. The price to pay is that the beamformer is non-coherent in the sense that it does not leverage full knowledge of the phase of each multipath component. A large variety of novel designs are proposed under different criterion and partial phase knowledge.

In wireless caching networks, a user generally has a concrete purpose of consuming contents in a certain preferred category, and requests multiple contents in sequence. While most existing research on wireless caching and delivery has focused only on one-shot requests, the popularity distribution of contents requested consecutively is definitely different from the one-shot request and has been not considered. Also, especially from the perspective of the service provider, it is advantageous for users to consume as many contents as possible. Thus, this paper proposes two cache allocation policies for categorized contents and consecutive user demands, which maximize 1) the cache hit rate and 2) the number of consecutive content consumption, respectively. Numerical results show how categorized contents and consecutive content requests have impacts on the cache allocation.

Railway communications has attracted significant attention from both academia and industries due to the booming development of railways, especially high-speed railways (HSRs). To be in line with the vision of future smart rail communications, the rail transport industry needs to develop innovative communication network architectures and key technologies that ensure high-quality transmissions for both passengers and railway operations and control systems. Under high mobility and with safety, eco-friendliness, comfort, transparency, predictability, and reliability. Fifth-generation (5G) technologies could be a promising solution to dealing with the design challenges on high reliability and high throughput for HSR communications. Based on our in-depth analysis of smart rail traffic services and communication scenarios, we propose a network slicing architecture for a 5G-based HSR system. With a ray tracing-based analysis of radio wave propagation characteristics and channel models for millimeter wave (mmWave) bands in railway scenarios, we draw important conclusions with regard to appropriate operating frequency bands for HSRs. mymargin Specifically, we have identified significant 5G-based key technologies for HSRs, such as spatial modulation, fast channel estimation, cell-free massive multiple-input-multiple-output (MIMO), mmWave, efficient beamforming, wireless backhaul, ultrareliable low latency communications, and enhanced handover strategies. Based on these technologies, we have developed a complete framework of 5G technologies for smart railways and pointed out exciting future research directions.

In wireless caching networks, the design of the content delivery method must consider random user requests, caching states, network topology, and interference management. In this article, we establish a general framework for content delivery in wireless caching networks without stringent assumptions that restrict the network structure and interference model. Based on the framework, we propose a dynamic and distributed link scheduling and power allocation scheme for content delivery that is assisted by belief-propagation (BP) algorithms. The proposed scheme achieves three critical purposes of wireless caching networks: 1) limiting the delay of user request satisfactions, 2) maintaining the power efficiency of caching nodes, and 3) managing interference among users. In addition, we address the intrinsic problem of the BP algorithm in our network model, proposing a matching algorithm for one-to-one link scheduling. Simulation results show that the proposed scheme provides almost the same delay performance as the optimal scheme found through an exhaustive search at the expense of a little additional power consumption and does not require a clustering method and orthogonal resources in a large-scale D2D network.

Development of a comprehensive channel propagation model for high-fidelity design and deployment of wireless communication networks necessitates an exhaustive measurement campaign in a variety of operating environments and with different configuration settings. As the campaign is time-consuming and expensive, the effort is typically shared by multiple organizations, inevitably with their own channel-sounder architectures and processing methods. Without proper benchmarking, it cannot be discerned whether observed differences in the measurements are actually due to the varying environments or to discrepancies between the channel sounders themselves. The simplest approach for benchmarking is to transport participant channel sounders to a common environment, collect data, and compare results. Because this is rarely feasible, this paper proposes an alternative methodology - which is both practical and reliable - based on a mathematical system model to represent the channel sounder. The model parameters correspond to the hardware features specific to each system, characterized through precision, in situ calibration to ensure accurate representation; to ensure fair comparison, the model is applied to a ground-truth channel response that is identical for all systems. Five worldwide organizations participated in the cross-validation of their systems through the proposed methodology. Channel sounder descriptions, calibration procedures, and processing methods are provided for each organization as well as results and comparisons for 20 ground-truth channel responses.

Proactive edge caching, as a promising approach to accommodate the explosively increased mobile data demand of Video on demand (VoD) service, has received extensive attention. However, although targeted to VoD service, existing caching policies are mainly designed for file downloading service while the unique requirement of quality of experience (QoE) for VoD service has been seldom considered. Aimed at maximizing the weighted average QoE of VoD service, this paper optimizes the proactive edge caching polices including the cached fraction and encoding bit rate of every video. We consider a two-tier network, where the helpers equipped with caching resource are deployed in the coverage area of traditional base stations. We formulate the caching optimization problems and show their hidden convexity properties, then we find that the optimal caching policy is determined by the weighted popularity-to-duration ratio of videos. Based on the result, we develop a low-complexity algorithm to find the optimal caching policy. Simulation results demonstrate evident performance gain of the proposed policy over the existing policy for VoD service.

Discriminating between line-of-sight (LOS) and non-line-of-sight (NLOS) conditions, orLOS identification, is important for a variety of purposes in wireless systems, including localization and channel modeling. LOS identification is especially challenging in vehicle-to-vehicle (V2V) networks since a variety of physical effects that occur at different spatial/temporal scales can affect the presence of LOS. This paper investigates machine learning techniques for LOS identification in V2V networks using an extensive set of measurement data and then develops robust and efficient identification solutions. Our approach exploits several static and time-varying features of the channel impulse response (CIR), which are shown to be effective. Specifically, we develop a fast identification solution that can be trained by using the power angular spectrum. Moreover, based on the measurement data, we also compare three different machine learning methods, i.e., support vector machine, random forest, and artificial neural network, in terms of their ability to train and generate the classifier. The results of our experiments conducted under various V2V environments, which were then validated using$K$-fold cross-validation, show that our techniques can distinguish the LOS/NLOS conditions with an error rate as low as 1%. In addition, we investigate the impact of different training and validating strategies on the identification accuracy.

Cache-aided wireless device-to-device (D2D) networks allow significant throughput increase, depending on the concentration of the popularity distribution of files. Many studies assume that all users have the same preference distribution; however, this may not be true in practice. This work investigates whether and how the information about individual preferences can benefit cache-aided D2D networks. We examine a clustered network and derive a network utility that considers both the user distribution and channel fading effects into the analysis. We also formulate a utility maximization problem for designing caching policies. This maximization problem can be applied to optimize several important quantities, including throughput, energy efficiency (EE), cost, and hit-rate, and to solve different tradeoff problems. We provide a general approach that can solve the proposed problem under the assumption that users coordinate, then prove that the proposed approach can obtain the stationary point under a mild assumption. Using simulations of practical setups, we show that performance can improve significantly with proper exploitation of individual preferences. We also show that different types of tradeoffs exist between different performance metrics and that they can be managed through caching policy and cooperation distance designs.

Channel estimation for the downlink of frequency division duplex (FDD) massive multiple-input-multiple output (MIMO) systems is well known to generate a large overhead as the amount of training generally scales with the number of transmit antennas in a MIMO system. In this paper, we consider the solution of extrapolating the channel frequency response from uplink pilot estimates to the downlink frequency band. This drastically reduces the downlink pilot overhead and completely removes the need for a feedback from the users. The price to pay is a degradation in the quality of the channel estimates, which reduces the downlink spectral efficiency. We first show that conventional estimators fail to achieve reasonable accuracy. We propose instead to use high-resolution channel estimation. We derive the Cramer-Rao lower bound (CRLB) of the mean squared error (MSE) of the extrapolated channel. Furthermore, a relationship between the imperfect channel state information (CSI) and the downlink user performance is derived. The extrapolation-based FDD massive MIMO performance is validated through numerical simulations and compared to a corresponding time division duplex (TDD) system. Considered figures of merit for extrapolation performance include channel MSE, beamforming efficiency, extrapolation range, spectral efficiency and uncoded symbol error rate. Our main conclusion is that channel extrapolation is a viable solution for FDD massive MIMO systems.

This paper investigates the capability of millimeter-wave (mmWave) channel sounders with phased arrays to perform super-resolution parameter estimation, i.e., determine the parameters of multipath components (MPC), such as direction of arrival and delay, with resolution better than the Fourier resolution of the setup. We analyze the question both generally, and with respect to a particular novel multi-beam mmWave channel sounder that is capable of performing multiple-input-multiple-output (MIMO) measurements in dynamic environments. We firstly propose a novel two-step calibration procedure that provides higher-accuracy calibration data that are required for Rimax or SAGE. Secondly, we investigate the impact of center misalignment and residual phase noise on the performance of the parameter estimator. Finally we experimentally verify the calibration results and demonstrate the capability of our sounder to perform super-resolution parameter estimation.

Future wireless networks are expected to operate in both the centimeter-wave and millimeter-wave bands. It is thus necessary to provide an accurate characterization of the {\em joint} channel properties in the two bands. Shadow fading is an important characteristic of wireless channels as it impacts achievable data rates and in particular outage probabilities. In this paper, we propose cross-band correlation models that can be used to capture the correlation over space and frequency. The models are designed to reduce to existing correlation models in the case of a single-band, which enables reuse of the large existing literature for (single-band) shadowing models. We extract and report the parameters of the proposed models in a micro-cellular environment. The important finding from our proposed models is that the cross-band spatial correlation function may not be derived just by scaling a single-band correlation function.

Caching at the wireless edge has proven to be a promising approach for efficient video distribution, especially when aided by device-to-device communication. A widely explored scheme is to sub- divide a cell into clusters, and allow one pair of users within each cluster to communicate in each time slot. As more devices are raising frequent requests for popular videos, activating multiple links simultaneously can potentially improve the throughput. However, allowing multiple links at the same time requires to solve the problems of avoiding request clashes, i.e., multiple users requesting transmission from the same caching node, as well as interference management. To address these issues, this paper proposes new designs of both the caching policy and the transmission policy (i.e., link scheduling and power control). Furthermore, the duration of each time slot is optimized to improve the throughput. Finally, some numerical results demonstrate the performance gain of the proposed designs.

On-demand video accounts for the majority of wireless data traffic. Video distribution schemes based on caching combined with device-to-device (D2D) communications promise order-of-magnitude greater spectral efficiency for video delivery, but hinge on the principle of concentrated demand distributions. This paper presents, for the first time, the analysis and evaluations of the throughput-outage tradeoff of such schemes based on measured cellular demand distributions. In particular, we use a dataset with more than 100 million requests from the BBC iPlayer, a popular video streaming service in the U.K., as the foundation of the analysis and evaluations. We present an achievable scaling law based on the practical popularity distribution, and show that such scaling law is identical to those reported in the literature. We find that also for the numerical evaluations based on a realistic setup, order-of-magnitude improvements can be achieved. Our results indicate that the benefits promised by the caching-based D2D in the literature could be retained for cellular networks in practice.

Downlink channel estimation in massive MIMO systems is well known to generate a large overhead in frequency division duplex (FDD) mode as the amount of training generally scales with the number of transmit antennas. Using instead an extrapolation of the channel from the measured uplink estimates to the downlink frequency band completely removes this overhead. In this paper, we investigate the theoretical limits of channel extrapolation in frequency. We highlight the advantage of basing the extrapolation on high-resolution channel estimation. A lower bound (LB) on the mean squared error (MSE) of the extrapolated channel is derived. A simplified LB is also proposed, giving physical intuition on the SNR gain and extrapolation range that can be expected in practice. The validity of the simplified LB relies on the assumption that the paths are well separated. The SNR gain then linearly improves with the number of receive antennas while the extrapolation performance penalty quadratically scales with the ratio of the frequency and the training bandwidth. The theoretical LB is numerically evaluated using a 3GPP channel model and we show that the LB can be reached by practical high-resolution parameter extraction algorithms. Our results show that there are strong limitations on the extrapolation range than can be expected in SISO systems while much more promising results can be obtained in the multiple-antenna setting as the paths can be more easily separated in the delay-angle domain.

Due to the concentrated popularity distribution of video files, caching of popular files on devices, and distributing them via device-to-device (D2D) communications allows a dramatic increase in the throughput of wireless video networks. However, since the popularity distribution is not static and the caching policy might be outdated, there is a need for replacement of cache content. In this work, by exploiting the broadcasting of the base station (BS), we model the caching content replacement in BS assisted wireless D2D caching networks and propose a practically realizable replacement procedure. Subsequently, by introducing a queuing system, the replacement problem is formulated as a sequential decision making problem, in which the long term average service rate is optimized under average cost constraint and queue stability. We propose a replacement design using Lyapunov optimization, which effectively solves the problem and makes decisions. Using simulations, we evaluate the proposed design. The results clearly indicate that, when dynamics exist, the systems exploiting replacement can significantly outperform the systems using merely the static policy.

Device-to-Device (D2D) communications has been embraced as a novel approach for extending coverage in fifth-generation (5G and beyond) wireless networks. Throughput and reliability assessment of such wireless networks are therefore of utmost importance for D2D communication systems design. In this paper, we consider the capacity results of wireless propagation channels in two 5G modes namely the sub-6 GHz centimeter-wave (cm-wave) and 60 GHz millimeter-wave (mm-wave) bands for a multiple-input-multiple-output (MIMO) D2D fading channel. Results presented in this paper were obtained from propagation channel measurements jointly conducted in both cm-wave (2-6 GHz) and mm-wave (59-63 GHz) bands using an 8 × 8 MIMO virtual array setup in an outdoor environment. These measurements were conducted at the exact same locations (with constituents unchanged) in both cm-wave and mm-wave bands for comparability of results. Capacity values were computed using two power policies (with and without channel state information (CSI)). We also investigated the eigenmode spectral structure to understand the spatial correlatedness of the channel and the Rician K-factor to analyze small-scale fading in the channel. The results presented in this paper can be used by wireless systems designer for assessing the performance of D2D systems operating in the cm-wave and/or mm-wave bands for this type of environment.

We present a distance estimation technique based on ultra-wideband time-of-arrival measurements and assess it with IEEE 802.15.4-2011 devices by Decawave. Experiments show that our technique is about 30 times faster than Decawave's out-of-the-box solution, which can be exploited to improve the precision by one order of magnitude.

In multiuser multiple-input multiple-output (MU-MIMO) systems, channel correlation is detrimental to system performance. We demonstrate that widely used, yet overly simplified, correlation models that generate identical correlation profiles for each terminal tend to severely underestimate the system performance. In sharp contrast, more physically motivated models that capture variations in the power angular spectra across multiple terminals, generate diverse correlation patterns. This has a significant impact on the system performance. Assuming correlated Rayleigh fading and downlink zero-forcing precoding, tight closed-form approximations for the average signal- to-noise-ratio, and ergodic sum spectral efficiency are derived. Our expressions provide clear insights into the impact of diverse correlation patterns on the above performance metrics. Unlike previous works, the correlation models are parameterized with measured data from a recent 2.53 GHz urban macrocellular campaign in Cologne, Germany. Overall, results from this paper can be treated as a timely re-calibration of performance expectations from practical MU-MIMO systems.

This paper presents results obtained from a vehicle- to-vehicle channel measurement campaign carried out in the millimeter-wave band around a 60 GHz center frequency and with 8 GHz of bandwidth. We characterize a situation of two oncoming cars on a two-lane road in the campus of the Brno University of Technology. For several vehicle passes we evaluate: (1) observed root mean square (RMS) delay spreads as a function of the received power, (2) temporal decorrelation of the channel impulse response and (3) a dependency of the Pearson correlation coefficient on the received power. For the measurement campaign, a correlative time-domain channel sounder was used.

Application of massive multiple-input multipleoutput (MIMO) systems to frequency division duplex (FDD) is challenging mainly due to the considerable overhead required for downlink training and feedback. Channel extrapolation, i.e., estimating the channel response at the downlink frequency band based on measurements in the disjoint uplink band, is a promising solution to overcome this bottleneck. This paper presents measurement campaigns obtained by using a wideband (350 MHz) channel sounder at 3.5 GHz composed of a calibrated 64 element antenna array, in both an anechoic chamber and outdoor environment. The Space Alternating Generalized Expectation- Maximization (SAGE) algorithm was used to extract the parameters (amplitude, delay, and angular information) of the multipath components from the attained channel data within the â€training†(uplink) band. The channel in the downlink band is then reconstructed based on these path parameters. The performance of the extrapolated channel is evaluated in terms of mean squared error (MSE) and reduction of beamforming gain (RBG) in comparison to the â€ground truthâ€, i.e., the measured channel at the downlink frequency. We find strong sensitivity to calibration errors and model mismatch, and also find that performance depends on propagation conditions: LOS performs significantly better than NLOS.

This paper presents the system design, calibration, and example measurements of a novel ultra-wideband, low-cost, real-time channel sounder. The channel sounder operates in the frequency range from 3 to 18 GHz with a 15-GHz bandwidth thus providing a Fourier delay resolution of 66.7 ps. It sequentially measures overlapping sub-bands of 1-GHz bandwidth to cover the whole frequency range. By using a waveform generator and a digitizer with relatively lower sample rates, the design significantly lowers the cost compared with sampling the whole band simultaneously. With the transmitting power of 40 dBm, the maximum measurable path loss is 132 dB without accounting for possible antenna gains. In this paper, we also describe the calibration procedure along with the method to stitch sub-bands into a combined channel response across 15 GHz of bandwidth. The channel sounder performance is compared with a vector network analyzer (VNA) measurements acquired in the same static channels. In contrast to VNAs, however, our channel sounder can measure the whole band in 6 ms and can, thus, operate in dynamic environments with coherence time more than 6 ms. Additionally, we present sample results from a measurement campaign performed in an indoor corridor investigating the delay spreads statistics over the range of 3–18 GHz.

Caching of video files at the wireless edge, i.e., at the base stations or on user devices, is a key method for improving wireless video delivery. While global popularity distributions of video content have been investigated in the past and used in a variety of caching algorithms, this paper investigates the statistical modeling of the individual user preferences. With individual preferences being represented by probabilities, we identify their critical features and parameters and propose a novel modeling framework by using a genre-based hierarchical structure as well as a parameterization of the framework based on an extensive real-world data set. Besides, the correlation analysis between parameters and critical statistics of the framework is conducted. With the framework, an implementation recipe for generating practical individual preference probabilities is proposed. By comparing with the underlying real data, we show that the proposed models and generation approach can effectively characterize the individual preferences of users for video content.

In localization, an outage occurs if the positioning mean squared error (MSE) exceeds a pre-defined threshold εth. For time-of-arrival-based localization, a key factor affecting the MSE is the relative positions of the anchors with respect to the target location. From a design point of view, characterizing the distribution of the MSE over an ensemble of anchor locations as seen from the perspective of a target is essential for providing probabilistic performance guarantees against outage. To solve this difficult problem, previous works have assumed all anchor-target links to have the same SNR (i.e., SNR homogeneity), which neglects the impact of link distance variation on the SNR and the positioning error; for instance, under an inverse-square law pathloss model, the outage probability can differ by orders of magnitude when compared with the homogeneous SNR assumption. In this paper, we derive an approximate expression for the MSE distribution under an inverse-square law pathloss model when the anchors are uniformly distributed around a target. Through simulations, we verify that our approximation can be used to estimate the number of anchors needed so that the outage probability is below 1%.

Outdoor to indoor penetration loss is one of the crucial challenges faced at millimeter-wave frequencies. This paper presents the results from 28-GHz channel sounding campaigns performed to investigate the impact of this phenomenon on the wireless propagation channel characteristics in small cell and fixed wireless access scenarios. The measurements are performed with a real-time channel sounder equipped with phased array antennas that allow beam-forming and electronic beam steering for directionally resolved measurements. Thanks to the short measurement time and the excellent phase stability of the system, we obtain both directional and omnidirectional channel power delay profiles without any delay uncertainty. We compare the measured path loss, delay spread, and angular spread for indoor and outdoor receiver locations for two different types of buildings. We find that the penetration loss strongly depends on the angle of incidence, and the scatterers on the outside of the building strongly impact how much power is coupled into the building. Based on the results, we provide statistical models for path loss, delay spread, and angular spread.

Analog beamforming is an attractive and cost-effective solution to exploit the benefits of massive multiple-input-multiple-output systems, requiring only one up/down-conversion chain. However, the presence of only one chain imposes a significant overhead in estimating the channel state information required for beamforming, when conventional digital channel estimation (CE) approaches are used. As an alternative, this paper proposes a novel CE technique, called periodic analog CE (PACE), which can be performed by analog hardware. By avoiding digital processing, the estimation overhead is significantly lowered and does not scale with the number of antennas. PACE involves the periodic transmission of a sinusoidal reference signal by the transmitter, the estimation of its amplitude and phase at each receive antenna via analog hardware, and using these estimates for beamforming. To enable such non-trivial operation, two reference tone recovery techniques and a novel receiver architecture for PACE are proposed and analyzed, both theoretically and via simulations. The results suggest that in sparse, wide-band channels and above a certain signal-to-noise ratio, PACE-aided beamforming suffers only a small loss in beamforming gain and enjoys a much lower CE overhead, in comparison to conventional approaches. The benefits of using PACE-aided beamforming during the initial access phase are also discussed.

Analog beamforming greatly reduces the implementation cost of massive antenna transceivers by using only one up/down-conversion chain. However, it incurs a large pilot overhead when used with conventional channel estimation (CE) techniques. This is because these CE techniques involve digital processing, requiring the up/down-conversion chain to be time-multiplexed across the antenna dimensions. This paper introduces a novel CE technique, called continuous analog channel estimation (CACE), that avoids digital processing, enables analog beamforming at the receiver and additionally provides resilience against oscillator phase-noise. By avoiding time-multiplexing of up/down-conversion chains, the CE overhead is reduced significantly and furthermore becomes independent of the number of antenna elements. In CACE, a reference tone is transmitted continuously with the data signals, and the receiver uses the received reference signal as a matched filter for combining the data signals, albeit via analog processing. We propose a receiver architecture for CACE, analyze its performance in the presence of oscillator phase-noise, and derive near-optimal system parameters and power allocation. Transmit beamforming and initial access procedure with CACE are also discussed. Simulations confirm that, in comparison to conventional CE, CACE provides phase-noise resilience and a significant reduction in the CE overhead, while suffering only a small loss in signal-to-interference-plus-noise-ratio.

Time-division multiplexed (TDM) channel sounders, in which a single RF chain is connected sequentially via an electronic switch to different elements of an array, are widely used for the measurement of double-directional/MIMO propagation channels. This paper investigates the impact of array switching patterns on the accuracy of parameter estimation of multipath components (MPC) for a time-division multiplexed (TDM) channel sounder. The commonly-used sequential (uniform) switching pattern poses a fundamental limit on the number of antennas that a TDM channel sounder can employ in fast time-varying channels. We thus aim to design improved patterns that relax these constraints. To characterize the performance, we introduce a novel spatio-temporal ambiguity function, which can handle the non-idealities of real-world arrays. We formulate the sequence design problem as an optimization problem and propose an algorithm based on simulated annealing to obtain the optimal sequence. As a result, we can extend the estimation range of Doppler shifts by eliminating ambiguities in parameter estimation. We show through Monte Carlo simulations that the root mean square errors of both direction of departure and Doppler are reduced significantly with the new switching sequence. The results are also verified with actual vehicle-to-vehicle (V2V) channel measurements.

This paper presents results from channel sounding campaigns that investigate the impact of foliage blockage on the wireless propagation channel characteristics at 28 GHz. The measurements are performed with a real-time channel sounder equipped with phased array antennas that allow beamforming and electronic beam steering for directionally resolved measurements. Thanks to the short measurement time and the excellent phase stability of the system, we obtain both directional and omnidirectional channel power delay profiles without any delay uncertainty. We derive models for the distance-dependent foliage attenuation for different receiver heights. Additionally, we compare the angular spread and delay spread for links with and without foliage blockage to understand the effects of foliage on all channel characteristics.

This paper investigates the effects of a human body shadowing for a device-to-device (D2D) communications scenario at 28 GHz. The measurements are performed with a real-time channel sounder equipped with fast-switching phased antenna arrays, which enables the directionally resolved wideband measurement of dynamic effects. By exploiting the phase coherence of the setup, the multi-path components can be tracked over time, observing the temporal variations of the channel characteristics. This paper presents results of the human body shadowing in an outdoor (plaza) environment at two different link distances: 5 m and 10 m. We then analyze results corresponding to the assumption of fixed beams with different beamwidth, a 12° directional single beam and a 102° sectoral combined beam. More importantly, for the first time, the time-varying angular power spectrum and the temporal evolutions of the mean angle and the angular spread statistics in a dynamic channel of walking pedestrians are presented, which cannot be measured with traditional horn antenna channel sounders.

Channel state information (CSI) is of vital importance in wireless communication systems. Existing CSI acquisition methods usually rely on pilot transmissions, and geographically separated base stations (BSs) with non-correlated CSI need to be assigned with orthogonal pilots which occupy excessive system resources. Our previous work adopts a data-driven deep learning based approach which leverages the CSI at a local BS to infer the CSI remotely, however the relevance of CSI between separated BSs is not specified explicitly. In this paper, we exploit a model-based methodology to derive the Cramer-Ran lower bound (CRLB) of remote CSI inference given the local CSI. Although the model is simplified, the derived CRLB explicitly illustrates the relationship between the inference performance and several key system parameters, e.g., terminal distance and antenna array size. In particular, it shows that by leveraging multiple local BSs, the inference error exhibits a larger power-law decay rate (w.r.t. number of antennas), compared with a single local BS; this explains and validates our findings in evaluating the deep-neural-network-based (DNN-based) CSI inference. We further improve on the DNN-based method by employing dropout and deeper networks, and show an inference performance of approximately 90% accuracy in a realistic scenario with CSI generated by a ray-tracing simulator.

Many 5G applications require low complexity transceivers that can exploit the benefits of massive antenna arrays while still maintaining low hardware, energy and computation costs. As a solution, this paper proposes a novel multi- antenna frequency shift reference (MA-FSR) receiver that uses only one down-conversion chain, supports single spatial-stream wide-band transmission with non-coherent demodulation, and can perform receive beamforming without requiring phase-shifters, explicit channel estimation, or complicated signal processing. In MA-FSR a reference tone is transmitted along with the data. At each receive antenna, the received signal for the data is correlated with the received signal for the reference, via a squaring operation, thereby compensating for the inter-antenna phase shift. The resulting signals are then summed and fed to a single down-conversion chain. In this paper, performance of the MA-FSR receiver is studied analytically and a near-optimal power allocation policy for the reference and data signals is proposed. Simulations suggest that the signal-to-noise ratio for MA-FSR is around 9 dB lower than that of coherent analog beamforming, although the bandwidth efficiency is also reduced to 50%. Several extensions to MA-FSR that can achieve better performance, though with a higher hardware cost, are also discussed.

This paper investigates the impact of array switching patterns on the accuracy of parameter estimation of multipath components for a time division multiplexed (TDM) channel sounder. To measure fast time- varying channels, the conventional uniform array switching pattern poses a fundamental limit of the number of antennas that a TDM channel sounder can utilize. We propose a method, which is based on the simulated annealing algorithm, to find non-uniform array switching patterns for realistic antenna arrays, so that we can extend the Doppler estimation range of the channel sounder by suppressing the high sidelobes in the spatio-temporal ambiguity function. Monte Carlo simulations demonstrate that the optimal switching sequence leads to significantly smaller root mean square errors of both direction of departure and Doppler. Results can be applied in both vehicle-to-vehicle and mobile millimeter wave MIMO channel measurements.

This paper presents the results from a 28 GHz channel sounding campaign performed to investigate the effects of outdoor to indoor penetration on the wireless propagation channel characteristics for an urban microcell in a fixed wireless access scenario. The measurements are performed with a real-time channel sounder, which can measure path loss up to 169 dB, and equipped with phased array antennas that allow electrical beam steering for directionally resolved measurements in dynamic environments. Thanks to the short measurement time and the excellent phase stability of the system, we obtain both directional and omnidirectional channel power delay profiles without any delay uncertainty. For outdoor and indoor receiver locations, we compare path loss, delay spreads and angular spreads obtained for two different types of buildings.

This work investigates the caching policy and cooperative distance designs for throughput and energy efficiency (EE) in base station (BS) assisted device-to-device (D2D) caching networks. To conduct the investigation in joint consideration of BS-, D2D-, and self-caching and the impact of cooperation distance, we configure a clustering network with specifically designed power control and resource reuse policies. After analyzing the throughput and EE of the network, their design problems and solving approaches are provided. By simulations, we validate our analyses and evaluate the proposed designs. Moreover, we show that the throughput and EE designs can significantly conflict with each other, and a trade-off design that provides a compromise between them is thus necessary for improving the system.

This work proposes a novel transmission scheme, namely Reference Tone Aided Transmission (RTAT), that can enable communication in massive MIMO systems with a single analog-to-digital converter at the receiver. Unlike conventional low complexity MIMO transceivers, it can perform receive beamforming without explicit channel estimation or use of phase shifters, thereby leading to a significant reduction in channel estimation overhead and simpler initial access. In RTAT, a sinusoidal reference tone is transmitted along with the data signals. At each receive antenna, the reference tone is recovered, and multiplied by the received signals, to obtain a base-band signal whose inter-antenna phase shift has been compensated. The resulting low-pass signals from all the antennas are then added, emulating maximal ratio combining at the receiver with imperfect channel knowledge. In this work, a receiver architecture for RTAT is proposed, its performance is analyzed and is compared with that of fully digital beamforming. The capacity maximizing power allocation problem is also considered, and one possible initial access protocol for RTAT is suggested. Simulation results show that, in comparison to hybrid beamforming, RTAT suffers only a small loss in signal-to-noise ratio, while providing a significant reduction in the channel estimation overhead.

Due to the short range of wireless Personal Area Networks (PANs), user proximity-induced effects have been identified as a critical issue. The development and performance simulation of PANs will therefore require an accurate channel model that considers the effects of the human body, in particular, various body sizes. This paper presents details of an extensive measurement campaign for wireless PAN Ultrawideband (UWB) multi-antenna propagation channels. The measurements were done in an anechoic chamber environment using a 1 × 4 Single- Input-Multiple-Output (SIMO) array channel sounding system operating in the 2-10 GHz frequency band. Using a total of 60 test subjects, we determine the Body Mass Index (BMI) dependence of channel parameters such as mean path gain, shadowing gain and delay spread. We find that in particular, PAN channel parameters of test subjects with BMI > 30 show considerable differences to those of subjects with typical BMI.

Spatial correlation across an antenna array is known to be detrimental to the terminal signal-to- interference-plus-noise-ratio (SINR) and system spectral efficiency. For a downlink multiuser multiple-input multiple-output system (MU-MIMO), we show that the widely used, yet overly simplified, correlation models which generate fixed correlation patterns for all terminals tend to underestimate the system performance. This is in contrast to more sophisticated, yet physically motivated, remote scattering models that generate variations in the correlation structure across multiple terminals. The remote scattering models are parameterized with measured data from a recent 2.53 GHz urban macrocellular channel measurement campaign in Cologne, Germany. Assuming spatially correlated Ricean fading, with maximum-ratio transmission precoding, tight closed-form approximations to the expected (average) SINR, and ergodic sum spectral efficiency are derived. The expressions provide clear insights into the impact of variable correlation patterns on the above performance metrics. Our results demonstrate the sensitivity of the MU-MIMO performance to different correlation models, and provide a cautionary tale of its impact.

This paper reports on a power-delay profile measurement campaign emulating a mobile vehicle-to-infrastructure urban-highway environment. The measured location is in the city of Brno, Czech Republic. Utilizing a correlative 50 GS/s time-domain channel sounder with a center frequency of 59.6 GHz and 8 GHz bandwidth and with open-ended WR15 waveguide antennas, we characterize the representative millimeter wave radio channels in terms of the RMS delay spread and its variability caused by mobile scatterers (i.e., vehicles). The RMS delay spread exhibits notable heteroscedasticity as its standard deviation can decrease by 40%. The measured high resolution power-delay profiles exhibit clustering behavior, where a typical number of reflected multipath components is four to five.

This paper presents results from the (to our knowledge) first dynamic double-directionally resolved measurement campaign at mm-wave frequencies for an outdoor microcellular scenario. The measurements are performed with USC's real- time channel sounder equipped with phased array antennas that can steer beams electrically in microseconds, allowing directional measurements in dynamic environments. Exploiting the phase coherency of the setup, the multi-path components can be tracked over time to investigate the temporal dependencies of the channel characteristics. We present results for time-varying path-loss, delay spread, mean angles and angular spreads observed at the transmitter (TX) and receiver (RX) in the presence of moving vehicles and pedestrians. Additionally, we investigate excess losses observed due to blockage by vehicles and compare the cases when TX and RX are using fixed beams or when they are capable of adjusting beam directions dynamically.

In this work, we analyze the performance of the downlink of a cell-free massive multiple-input multiple-output (mMIMO) system considering finite capacity fronthaul links. We model the locations of the remote radio heads (RRHs) and the users as two independent binomial point processes (BPPs). Conditioned on the locations of the RRHs and users, and considering imperfect channel state information (CSI) and conjugate beamforming at the RRHs, we derive an achievable rate for a randomly selected user in the network. Further, based on the dominant RRH approach, we provide an approximate but accurate expression to analytically evaluate this rate averaged over the spatial realizations of RRH and user locations. From our analysis, we arrive at the following conclusions: (1) the achievable average system sum-rate is a strictly quasi-concave function of the number of users in the network, (2) for the same number of antennas in the system, the optimal number of antennas per RRH to maximize the average user rate as well as average system sum-rate depends on the quality of the CSI. While for a high-quality CSI a more collocated system is preferred, for low-quality CSI it is better to consider a more distributed RRH deployment.

Body-to-Body (B2B) communication has been envisioned for next- generation communication systems as it affords the ability to share information between co-located users with low energy consumption and high spectral efficiency. It is also anticipated that the bodies of the users on which the communication devices are located will strongly influence the signal propagation. Therefore, it is important that any B2B propagation channel characterization consider the effect of the human body and in particular the size/shape of the body. In this paper, we provide a detailed description of a measurement campaign for wireless B2B propagation channels. Measurements are done in an anechoic chamber to clearly work out the impact of the body. We use a self-developed 4 × 4 Ultrawideband (UWB) Multiple-Input-Multiple- Output (MIMO) array channel sounding system operating in the 2-10 GHz frequency band. The impact of body mass index (BMI) on the B2B propagation channels is investigated. Using a total of 9 test subjects, we determine the BMI- dependence of channel paramaters such as path gain, shadowing gain and rms delay spread (ττms). We find that parameters obtained from measurements on test subjects with BMIs corresponding to large body sizes show considerable difference to those of subjects with typical BMI.

Localization using the time-of-arrival (ToA) of a wideband signal has the potential to achieve high accuracy, thereby making it a promising choice for a variety of applications. However, a major challenge impacting localization accuracy is multipath propagation, i.e., the existence of indirect paths from a target to an anchor (transceiver) via one or more obstacles present in the environment. In this paper, we provide an overview of the techniques proposed in the literature to analyze and address the effects of multipath on localization accuracy. For this purpose, we first cast the localization of one or more targets as a maximum a priori estimation problem, where the distribution of the amplitudes and ToAs of the indirect paths serves as a prior. Under this framework, we show that multipath can either be a blessing or a curse depending on the extent of prior knowledge available about the multipath statistics. Specifically, in the absence of any indirect path information, only the direct paths that go straight from a target to an anchor contain useful position information; in other words, multipath negatively impacts localization performance. Thus, in this case, it is important to detect the anchors having line-of-sight to the target(s), and we review the techniques reported in the literature to solve this problem. On the other hand, if complete indirect path information is available (i.e., the propagation paths of all the multipath components are known), then each indirect path is equivalent to a direct path from a virtual anchor, and hence, the spatial diversity offered by multipath can be exploited for improving localization accuracy.

Personal area networks (PANs) and body-to-body (B2B) networks are of increasing importance for applications ranging from medical monitoring to entertainment. The performance of such networks is largely determined by the propagation channels in which they operate, and which in turn are strongly impacted by the human body/bodies in these channels. It is thus essential to quantify the impact of different body types/sizes on the channel characteristics for PAN and B2B channels. In this paper, we present an extensive propagation channel measurement campaign in the 2-10 GHz frequency band for SIMO (single-input-multiple-output) PAN and MIMO (multiple-input-multiple-output) B2B channels. The measurements were done using 60 (for PAN) and 9 (B2B) human subjects, which had widely varying body mass index (BMI) values grouped into three categories. For each BMI category, parameters such as the average path gain, shadowing gain, root-mean-squared delay spread, channel spatial correlation coefficients, amplitude fading statistics, and channel capacity values were extracted for both PAN and B2B channels. Our analysis reveals considerable differences between these parameters for different BMI categories.

In localization applications, the line-of-sight between anchors and targets may be blocked by obstacles in the environment. If an insufficient number of anchors are visible (i.e., have line-of-sight) to a target, then the target cannot be unambiguously localized and is, therefore, said to be in a blind spot. In this paper, we analyze the blind spot probability of a typical target by using stochastic geometry to model the randomness in the obstacle and anchor locations. In doing so, we handle correlated anchor blocking induced by the obstacles, unlike previous works that assume independent anchor blocking. We first characterize the regime over which the independent blocking assumption underestimates the blind spot probability of the typical target, which in turn is characterized as a function of the distribution of the visible area surrounding the target location. Since this distribution is difficult to exactly characterize, we formulate the nearest two-obstacle approximation, which is equivalent to considering correlated blocking for only the nearest two obstacles from the target and assuming independent blocking for the remaining obstacles. Based on this, we derive an approximate expression for the blind spot probability, which helps to determine the anchor deployment intensity needed for the blind spot probability of a typical target to be bounded above by a threshold, μ.

This paper addresses the problem of localizing an unknown number of targets, all having the same radar signature, by a distributed MIMO radar consisting of single antenna transmitters and receivers that cannot determine directions of departure and arrival. Furthermore, we consider the presence of multipath propagation and the possible (correlated) blocking of the direct paths (going from the transmitter and reflecting off a target to the receiver). In its most general form, this problem can be cast as a Bayesian estimation problem where every multipath component is accounted for. However, when the environment map is unknown, this problem is ill-posed and hence, a tractable approximation is derived where only direct paths are accounted for. In particular, we take into account the correlated blocking by scatterers in the environment which appears as a prior term in the Bayesian estimation framework. A sub-optimal polynomial-time algorithm to solve the Bayesian multi-target localization problem with correlated blocking is proposed and its performance is evaluated using simulations. We found that when correlated blocking is severe, assuming the blocking events to be independent and having constant probability (as was done in previous papers) resulted in poor detection performance, with false alarms more likely to occur than detections.

Neighbor discovery (ND) is an essential prerequisite for any peer-to-peer communication. In general, minimizing the discovery time is the goal for ND schemes. In this paper, we study the average discovery time for directional random ND when nodes have prior information about their set of possible neighbors, which also helps identify the performance limits of random ND schemes. Typically, discovery time analysis is done for assumptions that simplify the network structure, such as uniform neighbor relations for all nodes. However, with prior information the directional transmission probabilities depend on the node and the direction. This complicates the analysis of the expected discovery time. We first provide a closed-form expression for the expected discovery time based on the non-uniform coupon collector problem. Next, we identify directional transmission probabilities of each node that achieve a small discovery time. Due to the mathematical complexity, we provide a lower and an upper bound on the expected discovery time, which allows us to write the problem as a convex optimization problem. Through simulations, we demonstrate the performance gain due to prior knowledge with the proposed methods as compared with when no prior information is available, as well as the impact of uncertainty in the prior knowledge.

Augmented information (AgI) services allow users to consume information that results from the execution of a chain of service functions that process source information to create real-time augmented value. Applications include real-time analysis of remote sensing data, real-time computer vision, personalized video streaming, and augmented reality, among others. We consider the problem of optimal distribution of AgI services over a wireless computing network, in which nodes are equipped with both communication and computing resources. We characterize the wireless computing network capacity region and design a joint flow scheduling and resource allocation algorithm that stabilizes the underlying queuing system while achieving a network cost arbitrarily close to the minimum, with a tradeoff in network delay. Our solution captures the unique chaining and flow scaling aspects of AgI services while exploiting the use of the broadcast approach coding scheme over the wireless channel.

Fifth generation cellular systems will operate in a wide range of frequencies, covering both the traditional cellular bands, and mm-wave frequency bands, either selecting a specific band or operating in multiple bands simultaneously. For the design of such systems, a detailed understanding of the frequency dependence of the propagation channel is essential. While many channel measurements exist in either microwave bands (20 GHz), the results are not easily comparable, and furthermore the transition between these bands is not well studied. This paper aims to bridge the gap by providing the results from a channel measurement campaign conducted in urban macro and micro-cellular environments, over the continuous frequency band of 3–18 GHz. We characterize the pathloss, shadow fading, root-mean-square (RMS) delay spreads, Ricean factor and coherence bandwidth in urban macro-cellular and urban micro-cellular environments. Measurements were taken in both line-of-sight (LOS) and non-line-of-sight (NLOS) environments. The pathloss exponents and the RMS delay spreads increase with the base station height in NLOS environment; but not much dependency is observed in the LOS environment. By dividing the wideband channel transfer function into subbands of 1 GHz each, we study the frequency dependence of pathloss, shadow fading, Ricean factor, delay spread, and coherence bandwidth in the 3–18 GHz band. The pathloss exponents vary significantly with frequency, but not-monotonically. The shadow fading and the Ricean factor increase with frequency. The RMS delay spreads decrease with frequency in the LOS environments, but they do not change significantly in the NLOS environments. The coherence bandwidth values do not change significantly with frequency in either environment.

Jamming, either intentional or unintentional, is a big, and increasingly prevalent, problem for many wireless communications systems. A well-known countermeasure is the application of spread spectrum techniques that spread the information over a bandwidth possibly larger than that of the jammer thereby exploiting processing gain for jammer suppression; an especially attractive spreading technique is multi-carrier code division multiple access (MC-CDMA), due to its flexibility to shape transmit spectra, resilience to inter-symbol interference, and ease of implementation. Such spreading needs to be combined with spectral notching at the receiver RF frontend, to avoid saturation of the low-noise amplifiers. Such notching, in turn, requires the receiver to sense the band over which the jammer is operating. In this paper, we consider an MC-CDMA transceiver that is being jammed by a wideband non-stationary jammer. For a Rayleigh fading channel, we derive the symbol error rate for the case that the jammer parameters are available at the receiver. We derive the Maximum-Likelihood-based and Log-Likelihood Ratio-based jammer parameter estimators for both the case where channel state information is available during jammer sensing and when it is not. The performance of various jammer parameter estimation schemes is studied using Monte Carlo simulations and with the channel impulse responses collected from the wireless propagation measurements conducted in the 10-12 GHz band.

This paper investigates the optimal caching policy and cooperation distance design from both throughput and energy efficiency (EE) perspectives in base station (BS)-assisted wireless device-to-device (D2D) caching networks. By jointly considering the effects of the BS transmission, D2D-caching, and self-caching, and the impact of the cooperation distance, a clustering approach is proposed with specifically designed power control and resource reuse policies. The throughput and EE of two network structures are comprehensively analyzed and designs aiming to optimize the throughout and EE, respectively, are proposed. We also characterize the trade-off between the throughput and EE and provide corresponding designs. Simulations considering practical parameters are conducted to verify the analyses and evaluate the proposed designs; they demonstrate superior performance compared with state-of-the art.

The high training cost of the massive multiple-input multiple-output (MIMO) systems motivates the use of hybrid digital/analog (HDA) beamforming structures. This paper considers the joint design of analog beamformers when both link ends of a millimeter (mm)-wave massive MIMO system are equipped with such HDA structures. We aim to maximize the multi-user MIMO net average throughput of the downlink in a frequency division duplex system. To achieve this, we develop an optimization framework, namely, user-centric virtual sectorization (UCVS), to explore the tradeoff of training overhead, beamforming gain, and spatial multiplexing gain. In the UCVS, both the channel-statistics-based analog beamforming design and a non-orthogonal downlink training scheme are investigated to reduce the necessary cost of instantaneous channel acquisition. By maximizing an approximate net average throughput, we devise efficient algorithms to realize the suboptimal UCVS. With generic mm-wave channel models, we demonstrate by simulations that our proposed scheme outperforms the state-of-the-art methods in various typical scenarios of mm-wave communications.

Considering the design of two-stage beamformers for the downlink of multi-user massive multiple-input multiple-output systems in frequency division duplexing mode, this paper investigates the case where both the link ends are equipped with hybrid digital/analog beamforming structures. A virtual sectorization is realized by channel-statistics-based user grouping and analog beamforming, where the user equipment only needs to feedback its intra-group effective channel, and the overall cost of channel state information (CSI) acquisition is significantly reduced. Under the Kronecker channel model assumption, we first show that the strongest eigenbeams of the receive correlation matrix form the optimal analog combiner to maximize the intra-group signal to inter-group interference plus noise ratio. Then, with the partial knowledge of instantaneous CSI, we jointly optimize the digital precoder and combiner by maximizing a lower bound of the conditional average net sum rate. Simulations over the propagation channels obtained from geometric-based stochastic models, ray tracing results, and measured outdoor channels, demonstrate that our proposed beamforming strategy outperforms the state-of-the-art methods.

Multidimensional characterization of outdoor urban macrocellular propagation channels is essential for the analysis and design of next-generation (5G and beyond) cellular massive MIMO (multiple-input-multiple-output) systems. Since most massive MIMO arrays will extend in two or three dimensions, an understanding of 3-D parameters (i.e., azimuth and elevation) of the multipath components (MPCs) is required. This paper presents an extensive measurement campaign for 3-D outdoor propagation channels in an urban macrocellular environment. Measurements were performed with a 20 MHz wideband polarimetric MIMO channel sounder centered at 2.53 GHz and MPCs were extracted using RIMAX-an iterative maximum likelihood algorithm. The physical propagation mechanisms of the observed discrete MPCs are explained in terms of waveguiding, over-the-rooftop propagation, and scattering by far-away objects. MPCs exhibit clustering in the temporal and spatial domains; both intra- and inter-cluster parameters and their relevant statistics are provided. We also extract diffuse MPCs, show that they can comprise a moderate portion of the overall energy, and provide a statistical characterization.

Wireless body area networks (BANs) have many important applications such as wearable communication devices and Internet of Things. Wireless propagation in on-body channels has been measured and modeled in the past. However, a crucial element that is usually ignored is the impact of the body size of the user-a 50-kg person obviously creates a different on-body channel than a 150-kg person. The status-quo “one-size-fits-all” approach to channel characterization in BAN is thus incomplete. In this paper, we provide a detailed description of a propagation measurement campaign that employs a self-developed 4 × 4 ultrawideband multiple-input-multiple-output array channel sounding system to perform BAN channel sounding both in an anechoic chamber and indoor laboratory environments. A total of 60 human subjects were investigated in our work. These human subjects had widely varying body mass index (BMI) values, which were grouped into three different categories, i.e., 20 per BMI category. Various propagation properties such as path gain, frequency-decay factor, shadowing gain, rms delay spread, amplitude fading, and spatial correlation are extracted for each on-body channel under consideration. A comparison of statistics among the BMI categories reveals considerable differences, emphasizing the fact that the aforementioned propagation properties are BMI dependent. Parameters such as path gain showed a monotonic decrease across the BMI categories with values ranging from 1-2 to almost 13 dB in some channels. This paper proposes a propagation channel model for the BMI-dependent parameters and validates that it can reproduce the measured channel capacities.

This paper presents results from the (to our knowledge) first double- directionally resolved measurement campaign at mm-wave frequencies in a suburban microcell. The measurements are performed with a real-time channel sounder equipped with phased antenna arrays that allows electrical beam steering in microseconds, and which can measure path-loss of up to 169 dB. Exploiting the phase coherency of the measurements in the different beams, we obtain both directional and omnidirectional channel power delay profiles without any delay uncertainty. We present statistics of channel characteristics such as path-loss, shadowing and delay spread results for line-of-sight and non-line-of-sight cases, as well as sample results for power angular spectrum and extracted multi- path components.

Video caching at the wireless edge is a promising approach to improve throughput and reliability of video delivery. While most existing literature considers homogeneous user preference modeling when designing caching policies, this paper investigates how to exploit statistical information on individual popularity preferences in the design of caching policies in base station assisted wireless device-to-device networks. Specifically, we formulate the caching policy design problem aiming to minimize average energy consumption and propose two approaches that solve the problem under different conditions: users can or cannot coordinatedly design their policies. By simulating systems using practical individual preference models, we show that the proposed designs are effective, and the performance gain brought by exploiting individual preferences can be clearly observed.

Caching of video files at the wireless edge, i.e., at the base stations or on user devices, is a key method for improving wireless video delivery. While global popularity distributions of video content have been investigated in the past, and used in a variety of caching algorithms, this paper investigates the statistical modeling of the individual user preferences. With individual preferences being represented by probabilities, we identify their critical features and parameters and propose a novel modeling framework as well as a parameterization of the framework based on an extensive real-world data set. Besides, an implementation recipe for generating practical individual preference probabilities is proposed. By comparing with the underlying real data, we show that the proposed models and generation approach can effectively characterize individual preferences of users for video content.

Considering the joint design of analog beamformers when both link ends of a millimeter (mm)-wave massive multiple input multiple-output (MIMO) system are equipped with hybrid digital/analog (HDA) structures, we aim to maximize the multi-user (MU) MIMO net average throughput of the downlink in a Frequency Division Duplex (FDD) system. To achieve this, we develop an optimization framework, namely user-centric virtual sectorization (UCVS), which explores the tradeoff of training overhead, beamforming gain, and spatial multiplexing gain. In the UCVS, both the channel-statistics based analog beamforming design and a non-orthogonal downlink training scheme are investigated to reduce the necessary cost of instantaneous channel acquisition. By maximizing an approximate net average throughput, we devise efficient algorithms to realize the suboptimal UCVS. With generic mm-wave channel models, we demonstrate by simulations that our proposed scheme outperforms state-of the-art methods in various scenarios typical for mm-wave communications.

Auto-correlation receivers, such as frequency shift reference systems, offer a low-complexity alternative to Rake receivers, for low data-rate ultra-wideband applications. In such systems, a data signal and a reference signal are simultaneously transmitted. At the receiver, the received signal corresponding to the data is correlated with the received signal corresponding to the reference signal, thereby accumulating the multi-path channel energies in-phase. For improving bandwidth and energy efficiency, multi- differential schemes have also been proposed where multiple data signals share the same reference signal. With the aim of further boosting the achievable data rates, in this work we propose a multi-differential frequency shift reference receiver that supports higher order modulation formats. We design the corresponding receiver architecture that can exploit both the in-phase and quadrature-phase signal components and analytically characterize the signal and noise components. We also study the problem of optimal bit and power allocation to the multiple data signals, both for uncoded and coded systems, as a function of channel metrics that can be easily tracked at the receiver.

Information theoretic analyses have shown the po- tential for a significant capacity gain in wireless systems by use of multiple antennas at both transmitter (TX) and receiver (RX) ends in various propagation channels. These channels are typically characterized by scenario-specific channel sounder systems. In this paper, we present a detailed description of a developed Ultrawideband (UWB) 4 x 4 multiple-input-multiple- output (MIMO) channel sounding system and capacity mea- surements results for Body area network (BAN) channels. The main focus of the investigation is the study of the impact that Body Mass Index (BMI) of a human being has on wireless body area network (BAN) measurement results in an anechoic and indoor lab environments. We found the capacities values to be different for various BMI categories. This paper also describes measurements done to examine how tissues and fabric influence (or distort) the radiation pattern of an antenna when in close proximity to it.

In an augmented information (Agi) service, users consume information that results from the execution of a chain of service functions that process source information to create real-time augmented value. Applications may include real-time analysis of remote sensing data, real-time computer vision, personalized video streaming, and augmented reality, among others. We consider the problem of optimal distribution of AgI services over a wireless computing network, in which nodes are equipped with both communication and computing resources. We characterize the wireless computing network capacity region and design a joint flow scheduling and resource allocation algorithm that stabilizes the underlying queuing system while achieving arbitrarily close to minimum network cost, with a tradeoff in network delay. Our solution captures the unique chaining and flow scaling aspects of AgI services, while exploiting the use of the broadcast approach over the wireless channel.

In this paper, we consider the design of analogue beamformers for the downlink of multi-user massive multiple-input-multiple-output (MIMO) systems. We specifically investigate systems where both link ends are equipped with hybrid digital/analog (HDA) beamforming structures, where the analog beamformers are adapted based on second order channel statistics, reducing the training overhead as well as hardware effort. We present a framework for the optimization of such beamformers operating in mm-wave channels, exploiting the directional characteristics and sparse nature of such channels. We develop an approximate upper bound of the ergodic sum capacity, based on which efficient beamforming algorithms are devised. Simulation results show significant performance improvements of the proposed algorithms compared to state-of-the-art algorithms.

Hybrid preprocessing, where an analogue preprocessing matrix is used to feed a large antenna array to fewer up/down-conversion chains, helps reduce hardware cost of massive Multiple-Input-Multiple-Output systems. Here, we analyze a variant of hybrid preprocessing, namely hybrid preprocessing with selection (HPwS), as an attractive solution to reduce this hardware cost while retaining good performance. In HPwS, the preprocessing matrix, built from radio frequency (RF) hardware, has a larger number of input ports L than the number of up/down-conversion chains K. A bank of RF switches connects the instantaneously best K input ports to the up/down-conversion chains. The preprocessor is designed based on average channel statistics and therefore needs to be updated only infrequently. This allows for a higher diversity-order and/or simpler RF hardware than some conventional hybrid preprocessing systems. In this paper, we propose a generic architecture of HPwS with a possibly non-unitary rectangular preprocessing matrix. A novel preprocessor is designed that maximizes a capacity lower bound for channels with isotropic scattering. In addition, we study how L, the number of users, and the use of low complexity RF hardware, such as discrete phase-shifters, impact system performance. We also present a method to extend the preprocessor design to anisotropic channels.

This paper introduces a real-time multiple input and multiple output (MIMO) channel sounder to measure the vehicle-to-vehicle (V2V) propagation channel at 5.9 GHz. Compared to the existing V2V channel sounders, our design emphasizes on improving the stability of the setup and increasing the MIMO snapshot rate. The system stability allows us to develop a high resolution parameter extraction algorithm for the data analysis, which jointly estimates parameters of multipath components such as time-of-arrival, direction of departure, direction of arrival and Doppler shift. The second emphasis increases the maximal absolute Doppler shift to 806 Hz, which the system can estimate without ambiguity. The increased snapshot rate also provides a more fluent representation of the channel dynamics. We verify the design of the channel sounder with actual V2V channel measurements. Results suggest that 80 percent of the sample snapshots have a diffuse power ratio less than 20 percent and the extracted dominant specular paths match well with the environment and dynamics of the measurement.

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

Distributed cloud networking builds on network functions virtualization (NFV) and software defined networking (SDN) to enable the deployment of network services in the form of elastic virtual network functions (VNFs) instantiated over general purpose servers at distributed cloud locations. We address the design of fast approximation algorithms for the NFV service distribution problem (NSDP), whose goal is to determine the placement of VNFs, the routing of service flows, and the associated allocation of cloud and network resources that satisfy client demands with minimum cost. We show that in the case of load-proportional costs, the resulting fractional NSDP can be formulated as a multi-commodity-chain flow problem on a cloud-augmented graph, and design a queue-length based algorithm, named QNSD, that provides an O(ε) approximation in time O (1/ε). We then address the case in which resource costs are a function of the integer number of allocated resources and design a variation of QNSD that effectively pushes for flow consolidation into a limited number of active resources to minimize overall cloud network cost.

We consider a wireless Device-to-Device (D2D) caching network, where users make arbitrary requests from a library of files and have pre-fetched (cached) information on their devices, subject to a per-node storage capacity constraint. The network is assumed to obey the “protocol model”, widely considered in the wireless network literature. Unlike other related works, which either restrict the communication to single-hop, or assume entire file caching, here we consider both multi-hop transmission and fully general caching strategies, including file subpacketization. We propose a caching strategy based on deterministic assignment of MDS-coded packets of the library files, and a coded multicast delivery strategy where the users send linearly coded messages to each other in order to collectively satisfy their demands. We show that our approach can achieve the information theoretic outer bound within a multiplicative constant factor in practical parameter regimes.

This paper presents first measurement results on truck-to-car and truck-to-truck propagation channels at 5.9 GHz. We measured the channel with a self-built real-time channel sounder with multiple antennas at both transmitter and receiver. We present sample results for two safety-related scenarios in a highway and urban environment, respectively. We analyze the measured data with a high-resolution parameter extraction algorithm, which provides both spatial and temporal characterization of the channel. The results show that the channel exhibits a higher Doppler dispersion in the highway scenario than in the urban scenario. Besides, the metallic trailer, which is higher than the antenna, acts as a strong reflector when the trucks face each other and an effective shadowing object when the trucks face away.

Neighbor discovery is an essential prerequisite for any device-to-device (D2D) communication. Unlike ad-hoc networks, the base station (BS) in D2D networks may facilitate the neighbor discovery process. However, device-to-BS channel states or device locations are not enough to provide BS with information on the channel quality between the devices. Thus, due to the inherent uncertainty of link quality between devices, the BS-assisted neighbor discovery cannot be treated as typical scheduling problem. In this paper, we investigate the assisted directional neighbor discovery in D2D networks. We first formulate the scheduling problem as an integer optimization problem that captures the uncertainty. Then we propose a greedy based centralized scheduling to determine directional pilot transmission instances. We also propose a one-way randomized discovery, where we choose the directional transmission probabilities based on two techniques, an intuitive and an optimized methods. Finally, we provide simulation results that assess the performance the schemes.

In this paper, we present a novel real-time MIMO channel sounder for 28 GHz. Until now, the common practice to investigate the directional characteristics of millimeter-wave channels has been using a rotating horn antenna. The sounder presented here is capable of performing horizontal and vertical beam steering with the help of phased arrays. Thanks to the fast beam-switching capability, the proposed sounder can perform measurements that are directionally resolved both at the transmitter (TX) and receiver (RX) as fast as 1.44 milliseconds compared to the minutes or even hours required for rotating horn antenna sounders. This does not only enable us to measure more points for better statistical inference but also allows us to perform directional analysis in dynamic environments. Equally important, the short measurement time combined with the high phase stability of our setup limits the phase drift between TX and RX, enabling phase-coherent sounding of all beam pairs even when TX and RX are physically separated and have no cabled connection for synchronization. This ensures that the measurement data is suitable for high-resolution parameter extraction algorithms. Along with the system design and specifications, this paper also discusses the measurements performed for verification of the sounder. Furthermore, we present sample measurements from a channel sounding campaign performed on a residential street.

This paper presents a detailed description of a measurement campaign for wireless Body Area Network (BAN) propagation channels in both an anechoic chamber and an indoor Lab environment. We use a self-developed 4 x 4 Ultrawideband (UWB) Multiple-input-multiple-output (MIMO) array channel sounding system operating in the 2 - 10 GHz frequency band. The main goal of the measurement is the determination of the impact of body mass index (BMI) on the on-body propagation channels. Using a total of 60 test subjects, we determine the BMI-dependence of channel parameters such as pathloss (G0), frequency-dependent decay factor (κ) and shadowing gain (χσ). We find that in particular, BAN channels of test subjects with BMI > 30 show considerable difference to those of subjects with typical BMI.

This paper reports the first truck-to-truck (T2T) wireless channel measurement conducted at 5.9GHz with multiple antennas at both transmitter and receiver. We present directional analysis of a sample route in downtown Los Angeles, CA USA, where two trucks drive towards each other. Measurements are performed with a self-built real-time MIMO channel sounder and evaluated with RiMAX, a high resolution parameter estimation algorithm, providing time-of-arrival, directional of departure, directional of arrival and Doppler shift for discrete multipath components as well as diffuse multipath scattering. Compared to vehicular channels for passenger cars, our measurements show that the metallic trailer, which is higher than the installed antennas, acts as a strong reflector in line-of-sight (LOS) condition and increases the average received power and angular spread. The extra power attenuation however can be as high as 35dB after two trucks pass each other and the trailers obstruct the LOS.

A new two-dimensional modulation technique called Orthogonal Time Frequency Space (OTFS) modulation designed in the delay-Doppler domain is introduced. Through this design, which exploits full diversity over time and frequency, OTFS coupled with equalization converts the fading, time-varying wireless channel experienced by modulated signals such as OFDM into a time-independent channel with a complex channel gain that is roughly constant for all symbols. Thus, transmitter adaptation is not needed. This extraction of the full channel diversity allows OTFS to greatly simplify system operation and significantly improves performance, particular in systems with high Doppler, short packets, and large antenna arrays. Simulation results indicate at least several dB of block error rate performance improvement for OTFS over OFDM in all of these settings. In addition these results show that even at very high Dopplers (500 Km/h), OTFS approaches channel capacity through linear scaling of throughput with the MIMO order, whereas the performance of OFDM under typical design parameters breaks down completely.

In the real-world environments, multipath components (MPCs) of wireless channels are generally distributed as groups, i.e., clusters. Modeling the clustered MPCs is important and necessary for channel modeling and an automatic clustering algorithm is thus required. This paper proposes a novel Kernel-power-density (KPD) based algorithm for MPC clustering. It uses the Kernel density to incorporate the modeled behavior of MPCs and takes into account the power of the MPCs. The proposed algorithm only considers the K nearest MPCs in the density estimation to better identify the local density variations of MPCs. Simulations validate the KPD algorithm and almost no performance degradation is found even with a large number of clusters and large cluster angular spread. The KPD algorithm enables applications with no prior knowledge about the clusters such as number and initial locations. It can be used for the cluster based channel modeling for 4G#x002F;5G communications.

There is considerable pressure to define the key requirements of 5G, develop 5G standards, and perform technology trials as quickly as possible. Normally, these activities are best done in series but there is a desire to complete these tasks in parallel so that commercial deployments of 5G can begin by 2020. 5G will not be an incremental improvement over its predecessors; it aims to be a revolutionary leap forward in terms of data rates, latency, massive connectivity, network reliability, and energy efficiency. These capabilities are targeted at realizing high-speed connectivity, the Internet of Things, augmented virtual reality, the tactile internet, and so on. The requirements of 5G are expected to be met by new spectrum in the microwave bands (3.3-4.2 GHz), and utilizing large bandwidths available in mm-wave bands, increasing spatial degrees of freedom via large antenna arrays and 3-D MIMO, network densification, and new waveforms that provide scalability and flexibility to meet the varying demands of 5G services. Unlike the one size fits all 4G core networks, the 5G core network must be flexible and adaptable and is expected to simultaneously provide optimized support for the diverse 5G use case categories. In this paper, we provide an overview of 5G research, standardization trials, and deployment challenges. Due to the enormous scope of 5G systems, it is necessary to provide some direction in a tutorial article, and in this overview, the focus is largely user centric, rather than device centric. In addition to surveying the state of play in the area, we identify leading technologies, evaluating their strengths and weaknesses, and outline the key challenges ahead, with research test beds delivering promising performance but pre-commercial trials lagging behind the desired 5G targets.

There are rapid developments towards the deployment of 5G and hardly a day goes by without the release of a major announcement concerning 5G. Many of these developments are happening in parallel as there is a race against time for 5G deployment. Various test beds have been established, laboratory trials and proof of concept trials of individual building blocks of 5G are already underway or have been completed, large scale system trials are also happening or are planned and standardisation efforts in the ITU, 3GPP, IEEE, etc. are also in progress. In the case of ITU/3GPP, the standards are expected to be completed by 2019. Many candidate bands for 5G in the centimetric and mm wave range were identified by the World Radio Conference (WRC) 2015 and are expected to be finalized by WRC 2019, while coexistence studies of 5G and existing systems in the new candidate bands are underway.

This is the second part of the papers in this special issue on deployment issues and performance challenges for 5G. The papers have been divided into five categories:•Massive MIMO•Co existence•Signal Processing and New Waveforms•Cloud RAN•Field Trials

We consider a wireless device-to-device network, where n nodes are uniformly distributed at random over the network area. We let each node caches M files from a library of size m ≥ M. Each node in the network requests a file from the library independently at random, according to a popularity distribution, and is served by other nodes having the requested file in their local cache via (possibly) multihop transmissions. Under the classical “protocol model” of wireless networks, we characterize the optimal per-node capacity scaling law for a broad class of heavy-tailed popularity distributions, including Zipf distributions with exponent less than one. In the parameter regime of interest, i.e., m=o(nM), we show that a decentralized random caching strategy with uniform probability over the library yields the optimal per-node capacity scaling of Θ(√M/m) for heavy-tailed popularity distributions. This scaling is constant with n , thus yielding throughput scalability with the network size. Furthermore, the multihop capacity scaling can be significantly better than for the case of single-hop caching networks, for which the per-node capacity is Θ (M/m). The multihop capacity scaling law can be further improved for a Zipf distribution with exponent larger than some threshold > 1, by using a decentralized random caching uniformly across a subset of most popular files in the library. Namely, ignoring a subset of less popular files (i.e., effectively reducing the size of the library) can significantly improve the throughput scaling while guaranteeing that all nodes will be served with high probability as n increases.

By caching files at users, content delivery traffic can be offloaded via device-to-device (D2D) links if a helper user is willing to transmit the cached file to the user who requests the file. In practice, the user device has limited battery capacity, and may terminate the D2D connection when its battery has little energy left. Thus, taking the battery consumption allowed by the helper users to support D2D into account introduces a reduction in the possible amount of offloading. In this paper, we investigate the relationship between offloading gain of the system and energy cost of each helper user. To this end, we introduce a user-centric protocol to control the energy cost for a helper user to transmit the file. Then, we optimize the proactive caching policy to maximize the offloading opportunity and the transmit power at each helper to maximize the offloading probability. Finally, we evaluate the overall amount of traffic offloaded to D2D links and the average energy consumption at each helper, with the optimized caching policy and transmit power. Simulations show that a significant amount of traffic can be offloaded even when the energy cost is kept low.

Cluster-based channel modeling has been an important trend in the development of channel model, as it maintains accuracy while reducing complexity. Whereas a large number of channel measurements have shown that multipath components (MPCs) are distributed as groups, i.e., clusters, existing clustering algorithms have various drawbacks with respect to complexity, threshold choices, and/or assumptions about prior knowledge. In this paper, a kernel-power-density (KPD)-based algorithm is proposed for MPC clustering. It uses the kernel density of MPCs to incorporate the modeled behavior of MPCs and takes into account the power of the MPCs. Furthermore, the KPD algorithm only considers the K nearest MPCs in the density estimation to better identify the local density variations of MPCs. A heuristic approach of cluster merging is used to improve the performance. Both simulation and channel measurements validate the KPD algorithm, and almost no performance degradation is found even with a large number of clusters and large cluster angular spread, which outperforming other algorithms. The KPD algorithm enables applications in multipleinput-multiple-output channels with no prior knowledge about the clusters, such as number and initial locations. It also has a fairly low computational complexity and can be used for clusterbased channel modeling.

This paper considers a fundamental issue of path loss (PL) modeling in urban micro cell (UMi) environments, namely the spatial consistency of the model as the mobile station moves along a trajectory through street canyons. This paper is motivated by the observed non-stationarity of the PL. We show that the traditional model of power law PL plus lognormally distributed variations can provide misleading results that can have serious implications for system simulations. Rather, the PL parameters have to be modeled as random variables that change from street to street and also as a function of the street orientation. Variations of the PL, taken over the ensemble of the whole cell (or multiple cells), thus consist of the compound effect of these PL parameter variations together with the traditional shadowing variations along the trajectory of movement. Ray-tracing results demonstrate that ignoring this effect can lead to a severe overestimation of the local standard deviation in a given area. Then, a spatially consistent stochastic street-by-street PL model is established, and a parameterization for 28-GHz UMi cells is given. The model correctly describes the PL as a function of the street orientation as well as the large variance observed for all the PL model parameters.

The millimeter-wave (mm-wave) band will be a key component of fifth-generation (5G) wireless communication systems. This paper presents radio propagation measurements and analysis investigating the wideband directional channel characteristics of the mm-wave transmission for in-building and urban cellular communication systems in the 28-GHz band. Based on the measurements, we analyze and model the spatio-temporal channel characteristics such as multipath delay, angular statistics, and path loss. In particular we investigate the clustering of the multipath components, and investigate both the intra-cluster and inter-cluster distributions. Based on these investigations, we present a complete channel model suitable for system simulations in the in-building and urban environments.

The 3-D multiple-input multiple-output (3-D MIMO) systems have received great interest recently because of the spatial diversity advantage and capability for full-dimensional beamforming, making them promising candidates for practical realization of massive MIMO. In this paper, we present a low-cost test equipment (channel sounder) and post-processing algorithms suitable for investigating the 3-D MIMO channels as well as the results from a measurement campaign for obtaining elevation and azimuth characteristics in an outdoor-to-indoor (O2I) environment. Due to limitations in available antenna switches, our channel sounder consists of a hybrid switched/virtual cylindrical array with effectively 480 antenna elements at the base station. The virtual setup increased the overall MIMO measurement duration, thereby introducing phase drift errors in the measurements. Using reference antenna measurements, we estimate and correct for the phase errors during post-processing. We provide the elevation and azimuth angular spreads for the measurements done in an urban macro-cellular and urban micro-cellular environments, and study their dependence on the user equipment (UE) height. Based on the measurements done with UE placed on different floors, we study the feasibility of separating users in the elevation domain. The measured channel impulse responses are also used to study the channel hardening aspects of the massive MIMO and the optimality of the maximum ratio combining receiver.

In this paper, we consider the design of analog beamformers for the downlink of multi-user massive multiple-input-multiple-output (MIMO) systems. We specifically investigate systems, where both link ends are equipped with hybrid digital/analog beamforming structures, where the analog beamformers are adapted based on second order channel statistics, reducing the training overhead as well as hardware effort. We present a framework for the optimization of such beamformers operating in mm-wave channels, exploiting the directional characteristics and sparse nature of such channels. We develop an approximate upper bound of the ergodic sum capacity, based on which efficient beamforming algorithms are devised. Simulation results show significant performance improvements of the proposed algorithms compared with the state-of-the-art algorithms.

Line-of-sight wireless communications can benefit from the simultaneous transmission of multiple independent data streams through the same medium in order to increase system capacity. A common approach is to use conventional spatial multiplexing with spatially separated transmitter/receiver antennae, for which inter-channel crosstalk is reduced by employing multiple-input-multiple-output (MIMO) signal processing at the receivers. Another fairly recent approach to transmitting multiple data streams is to use orbital-angular-momentum (OAM) multiplexing, which employs the orthogonality among OAM beams to minimize inter-channel crosstalk and enable efficient (de)multiplexing. In this paper, we explore the potential of utilizing both of these multiplexing techniques to provide system design flexibility and performance enhancement. We demonstrate a 16 Gbit/s millimeter-wave link using OAM multiplexing combined with conventional spatial multiplexing over a short link distance of 1.8 meters (shorter than Rayleigh distance). Specifically, we implement a spatial multiplexing system with a 2 × 2 antenna aperture architecture, in which each transmitter aperture contains two multiplexed 4 Gbit/s data-carrying OAM beams. A MIMO-based signal processing is used at the receiver to mitigate channel interference. Our experimental results show performance improvements for all channels after MIMO processing, with bit-error rates of each channel below the forward error correction limit of 3.8 × 10-3. We also simulate the capacity for both the 4 × 4 MIMO system and the 2 × 2 MIMO with OAM multiplexing. Our work indicates that OAM multiplexing and conventional spatial multiplexing can be simultaneously utilized to provide design flexibility. The combination of these two approaches can potentially enhance system capacity given a fixed aperture area of the transmitter/receiver (when the link distance is within a few Rayleigh distances).

This paper describes a high-resolution parameter estimation algorithm applied to the vehicle-to-vehicle (V2V) multi-dimensional channel measurements. The algorithm, which is based on a maximum likelihood estimator, jointly estimates parameters of multipath components, such as time-of-arrival, direction of departure, direction of arrival, and Doppler shift. It serves to provide a comprehensive understanding of the V2V propagation channel. We propose computationally attractive methods to initialize parameters in the global search and evaluate key components in the local optimization. We also apply the algorithm to actual V2V channel measurement data. The results suggest an overall good performance of the estimator, where 80% of the snapshots have a diffuse power ratio less than 20% and the dominant specular paths match well with the environment and dynamics of the measurement.

Many wireless transceivers, such as massive-MIMO and Rake, are designed to provide high diversity order. In order to reduce cost, low-complexity switched transceivers, e.g., hybrid antenna preprocessing with selection, or S-Rake, which employ a limited number of RF chains or correlators, are popular. This paper shows that the presence of limited number of RF chains or correlators in such transceivers introduces an inherent trade-off between system performance and the channel estimation overhead. We prove that to maximize achievable rate, it is optimal to perform channel estimation for only a subset of diversity branches with the highest second moments, if the diversity branches have the same fading parameters but different mean powers. We also prove that the achievable data-rate is a unimodal function of the size of this subset L, which ensures that any locally optimum L is also globally optimum. A computationally efficient approximation for the ergodic capacity is introduced, which reduces the cost of finding the optimal L significantly. Simulation results for some practically important settings suggest that the optimal choice of L can improve the data rate by a factor of 20-30%.

This paper reports an experimental investigation of using orthogonal frequency division multiplexing (OFDM) on orbital-angular momentum (OAM) channels when there is intersymbol interference (ISI) caused by multipath effects. The impulse response measurement indicates that due to the divergence of OAM beams, channels of larger OAM number are more affected by the ISI caused the reflections from surrounding objects. Channel performance of three different OAM numbers l = 0, +1 and +3 are studied. When a single QPSK channel is used, the more channel performance degradation in term of BER and EVM is observed for higher OAM channels. OFDM with 4 sub-carriers is used on the OAM channels to help reduce the ISI and improve the channel performance to some extent.

Distributed cloud networking enables the deployment of network services in the form of interconnected virtual network functions instantiated over general purpose hardware at multiple cloud locations distributed across the network. The service distribution problem is to find the placement of virtual functions and the routing of network flows that meet a given set of demands with minimum cost. In this paper, we address the design of distributed online solutions that drive local routing, processing, and resource allocation decisions while providing global objective guarantees. We present a distributed joint transmission-processing flow scheduling and resource allocation algorithm that stabilizes the underlying cloud network queuing system, while achieving arbitrarily close to minimum average network cost (with a tradeoff in network delay) with probability 1. We further enhance our algorithm with a shortest transmission-plus-processing distance bias that improves the delay performance without compromising throughput or overall cloud network cost. We provide simulation results that confirm our theoretical analysis, illustrate the effect of the shortest transmission-plus-processing distance bias, and demonstrate remarkably good convergence to the optimal cloud network configuration.

The path loss model describing the power-law dependency on distance plus a log-normally distributed shadowing attenuation, is a staple of link budgets and system simulations. Determination of the parameters of this model is usually done from measurements and ray tracing. We show that the typical least-square fitting to those data points is inherently biased to give the best fitting to the link distances that happen to have more evaluation points; this bias might be highly undesirable in various types of simulations that use the resulting model. In this paper we present a weighted fitting method to address this issue. While it is unavoidable that fits are better for one distance range than another, we argue that such a decision should be made consciously, and adjusted to the type of simulation for which the path loss model should be used. We discuss the weighting functions for different purposes, and show their impact on prediction accuracy of signal level, interference level, and capacity in a hexagonal cellular grid simulation. As examples, weighted fitting models are presented for 28 GHz channels in urban macrocells, and it is shown that the fitting accuracy can be improved by our approach.

In this paper, we consider the design of two-stage beamformers for the downlink of multi-user frequency-division-duplexing (FDD) massive multiple-input multiple-output (MIMO) systems. We consider the case that both link ends are equipped with hybrid analog/digital (HDA) beamforming structures. With analog beamforming and user grouping based on the second-order channel statistics, the user equipment (UE) only needs to feed back its intra-group effective channel. We first show that the strongest eigenbeams of the receive correlation matrix form the optimal analog combiner under the Kronecker channel model assumption. Then, with limited instantaneous channel state information, we jointly optimize the digital precoder and combiner for conditional average net sum-rate maximization by maximizing its lower bound. To initialize our algorithm efficiently, we present a digital precoder design to maximize the conditional average signal-to-leakage-plus-noise ratio (SLNR). Simulation results show significant performance improvements compared to state-of-the-art algorithms.

Wireless localization systems based on determination of signal runtime (TOA/TDOA) are of great importance for a variety of applications. In many cases, synchronization of the clocks of the agent nodes to those of the network nodes (anchors) has to be performed together with the localization. The current paper investigates the fundamental accuracy limits of such a joint localization/synchronization. In particular we analyze the impact of allocating power to the different anchor nodes, and optimize this power allocation to maximize accuracy. Simulation results confirm the importance of proper power allocation; known special cases (TDOA localization, localization with already-synchronized clocks) are recovered from our general solution.

In this paper, the generation and steering of beams carrying orbital angular momentum utilizing a custom-designed circular antenna array has been demonstrated at 28 GHz. A steering angle as large as 30 degrees for an orbital angular momentum (OAM) beam has been achieved. The effect of number of antennas and the distance from antennas to the array center to the quality of beam generation and beam steering is investigated through both experiments and simulations. Our results indicate that: (1) As the steering angle increases, the mode purity of the generated OAM beams decreases; (2) Increasing the number of antennas improves the OAM mode purity; (3) For a fixed number of antennas, high mode purity is observed for lower order OAM modes; (4) Placing the antennas farther away from the array center allows for reduced divergence of the generate OAM beams.

This paper reports an experimental demonstration of a 32-Gbit/s wireless link using orbital angular momentum (OAM) and polarization multiplexing in a millimeter-wave regime at 60 GHz. Results of the analysis show that a higher carrier frequency reduces the propagation loss as well as the size of the transmitter and receiver, particularly for OAM channels with higher OAM values. Further, two different OAM channels (with l = +1 and l = +3) on each of the two polarizations are spatially multiplexed, and each channel carries 2-Gbaud signals with 16-QAM modulation. Spiral phase plates are used to generate 60-GHz OAM beams. The simulation results show that a higher carrier frequency plays a more significant role in reducing both the aperture size and the transmission loss for channels with higher OAM values. The bit error rates (BERs) of 4 channels are measured, and the raw BERs are found to be less than 3.8 × 10−3

We present a short-range experimental orbital-angular-momentum (OAM) multiplexing millimeter-wave communication system using patch antenna arrays. The dependence of the evolution of OAM beams on the number of array elements and array radius are analyzed. We also find phase delay deviation of 30 degrees leads to mode purity degrading to ∼1% and power deviation of 6 dB reduces the purity by <40%. An obstruction of size <10mm has not much influence on the evolution of OAM beams generated from antenna arrays with r = 8mm, which enables the stacking of multiple antenna arrays. We also use these patch antenna arrays to demonstrate a 60 GHz wireless communication link using two multiplexed OAM modes, each carrying a 500-Mbaud 16-QAM or 2-Gbaud QPSK signal. A channel crosstalk of less than −20 dB and bit-error-rates (BER) of less than 3.8 × 10−3 are achieved.

For the development of new 5G systems to operate in bands up to 100 GHz, there is a need for accurate radio propagation models at these bands that currently are not addressed by existing channel models developed for bands below 6 GHz. This document presents a preliminary overview of 5G channel models for bands up to 100 GHz. These have been derived based on extensive measurement and ray tracing results across a multitude of frequencies from 6 GHz to 100 GHz, and this document describes an initial 3D channel model which includes: 1) typical deployment scenarios for urban microcells (UMi) and urban macrocells (UMa), and 2) a baseline model for incorporating path loss, shadow fading, line of sight probability, penetration and blockage models for the typical scenarios. Various processing methodologies such as clustering and antenna decoupling algorithms are also presented.

In this paper, we propose a novel channel impulse response (CIR) clustering algorithm using a sparsity-based method, which exploits the feature of CIR that power of multipath component (MPC) is exponentially decreasing with increasing delay. We first use a sparsity-based optimization to recover CIRs, which can be well solved by using reweighted ℓ1minimization. Then a heuristic approach is provided to identify clusters in the recovered CIRs, which leads to improved clustering accuracy in comparison to identifying clusters directly in the raw CIRs. The proposed algorithm incorporates the physical behaviors of MPCs into the clustering framework and enables applications with no prior knowledge of the clusters, such as number and initial locations of clusters. The results in this paper can be used to parameterize the CIR model of radio channels.

It has been widely recognized that relaying is an important method for increasing the reliability and spectral efficiency of communications systems, and it is thus helpful for improving the performance of vehicle-to-vehicle (V2V) communication systems. However, designing and evaluating V2V relay networks require understanding the effect of shadowing, as this critically impacts the performance of the relay system. Even though the theoretic performances of various relaying schemes have been well investigated, there is a lack of empirical test that incorporates realistic shadowing effects. In this paper, we analyze the performance of relaying transmission in V2V scenarios based on measurements in scenarios where shadowing occurs through large vehicles such as buses. We investigate several potential locations for the relay nodes, and the measurements are performed with two static transmitters (TX) and one dynamic receiver (RX). Outage probabilities of several relaying schemes such as multi-hop decode-and- forward, multi-hop amplify-and-forward, and diversity-amplify-and-forward are estimated and discussed based on the measured instantaneous end- to-end signal-to-noise ratio (SNR). It is found that: (i) shadowing effect caused by the bus between V2V line-of-sight (LOS) links increases the outage probability for the non-LOS (NLOS) direct transmission; (ii) using relay node on the bus roof can significantly improve transmission, however, a strong shadowing effect may reduces the acceptable communication distance of relaying scheme; and (iii) the diversity-amplify-and- forward relaying scheme generally has the best performance. Our results can be used to design a relay system for V2V communications.

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

As wireless video is the fastest growing form of data traffic, methods for spectrally efficient on-demand wireless video streaming are essential to both service providers and users. A key property of video on-demand is the asynchronous content reuse, such that a few popular files account for a large part of the traffic but are viewed by users at different times. Caching of content on wireless devices in conjunction with device-to-device (D2D) communications allows to exploit this property, and provide a network throughput that is significantly in excess of both the conventional approach of unicasting from cellular base stations and the traditional D2D networks for “regular” data traffic. This paper presents in a tutorial and concise form some recent results on the throughput scaling laws of wireless networks with caching and asynchronous content reuse, contrasting the D2D approach with other alternative approaches such as conventional unicasting, harmonic broadcasting, and a novel coded multicasting approach based on caching in the user devices and network-coded transmission from the cellular base station only. Somehow surprisingly, the D2D scheme with spatial reuse and simple decentralized random caching achieves the same near-optimal throughput scaling law as coded multicasting. Both schemes achieve an unbounded throughput gain (in terms of scaling law) with respect to conventional unicasting and harmonic broadcasting, in the relevant regime where the number of video files in the library is smaller than the total size of the distributed cache capacity in the network. To better understand the relative merits of these competing approaches, we consider a holistic D2D system design incorporating traditional microwave (2 GHz) and millimeter-wave (mm-wave) D2D links; the direct connections to the base station can be used to provide those rare video requests that cannot be found in local caches. We provide extensive simulation results under a variety of system settings and compare our scheme with the systems that exploit transmission from the base station only. We show that, also in realistic conditions and nonasymptotic regimes, the proposed D2D approach offers very significant throughput gains.

Network localization is a key feature in many wireless services and applications. In typical range-based non-cooperative localization techniques, agents try to perform position estimation through ranging with respect to anchors with known positions. Based on the definition of squared positional error bound, the localization accuracy can be determined by the transmit power, carrier frequency, and signal bandwidth. This paper analyzes the joint power and spectrum allocation (JPSA) optimization problems in resource restricted wireless localization systems. We first formulate both optimal interference-free and interference affected JPSA problems. We then formulate the robust counterparts of the problems in the presence of uncertainty of the agents' positions. Since all JPSA problems are non-convex, we show that they can be modeled and solved as geometric programming (GP) by proper approximations. Numeric results validate our analysis and show that the developed algorithms are able to find solutions close to the global optimum in the investigated cases. We can find the optimal/robust resource deployment in non-cooperative wireless localization networks based on the proposed frameworks.

We suggest and analyze algorithms for routing in multi-hop wireless ad-hoc networks that exploit mutual information accumulation as the physical layer transmission scheme, and are capable of routing multiple packet streams (commodities) when only the average channel state information is present, and that only locally. The proposed algorithms are the modifications of the diversity backpressure (DIVBAR) algorithm, under which the packet whose commodity has the largest “backpressure metric” is chosen to be transmitted and is forwarded through the link with the largest differential backlog (queue length). In contrast to traditional DIVBAR, each receiving node stores and accumulates the partially received packet in a separate “partial packet queue”, thus increasing the probability of successful reception during a later possible retransmission. We present two variants of the algorithm: DIVBAR-renewal mutual information accumulation (RMIA), under which all the receiving nodes clear the received partial information of a packet once one or more receiving nodes firstly decode the packet; and DIVBAR-full mutual information accumulation (FMIA), under which all the receiving nodes retain the partial information of a packet until the packet has reached its destination. We characterize the network capacity region with the RMIA transmission scheme and prove that (under certain mild conditions) it is strictly larger than the network capacity region with the repetition (REP) transmission scheme that is used by the traditional DIVBAR. We also prove that DIVBAR-RMIA is throughput-optimal among the policies with RMIA, i.e., it achieves the network capacity region with RMIA, which in turn demonstrates that DIVBAR-RMIA outperforms traditional DIVBAR with respect to the achievable throughput. Moreover, we prove that DIVBAR-FMIA performs at least as well as DIVBAR-RMIA with respect to throughput. Simulations also confirm these results.

We consider a wireless device-to-device (D2D) network where communication is restricted to be single-hop. Users make arbitrary requests from a finite library of files and have pre-cached information on their devices, subject to a per-node storage capacity constraint. A similar problem has already been considered in an infrastructure setting, where all users receive a common multicast (coded) message from a single omniscient server (e.g., a base station having all the files in the library) through a shared bottleneck link. In this paper, we consider a D2D infrastructureless version of the problem. We propose a caching strategy based on deterministic assignment of subpackets of the library files, and a coded delivery strategy where the users send linearly coded messages to each other in order to collectively satisfy their demands. We also consider a random caching strategy, which is more suitable to a fully decentralized implementation. Under certain conditions, both approaches can achieve the information theoretic outer bound within a constant multiplicative factor. In our previous work, we showed that a caching D2D wireless network with one-hop communication, random caching, and uncoded delivery (direct file transmissions) achieves the same throughput scaling law of the infrastructure-based coded multicasting scheme, in the regime of large number of users and files in the library. This shows that the spatial reuse gain of the D2D network is order-equivalent to the coded multicasting gain of single base station transmission. It is, therefore, natural to ask whether these two gains are cumulative, i.e., if a D2D network with both local communication (spatial reuse) and coded multicasting can provide an improved scaling law. Somewhat counterintuitively, we show that these gains do not cumulate (in terms of throughput scaling law). This fact can be explained by noticing that the coded delivery scheme creates messages that are useful to multiple nodes, such that it benefits from broadcasting to as many nodes as possible, while spatial reuse capitalizes on the fact that the communication is local, such that the same time slot can be reused in space across the network. Unfortunately, these two issues are in contrast with each other.

On-demand video streaming is becoming a killer application for wireless networks. Recent information-theoretic results have shown that a combination of caching on the users' devices and device-to-device (D2D) communications yields throughput scalability for very dense networks, which represent critical bottlenecks for conventional cellular and wireless local area network (WLAN) technologies. In this paper, we consider the implementation of such caching D2D systems where each device pre-caches a subset of video files from a library, and users requesting a file that is not already in their library obtain it from neighboring devices through D2D communication. We develop centralized and distributed algorithms for the delivery phase, encompassing a link scheduling and a streaming component. The centralized scheduling is based on the max-weighted independent set (MWIS) principle and uses message-passing to determine max-weight independent sets. The distributed scheduling is based on a variant of the FlashLinQ link scheduling algorithm, enhanced by introducing video-streaming specific weights. In both cases, the streaming component is based on a quality-aware stochastic optimization approach, reminiscent of current Dynamic Adaptive Streaming over HTTP (DASH) technology, for which users sequentially request video “chunks” by choosing adaptively their quality level. The streaming and the scheduling components are coupled by the length of the users' request queues. Through extensive system simulation, the proposed approaches are shown to provide sizeable gains with respect to baseline schemes formed by the concatenation of off-the-shelf FlashLinQ with proportional fair link scheduling and DASH at the application layer.

We consider the problem of acquiring channel state information (CSI) in base-station (BS) controlled device-to-device (D2D) networks. Obtaining high-quality CSI requires a tradeoff between interference, outdatedness of CSI, and noise. Thus, the goal is to find an efficient pilot scheduling scheme that minimizes errors in the estimates. In this paper, we present the location aware training scheme (LATS) as simple yet efficient training technique. Assuming that the devices are aware of their location, LATS groups the devices into geographical segments and assigns a frequency reuse pattern to them. To identify the parameters of the scheme (segmentation and guard parameters, Snand Gn, respectively), we use the average normalized mean square error (NMSE) as a metric, which combines the effects of outdatedness, noise, and interference. We derive an approximation of the average NMSE based on statistics of the devices and LATS structure. We present simulation results that illustrate LATS behavior and show that it outperforms TDMA- and CSMA-based schemes. Furthermore, we consider some practical challenges in using location information and evaluate their effects on the scheme.

With growing base-station density and decreasing frequency reuse factor, intercell interference and low cell-edge user rates are becoming serious problems. Legacy solutions like fractional frequency reuse are simple to implement but are suboptimal. In this paper, we investigate interlaced clustering as a solution to the edge user problem for a general distributed cellular transmission system. In interlaced clustering, several different coverage patterns coexist on disjoint parts of the spectrum. We demonstrate how various previously suggested network architectures can be interpreted as special cases of interlaced clustering. We then characterize the downlink user throughputs at the proportional fairness operating point of the rate region for both of the cases that the transmitter does, or does not, have channel state information. Based on this derivation, we develop a novel algorithm to solve the resource allocation problem for systems with interlaced clustering. Simulations based on practical cell parameters show that interlaced clustering can provide, on an average, a 100% gain on edge user rate without appreciable loss in rates elsewhere. We also verify that this result is robust to irregular deployment of the remote antenna units.

Many practical communication systems have some form of restricted precoding or postcoding, such as antenna selection, selection combining, beam selection, and limited feedback precoding, to name a few. The capacity analysis of such systems is, in general, difficult and previous works in the literature provide results only for certain simplified cases. This paper derives a novel approach to analyze the capacity for such systems under a very generic setting. The results are based on asymptotic closed-form expressions for the second-order statistics and joint distributions of eigenvalues for a set of coupled, doubly correlated Wishart matrices. A tight approximation to the joint distribution of the eigenvalues in the non-asymptotic regime is also proposed. These results are then used to show that the system capacity can be approximated as the largest element of a correlated Gaussian vector. Showing that this is equivalent to the problem of finding the distribution of sum of lognormals, we propose a novel approach to characterize its distribution. As an application, the capacity for an antenna selection system and a limited feedback precoding system is compared with their respective approximations. This paper also demonstrates how the results can be used to design the precoding codebook in limited feedback systems.

Cooperative localization can enhance the accuracy of wireless network localization by incorporating range information among agent nodes in addition to those between agents and anchors. In this paper, we investigate the optimal allocation of the restricted resources, namely, power and bandwidth, to different nodes. We formulate the optimization problems for both synchronous networks and asynchronous networks, where one way and round trip measurements are applied for range estimation, respectively. Since the optimization problems are nonconvex, we develop an iterative linearization-based technique, and show by comparison with brute-force search that it provides near-optimal performance in the investigated cases. We also show that especially in the case of inefficient anchor placement and/or severe shadowing, cooperation among agents is important and more resources should be allocated to the agents correspondingly.

Abstract Wireless localization systems based on determination of signal runtime, such as time of arrival and time difference of arrival, are of great importance for a variety of applications. In many cases, synchronization of the clocks of the agent nodes to those of the network nodes (anchors) has to be performed together with the localization. The current paper investigates the fundamental accuracy limits of such a joint localization/synchronization. In particular, we analyze the impact of allocating power to the different anchor nodes and optimize this power allocation to maximize accuracy. Optimal trade‐offs between localization and synchronization can be drawn according to the presented frameworks. Simulation results confirm the importance of proper power allocation; known special cases (time difference of arrival localization, localization with already‐synchronized clocks) are recovered from our general solution. Copyright © 2016 John Wiley & Sons, Ltd.

3-dimensional Multiple-Input Multiple-Output (3D MIMO) systems have received great interest recently because of the spatial diversity advantage and capability for full-dimensional beamforming, making them promising candidates for practical realization of massive MIMO. For 3D MIMO system design, it is important to have full characterization of the 3D MIMO propagation channel, i.e., characterize both the elevation and azimuth characteristics of the wireless propagation channel, especially at the base station end. In this paper, we present first results from a measurement campaign for obtaining these characteristics. For those measurements we use a hybrid switched/virtual cylindrical array with 480 antenna elements at the base station (BS), and a switched array with 24 antenna elements at the user equipment (UE). We perform outdoor-to-indoor (O2I) channel measurements in an urban macro-cellular (UMa) and a micro-cellular (UMi) environments. We provide the elevation and azimuth angular spreads at the transmitter and receiver, and study their dependence on the UE height. With the increase in the UE height, the BS elevation spreads decreased from 1.24 deg to 1 deg and 1.15 deg to 0.78 deg respectively for UMa and UMi; the azimuth spreads remained approximately the same (7.5 deg). Based on the measurements done with the UE placed on different floors, we study the feasibility of separating users in the elevation domain. Users were separable in 44% and 54.17% scenarios respectively for the UMa and UMi environments.

Polarization diversity is an important candidate for improving the channel capacity in multiple-input multiple-output (MIMO) communication systems. The use of polarization-agile antennas enables to reap important benefits of polarization diversity without the need for additional up/down-conversion chains and their associated cost and energy consumption. In this paper we propose a novel scheme of polarization pre-post coding based on the theoretical (approximate) closed-form derivation of optimal transmit/receive polarization vectors to maximize channel capacity when polarization agility is available only at one link end. We use those as basis for an iterative joint polarization pre-post coding for polarization agility at both link ends. Simulations validate that the obtained capacity is in a close agreement with that obtained by exhaustive numerical search for optimal transmit/receive polarization orientation. Utilizing our scheme of joint polarization pre-post coding improves system performance by 3 dB to 5 dB in terms of the SNR.

We introduce a new neighbor discovery method for wireless nodes with adaptive antennas, called "directional ZigZag." Adaptive antennas are capable of electronically changing their gain pattern and in particular form beams and steer them in arbitrary directions. Despite improving range, this makes neighbor discovery more difficult. We consider three cases; namely, nodes transmit with beamsteering and receive in omni-directional mode (DTOR), transmit with omni-directional antenna and receive with beam- steering (OTDR), and use beamsteering (directional antennas) for both transmitting and receiving (DTDR). We show that directional ZigZag detects the neighbors with vanishing probability of error. Furthermore, it is the first algorithm that requires a discovery period that scales on linearly with the average number of neighbors. This is proven through establishing a connection between ZigZag and message passing decoding.

In this paper, we consider joint routing, scheduling, and resource allocation to maximize the throughput of OFDMA based wireless ad-hoc networks subject to MAC layer and network layer constraints. Comparing with previous work (e.g., Rashtchi et al., ICC 2012) that assumes each subchannel to be orthogonally accessed by all network links through time sharing, we schedule the orthogonal multiple access (e.g., CDMA) among the outgoing links of each node to each subchannel and treat the interferences caused by other nodes as noise. We propose an iterative heuristic approach to decompose the original problem into subproblems, each of which can be solved approximately through linearization. Simulations demonstrate that, particularly in large networks, our proposed cross-layer design significantly outperforms the previously proposed “orthogonal-only” access.

Low signal strength and high interference lead to significantly reduced data-rates at the cell edge. With rising demand for spectrum and systems moving closer to universal frequency reuse, the problem has become more pronounced. Techniques like Fractional Frequency Reuse boost the edgeuser performance at the cost of the spectral efficiency and are therefore sub-optimal. In this paper we investigate interlaced clustering as a solution to the edge user problem for a general distributed cellular transmission system, and explore a multicell Distributed Antenna System as a particular example. In interlaced clustering, different parts of the spectrum use coverage patterns that are spatially shifted (by less than a cell size) replicas of each other. An information theoretic analysis is presented to characterize the proportional fairness boundary point of the achievable rate region. In the process, the joint resource allocation problem is formulated and shown to be convex. As opposed to using interior point methods which are relatively slower, the current paper proposes a novel gradient-search algorithm to solve the resource allocation problem. It is demonstrated that fractional frequency reuse can in fact be represented as a special (albeit sub-optimal) case of interlaced clustering. Simulation results show that interlaced clustering can boost the edge-user rates by a factor of 2 with negligible degradation of rates in the cell interior. Results also show that interlaced clustering outperforms the edge-user rates achieved with fractional frequency reuse by a factor of 1.5. The theoretical results are validated by comparing performance to a practical proportional fairness scheduler.

This paper presents a detailed description of a propagation channel measurement campaign performed in a warehouse environment and provide a comprehensive channel model for this environment. Using a vector network analyzer, we explored both line-of-sight (LOS) and non-line-of-sight (NLOS) scenarios over a 2-8 GHz frequency range. We extracted both small-scale and large-scale channel parameters such as distance-dependent path loss exponent, frequency-dependent path loss exponent, shadowing variance, and amplitude fading statistics of the channel. We also provide the clustering analysis of the channel impulse responses by using a modified Saleh-Valenzuela approach. Our model is validated by comparing the distributions of the root-mean-square (RMS) delay spread obtained from our model and measurement data, respectively. The model developed can be used for realistic performance evaluations of ultrawideband (UWB) communications and localization systems in warehouse environments.

Accurate wireless timing synchronization has been an extremely important topic in wireless sensor networks, required in applications ranging from distributed beam forming to precision localization and navigation. However, it is very challenging to realize, in particular when the required accuracy should be better than the runtime between the nodes. This work presents, to our knowledge for the first time, an experimental timing synchronization scheme that achieves a timing accuracy better than 5-ns rms in a network with 4 nodes. The experimental hardware is built from commercially available components and based on software-defined ultra-wideband transceivers. The protocol for establishing the synchronization is based on our recently developed “blink” protocol that can scale from the small network demonstrated here to larger networks of hundreds or thousands of nodes.

This paper reports on an experimental measurement and analysis of multipath-induced intra- and interchannel crosstalk effects in a mm-wave communications link using orbital angular momentum multiplexing at 28 GHz. The reflection is from an ideal reflector parallel to the propagation path. The intra-channel crosstalk effect is measured when a single OAM beam is transmitted, and inter-channel crosstalk effect is measured when 2 multiplexed OAM beams are transmitted. Both simulation and experimental results show that OAM channels with larger OAM number ℓ tend to have stronger intra-channel crosstalk because less power is received from the direct path and more power is received from the reflected path. This effect is caused by OAM beam divergence, as OAM beams with larger ℓ spread into a larger beam size and have less power in the beam center. For the same reason, OAM beams of larger ℓ lead to stronger inter-channel crosstalk with the other OAM channels.

We present the design and performance characterization of a thin metamaterial plate for generation of orbital angular momentum (OAM) modes of a millimeter-wave beam, which can carry independent data streams over the same physical medium. The plate has a thickness of 1.56 mm, and consists of 3.06 × 0.68 mm rectangular apertures with spatial variant orientations. It generates OAM beams l = +1 and l = +3 with mode purity larger than 77.5% over a bandwidth of 6 GHz (25-31 GHz). We then use these streams to experimentally demonstrate a 16-Gbit/s millimeter-wave wireless communications link using two multiplexed OAM modes, each carrying a 2-Gbaud 16-QAM signal. A channel crosstalk less than -20 dB over a bandwidth of 4 GHz (26-30 GHz) and biterror-rates (BER) less than 3.8 × 10-3are achieved.

When a mobile station moves along a trajectory, it will see different parts of the same scatterers or scatterer groups during its movement. In this paper, we present a new method to identify such physical clusters of scatterers, and the corresponding groups of multipath components (MPCs) interacting with those clusters, from measurements of channel impulse responses. The method is based on identifying MPCs that have similar long-term properties - in the sense that they ``effectively interact with'' the same physical clusters, The method consists of four steps: (1) estimate the delays, the DOAs and the amplitudes of the MPCs in each time snapshot; (2) track the MPCs in the time-delay domain; (3) localize all the scattering points in a 2-D Cartesian coordinate system and cluster them on a map with a traditional Kmeans clustering algorithm; (4) merge the scattering points of different MPCs into physical clusters. The method is evaluated using both synthetic data and real measurement data from a suburban area. In the latter case, the locations and the sizes of the interacting objects identified from the measurements show excellent agreement with the location of physical objects in the environment such as pillars and buildings.

This paper revisits estimation and tracking of channel parameters of multiple-input multiple-output (MIMO) systems. Since initialization of channel parameters has always been regarded as a computationally complex problem, we develop a new parameter initialization method that exploits the nature of the data structure of channel measurements. For medium or large problems it leads to a more than 500 times runtime acceleration for parameter initialization. We show that the runtime of the new initialization method increases much more slowly compared with that of the traditional initialization method when the dimensions of measurement data or search grids grow.

We consider a wireless device-to-device (D2D) network where the nodes have precached information from a library of available files. Nodes request files at random. If the requested file is not in the on-board cache, then it is downloaded from some neighboring node via one-hop local communication. An outage event occurs when a requested file is not found in the neighborhood of the requesting node, or if the network admission control policy decides not to serve the request. We characterize the optimal throughput-outage tradeoff in terms of tight scaling laws for various regimes of the system parameters, when both the number of nodes and the number of files in the library grow to infinity. Our analysis is based on Gupta and Kumar protocol model for the underlying D2D wireless network, widely used in the literature on capacity scaling laws of wireless networks without caching. Our results show that the combination of D2D spectrum reuse and caching at the user nodes yields a per-user throughput independent of the number of users, for any fixed outage probability in (0, 1). This implies that the D2D caching network is scalable: even though the number of users increases, each user achieves constant throughput. This behavior is very different from the classical Gupta and Kumar result on ad hoc wireless networks, for which the per-user throughput vanishes as the number of users increases. Furthermore, we show that the user throughput is directly proportional to the fraction of cached information over the whole file library size. Therefore, we can conclude that D2D caching networks can turn memory into bandwidth (i.e., doubling the on-board cache memory on the user devices yields a 100% increase of the user throughout).

Vehicular communication channels are characterized by a non-stationary time- and frequency-selective fading process due to fast changes in the environment. We characterize the distribution of the envelope of the first delay bin in vehicle-to-vehicle channels by means of its Rician$K$-factor. We analyze the time–frequency variability of this channel parameter using vehicular channel measurements at 5.6 GHz with a bandwidth of 240 MHz for safety-relevant scenarios in intelligent transportation systems (ITS) . This data enables a frequency-variability analysis from an IEEE 802.11p system point of view, which uses 10 MHz channels. We show that the small-scale fading of the envelope of the first delay bin is Rician distributed with a varying$K$-factor. The later delay bins are Rayleigh distributed. We demonstrate that the$K$-factor cannot be assumed to be constant in time and frequency. The causes of these variations are the frequency-varying antenna radiation patterns, as well as the time-varying number of active scatterers, and the effects of vegetation. We also present a simple but accurate bimodal Gaussian mixture model, which allows to capture the$K$-factor variability in time for safety-relevant ITS scenarios.

The high-speed railway (HSR) propagation channel has a significant impact on the design and performance analysis of wireless railway control systems. This paper derives a stochastic model for the HSR wireless channel at 930 MHz. The model is based on a large number of measurements in 100 cells using a practically deployed and operative communication system. We use the Akaike information criterion to select the distribution of the parameter distributions, including the variations from cell to cell. The model incorporates the impact of directional base station (BS) antennas, includes several previously investigated HSR deployment scenarios as special cases, and is parameterized for practical HSR cell sizes, which can be several kilometers. The proposed model provides a consistent prediction of the propagation in HSR environments and allows a straightforward and time-saving implementation for simulation.

It is well known that the performance of frequency-division-duplex (FDD) massive MIMO systems with i.i.d. channels is disappointing compared with that of time-division-duplex (TDD) systems, due to the prohibitively large overhead for acquiring channel state information at the transmitter (CSIT). In this paper, we investigate the achievable rates of FDD massive MIMO systems with spatially correlated channels, considering the CSIT acquisition dimensionality loss, the imperfection of CSIT and the regularized-zero-forcing linear precoder. The achievable rates are optimized by judiciously designing the downlink channel training sequences and user CSIT feedback codebooks, exploiting the multiuser spatial channel correlation. We compare our achievable rates with TDD massive MIMO systems, i.i.d. FDD systems, and the joint spatial division and multiplexing (JSDM) scheme, by deriving the deterministic equivalents of the achievable rates, based on the one-ring model and the Laplacian model. It is shown that, based on the proposed eigenspace channel estimation schemes, the rate-gap between FDD systems and TDD systems is significantly narrowed, even approached under moderate number of base station antennas. Compared to the JSDM scheme, our proposal achieves dimensionality-reduction channel estimation without channel pre-projection, and higher throughput for moderate number of antennas and moderate to large channel coherence block length, though at higher computational complexity.

This paper reports an experimental demonstration of a 16 Gbit/s millimeter-wave communication link using MIMO processing of 2 OAM modes on each of two transmitter/receiver antenna apertures. Two groups of multiplexed OAM beams, each containing OAM beams of ℓ =1 and +3 are generated and transmitted through two transmitter apertures respectively. The two transmitter apertures are separated with a certain distance such that the two groups of OAM beams are spatially overlapping at the receiver aperture plane. Each channel carries 1-GBaud 16-QAM signals at the same carrier frequency of 28 GHz. Our experimental results show that MIMO equalization processing can help mitigate the interferences from the other OAM channels and the BER performance of each channel improves significantly after MIMO processing. Our results indicate that OAM multiplexing and traditional spatial multiplexing combined with MIMO processing can be compatible and complementary with each other.

Vehicle-to-Vehicle (V2V) communication is an en-abler for improved traffic safety and congestion control. As for any wireless system the ultimate performance limit is determined by the propagation channel. A particular point of interest is the shadowing effect of large vehicles such as trucks and buses, as this might affect the communication range significantly. In this paper we present measurement results and model the propagation channel in which a bus acts as a shadowing object between two passenger cars. The measurement setup is based on a WARP FPGA software radio as transmitter, and a Tektronix RSA5106A real-time complex spectrum analyzer as receiver. We analyze the influence of the bus location and car separation distance on the large-scale path loss, shadowing, and small-scale fading. The main effect of the bus is that it is acting as an obstruction creating an additional 15–20 dB attenuation. A Nakagami distribution is found to describe the statistics of the small-scale fading, by using Akaike's Information Criterion and the Kolmogorov-Smirnov test. The distance-dependency of the path loss is analyzed, and a stochastic model is developed to reflect the impact.

Millimeter-wave (mm-wave) transmission is a promising method for increasing capacity in next-generation cellular systems. However, since mm-wave signals are too weak to (i) do long-distance communication and (ii) survive in non-line-of-sight situations, multi-hop relaying is required. We thus study in this paper a multi-hop routing protocol at mm-wave (38 GHz and 28GHz) frequencies. Due to the great and steadily increasing importance of video streaming in cellular applications, we pay special attention to the maximization of video transmission quality, which makes the optimization problem different from the usual sum-rate maximization. Specifically, we develop a protocol that optimizes the “sum quality” of the transmission of different video streams, subject to constraints (minimum quality) of separate streams. We take the unique properties of mm-waves (sparse angular power spectrum, leading to low interference) into account in our method. Extensive simulation results show the superiority of our approach compared to max-min flow routing which is widely used in QoS-sensitive applications.

Ultra-wideband (UWB) technology is a good candidate to provide accurate position information indoors and in dense urban environments where Global Positioning System (GPS) is usually not reliable. This paper provides the results of a UWB ranging measurement campaign carried out in a dense urban environment. Measurements were taken with two different antenna heights in line-of-sight (LOS) and non-line-of-sight (NLOS) conditions. It is observed that the ranging errors increases when the antennas are closer to the ground, and is more significant in NLOS conditions. Also, in NLOS conditions ranging errors of more than 10 meters were observed when the LOS component is completely blocked by a building. Errors of such magnitude are typically not captured by IEEE 802.15.4a CM6 (outdoor NLOS) channel model. Since the conventional thresholding schemes provide bad ranging accuracy in presence of multiuser interference (MUI), we propose a nonlinear processing scheme of time-hopping impulse radio (TH-IR) and apply it to our measurements to show that it gives much better ranging accuracy.

This paper reports an experimental demonstration of modal multiplexing using orbital angular momenta (OAM) in the millimeter-wave regime (28 GHz). Using 1 GBaud/s signals with 16-QAM modulation, a data rate of 32 Gbit/s is obtained by employing 4 different OAM modes (l= -3,-1,+1 and +3) on two orthogonal linear polarization states. Without any digital post-processing, the receiver can separate the different data streams since crosstalk among the OAM channels is low: measured to be better than -23dB with a 28 GHz narrowband carrier on a single polarization, and better than -16dB on both polarization states. The bit-error rate (BER) of 8 channels is measured, and found the raw BER is below 3.8×10-3, thus allowing efficient use of forward error correction (FEC) codes.

Localization of wireless node is a key feature in many applications. Traditional localization has exploited the signal runtime between “agent” nodes that are to be localized and a set of “anchor” nodes with known position. Recently, cooperative localization that also uses runtime measurement between agent nodes has been shown to provide superior performance. This paper analyzes the optimum power and bandwidth allocation in such systems. We first formulate the general optimization problem and show that it is non-convex. We then develop an approximate algorithm based on Taylor expansion and iterative optimization of power and bandwidth separately to find an approximate solution; simulations show that results are close to the optimum solution (which is NP-hard). We also find that the importance of cooperative localization increases (and agents get assigned more resources) if the anchor deployment is bad in the sense that it provides high geometric dilution of precision and/or suffers from significant blockage between anchors and agents.

There has been an increasing interest in Full- Dimension (FD) MIMO, i.e., exploiting the elevation domain (in addition to the azimuth domain) for increasing the capacity of multi-antenna systems. A first prerequisite for designing FD MIMO is an understanding of the elevation characteristics of wireless propagation channels, in particular elevation at the base station. This paper utilizes a state-of-art ray-tracing simulation software to investigate and quantify these elevation characteristics in urban macro cellular (UMa) environments. We study in detail the height and distance dependency of these channel parameters such as mean elevation angle and elevation spread. More importantly we find that several of these parameters exhibit markedly different characteristics depending on whether the UEs are close to exterior walls or they are deeper inside the buildings. Thus, the traditional method of "factoring" the propagation into an outdoor part and an indoor part is not applicable, and we propose more accurate fitting equations.

Massive MIMO systems are well-suited for mm-Wave communications, as large arrays can be built with reasonable form factors, and the high array gains enable reasonable coverage even for outdoor communications. One of the main obstacles for using such systems in frequency-division duplex mode, namely, the high overhead for the feedback of channel state information (CSI) to the transmitter, can be mitigated by the recently proposed joint spatial division and multiplexing (JSDM) algorithm. In this paper, we analyze the performance of this algorithm in somerealisticpropagation channels that take into account the partial overlap of the angular spectra from different users, as well as the sparsity of mm-Wave channels. We formulate the problem of user grouping for two different objectives, namely, maximizing spatial multiplexing and maximizing total received power in a graph-theoretic framework. As the resulting problems are numerically difficult, we proposed (sub optimum) greedy algorithms as efficient solution methods. Numerical examples show that the different algorithms may be superior in different settings. We furthermore develop a new, “degenerate” version of JSDM that only requires average CSI at the transmitter and thus greatly reduces the computational burden. Evaluations in propagation channels obtained from ray tracing results, as well as inmeasuredoutdoor channels, show that this low-complexity version performs surprisingly well in mm-Wave channels.

This paper aims to explore the potential of a high spectrum efficiency (SE) technology, coordinated multi-point (CoMP) transmission, for improving energy efficiency (EE) of downlink cellular networks. To this end, a traffic-aware mechanism, named cooperative base station (BS) doze, is introduced and optimized. The key idea is to allow BS idling by exploiting the delay tolerance of some users as well as the short-term spatio-temporal traffic fluctuations in the network, and to increase the opportunity of the idling by using CoMP transmission. The cooperative BS doze strategy involves BS time-slot doze pattern, and multicell user scheduling and cooperative precoding with different amount of data sharing, which are jointly optimized in a unified framework. To ensure various performance requirements of multiple users including delay tolerance and data rate, we maximize the network EE under different time-average rate constraints for different users, where the consumptions on transmit power, circuitry power and backhauling power are taken into account. We then propose a hierarchical iterative algorithm to solve the optimization problem. Simulations under practical power consumption parameters demonstrate that cooperative BS doze can provide substantial EE gain and support high data rate services with high achievable SE.

We analyze a novel architecture for caching popular video content to enable wireless device-to-device (D2D) collaboration. We focus on the asymptotic scaling characteristics and show how they depend on video content popularity statistics. We identify a fundamental conflict between collaboration distance and interference and show how to optimize the transmission power to maximize frequency reuse. Our main result is a closed form expression of the optimal collaboration distance as a function of the model parameters. Under the common assumption of a Zipf distribution for content reuse, we show that if the Zipf exponent is greater than 1, it is possible to have a number of D2D interference-free collaboration pairs that scales linearly in the number of nodes. If the Zipf exponent is smaller than 1, we identify the best possible scaling in the number of D2D collaborating links. Surprisingly, a very simple distributed caching policy achieves the optimal scaling behavior.

Vehicle-to-vehicle (V2V) communication is an enabler for improved traffic safety and congestion control. As for any wireless system, the ultimate performance limit is determined by the propagation channel. A particular point of interest is the shadowing effect of large vehicles such as trucks and buses, as this might affect the communication range significantly. In this paper we present measurement results and model the propagation channel, in which a bus acts either as a shadowing object or as a relay between two passenger cars. The measurement setup is based on a Wireless Open-Access Research Platform (WARP) Field-Programmable Gate Array (FPGA) software radio as transmitter and a Tektronix RSA5106A real-time complex spectrum analyzer as receiver. We analyze the influence of the bus location and car separation distance on the path loss, shadowing, small-scale fading, delay spread, and cross correlation. The main effect of the bus is that it is acting as an obstruction creating an additional 15- to 20-dB attenuation and an increase in the root-mean-square delay spread by roughly 100 ns. A Nakagami distribution is found to well describe the statistics of the small-scale fading, by using Akaike's Information Criterion and the Kolmogorov-Smirnov test. The distance dependence of the path loss is analyzed and a stochastic model is developed.

We propose a new scheme for increasing the throughput of video files in cellular communications systems. This scheme exploits (1) the redundancy of user requests as well as (2) the considerable storage capacity of smartphones and tablets. Users cache popular video files and-after receiving requests from other users-serve these requests via device-to-device localized transmissions. The file placement is optimal when a central control knows a priori the locations of wireless devices when file requests occur. However, even a purely random caching scheme shows only a minor performance loss compared to such a “genie-aided” scheme. We then analyze the optimal collaboration distance, trading off frequency reuse with the probability of finding a requested file within the collaboration distance. We show that an improvement of spectral efficiency of one to two orders of magnitude is possible, even if there is not very high redundancy in video requests.

Roundtrip time-of-arrival (ToA) measurements employing ultra-wideband (UWB) signals can provide high-precision ranging information. However, the accuracy is degraded by multiuser interference (MUI), in particular in the presence of multipath propagation. While the processing gain of time-hopping impulse radio (TH-IR) can be used to suppress the MUI, this is often insufficient. We propose instead a nonlinear processing scheme of TH-IR that effectively suppresses MUI without requiring knowledge of the time-hopping sequences of the interfering users. The principle is that multipath components (MPCs) of interferers do not align closely, for the majority of transmission frames, with the MPCs of the desired signal. Through a judicious choice of algorithm parameters we show that our algorithm is superior to existing (realizable) thresholding and median filter algorithms, and in some cases can even beat genie-aided thresholding algorithms. The performance is robust to both strength and number of the interferers. The results are validated with both standardized 802.15.4a channel models and measured outdoor UWB channels.

Large-scale distributed Multiuser MIMO (MU-MIMO) is a promising wireless network architecture that combines the advantages of "massive MIMO" and "small cells." It consists of several Access Points (APs) connected to a central server via a wired backhaul network and acting as a large distributed antenna system. We focus on the downlink, which is both more demanding in terms of traffic and more challenging in terms of implementation than the uplink. In order to enable multiuser joint precoding of the downlink signals, channel state information at the transmitter side is required. We consider Time Division Duplex (TDD), where the downlink channels can be learned from the user uplink pilot signals, thanks to channel reciprocity. Furthermore, coherent multiuser joint precoding is possible only if the APs maintain a sufficiently accurate relative timing and phase synchronization. AP synchronization and TDD reciprocity calibration are two key problems to be solved in order to enable distributed MU-MIMO downlink. In this paper, we propose novel over-the-air synchronization and calibration protocols that scale well with the network size. The proposed schemes can be applied to networks formed by a large number of APs, each of which is driven by an inexpensive 802.11-grade clock and has a standard RF front-end, not explicitly designed to be reciprocal. Our protocols can incorporate, as a building block, any suitable timing and frequency estimator. Here we revisit the problem of joint ML timing and frequency estimation and use the corresponding Cramer-Rao bound to evaluate the performance of the synchronization protocol. Overall, the proposed synchronization and calibration schemes are shown to achieve sufficient accuracy for satisfactory distributed MU-MIMO performance.

This paper addresses the problem of multi-target localization with distributed multiple-input-multiple-output (MIMO) radar systems without angular resolution capability-i.e., only based on the measurements of the signal propagation delays. In particular, we address the case that multiple targets cannot be distinguished by their “signatures” or other unique characteristics. Further complications in realistic settings arise from the following factors: 1) the direct path (DP) between transmitter/receiver and target might be blocked, and 2) indirect paths (IPs) arising from multi path propagation might be present. We present in this paper a novel algorithm that can accurately localize multiple targets even under these difficult circumstances. This algorithm is based on an iterative approximation to an exhaustive maximum likelihood (EML) estimation of the target locations, yet avoiding the exponential complexity that an EML requires. It utilizes a clustering technique that gives the number and locations of targets, and incorporates an IP detection mechanism based on the likelihood of several propagation delay measurement (PDM) correspond to DPs from the same target. Despite its lower complexity, our algorithm achieves almost the same performance as the EML in a variety of simulation settings. We finally study the choice of algorithm parameters and the configurations of the MIMO system nodes to achieve the best localization performance.

This paper addresses the problem of estimating the location of a target based on measurements of the signal run length from one transmitter, via the (passive) target, to multiple receivers. In particular we address the case that there are multiple targets that differ in their locations, but otherwise have the same characteristics, and thus cannot be distinguished by their “signatures” or other unique characteristics. Thus localization requires to correctly “pair” the time-of-arrivals (TOAs) of the reflections arriving at the different receivers. As a further challenge, in practical scenarios the number of the targets are unknown, and the wireless signal propagation can suffer from indirect paths (IPs). We present in this paper an algorithm that can localize the targets even under these difficult circumstances. Its steps include a clustering stage that gives the number and locations of targets, and an IP detection mechanism based on the likelihood of several TOA measurements being direct paths from the same target. The algorithm has much lower computational complexity than the exhaustive maximum likelihood estimation (EMLE), while our simulations demonstrate that it achieves almost the same performance as the EMLE.

Shutdown of low traffic load base stations (BSs) is recognized as a promising approach to increase energy-efficiency (EE) and reduce total power consumption, especially for small cell networks (SCN). In this paper, we study BS closing strategies for downlink multi-antenna multi-carrier SCN supporting best effort traffics. We formulate the optimization problem of long-term BS closing, BS-user association and subcarrier allocation to maximize EE or minimize total power consumption under the constraints of average sum rate and rate proportion. We obtain an optimal solution for maximizing EE and a suboptimal solution for minimizing total power consumption. Simulation results show that the solutions provide substantial gain both in saving power consumption and increasing EE, and minimizing the total power consumption will not lead to the maximal EE.

Wireless streaming of high-definition video is a promising application for 60GHz links, since multi-Gigabit/s data rates are possible. In particular we consider a sports stadium broadcasting system where video signals from multiple cameras are transmitted to a central location. Due to the high pathloss of 60GHz radiation over the large distances encountered in this setting, the use of relays is required. This paper designs a quality-aware coding and relaying algorithm for maximization of the overall video quality. We consider the setting that the source can split its data stream into parallel streams, which can be transmitted via different relays to the destination. For this, we derive the related formulation and re-formulate it as convex programming, which can guarantee optimal solutions.

We consider a wireless device-to-device (D2D) network where the nodes have cached information from a library of possible files. Inspired by the current trend in the standardization of the D2D mode for 4th generation wireless networks, we restrict to one-hop communication: each node places a request to a file in the library, and downloads from some other node which has the requested file in its cache through a direct communication link, without going through a base station. We describe the physical layer communication through a simple “protocol-model”, based on interference avoidance (independent set scheduling). For this network we define the outage-throughput tradeoff problem and characterize the optimal scaling laws for various regimes where both the number of nodes and the files in the library grow to infinity.

We consider a wireless Device-to-Device (D2D) network where communication is restricted to be single-hop, users make arbitrary requests from a finite library of possible files and user devices cache information in the form of carefully designed sets of packets from all files in the library. We consider the combined effect of coding in the delivery phase, achieving “coded multicast gain”, and of spatial reuse due to local short-range D2D communication. Somewhat counterintuitively, we show that the coded multicast gain and the spatial reuse gain do not cumulate, in terms of the throughput scaling laws. In particular, the spatial reuse gain shown in our previous work on uncoded random caching and the coded multicast gain shown in this paper yield the same scaling laws behavior, but no further scaling law gain can be achieved by using both coded caching and D2D spatial reuse.

This demo abstract describes an initial design of a new adaptive video streaming protocol for device-to-device WiFi-based mobile platforms and its software implementation. For the demonstration, two mobile servers and two mobile users will be deployed verifying that our device-to-device adaptive video streaming implementation works with desirable user experience.

This paper proposes a deterministic path-loss model for the open-sea environment. The model accounts for different effects including effective reflection, divergence, and diffraction due to rough sea and earth curvature. The model results show excellent agreement with experimental results from our recent measurement campaign, which investigated propagation at 2 GHz with a maximum distance of 45 km. Channel parameters like mean-square surface slope and standard deviation of surface height are evaluated, from which it can be concluded that the shadowing and scattering effects on the reflection ray will influence the fading amplitude within the distance of 0.6 First Fresnel Zone clearance.

The high-speed railway propagation channel has significant effect on the design and performance analysis of wireless railway control systems. An important feature of the high-speed railway communications is the usage of directional transmitting antennas, due to which the receiver may experience strong attenuation of the line-of-sight (LOS) path under the base station (BS). This leads to a drop, and strong variations, of the signal strength under the BS. While the physical origin of the signal variations is different from conventional shadowing, it can be described by similar statistical methods. However, the effect has been largely neglected in the literature. In this paper we first define the region of the bottom of the BS, and then present a simple shadow fading model based on the measurements performed in high-speed railways at 930 MHz. It is found that the bottom area of the BS has a range of 400 m – 800 m; the standard deviation of the shadowing also follows a Gaussian distribution; the double exponential model fits the autocovariance of the shadow fading very well. We find that the directivity of the transmitting antenna leads to a higher standard deviation of shadowing and a smaller decorrelation distance under the BS compared to the region away from the BS.

Single receive antenna selection (AS) is a popular method for obtaining diversity benefits without the additional costs of multiple radio receiver chains. Since only one antenna receives at any time, the transmitter sends a pilot multiple times to enable the receiver to estimate the channel gains of its N antennas to the transmitter and select an antenna. In time-varying channels, the channel estimates of different antennas are outdated to different extents. We analyze the symbol error probability (SEP) in time-varying channels of the N-pilot and (N + 1)-pilot AS training schemes. In the former, the transmitter sends one pilot for each receive antenna. In the latter, the transmitter sends one additional pilot that helps sample the channel fading process of the selected antenna twice. We present several new results about the SEP, optimal energy allocation across pilots and data, and optimal selection rule in time-varying channels for the two schemes. We show that due to the unique nature of AS, the (N +1)-pilot scheme, despite its longer training duration, is much more energy-efficient than the conventional N-pilot scheme. An extension to a practical scenario where all data symbols of a packet are received by the same antenna is also investigated.

Video on-demand streaming from Internet-based servers is becoming one of the most important services offered by wireless networks today. In order to improve the area spectral efficiency of video transmission in cellular systems, small cells heterogeneous architectures (e.g., femtocells, WiFi off-loading) are being proposed, such that video traffic to nomadic users can be handled by short-range links to the nearest small cell access points (referred to as “helpers”). As the helper deployment density increases, the backhaul capacity becomes the system bottleneck. In order to alleviate such bottleneck we propose a system where helpers with low-rate backhaul but high storage capacity cache popular video files. Files not available from helpers are transmitted by the cellular base station. We analyze the optimum way of assigning files to the helpers, in order to minimize the expected downloading time for files. We distinguish between the uncoded case (where only complete files are stored) and the coded case, where segments of Fountain-encoded versions of the video files are stored at helpers. We show that the uncoded optimum file assignment is NP-hard, and develop a greedy strategy that is provably within a factor 2 of the optimum. Further, for a special case we provide an efficient algorithm achieving a provably better approximation ratio of 1-(1-1/d )d, where d is the maximum number of helpers a user can be connected to. We also show that the coded optimum cache assignment problem is convex that can be further reduced to a linear program. We present numerical results comparing the proposed schemes.

This paper reports (i) a set of measurements of the wireless propagation channel at 930 MHz, conducted along the "Zhengzhou-Xian" high-speed railway of China in various railway viaduct scenarios, and (ii) an analysis and modeling of the small-scale and large-scale channel parameters based on those measurements. The environment can be categorized into four cases, covering viaducts with different heights and in different suburban environments. Small values of fade depth, level crossing rates, and average fade duration are observed. Akaike's Information Criterion (AIC)-based evaluation indicates that the Ricean distribution is the best to describe small-scale amplitude fading. An analysis of the envelope autocovariance function shows that the coherence distance is less than 10 cm. The Ricean K-factor is modeled as a piecewise-linear function of distance. Moreover, a breakpoint path loss model is developed and shadow fading is investigated using the same break point as for the distance-dependent K-factor model. The Suzuki distribution is found to offer a good fit for the composite multipath/shadowing channels. We find that the viaduct height H, together with the number of surrounding scatterers, significantly affects the small- and large-scale channel parameters. These results are applicable to both normal-speed and high-speed railways, and will be useful in the modeling of railway viaduct channels and the design of railway wireless communication systems.

Coordinated multi-point transmission (CoMP) joint processing (CoMP-JP) is a promising technique for supporting high spectral efficiency in future cellular systems. However, this technique requires high-capacity backhaul links for connecting multiple base stations (BSs), which leads to high deployment costs. CoMP coordinated beamforming (CoMP-CB) needs less backhaul capacity, but with lower maximal multiplexing gain. In this paper, we study downlink CoMP strategies with limited-capacity backhaul. We start by investigating the optimal interference-free transmission strategy under backhaul rate constraints, where only a part of the data is shared among the BSs. We then study soft and hard switching between the CoMP-JP and CoMP-CB modes. Finally, a closed-form semi-dynamic distributed hard switching scheme is proposed, which depends on the local large-scale channel gains of the users. Simulation results show that both the soft and hard mode switching transmission perform closely to the optimal strategy and outperform the single-mode CoMP transmission especially when the backhaul capacity is very limited.

Video is the main driver for the inexorable increase in wireless data traffic. In this paper we analyze a new architecture in which device-to-device (D2D) communications is used to drastically increase the capacity of cellular networks for video transmission. Users cache popular video files and - after receiving requests from other users - serve these requests via D2D localized transmissions; the short range of the D2D transmission enables frequency reuse within the cell. We analyze the scaling behavior of the throughput with the number of devices per cell. The user content request statistics, as well as the caching distribution, are modeled by a Zipf distribution with parameters γrand γc, respectively. For the practically important case γr0> 1, we derive a closed form expression for the scaling behavior of the number of D2D links that coexist without interference. Our analysis relies on a novel Poisson approximation result for wireless networks obtained through the Chen-Stein Method.

Single receive antenna selection (AS) allows single-input single-output (SISO) systems to retain the diversity benefits of multiple antennas with minimum hardware costs. We propose a single receive AS method for time-varying channels, in which practical limitations imposed by next-generation wireless standards such as training, packetization and antenna switching time are taken into account. The proposed method utilizes low-complexity subspace projection techniques spanned by discrete prolate spheroidal (DPS) sequences. It only uses Doppler bandwidth knowledge, and does not need detailed correlation knowledge. Results show that the proposed AS method outperforms ideal conventional SISO systems with perfect CSI but no AS at the receiver and AS using the conventional Fourier estimation/prediction method. A closed-form expression for the symbol error probability (SEP) of phase-shift keying (MPSK) with symbol-by-symbol receive AS is derived.

We suggest and analyze algorithms for routing in ad-hoc networks that exploit mutual-information accumulation at receiving nodes, and are capable of routing multiple data streams (commodities) when only mean channel state information is present, and that only locally. The algorithm is a generalization of the DIVBAR algorithm, which in turn is based on backpressure routing. Packets are transmitted by each node on the links seeing the largest "backpressure", a measure for the differential queue lengths for a specific commodity at the considered nodes times the success probability for packets on that link. In contrast to traditional DIVBAR, nodes store and exploit partially received packets, thus increasing the probability of successful reception at retransmission, where the information is stored in separate "partial queues" at each node. We present two variants of our algorithm: DIVBAR-RMIA, which clears the partial queues whenever a packet is firstly decoded by one or more receiving nodes; DIVBAR-MIA, which retains the information about a specific packet in the partial queues until the packet has reached its destination. We prove that DIVBAR-RMIA performs strictly better than conventional DIVBAR (under some mild assumptions about the channel states), and that DIBVAR-MIA performs at least as well as DIVBAR-RMIA. Simulations not only confirm these results, but also demonstrate the impact of packet entropy on the achievable throughput.

We propose a new scheme for increasing the throughput of video files in cellular communications systems. This scheme exploits (i) the redundancy of user requests as well as (ii) the considerable storage capacity of smartphones and tablets. Users cache popular video files and - after receiving requests from other users - serve these requests via device-to-device localized transmissions. We investigate what is the optimal collaboration distance, trading off frequency reuse with the probability of finding a requested file within the collaboration distance. We show that an improvement of spectral efficiency of one to two orders of magnitude is possible, even if there is not very high redundancy in video requests.

We suggest a novel approach to handle the ongoing explosive increase in the demand for video content in mobile devices. We envision femtocell-like base stations, which we call helpers, with weak backhaul links but large storage capabilities. These helpers form a wireless distributed caching network that assists the macro base station by handling requests of popular files that have been cached. We formalize the wireless distributed caching optimization problem for the case that files are encoded using fountain/MDS codes. We express the problem as a convex optimization. By adding additional variables we reduce it to a linear program. On the practical side, we present a detailed simulation of a university campus scenario covered by a single 3GPP LTE R8 cell and several helper nodes using a simplified 802.11n protocol. We use a real campus trace of video requests and show how distributed caching can increase the number of served users by as much as 600-700%.

Due to the fine delay resolution in ultra-wideband (UWB) wireless propagation channels, a large number of multipath components (MPC) can be resolved; and the first arriving MPC might not be the strongest one. This makes time-of-arrival (ToA) estimation, which essentially depends on determining the arrival time of the first MPC, highly challenging. In this paper, we consider non-coherent ToA estimation given a number of measurement trials, at moderate sampling rate and in the absence of knowledge of pulse shape. The proposed ToA estimation is based on detecting the presence of a signal in a moving time delay window, by using the largest eigenvalue of the sample covariance matrix of the signal in the window as the test statistic. We show that energy detection can be viewed as a special case of the eigenvalue detection. Max-eigenvalue detection (MED) generally has superior performance, due to the following reasons: 1) MED collects less noise, namely only the noise contained in the signal space, and 2) if multiple channel taps fall into the time window, the MED detector can collect energy from all of them. Simulation results confirm that MED outperforms the energy detection in IEEE 802.15.3a and 802.15.4a channels. Finally, the selection of the threshold of the MED is studied both by simulations and by random matrix theory.

We suggest a novel approach to handle the ongoing explosive increase in the demand for video content in wireless/mobile devices. We envision femtocell-like base stations, which we call helpers, with weak backhaul links but large storage capacity. These helpers form a wireless distributed caching network that assists the macro base station by handling requests of popular files that have been cached. Due to the short distances between helpers and requesting devices, the transmission of cached files can be done very efficiently. A key question for such a system is the wireless distributed caching problem, i.e., which files should be cached by which helpers. If every mobile device has only access to a exactly one helper, then clearly each helper should cache the same files, namely the most popular ones. However, for the case that each mobile device can access multiple caches, the assignment of files to helpers becomes nontrivial. The theoretical contribution of our paper lies in (i) formalizing the distributed caching problem, (ii) showing that this problem is NP-hard, and (iii) presenting approximation algorithms that lie within a constant factor of the theoretical optimum. On the practical side, we present a detailed simulation of a university campus scenario covered by a single 3GPP LTE R8 cell and several helpers using a simplified 802.11n protocol. We use a real campus trace of video requests and show how distributed caching can increase the number served users by as much as 400 - 500%.

We introduce a novel wireless device-to-device (D2D) collaboration architecture that exploits distributed storage of popular content to enable frequency reuse. We identify a fundamental conflict between collaboration distance and interference and show how to optimize the transmission power to maximize frequency reuse. Our analysis depends on the user content request statistics which are modeled by a Zipf distribution. Our main result is a closed form expression of the optimal collaboration distance as a function of the content reuse distribution parameters. We show that if the Zipf exponent of the content reuse distribution is greater than 1, it is possible to have a number of D2D interference-free collaboration pairs that scales linearly in the number of nodes. If the Zipf exponent is smaller than 1, we identify the best possible scaling in the number of D2D collaborating links. Surprisingly, a very simple distributed caching policy achieves the optimal scaling behavior and therefore there is no need to centrally coordinate what each node is caching.

The assumption of wide-sense-stationarity (WSS) and uncorrelated scattering (US) of wireless propagation channels is widespread for the design and analysis of wireless propagation channels. However, the assumption is only valid within a limited range. In this paper, we investigate the extension of this range not only in time (WSS), but also in frequency (US), for measured vehicular propagation channels. We do this by using the collinearity of the local scattering function, which is a bounded spectral distance metric, and thus allows us to set an indicative threshold, which in turn enables a WSS and a US test. We prove that the fading process in vehicular communications is strongly non-WSS, which agrees with results previously reported in the literature. Furthermore, for the first time (to the authors' knowledge), we show their non-US behavior as well, mainly in scenarios with rich scattering. The dimensions of the minimum stationarity region are on the order of 40 ms in time and 40MHz in frequency.

Coordinated multi-point transmission (CoMP) has been widely recognized as a promising technique for improving spectral efficiency in future cellular systems. However, it may require high-capacity backhaul links, which can increase expense. In this paper, we study the cooperative transmission method with limited-capacity backhaul. Downlink CoMP transmission requires the exchange of information between base stations, depending on the type of CoMP schemes. Either channel state information (CSI) information of all users (for coordinated beamforming), or both data and CSI (for joint processing) need to be shared among all cooperative base stations. The latter method exhibits better performance, but requires high capacity backhaul links. For this reason, we propose in this paper a transmission mode selection method under capacity constrained backhaul. We start by analyzing the performance of two kinds of CoMP transmission modes: joint processing and coordinated beamforming with various types of user scheduling. Then we propose a closed-form decision rule, which depends on the strongest average channel gains of the users co-scheduled in the same time-frequency resource. Simulation results show that the proposed transmission mode selection outperforms the single-mode CoMP transmission.

Wireless technology enables novel approaches to healthcare, in particular the remote monitoring of vital signs and other parameters indicative of people's health. This paper considers a system scenario relevant to such applications, where a smart-phone acts as a data-collecting hub, gathering data from a number of wireless-capable body sensors, and relaying them to a healthcare provider host through standard existing cellular networks. Delay of critical data and sensors' energy efficiency are both relevant and conflicting issues. Therefore, it is important to operate the wireless body-area sensor network at some desired point close to the optimal energy-delay tradeoff curve. This tradeoff curve is a function of the employed physical-layer protocol: in particular, it depends on the multiple-access scheme and on the coding and modulation schemes available. In this work, we consider a protocol closely inspired by the widely-used Bluetooth standard. First, we consider the calculation of the minimum energy function, i.e., the minimum sum energy per symbol that guarantees the stability of all transmission queues in the network. Then, we apply the general theory developed by Neely to develop a dynamic scheduling policy that approaches the optimal energy-delay tradeoff for the network at hand. Finally, we examine the queue dynamics and propose a novel policy that adaptively switches between connected and disconnected (sleeping) modes. We demonstrate that the proposed policy can achieve significant gains in the realistic case where the control "NULL" packets necessary to maintain the connection alive, have a non-zero energy cost, and the data arrival statistics corresponding to the sensed physical process are bursty.

Receive antenna selection (AS) has been shown to maintain the diversity benefits of multiple antennas while potentially reducing hardware costs. However, the promised diversity gains of receive AS depend on the assumptions of perfect channel knowledge at the receiver and slowly time-varying fading. By explicitly accounting for practical constraints imposed by the next-generation wireless standards such as training, packetization and antenna switching time, we propose a single receive AS method for time-varying fading channels. The method exploits the low training overhead and accuracy possible from the use of discrete prolate spheroidal (DPS) sequences based reduced rank subspace projection techniques. It only requires knowledge of the Doppler bandwidth, and does not require detailed correlation knowledge. Closed-form expressions for the channel prediction and estimation error as well as symbol error probability (SEP) of M-ary phase-shift keying (MPSK) for symbol-by-symbol receive AS are also derived. It is shown that the proposed AS scheme, after accounting for the practical limitations mentioned above, outperforms the ideal conventional single-input single-output (SISO) system with perfect CSI and no AS at the receiver and AS with conventional estimation based on complex exponential basis functions.

An important prerequisite for precise localization, such as in radar systems, is to discern "direct paths" (DP), i.e., multipath components (MPCs) where the signal propagates directly between the target and the localization system nodes (transmitters or receivers), from indirect paths (IPs). This paper describes how to distinguish between DPs and IPs purely based on measurements of observable propagation parameters, namely the time of arrival (TOA), direction of departure (DOD), and direction of arrival (DOA). Any combination of two of those parameters allow the computation of a reflection point, and - in the absence of noise - the points computed from the different combinations are consistent in the case of a DP. In the presence of noise, the computed points deviate from each other, and we establish rules for how large this deviation might be to consider a path an IP. Several different decision rules are discussed and compared. The paper derives closed-form equations for these decision criteria and the resulting performance. The situations when the proposed decision rules do not work well are also studied. Simulation results show that under common localization scenarios employing ultrawideband signals, the proposed algorithms can effectively detect IPs and significantly improve localization accuracy.

Localization of objects is fast becoming a major aspect of wireless technologies, with applications in logistics, surveillance, and emergency response. Time-of-arrival (TOA) localization is ideally suited for high-precision localization of objects in particular in indoor environments, where GPS is not available. This paper considers the case where one transmitter and multiple, distributed, receivers are used to estimate the location of a passive (reflecting) object. It furthermore focuses on the situation when the transmitter and receivers can be synchronized, so that TOA (as opposed to time-difference-of-arrival (TDOA)) information can be used. We propose a novel, Two-Step estimation (TSE) algorithm for the localization of the object. We then derive the Cramer-Rao Lower Bound (CRLB) for TOA and show that it is an order of magnitude lower than the CRLB of TDOA in typical setups. The TSE algorithm achieves the CRLB when the TOA measurements are subject to small Gaussian-distributed errors, which is verified by analytical and simulation results. Moreover, practical measurement results show that the estimation error variance of TSE can be 33 dB lower than that of TDOA based algorithms.

Two-way relaying can significantly improve the spectrum efficiency of bi-directional information transmission between two nodes with the help of half-duplex relays. However, in the general case of asymmetric channel gains, the achievable rates of both directions are restricted by the link with the worse channel quality. In this paper, we propose a new three-phase cooperative transmission protocol, which exploits the direct link and the time resource of a two-way relay system more efficiently. We derive the rate region outer bound of this protocol, and propose a practical transmission scheme based on doubly nested lattice codes, which can achieve this outer bound within 0.5 bit. Numerical results show the superiority of this protocol over two other classical protocols in asymmetric channels.

Passive target localization is an important prerequisite for applications ranging from security to logistics to emergency response. The localization system estimates the location of the target by utilizing runtime and direction of the signal (multipath component, MPC) directly reflected by the target. Indirect paths (IPs), i.e., MPCs that interact with environmental objects as well as the target, need to be discerned from the direct paths; failing to do so brings bias to location estimate, and is a major source of localization error. This paper proposes a novel scheme for the detection of IPs in the presence of noise, jointly employing the time of arrival (TOA), direction of arrival (DOA) and direction of departure (DOD) estimates. The decision statistic of this scheme is the distance between the intersection of DOA and DOD directions and the TOA ellipse. Under the assumption of Gaussian errors of TOA, DOA and DOD, the scheme is implemented to maximize the probability of detection given a fixed probability of false alarm. The paper derives closed-form equations for the decision criterion and the resulting performance. Simulation results are given to support the validity of our scheme.

We develop a recursive least-squares (RLS) algorithm which employs L1-Lqregularized sparse regressions to estimate a sparse channel matrix in frequency-and-time selective fading for multi-input multi-output (MIMO) wireless communications. We propose an improved sparse RLS by using an order extension technique for rapid fading channels. Simulation results demonstrate that the proposed sparse RLS algorithm offers a significant improvement over the conventional RLS algorithm.

In this work, we propose a super-resolution blind channel modeling algorithm to characterize wide-band channels comprised of disjoint frequency subbands. Since sounding signals are not available over the frequency guard bands separating adjacent subbands, conventional channel modeling methods suffer from poor performance in modeling the channel frequency response over the guard bands. To circumvent this obstacle, a three-step super-resolution blind algorithm is developed. First, the path delays are estimated by exploiting super-resolution algorithms such as MUSIC or ESPRIT. After that, the proposed algorithm performs blind channel estimation over the guard bands and subsequently derive the frequency response over the whole wideband channel. Finally, estimates derived from different subbands are combined via a soft combining technique. It is shown by computer simulations that the proposed superresolution blind algorithm can achieve a significant performance gain over conventional methods.

This paper proposes a joint optimization framework for minimizing high-definition (HD) video coding rates and selecting optimal relay nodes in 60 GHz millimeter-wave (mmWave) IEEE 802.11ad very high throughput (VHT) wireless systems. While IEEE 802.11ad VHT aims to support uncompressed HD video wireless transmission, its major limitation is the extremely high attenuation even in line-of-sight situations, which leads to a short admissible distance between transmitter and receivers and/or the necessity to compress video. To deal with this problem, the IEEE 802.11ad VHT draft standard defines efficient relaying protocols to extend the network coverage. When multiple source-destination pairs and multiple relays are present, a key question is which relay should help in the forwarding of which source flow. This selection should be done in such a way that the average video quality of the streams, which is related to the throughput in a nonlinear way, is maximized. We solve this problem by an integer programming framework that selects optimal relay nodes, their cooperation modes (i.e., amplify-and-forward, decode-and-forward, or non-cooperation), and the video coding (compression) rates which can maximize transmission quality.

This paper presents a double directional analysis of vehicle-to-vehicle channel measurements conducted in three different traffic scenarios. Using a high- resolution algorithm, we derive channel parameters like Angle-of-Arrival (AOA), Angleof- Departure (AOD), propagation delay and Doppler shift and identify underlying propagation mechanisms by combining these estimates with maps of the measurement sites. The results show that first-order reflections from a small number of interacting objects can account for a large part of the received signal in the absence of line-of-sight (LOS). This effect is especially pronounced in the two traffic scenarios where the road is not lined with buildings. We also found that the direction spread is low (and conversely that the antenna correlation is high) in such scenarios, which suggests that beamforming rather than diversity-based methods should be used if multiple antenna elements are available. The situation is reversed, however, in the third scenario, a narrow urban intersection, where a larger number of higher-order reflections is found to result in a higher direction spread.

Inside a tunnel, electromagnetic wave propagation differs strongly from the well understood "open-air" situation. The characterization of the tunnel environment is crucial for deploying vehicular communication systems. In this paper we evaluate vehicle-to-vehicle (V2V) radio channel measurements inside a tunnel. We estimate the time-varying root mean square (rms) delay and Doppler spreads, as well as the excess delay and the maximum Doppler dispersion. The fading process in V2V communications is inherently non-stationary. Hence, we characterize the stationarity time, for which we can consider the fading process to be wide sense stationary. We show that the spreads, excess delay, and maximum Doppler dispersion are larger on average when both vehicles are inside the tunnel compared to the "open-air" situation. The temporal evolution of the stationarity time is highly influenced by the strength of time-varying multipath components and the distance between vehicles. Furthermore, we show the good fit of the rms delay and Doppler spreads to a lognormal distribution, as well as for the stationarity time. From our analysis we can conclude that the IEEE 802.11p standard will be robust towards inter-symbol and inter-carrier interference inside a tunnel.

Directional properties of the radio channel are of high importance for the development of reliable wireless systems operating in the 60 GHz frequency band. Using transfer functions measured from 61 to 65 GHz in a conference room we have extracted estimates of the multi-path component parameters using the SAGE algorithm. In the paper we compare results for line-of-sight (LOS) scenarios and the corresponding non-line-of-sight (NLOS) scenarios and present values of the direction spread at the Tx and the Rx.

A millimeter-wave (mm-wave) impulse radio is an attractive alternative to existing high-speed mm- wave radio systems because of the potential for a simpler transceiver architecture. This paper studies the feasibility of a mm-wave impulse radio system for home and office use by considering state-of-the- art transceivers and multiple-input multiple-output measured propagation channels as well as the IEEE 802.15.3c channel model. Our analysis reveals that reliable data transmission is infeasible even in line-of-sight (LOS) scenarios because of a low power level at the Rx if we do not use beamforming. However, introducing 7×7 beamforming at the Tx dramatically improves the coverage beyond 5 m distance in the LOS scenario and up to 5 m in a non- line-of-sight (NLOS) scenario, though the performance varies in the NLOS scenario, depending on the Tx and Rx locations. We propose an adaptive transmit signaling scheme that adjusts the pulse repetition frequency depending on the delay dispersion of the propagation channel in order to avoid intersymbol interference and keep the Rx structure simple. The proposed transmit signaling scheme leads to a pulse transmission rate of 250 Mpulses/s in all measured channels while the rate is lower when the 802.15.3c model is considered because of a more multipath-rich characteristics than our measured channels.

In a multiple-input multiple-output (MIMO) system, cross-polarized antenna selection yields significant reduction in cost and hardware size. However, actual benefits of the technique are dependent on the propagation characteristics including channel polarization. To accurately characterize the target 2 GHz-band MIMO channels, the authors conduct 2 GHz cross-polarized channel measurement campaigns. Based on the measured data, novel channel models specifically for the 2 GHz bands are established. In addition, we evaluate the performance improvement obtained with cross-polarized antenna selection using the channel models. Simulation results reveal that antenna selection is particularly useful in the low SNR regime, and that the system capacity at cell edges can be increased up to 13%.

To make transportation safer, more efficient, and less harmful to the environment, traffic telematics services are currently being intensely investigated and developed. Such services require dependable wireless vehicle-to-infrastructure and vehicle-to-vehicle communications providing robust connectivity at moderate data rates. The development of such dependable vehicular communication systems and standards requires accurate models of the propagation channel in all relevant environments and scenarios. Key characteristics of vehicular channels are shadowing by other vehicles, high Doppler shifts, and inherent nonstationarity. All have major impact on the data packet transmission reliability and latency. This paper provides an overview of the existing vehicular channel measurements in a variety of important environments, and the observed channel characteristics (such as delay spreads and Doppler spreads) therein. We briefly discuss the available vehicular channel models and their respective merits and deficiencies. Finally, we discuss the implications for wireless system design with a strong focus on IEEE 802.11p. On the road towards a dependable vehicular network, room for improvements in coverage, reliability, scalability, and delay are highlighted, calling for evolutionary improvements in the IEEE 802.11p standard. Multiple antennas at the onboard units and roadside units are recommended to exploit spatial diversity for increased diversity and reliability. Evolutionary improvements in the physical (PHY) and medium access control (MAC) layers are required to yield dependable systems. Extensive references are provided.

Block transmission with cyclic prefix is a promising technique to realize high-speed data rates in frequency-selective fading channels. Many popular linear precoding schemes, including orthogonal frequency-division multiplexing (OFDM), single-carrier (SC) block transmission, and time-reversal (TR), can be interpreted as such a block transmission. This paper presents a unified performance analysis that shows how the optimal precoding strategy depends on the optimization criterion such as capacity, mean-square error, and secrecy. We analyze three variants of TR methods (based on maximum-ratio combining, equal-gain combining and selective combining) and two-types of pre-equalization methods (zero-forcing and minimum mean-square error). As one application of our framework, we derive optimal precoding (i.e., OFDM with optimal power and phase control) in the presence of interference limitation for distributed antenna systems; we find that without power/phase control, OFDM does not have any capacity advantage over SC transmissions. When comparing SC and TR, we verify that for single-antenna systems in the high SNR regimes, SC has a capacity advantage; however, TR performs better in the low SNR regime. For distributed multiple-antenna systems, TR always provides higher capacity, and the capacity of TR can approach that of optimal precoders with a large number of distributed antennas. Furthermore, we make an analysis of secrecy capacity which shows how high-rate messages can be transmitted towards an intended user without being decoded by the other users from the viewpoint of information-theoretic security. We demonstrate that TR precoding can be the best candidate among the non-optimal precoders for achieving high secrecy capacity, while the optimal precoder offers a significant gain over those non-optimal precoders.

Spectral efficiency is a key characteristic of cellular communications systems, as it quantifies how well the scarce spectrum resource is utilized. It is influenced by the scheduling algorithm as well as the signal and interference statistics, which, in turn, depend on the propagation characteristics. In this paper we derive analytical expressions for the short-term and long-term channel-averaged spectral efficiencies of the round robin, greedy Max-SINR, and proportional fair schedulers, which are popular and cover a wide range of system performance and fairness trade-offs. A unified spectral efficiency analysis is developed to highlight the differences among these schedulers. The analysis is different from previous work in the literature in the following aspects: (i) it does not assume the co-channel interferers to be identically distributed, as is typical in realistic cellular layouts, (ii) it avoids the loose spectral efficiency bounds used in the literature, which only considered the worst case and best case locations of identical co-channel interferers, (iii) it explicitly includes the effect of multi-tier interferers in the cellular layout and uses a more accurate model for handling the total co-channel interference, and (iv) it captures the impact of using small modulation constellation sizes, which are typical of cellular standards. The analytical results are verified using extensive Monte Carlo simulations.

Cooperation between the nodes of wireless multihop networks can increase communication reliability, reduce energy consumption, and decrease latency. The possible improvements are even greater when nodes perform mutual information accumulation. In this paper, we investigate resource allocation for unicast and multicast transmission in such networks. Given a network, a source, and a destination, our objective is to minimize end-to-end transmission delay under energy and bandwidth constraints. We provide an algorithm that determines which nodes should participate in forwarding the message and what resources (time, energy, bandwidth) should be allocated to each. Our approach factors into two sub-problems, each of which can be solved efficiently. For any transmission order we show that solving for the optimum resource allocation can be formulated as a linear programming problem. We then show that the transmission order can be improved systematically by swapping nodes based on the solution of the linear program. Solving a sequence of linear programs leads to a locally optimal solution in a very efficient manner. In comparison to the proposed cooperative routing solution, it is observed that conventional shortest path multihop routing typically incurs additional delays and energy expenditures on the order of 70%. Drawing inspiration from this first, centralized, algorithm, we also present two distributed algorithms. These algorithms require only local channel state information. Simulations indicate that they yield solutions about two to five percent less efficient than the centralized algorithm.

Receive antenna selection (AS) provides many benefits of multiple-antenna systems at drastically reduced hardware costs. In it, the receiver connects a dynamically selected subset of N available antennas to the L available RF chains. Due to the nature of AS, the channel estimates at different antennas, which are required to determine the best subset for data reception, are obtained from different transmissions of the pilot sequence. Consequently, they are outdated by different amounts in a time varying channel. We show that a linear weighting of the estimates is necessary and optimum for the subset selection process, where the weights are related to the temporal correlation of the channel variations. When L is not an integer divisor of N, we highlight a new issue of "training voids", in which the last pilot transmission is not fully exploited by the receiver. We then present new "void filling" methods that exploit these voids and greatly improve the performance of AS. The optimal subset selection rules with void filling, in which different antennas turn out to have different numbers of estimates, are also explicitly characterized. Closed form equations for the symbol error probability with and without void-filling are also developed.

We consider a two-way relay network consisting of multiple pairs of single-antenna users and multiple distributed single-antenna relays. The two communication peers in each pair of users transmit simultaneously to the relays in the first time slot, and the relays rebroadcast the received sum signal weighted by a complex gain, in the second time slot. For multi-user systems, the signal arriving at the users contains not only self interference from the back-propagation of user signals, but also inter-pair interferences from other pairs of users. In this paper, we use zero-forcing (ZF) to cancel the inter-user interference, assuming that channel-state information for all relay-peer connections are known at every relay, but no data exchange occurs between relays. We also derive two closed-form expressions for zero-forcing beamforming weights, corresponding to two different relay power constraints, which can be implemented in a distributed manner. The first approach uses standard ZF to null out every inter-pair interference and the second approach sets the total inter-pair interference to zero. We also derive a closed-form upper bound of the achievable sum-rate and show that both methods achieve the same multiplexing gain when the number of relays N is sufficient for perfect zero-forcing, namely 2K2+ K, where K is the number of user pairs. For the case of insufficient number of relays, we also propose two solutions for beamforming weights, i.e., based on diagonal loading and use of the pseudo-inverse, and compare their advantages and weaknesses.

Beamforming is an important feature of 60 GHz communications. We present an analysis of the influence of beamforming in indoor ultrawideband radio channels measured in the mm-wave 60 GHz band. The performance of narrowband and wideband direction-based beamformers is investigated in terms of improving channel metrics such as the delay spread, excess delay, and the signal-to-noise ratio (SNR). The performance of the direction-based beamformers is compared with dominant eigenmode transmission and statistical beamforming. Our analysis reveals that in line-of-sight (LOS) scenarios, the two direction-based beamformers have a similar performance that approaches the upper bound set by dominant eigenmode transmission. In non-LOS (NLOS) scenarios, the direction-based beamformers show a performance degradation in relation to the upper bound, with the narrowband beamformer worse off than the wideband variant. The array gain in our measured NLOS scenarios is observed to exceed the theoretical upper limit valid for a rich scattering environment. We show that this result follows from the spatial structure of the measured NLOS channels that has only a few strong reflected components. We investigate the influence of array size on beamforming performance; 5×5 planar arrays are observed to improve the channel's delay metrics as well as the larger 7×7 planar arrays.

Antenna selection (AS) provides most of the benefits of multiple-antenna systems at drastically reduced hardware costs. In receive AS, the receiver connects a dynamically selected subset of N available antennas to the L available RF chains. The "best" subset to be used for data reception is determined by means of channel estimates acquired using training sequences. Due to the nature of AS, the channel estimates at different antennas are obtained from different transmissions of the pilot sequence, and are, thus, outdated by different amounts in a time-varying channel. We show that a linear weighting of the estimates is optimum for the subset selection process, where the weights are related to the temporal correlation of the channel variations. When L is not an integer divisor of N, we highlight a new issue of "training voids", in which the last pilot transmission is not fully exploited by the receiver. We present a "void-filling" method for fully exploiting these voids, which essentially provides more accurate training for some antennas, and derive the optimal subset selection rule for any void-filling method. We also derive new closed-form equations for the performance of receive AS with optimal subset selection.

Receive antenna selection (AS), in which only a subset of antennas receive simultaneously at any time, requires the transmitter to send pilots multiple times so that the receiver can acquire channel state of all antennas and select the best subset. In conventional AS training, sensitivity of coherent reception to channel estimation errors forces the transmitter to boost the energy allocated to all pilots to ensure accurate channel estimates. Energy for pilots received by unselected antennas is mostly wasted, especially since the selection process is robust to estimation errors. In this paper, we propose a novel training method uniquely tailored for AS. Using one extra pilot symbol, accurate channel estimates get generated for the antenna subset that actually receives data. Consequently, the transmitter can selectively boost the energy allocated to the extra pilot. Other pilots, which now primarily help in subset selection, get allocated less energy. We derive closed-form expressions for the proposed scheme's symbol error probability, and optimize the energy allocated to pilot and data symbols. We show that the optimal solution achieves full diversity; it is provably unique and strictly better than the conventional model.

In this paper we analyze the Ricean K-factor for vehicle-to-vehicle (V2V) communications in a typical open sub-urban street crossing. The channel conditions vary from non line-of sight (NLOS) to line-of-sight (LOS). The antenna arrays used for recording the radio channels consist of 4 elements with directional radiation patterns. We measured 16 individual single-input single-output channels, with a bandwidth of 240MHz for a duration of 20 s. We performed two kind of evaluations. For the first analysis we partitioned the 240MHz bandwidth into 24 sub-bands with 10MHz each, according to 802.11p. The small-scale fading of the first delay bin is Ricean distributed with a time-varying K-factor. The later delay bins are mostly Rayleigh distributed. We observe that the large/small K-factor values are not necessarily correlated with the received power. We show that the K-factor can not be assumed to be constant in time, frequency, and space. The antenna radiation patterns, and the illuminated objects by them at different time instances are the cause of these variations. The second evaluation considers the 240MHz bandwidth, and the narrow-band K-factor is calculated for each frequency bin, with Δf = 312 kHz. We corroborate the need to consider the frequency variation of the K-factor. We conclude that a multi-dimensional varying K-factor models the large-scale statistical behaviour more accurately than a constant K-factor.

This paper presents the results of an empirical study of wireless propagation channels for vehicle-to-vehicle communications in street intersections, a scenario especially important for collision avoidance applications. The results are derived from a channel measurement campaign performed at 5.6 GHz in four different types of urban intersections. We present results on typical power delay profiles, pathloss and delay spreads and discuss important propagation mechanisms. By comparing the results of the different intersections, we find that absence of line-of-sight is problematic for system coverage, especially when there are few other significant scattering objects in and around the intersection. Roadside buildings can create important propagation paths that account for a considerable part of the total received power.

This study presents sample measurements and analysis characterising the radio channel for outdoor short-range sensor networks. A number of transmit and receive antennas are placed on the ground in an open area. The measured propagation channel is time varying because of the controlled motion of a person walking in the vicinity of the nodes. The statistics of both the line-of-sight (LOS) path and the scattered component of the measured channel are observed to be non-stationary. The channel (power) gains are found to be significantly influenced by the pedestrian movement, only when the LOS path is momentarily blocked. The authors present a generic approach to model receive signal fluctuations because of body blockage of the LOS path. Our approach, which is similar to the referenced work of Pagani and Pajusco, additionally models the time-variant Doppler spectrum of the residue (scattered) component of the measured channel, that is the remainder of the measured channel after the LOS path has been extracted. The proposed modelling approach is parameterised and validated from the measurements.

Hardware constraints, which motivate receive antenna selection, also require that various antenna elements at the receiver be sounded sequentially to obtain estimates required for selecting the 'best' antenna and for coherently demodulating data thereafter. Consequently, the channel state information at different antennas is outdated by different amounts and corrupted by noise. We show that, for this reason, simply selecting the antenna with the highest estimated channel gain is not optimum. Rather, a preferable strategy is to linearly weight the channel estimates of different antennas differently, depending on the training scheme. We derive closed-form expressions for the symbol error probability (SEP) of AS for MPSK and MQAM in time-varying Rayleigh fading channels for arbitrary selection weights, and validate them with simulations. We then characterize explicitly the optimal selection weights that minimize the SEP. We also consider packet reception, in which multiple symbols of a packet are received by the same antenna. New suboptimal, but computationally efficient weighted selection schemes are proposed for reducing the packet error rate. The benefits of weighted selection are also demonstrated using a practical channel code used in third generation cellular systems. Our results show that optimal weighted selection yields a significant performance gain over conventional unweighted selection.

The immediate environment of handset antennas, including the casings and the users holding the handsets, has a strong impact on the radio channel in mobile communication. In this paper we investigate a composite channel method that synthetically combines double-directional measurements of the user-less propagation channel with measured super-antenna patterns, i.e., patterns of the combined antenna-casing-user arrangement. We experimentally evaluate the method by comparing results (power, capacity, and eigenvalue distribution) obtained from this composite method with direct measurements in the same environment. The measurements were done in two static 8 × 4 MIMO scenarios at 2.6 GHz, with the user indoors and the base station located outdoors and indoors, respectively. A realistic user phantom together with a"smart-phone" handset mock-up with four antenna elements was used, and different configurations and orientations were tested. The method gives statistical distributions of the MIMO eigenvalues, that are close to the measured. By using the composite method, we found that the user, apart from introducing hand and body loss that mainly decreases the SNR of the channel, slightly increases the correlation between the fading at the antenna elements.

Recent years have seen an increasing attention given to wireless Personal Area Networks (PANs), which are typically networks with small transmitter-receiver separation. The desire for high data rates has led to an interest in deploying multipleinput multiple-output (MIMO) transmission for such systems, but up until this date there exists, to the authors¿ best knowledge, no MIMO channel model that enables performance simulations of such systems. An important characteristic of PANs, and at the same time an important difference to regular wireless local area networks, is the interaction between the antenna array and the user. In conjunction with the irregular antenna arrangements that are typical for PAN devices, this has been shown to lead to flexible channel statistics. In this paper we present a MIMO model for PANs that incorporates these effects by prescribing different small-scale statistics and gains to different antenna elements. The proposed model can thus be seen as a generalization of the classical MIMO model for line-of-sight situations. The model is compared to several sets of measurement data and found to provide a very good description of the essential PAN channel characteristics. We also provide a detailed parameterization of the model for a particular PAN scenario.

In receive antenna selection (AS), only signals from a subset of the antennas are processed at any time by the limited number of radio frequency (RF) chains available at the receiver. Hence, the transmitter needs to send pilots multiple times to enable the receiver to estimate the channel state of all the antennas and select the best subset. Conventionally, the sensitivity of coherent reception to channel estimation errors has been tackled by boosting the energy allocated to all pilots to ensure accurate channel estimates for all antennas. Energy for pilots received by unselected antennas is mostly wasted, especially since the selection process is robust to estimation errors. In this paper, we propose a novel training method uniquely tailored for AS that transmits one extra pilot symbol that generates accurate channel estimates for the antenna subset that actually receives data. Consequently, the transmitter can selectively boost the energy allocated to the extra pilot. We derive closed-form expressions for the proposed scheme's symbol error probability for MPSK and MQAM, and optimize the energy allocated to pilot and data symbols. Through an insightful asymptotic analysis, we show that the optimal solution achieves full diversity and is better than the conventional method.

This paper presents results from an outdoor measurement campaign for ultra-wideband channels at gas stations. The results are particularly relevant for "infostations" where large amounts of data are downloaded to a user within a short period of time. We describe the measurement setup and present a novel high-resolution algorithm that allows the identification of the scatterers that give rise to multipath components. As input, the algorithm uses measurements of the transfer function between a single-antenna transmitter and a long uniform linear virtual array as receiver. The size of the array ensures that the incoming waves are spherical, which improves the estimation accuracy of scatterer locations. Insight is given on how these components can be tracked in the impulse response of a spatially varying terminal. We then group the detected scatterers into clusters, and investigate the angular power variations of waves arriving at the receiver from the clusters. This defines the cluster's "radiation pattern." Using sample measurements we show how obstacles obstruct the line-of-sight component -- a phenomenon commonly referred to as "shadowing." We compare the measurement data in the shadowing regions (locations of the receiver experiencing shadowing) with the theoretical results predicted by diffraction theory and find a good match between the two.

In this paper we establish a geometry-based stochastic ultra-wideband channel model for gas stations. We statistically describe the two-dimensional spatial location and power of clustered scatterers, and the shape of their visibility and shadowing regions. We also separately model the diffuse part of the impulse response (i.e., the part that cannot be explained by the scatterers' multipath components), and show that its amplitude fading statistics can be best described by a Weibull distribution with a delay dependent beta-parameter. A step-by-step implementation recipe demonstrates how the model can be built. Finally, we validate our model by comparing simulated and measured channel parameters such as the rms delay spread.

We develop an alternate characterization of the statistical distribution of the inter-cell interference power observed in the uplink of CDMA systems. We show that the lognormal distribution better matches the cumulative distribution and complementary cumulative distribution functions of the uplink interference than the conventionally assumed Gaussian distribution and variants based on it. This is in spite of the fact that many users together contribute to uplink interference, with the number of users and their locations both being random. Our observations hold even in the presence of power control and cell selection, which have hitherto been used to justify the Gaussian distribution approximation. The parameters of the lognormal are obtained by matching moments, for which detailed analytical expressions that incorporate wireless propagation, cellular layout, power control, and cell selection parameters are developed. The moment-matched lognormal model, while not perfect, is an order of magnitude better in modeling the interference power distribution.

Abstract The IEEE 802.16/WiMAX standard has fully embraced multi‐antenna technology and can, thus, deliver robust performance and high transmission rates. Nevertheless, due to its inherent cost concerns, a WiMAX mobile station (MS) should preferably contain fewer radio frequency (RF) chains than antenna elements. This is because the former is often substantially more expensive than the latter. Thus, antenna selection, wherein a subset of antennas is dynamically selected to connect to the limited number of RF chains for transceiving, is a highly appealing performance enhancement technique for multi‐antenna WiMAX terminals. In this paper, a novel protocol for antenna selection in space division multiple access (SDMA) transmission is developed for the next‐generation IEEE 802.16 mobile stations. Both locally and globally optimal selection rules are considered together with various linear precoding designs at the base station (BS). The proposed protocol can readily accommodate various channel situations (e.g., reciprocal and non‐reciprocal channels). As demonstrated by analysis and simulation, the proposed protocol delivers considerable performance improvement over conventional IEEE 802.16 terminals that lack antenna selection capability. Moreover, the proposed protocol leverages the existing signaling method defined in IEEE 802.16, thereby incurring a negligible signaling overhead and requiring only minimal modifications of the standard. To the best of our knowledge, this paper represents the first effort to support antenna selection capability for SDMA transmissions in IEEE 802.16 mobile stations. Copyright © 2009 John Wiley & Sons, Ltd.

A detailed characterization of interference power statistics in CDMA systems is of considerable practical and theoretical interest. Such a characterization for uplink inter-cell interference has been difficult because of transmit power control, randomness in the number of interfering mobile stations, and randomness in their locations. We develop a new method to model the uplink inter-cell interference power as a lognormal distribution, and show that it is an order of magnitude more accurate than the conventional Gaussian approximation even when the average number of mobile stations per cell is relatively large and even outperforms the moment-matched lognormal approximation considered in the literature. The proposed method determines the lognormal parameters by matching its moment generating function with a new approximation of the moment generating function for the inter-cell interference. The method is tractable and exploits the elegant spatial Poisson process theory. Using several numerical examples, the accuracy of the proposed method in modeling the probability distribution of inter-cell interference is verified for both small and large values of interference.

Vehicle-to-vehicle (VTV) communications are of interest for applications within traffic safety and congestion avoidance, but the development of suitable communications systems requires accurate models for VTV propagation channels. This paper presents a new wideband MIMO (multiple-input- multiple-output) channel model for VTV channels based on extensive MIMO channel measurements performed at 5.2 GHz in rural environments in Lund, Sweden. The measured channel characteristics, in particular the non-stationarity of the channel statistics, motivate the use of a geometry-based stochastic channel model (GSCM) instead of the classical tapped-delay line model. We introduce generalizations of the generic GSCM approach and find it suitable to distinguish between diffuse and discrete scattering contributions. The time-variant contributions from discrete scatterers are tracked over time and delay using a high resolution algorithm, and our observations motivate their power being modeled as a combination of a deterministic part and a stochastic part. The paper gives a full model parameterization and the model is verified by comparison of MIMO antenna correlations derived from the channel model to those obtained directly from measurements.

Receive antenna selection (AS) reduces the hardware complexity of multi-antenna receivers by dynamically connecting an instantaneously best antenna element to the available radio frequency (RF) chain. Due to the hardware constraints, the channels at various antenna elements have to be sounded sequentially to obtain estimates that are required for selecting the "best" antenna and for coherently demodulating data. Consequently, the channel state information at different antennas is outdated by different amounts. We show that, for this reason, simply selecting the antenna with the highest estimated channel gain is not optimum. Rather, the channel estimates of different antennas should be weighted differently, depending on the training scheme. We derive closed-form expressions for the symbol error probability (SEP) of AS for MPSK and MQAM in time-varying Rayleigh fading channels for arbitrary selection weights, and validate them with simulations. We then derive an explicit formula for the optimal selection weights that minimize the SEP. We find that when selection weights are not used, the SEP need not improve as the number of antenna elements increases, which is in contrast to the ideal channel estimation case. However, the optimal selection weights remedy this situation and significantly improve performance.

Relays play an important role for increasing rate and reducing energy consumption of wireless networks. In this paper we consider a three-node network (source, relay, destination) in which we want to minimize total energy consumption for a given transmission rate. We analyze and optimize the Split-Combine-Relaying (SCR) protocol that for many typical parameter settings performs better than traditional decode-and- forward. SCR splits a data packet into two fragments, which are then transmitted in two phases. In the first phase of transmission, the source sends the first fragment to the relay. In the second phase of the transmission, the source sends the second fragment to the destination, while, at the same time, the relay forwards the first fragment to the destination. We provide an optimization framework to decide the amount of data in each fragment, the amount of time spent in each of the phases, and the corresponding transmission powers. We also show that an extension of SCR that employs Slepian-Wolf coding of the fragments leads to further reduction of energy consumption.

The IEEE 802.16/WiMAX standards has fully embraced multi-antenna technology and can, thus, deliver robust performance and high transmission rates. Nevertheless, due to its inherent cost concerns, a WiMAX mobile station (MS) should preferably contain fewer radio frequency (RF) chains than antenna elements. Thus, antenna selection, wherein a subset of antennas is dynamically selected to connect to the limited number of RF chains for transceiving, is a highly appealing performance enhancement technique for multiantenna WiMAX terminals. In this paper, a novel protocol for antenna selection in space division multiple access (SDMA) transmission is developed for the next-generation IEEE 802.16 mobile stations. Both locally and globally optimal selection rules are considered at the base station (BS). The proposed protocol can readily accommodate various channel situations (e.g., reciprocal and non-reciprocal channels). As demonstrated by analysis and simulation, the proposed protocol delivers considerable performance improvement over conventional IEEE 802.16 terminals that lack antenna selection capability. Moreover, the proposed protocol leverages the existing signaling method defined in IEEE 802.16, thereby incurring a negligible signaling overhead and requiring only minimal modifications of the standard.

Understanding ultra-wide-band (UWB) propagation channels is a prerequisite for UWB system design as well as communication-theoretic and information-theoretic investigations. This paper surveys the fundamental properties of UWB channels, pointing out the differences to conventional channels. If the relative bandwidth is large, the propagation processes, and therefore path loss and shadowing, become frequency-dependent, and the well-known wide-sense stationary uncorrelated scattering model is not applicable anymore. If the absolute bandwidth is large, the shape of the impulse responses as well as the fading statistics change. This paper also describes methods for measuring UWB channels and extracing channel parameters. Throughout this paper, the relationship between channel properties and other areas of UWB research are pointed out.

Cognitive radios are expected to work in bands below about 3.5 GHz and may be used for a variety of applications, e.g., broadband fixed wireless access, mobile and nomadic access, etc. Cognitive radio system designers must have access to a wide range of channel models covering a wide span of operating frequencies, carrier bandwidths, deployment conditions, and environments. This paper provides a comprehensive overview of the propagation channel models that will be used for the design of cognitive radio systems. We start with classical models for signal loss versus distance and discuss their dependence on the physical properties of the environment and operating frequency. Here we also introduce the concept of log-normal shadowing resulting from signal blockage by man-made and natural features. Next, we discuss the time-varying nature of the wireless channel, introduced as a result of the motion of objects in the channel. This is followed by a discussion on the dispersion of the signal caused by various effects of propagation, especially in the time and frequency domains. Angular dispersion, which is discussed next, is important because cognitive radios may be based on modems that exploit the spatial domain. Lastly, we summarize channel models that have been standardized for fixed and mobile systems.

Wireless sensor networks are emerging as an important area for communications. They enable a wealth of new applications including surveillance, building control, factory automation, and in-vehicle sensing. The sensor nodes have to operate under severe constraints on energy consumption and form factor, and provide the ability for precise self-location of the nodes. These requirements can be fulfilled very well by various forms of ultra-wide-band (UWB) transmission technology. We discuss various techniques and tradeoffs in UWB systems and indicate that time-hopping and frequency-hopping impulse radio physical layers combined with simple multiple-access techniques like ALOHA are suitable designs. We also describe the IEEE 802.15.4a standard, an important system that adopts UWB impulse radio to ensure robust data communications and precision ranging. In order to accommodate heterogeneous networks, it uses specific modulation, coding, and ranging waveforms that can be detected well by both coherent and noncoherent receivers.

Vehicle-to-vehicle (VTV) wireless communications have many envisioned applications in traffic safety and congestion avoidance, but the development of suitable communications systems and standards requires accurate models for the VTV propagation channel. In this paper, we present a new wideband multiple-input-multiple-output (MIMO) model for VTV channels based on extensive MIMO channel measurements performed at 5.2 GHz in highway and rural environments in Lund, Sweden. The measured channel characteristics, in particular the nonstationarity of the channel statistics, motivate the use of a geometry-based stochastic channel model (GSCM) instead of the classical tapped-delay line model. We introduce generalizations of the generic GSCM approach and techniques for parameterizing it from measurements and find it suitable to distinguish between diffuse and discrete scattering contributions. The time-variant contribution from discrete scatterers is tracked over time and delay using a high resolution algorithm, and our observations motivate their power being modeled as a combination of a (deterministic) distance decay and a slowly varying stochastic process. The paper gives a full parameterization of the channel model and supplies an implementation recipe for simulations. The model is verified by comparison of MIMO antenna correlations derived from the channel model to those obtained directly from the measurements.

Sensor networks and ad-hoc networks, where nodes inter-communicate without fixed infrastructure, have recently attracted interest due to potential use in industrial, environmental, and safety-related applications. The fading statistics of the propagation channels between sensor nodes are essential to determine the possible data rate, outage, and latency of sensor networks. This paper presents (to the best of our knowledge) the first in-depth analysis, based on measurements, of the propagation channels between typical sensor node locations in office environments. We find that the amplitude fading distribution can be characterized as Ricean. The Rice factor is analyzed as a function of distance and it is determined that it is not a monotonically decreasing function. Even in pure line-of-sight situations, Rice factors show a random behavior and are on the order of 10 or less. We propose models for the small- and large-scale fading correlation. A simulation model based on our analysis is also provided. Our results have relevance for the analysis of bit error rates and diversity order in clustered sensor networks.

Many wireless applications demand a fast mechanism to detect the packet from a node with the highest priority (ldquobest noderdquo) only, while packets from nodes with lower priority are irrelevant. In this paper, we introduce an extremely fast contention-based multiple access algorithm that selects the best node and requires only local information of the priorities of the nodes. The algorithm, which we call variable power multiple access selection (VP-MAS), uses the local channel state information from the accessing nodes to the receiver, and maps the priorities onto the receive power. It is based on a key result that shows that mapping onto a set of discrete receive power levels is optimal, when the power levels are chosen to exploit packet capture that inherently occurs in a wireless physical layer. The VP-MAS algorithm adjusts the expected number of users that contend in each step and their respective transmission powers, depending on whether previous transmission attempts resulted in capture, idle channel, or collision. We also show how reliable information regarding the total received power at the receiver can be used to improve the algorithm by enhancing the feedback mechanism. The algorithm detects the packet from the best node in 1.5 to 2.1 slots, which is considerably lower than the 2.43 slot average achieved by the best algorithm known to date.

Contention-based multiple access is a crucial component of many wireless systems. Multiple-packet reception (MPR) schemes that use interference cancellation techniques to receive and decode multiple packets that arrive simultaneously are known to be very efficient. However, the MPR schemes proposed in the literature require complex receivers capable of performing advanced signal processing over significant amounts of soft undecodable information received over multiple contention steps. In this paper, we show that local channel knowledge and elementary received signal strength measurements, which are available to many receivers today, can actively facilitate multi-packet reception and even simplify the interference canceling receiver's design. We introduce two variants of a simple algorithm called dual power multiple access (DPMA) that use local channel knowledge to limit the receive power levels to two values that facilitate successive interference cancellation. The resulting receiver structure is markedly simpler, as it needs to process only the immediate received signal without having to store and process signals received previously. Remarkably, using a set of three feedback messages, the first variant, DPMA-Lite, achieves a stable throughput of 0.6865 packets per slot. Using four possible feedback messages, the second variant, turbo-DPMA, achieves a stable throughput of 0.793 packets per slot, which is better than all contention algorithms known to date.

We consider relay cooperation with imperfect channel state information (CSI) in the downlink of wireless networks. In particular, we consider a two-phase transmission where in the first phase the base station broadcasts information to the relays; the relays decode the data fully or partially depending on the transmission rate and the quality of their corresponding communication links. During the second phase, the relays cooperate by jointly beamforming information to multiple users given that channel mean and covariance are available at the transmitter side. The goal is to optimize the total network throughput (taking into account both transmission phases) by proper choice of the transmission rates, cooperation architecture and beamforming transmit vectors from the relays. The key contribution of this paper lies in the consideration of the impact of CSI imperfections in such a system. We first formulate the problem of finding the optimum throughput, which is not amenable to analytical solution. We therefore derive a suboptimum adaptive beamforming strategy that maximizes a derived upper bound on the average system throughput. Even though the relays have imperfect CSI, it is shown that relay cooperation can significantly improve the overall system throughput.

Given a wireless network where each link undergoes small-scale (Rayleigh) fading, we consider the problem of routing a message from a source node to a target node while minimizing energy or power expenditure given a fixed time budget, or vice versa. Given instantaneous channel state information, we develop tight hyperbolic bounds on the quantities of interest and solve the related optimizations in closed form or via lightweight computations. If only average channel state information is available, probabilistical performance measures must be introduced. We therefore develop another set of bounds that supports resource-optimal routing with a guaranteed success probability. Our results rest on novel formulations and solution methods for hyperbolic convex programs and, more generally, nonlinear multicriterion combinatorial optimization.

In many wireless ad-hoc networks it is important to find a route that delivers a message to the destination within a certain deadline (delay constraint). We propose to identify such routes based on average channel state information (CSI) only, since this information can be distributed more easily over the network. Such cases allow probabilistic QoS guarantees i.e., we maximize and report the probability of on-time delivery. We develop a convolution-free lower bound on probability of on-time arrival, and a scheme to rapidly identify a path that maximizes this bound. This analysis is motivated by a class of infinite variance subexponential distributions whose properties preclude the use of deviation bounds and convolutional schemes. The bound then forms the basis of an algorithm that finds routes that give probabilistic delay guarantees. Simulations demonstrate that the algorithm performs better than shortest-path algorithm based on statistics of path loss or CSI.

Cooperation between the nodes of wireless multi-hop networks can increase communication reliability, reduce energy consumption, and decrease latency. The possible improvements are even greater when nodes perform mutual-information accumulation, e.g., by using rateless codes. In this paper, we investigate routing problems in such networks. Given a network, a source and a destination, our objective is to minimize end-to-end transmission delay under a sum energy constraint. We provide an algorithm that determines which nodes should participate in forwarding the message and what resources (time, energy, bandwidth) should be allocated to each. Our approach factors into two sub-problems, each of which can be solved efficiently. For any node decoding order we show that solving for the optimum resource allocation can be formulated as a linear problem. We then show that the decoding order can be improved systematically by swapping nodes based on the solution of the linear program. Solving a sequence of linear program leads to a locally optimum solution in a very efficient manner. In comparison to the cooperative routings, it is observed that conventional shortest-path multihop routings incur additional delays and energy expenditures on the order of 70%. Since this initial solution is centralized, requiring full channel state information, we exploit the insights to design two distributed routing algorithms that require only local channel state information. We provide simulations showing that in the same networks the distributed algorithms find routes that are only about 2-5% less efficient than the centralized solution.

The IEEE 802.16/WiMAX standard has fully embraced multi-antenna technology and can, thus, deliver robust and high transmission rates and higher system capacity. Nevertheless, due to its inherent form-factor constraints and cost concerns, a WiMAX mobile station (MS) should preferably contain fewer radio frequency (RF) chains than antenna elements. This is because RF chains are often substantially more expensive than antenna elements. Thus, antenna selection, wherein a subset of antennas is dynamically selected to connect to the limited RF chains for transceiving, is a highly appealing performance enhancement technique for multi-antenna WiMAX terminals. In this paper, a novel antenna selection protocol tailored for next-generation IEEE 802.16 mobile stations is proposed. As demonstrated by the extensive OPNET simulations, the proposed protocol delivers a significant performance improvement over conventional 802.16 terminals that lack the antenna selection capability. Moreover, the new protocol leverages the existing signaling methods defined in 802.16, thereby incurring a negligible signaling overhead and requiring only diminutive modifications of the standard. To the best of our knowledge, this paper represents the first effort to support antenna selection capability in IEEE 802.16 mobile stations.

In many wireless applications, it is highly desirable to have a fast mechanism to resolve or select the packet from the user with the highest priority. Furthermore, individual priorities are often known only locally at the users. In this paper we introduce an extremely fast, local-information-based multiple access algorithm that selects the best node in 1.8 to 2.1 slots, which is much lower than the 2.43 slot average achieved by the best algorithm known to date. The algorithm, which we call Variable Power Multiple Access Selection (VP-MAS), uses the local channel state information from the accessing nodes to the receiver, and maps the priorities into the receive power. It is inherently distributed and scales well with the number of users. We show that mapping onto a discrete set of receive power levels is optimal, and provide a complete characterization for it. The power levels are chosen to exploit packet capture that inherently occurs in a wireless physical layer. The VP-MAS algorithm adjusts the expected number of users that contend in each step and their respective transmission powers, depending on whether previous transmission attempts resulted in capture, idle channel, or collision.

The immediate environment of the transmit (TX) and receive (RX) antennas, including the antenna casings and the users holding the antennas, has a strong impact on the propagation channel and thus on wireless systems. In this paper we experimentally evaluate a method that synthetically combines double-directional measurements of the propagation channel (without the user influence) with measured antenna patterns of antennas-plus-users, by comparing obtained sample results with direct measurements in the same environment. The measurements are done for a static microcell 8times4 MIMO scenario at 2.6 GHz. A realistic user phantom was used together with a test terminal prototype with four antenna elements, and a number of different configurations and orientations of the phantom were tested. In average over all test cases, the mean signal power deviation between composite channel method and measurements was well within 1 dB. The composite method shows 6% higher terminal antenna correlation but similar statistical distributions as the measured. The differences between the model and measurements for the strongest eigenvalue (relevant for MRC combining) was found to be within 1 dB above 10% outage level. Relative deviations in the ergodic MIMO capacity were smaller than 10%.

We present a new high delay resolution method to detect ultra-wideband (UWB) scatterers when using frequency domain measurements. Our approach makes use of the impulse response envelope amplitudes and delays measured over a distance that is larger than the region of stationarity, and detects the 2D coordinates of the channel scatterers, assuming that only single-scattering (single-interaction) processes occur. The identification methodology is based on multiple application of interference cancellation: at every step, we detect the strongest scatterer from an array of measurements, save its information, cancel it from the channel and search for the next strongest scatterer. To precisely define the strength of each scatterer, we present a method to define its birth and death locations along the measurement array. Finally, we verify the method by applying it to measurement results in an outdoor environment; the scatterer locations identified from the measurements show excellent agreement with the physically present objects like walls and columns.

Contention-based multiple access is a crucial component of many wireless systems. It is known that using interference cancellation techniques to receive and decode multiple packets that arrive simultaneously can improve the efficiency of multiple access. However, such multi-packet reception (MPR) schemes proposed in the literature require complex receivers capable of performing advanced signal processing over significant amounts of soft undecodable information received over multiple contention steps. In this paper, we show that local channel knowledge and elementary received signal strength measurements, which are made by many receivers today, can actively facilitate multi-packet reception and even simplify the interference canceling receiver's design. We introduce a simple algorithm called turbo-dual power multiple access (turbo-DPMA) that uses local channel knowledge to limit the receive power levels to two discrete values that are carefully chosen to facilitate successive interference cancellation. As we shall see, limiting the receive power in such a manner not only facilitates the simultaneous reception of up to two packets, but it also enables the receiver to derive additional useful information about the contending users from its received signal strength indicator. The resulting receiver structure is markedly simpler, as it needs to process only the immediate received signal, without having to store and process signals received previously. Even more remarkably, the turbo-DPMA is stable for packet arrival rates as high as 0.793 packets/slot, which is significantly better than all the contention algorithms known to date.

Wireless relays extend coverage, improve spectral efficiency, and enhance reliability and rates of wireless cellular communication systems. In this work, we introduce the fundamental notion of asymmetric cooperation among cooperating relays in cellular downlinks - different relays are party to different but overlapping knowledge about the messages transmitted from the base station. We argue that asymmetric cooperation arises naturally in most two-phase protocols in which the base station first transmits information to multiple relays that then cooperatively forward the information to the recipient mobile stations in the cell. For a system in which two relays are of the decode-and-forward type and cooperate using linear precoding to communicate with two mobile stations, we formulate the general, but complicated, throughput optimization problem and derive several results that considerably simplify the optimization. We show that under different channel configurations and fairness criteria, asymmetric cooperation is often the throughput-maximizing option. Under typical configurations, a 20-30% throughput enhancement is achieved compared to conventional full-cooperation systems.

Personal area networks (PANs) are wireless communications systems with high data rates but small coverage area. PAN propagation channels differ from the well-explored propagation channels of wide-area networks due to several reasons: (i) the distances are typically very small, (ii) the antenna arrangements can be quite different, and (iii) the influence from human presence in the environment is different. The current paper presents results of a channel measurement campaign, where measurements are conducted over distances of 1-10 m using several multi-antenna devices, combined to create different PAN scenarios. For each measured Tx-Rx separation, channel realizations are obtained by small spatial movements of the antenna devices, and by rotating the persons holding the devices. From the results, we draw two main conclusions: (i) The small scale amplitude statistics, analyzed as the variations over a small sampling area and frequency subchannels, cannot be described in a satisfactory way using only the Rayleigh or Ricean distribution, rather a mixed distribution, the generalized gamma distribution, is more suitable; (ii) it is advantageous to distinguish between two types of large-scale fading: body shadowing (due to the orientation of the person holding the device) and shadowing due to surrounding objects (lateral movement). We also define and parameterize a complete statistical model for all fading.

We consider a cooperative wireless network where a set of nodes cooperate to relay in parallel the information from a source to a destination using a decode-and-forward approach. The source broadcasts the data to the relays, some or all of which cooperatively beamform to forward the data to the destination. We generalize the standard approaches for cooperative communications in two key respects: (i) we explicitly model and factor in the cost of acquiring channel state information (CSI), and (ii) we consider more general selection rules for the relays and compute the optimal one among them. In particular, we consider simple relay selection and outage criteria that exploit the inherent diversity of relay networks and satisfy a mandated outage constraint. These criteria include as special cases several relay selection criteria proposed in the literature. We obtain expressions for the total energy consumption for general relay selection and outage criteria for the non-homogeneous case, in which different relay links have different mean channel power gains, and the homogeneous case, in which the relay links statistics are identical. We characterize the structure of the optimal transmission scheme. Numerical results show that the cost of training and feedback of CSI is significant. The optimal strategy is to use a varying subset (and number) of relay nodes to cooperatively beamform at any given time. Depending on the relative location of the relays, the source, and the destination, numerical computations show energy savings of about 16% when an optimal relay selection rule is used. We also study the impact of shadowing correlation on the energy consumption for a cooperative relay network.

This paper considers a multi-hop network in which relay nodes cooperate to minimize the total energy consumed in transmitting a (unicast) packet from a source to a destination. We propose the Progressive Accumulative Routing (PAR) algorithm, which progressively performs relay discovery, relay ordering and relay power allocation in a distributed manner, such that each relay node only needs local information. We assume Destination Energy Accumulation, in which the destination accumulates the energy of multiple received copies of a packet, each of which is too weak to be reliably decoded by itself, while the lower complexity relay nodes use a decode-and-forward approach. We also provide a closed-form analysis of the energy-savings achieved by the PAR when a relay node is added to an already existing DEA route. Simulations verify that the algorithm considerably reduces the total energy consumption, and can be implemented efficiently.

Cooperative transmission by base stations (BSs) can significantly improve the spectral efficiency of multiuser, multicell, multiple input multiple output (MIMO) systems. We show that contrary to what is often assumed in the literature, the multiuser interference in such systems is fundamentally asynchronous. Intuitively, perfect timing-advance mechanisms can be best only ensure that the desired signal components- but not also the interference components- are perfectly aligned at their intended mobile stations. We develop an accurate mathematical model for the asynchronicity, and show that it leads to a significant performance degradation of existing designs that ignore the asynchronicity of interference. Using three previously proposed linear precoding design methods for BS cooperation, we develop corresponding algorithms that are better at mitigating the impact of the asynchronicity of the interference. Furthermore, we also address timing-advance inaccuracies (jitter), which are inevitable in a practical system. We show that using jitter-statistics-aware precoders can mitigate the impact of these inaccuracies as well. The insights are critical for the practical implementation of BS cooperation in multiuser MIMO systems, a topic that is typically oversimplified in the literature.

Fixed and mobile relays are used, among other applications, in the downlink of cellular communications systems. Cooperation between relays can greatly increase their benefits in terms of extended coverage, increased reliability, and improved spectral efficiency. In this paper, we introduce the fundamental notion of asymmetric cooperation. For this, we consider a two-phase transmission protocol where, in the first phase, the base station (BS) sends several available messages to the relays over wireless links. But, depending on the channel state and the duration of the BS transmission, not all relays decode all messages. In a second phase, the relays, which may now have asymmetric message knowledge, use cooperative linear precoding for the transmission to the mobile stations. We show that for many channel configurations, asymmetric cooperation, although (slighlty) sub-optimum for the second phase, is optimum from a total-throughput point of view, as it requires less time and energy in the first phase. We give analytical formulations for the optimum operating parameters and the achievable throughput, and show that under typical circumstances, 20-30% throughput enhancement can be achieved over conventional systems.

Ultrawideband communications often occur in heterogeneous networks where different receivers have different complexity and energy consumption requirements. In this case it is desirable to have a modulation scheme that works well with coherent receivers as well as simpler receivers, namely transmitted-reference (TR) receivers. In particular, we consider a TR scheme that employs slightly frequency-shifted reference (FSR) signals [15] and thus avoids one of the main drawbacks of conventional TR schemes, namely the need to implement a delay line. We propose and analyze a modulation scheme that works well with both FSR receivers (where it has the same performance as conventional TR modulation), and coherent receivers. Coherent receivers receiving conventional TR modulation suffer a 3 dB penalty, because they cannot make use of the energy invested into the reference pulse. Our proposed scheme avoids this drawback by including a data preprocessor that can be viewed as a nonsystematic rate-1/2 convolutional code with a high constraint length. These codes give 1.5 dB gain over our previously proposed constraint-length-two systematic codes at a BER of 1 times 10-4in 802.15.3a CM4 multipath fading channels.

Ad-hoc networks and sensor networks, where nodes communicate with each other without fixed infrastructure, are of great importance for many industrial, environmental, and safety-related applications. The fading statistics of the propagation channels between sensor nodes are essential to determine the possible data rate, outage, and latency of sensor networks. In this paper, we present results from a recent measurement campaign that for the first time provides an in-depth analysis of the propagation channels between typical sensor node locations in office environments. We analyze the Rice factor as a function of distance and find that it is not a monotonically decreasing function. Even in pure LOS situations, Rice factors are on the order of 10 or less. This has important consequences for analysis of bit error rates, diversity order, and diversity/multiplexing tradeoff in clustered sensor networks.

Accurate system planning and performance evaluation requires knowledge of the joint impact of scheduling, interference, and fading. However, current analyses either require costly numerical simulations or make simplifying assumptions that limit the applicability of the results. In this paper, we derive analytical expressions for the spectral efficiency of cellular systems that use either the channel-unaware but fair round robin scheduler or the greedy, channel-aware but unfair maximum signal to interference ratio scheduler. As is the case in real deployments, non-identical co-channel interference at each user, both Rayleigh fading and lognormal shadowing, and limited modulation constellation sizes are accounted for in the analysis. We show that using a simple moment generating function-based lognormal approximation technique and an accurate Gaussian-Q function approximation leads to results that match simulations well. These results are more accurate than erstwhile results that instead used the moment-matching Fenton-Wilkinson approximation method and bounds on the Q function. The spectral efficiency of cellular systems is strongly influenced by the channel scheduler and the small constellation size that is typically used in third generation cellular systems.

Cooperative transmission by base stations can significantly improve the spectral efficiency of multiuser, multi-cell multiple input multiple output systems. We show that in such systems the multiuser interference is asynchronous by nature, even when perfect timing-advance mechanisms ensure that the desired signal components arrive synchronously. We establish an accurate mathematical model for the asynchronism, and use it to show that the asynchronism leads to a significant performance degradation of existing linear preceding designs that assumed synchronous interference. We consider three different previously proposed precoding designs, and show how to modify them to effectively mitigate asynchronous interference.

Car-to-car and car-to-infrastructure (henceforth called C2X) communications are constantly gaining importance for road- safety and other applications. In order to design efficient C2X systems, an understanding of realistic C2X propagation channels is required, but currently, only few measurements have been published. This paper presents a description of an extensive measurement campaign recently conducted in an urban scenario, a rural scenario, and on a highway. We focused on 4 x 4 multiple-input multiple-output (MHVIO) measurements at a center frequency of 5.2 GHz with high Doppler resolution. As first results from evaluating the measurement data we present the power-delay profile and the delay-Doppler spectrum from a selected, especially interesting measurement run from an urban measurement route. We observe dispersed Doppler contributions between zero and the Doppler shift corresponding to the relative speed of the cars, and very concentrated (in the Doppler domain), contributions from double reflections. Surprisingly, we also found paths with larger delays and zero Doppler shifts.

We propose a modified layered structure for multiple-input multiple-output systems, where the layer detection order is fixed and the data rate for each layer is allocated based on the detection order and channel statistics. Using a Gaussian approximation of the layer capacities, we derive an asymptotic optimum data-rate-allocation approach. For optimum data-rate allocation, the amount of backoff from the mean layer capacity is proportional to the standard deviation of the layer capacity. With statistical data-rate allocation, only limited channel feedback is needed to update channel statistics at the transmitter. Simulation results show significant performance improvement with the proposed algorithm. We also find that the performance gap between the layered structure and the channel capacity diminishes with increasing ergodicity within each codeword. Numerical results show a singal-to-noise ratio improvement of 6.3 and 3.6 dB for TGn channels B and D, respectively, for 1% outage probability and 9 b/s/Hz spectral efficiency.

In time hopping impulse radio, Nfpulses of duration Tcare transmitted for each information symbol. This gives rise to two types of processing gains: (i) pulse combining gain, which is a factor Nf, and (ii) pulse spreading gain, which is Nc= Tf/Tc, where Tfis the mean interval between two subsequent pulses. This paper investigates the tradeoff between these two types of processing gains in the presence of timing jitter. First, an additive white Gaussian noise (AWGN) channel is considered, and approximate closed-form expressions for bit error probability (BEP) are derived for impulse radio systems with and without pulse-based polarity randomization. Both symbol-synchronous and chip-synchronous scenarios are considered. The effects of multiple-access interference (MAI) and timing jitter on the selection of optimal system parameters are explained through theoretical analysis. Finally, a multipath scenario is considered, and the tradeoff between processing gains of a synchronous impulse radio system with pulse-based polarity randomization is analyzed. The effects of the timing jitter, MAI, and interframe interference (IFI) are investigated. Simulation studies support the theoretical results.

In marked contrast with the ideal error-free feedback assumption that is common in the literature, practical systems are likely to have severely bandwidth-limited, error-prone feedback channels. We consider the scenario where feedback from the receiver is used by the transmitter to select the best antenna, out of many available antennas, for data transmission. Feedback errors cause the transmitter to select an antenna different from the one signaled by the receiver. We show that optimizing the signaling assignment, which maps the antenna indices to the feedback codewords, improves performance without introducing any additional redundancy. For a system that uses error-prone feedback to transmit quadrature-phase-shift-keying-modulated data from a single antenna selected from many available spatially correlated antennas, we derive closed-form approximations for the data symbol error probability for an arbitrary number of receive antennas. We use these to systematically find the optimal signaling assignments using a low-complexity algorithm. The optimal signaling is intimately coupled to how the receiver performs selection verification, i.e., how it decodes the data signal when, due to feedback errors, it does not always know which antenna was used for data transmission. We show that ignoring feedback errors at the receiver can lead to an unacceptable performance degradation, and develop optimal and suboptimal, blind and nonblind selection-verification methods. With a small side-information overhead, nonblind verification approaches the ideal perfect selection-verification performance

One of the major issues for the design of ultra-wideband (UWB) receivers is the need to recover the signal energy dispersed over many multipath components, while keeping the receiver complexity low. To this aim we consider two schemes for reduced-complexity UWB Rake receivers, both of which combine a subset of the available resolved multipath components. The first method, called partial Rake (PRake), combines theirs/ arriving multipath components. The second is known as selective Rake (SRake) and combines the instantaneously strongest multipath components. We evaluate and compare the link performance of these Rake receivers in different UWB channels, whose models are based on extensive propagation measurements. We quantify the effect of the channel characteristics on the receiver performance, analyzing in particular the influence of small-scale fading statistics. We find that for dense channels the performance of the simpler PRake receiver is almost as good as that of the SRake receiver, even for a small number of fingers. In sparse channels, however, the SRake outperforms the PRake significantly. We also show that for a fixed transmitted energy there is an optimum transmission bandwidth

The results of three ultra-wideband (UWB) measurement campaigns conducted in two different industrial environments are presented. A frequency range of 3.1-10.6 or 3.1-5.5 GHz was measured using a vector network analyzer and a virtual array technique enabling the investigation of small-scale statistics. The results show that the energy arrives in clusters, and that the abundance of metallic scatterers present in the factory hall causes dense multipath scattering. The latter produces a small-scale fading that is mostly Rayleigh distributed; the only exception being the delay bin containing the line-of- sight component. The power delay profile can be modeled by a generalized Saleh-Valenzuela model, where different clusters have different ray power decay constants. It is also noted that the number of multipath components required to capture a majority of the energy is quite large. More than a hundred components can be needed to capture 50% of the total available energy.

A simple, novel, and general method is presented in this paper for approximating the sum of independent or arbitrarily correlated lognormal random variables (RV) by a single lognormal RV. The method is also shown to be applicable for approximating the sum of lognormal-Rice and Suzuki RVs by a single lognormal RV. A sum consisting of a mixture of the above distributions can also be easily handled. The method uses the moment generating function (MGF) as a tool in the approximation and does so without the extremely precise numerical computations at a large number of points that were required by the previously proposed methods in the literature. Unlike popular approximation methods such as the Fenton-Wilkinson method and the Schwartz-Yeh method, which have their own respective short-comings, the proposed method provides the parametric flexibility to accurately approximate different portions of the lognormal sum distribution. The accuracy of the method is measured both visually, as has been done in the literature, as well as quantitatively, using curve-fitting metrics. An upper bound on the sensitivity of the method is also provided.

Cooperative communications, where parallel relays forward information to a destination node, can greatly improve the energy efficiency and latency in ad-hoc networks. However, current networks do not fully exploit its potential as they only use traditional energy-accumulation, which is often used in conjunction with repetition coding or cooperative space-time codes. In this paper, we show that the concept of mutual- information-accumulation can be realized with the help of fountain codes, and leads to a lower energy expenditure and a lower transmission time than energy accumulation. We then provide an analysis of the performance of mutual information accumulation in relay networks with N relay nodes. We first analyze the quasi-synchronuous scenario where the source stops transmitting and the relay nodes start transmitting after L relay nodes have successfully decoded the source data. We show that an optimum L exists, and is typically on the order of 3 or 4. We also give closed-form equations for the energy savings that can be achieved by the use of mutual-information-accumulation at the receiver. We then analyze and provide bounds for an alternate scenario where each relay node starts its transmission to the destination as soon as it has decoded the source data, independent of the state of the other relay nodes. This approach further reduces the transmission time, because the transmission by the relay nodes helps the other relay nodes that are still receiving.

This paper considers signaling schemes for heterogeneous ultrawideband communications networks that contain both coherent (rake) and transmitted-reference (TR) receivers. While coherent receivers are capable of receiving TR signals, they do so with a 3 dB penalty, because they cannot make use of the energy invested into the reference pulse. We propose a new signaling scheme that avoids this drawback, by encoding redundant information on the reference pulse. The resulting scheme does not affect the operation of a TR receiver, while recovering the 3 dB penalty and furthermore providing an additional 1.7 dB coding gain to a coherent uncoded binary scheme. This can be explained by interpreting the scheme as a trellis-coded modulation. We also provide an alternative implementation that can be viewed as a recursive systematic convolutional encoder. Combining this version further with a simple forward error correction encoder results in a concatenated code that can be decoded iteratively, providing a bit-error rate of 10-3at 2.8 dB signal-to-noise ratio in additive white Gaussian noise. The convergence behavior of this iterative code is analyzed by using extrinsic information transfer charts.

This paper presents results from the first double-directional measurements of the multiple-input multiple-output (MIMO) peer-to-peer radio channel in the 300 MHz frequency range. The measurements were performed with vertically polarized circular antenna arrays mounted on cars driving along a route in a semi rural and sub-urban environment. We evaluate the measurements with a high resolution algorithm, thus obtaining a wideband, double-directional characterization of the propagation channel. The delay- and directional spread, as well as the small-scale fading statistics are analyzed along the measurement routes and major scatterers are identified. The measurements, performed with low antennas heights, show high angular spreads, caused by reflections from trees and buildings and by diffractions over terrain irregularities. We further observe multiple clusters in both the delay and angular domains. The large spreads in these domains indicate a high degree of available diversity, and rich multipath in the considered environment.

We consider a MIMO system where error-prone feedback from the receiver is used by the transmitter to select a single optimum antenna to transmit data. Such error-prone feedback is common in the bandwidth-limited real systems, and is in marked contrast with the idealizations assumed in the selection literature. We show how the signaling assignment, which maps the antenna indices to the codewords that are fed back to indicate the index of the best transmit antenna, affects the performance of transmit antenna selection. The impact is intimately coupled with the receiver design. We derive approximate closed-form expressions for the average symbol error probability of QPSK modulated data in a spatially correlated channel, and then systematically find the optimal signaling assignment. Performance improvements are demonstrated for different antenna topologies without introducing any additional redundancy.

We consider a cooperative wireless network where the source broadcasts data to relays, some or all of which cooperatively beamform to forward the data to the destination. The network is subject to an overall outage constraint. We generalize the standard approaches for cooperative communications in two respects: (i) we explicitly model and factor in the cost of acquiring channel state information (CSI), and (ii) we consider more general, yet simple, selection rules for the relays and compute the optimal one among them. These rules include as special cases several relay selection criteria proposed in the literature. We present analytical results for the homogeneous case, where the links have identical mean channel gains. For this case, we show that the optimal transmission scheme is simple and can be computed efficiently. Numerical results show that while the cost of training and feedback of CSI is significant, relay cooperation is still beneficial.

We investigate a cooperative communications scheme withNparallel relays, where both the transmissions from the source to the relays and from the relays to the destination use fountain codes. Receivers for fountain codes can accumulate mutual information, while traditional energy collection methods, such as repetition or cooperative space-time codes, only accumulate energy. As a consequence, using fountain codes can reduce the total energy required for transmitting data from the source to the destination. We first analyze the scenario where the source stops transmitting and the relay nodes start transmitting afterLrelay nodes have successfully decoded the source data. We optimizeL, and also give closed-form equations for the energy savings that can be achieved by the use of mutual-information-collection at the receiver instead of traditional energy-collection methods. We then analyze an alternate scenario where each relay node starts its transmission to the destination as soon as it has decoded the source data, and helps the other relay nodes that are still in reception mode. Doing so further reduces the total transmission time and energy consumption.

This paper considers a sensor network where relay nodes cooperate in order to minimize the total energy consumption for the unicast transmission of a message from a single source to a single destination. We assume Destination Energy Accumulation, i.e., the destination can accumulate the energy of multiple copies of the message, each of which is too weak to be reliably decoded by itself, while the relay nodes use a decode-and-forward approach. We propose the Progressive Accumulative Routing (PAR) algorithm, which performs relay discovery, relay ordering and power allocation in a distributed manner so that each relay node only needs information about its neighboring nodes. Simulations verify that the algorithm considerably reduces the total energy consumption, and can be implemented efficiently. Furthermore, it performs close to the optimal DEA route with high probability.

Due to the fine delay resolution of typical ultra wideband (UWB) systems, Rake receivers need to combine samples from a large number of multipath components (MPCs) in order to collect sufficient signal energy for reliable decisions. In addition, these samples need to be combined optimally in order to minimize bit error probability. The optimal linear minimum mean square error (MMSE) combining scheme might require inversion of a large matrix depending on channel and system parameters. Therefore, suboptimal algorithms with lower computational complexity but close-to-optimal performance are desirable. In this paper, a low-complexity combining scheme is proposed for that purpose, which divides samples into a number of groups and performs MMSE combining in two steps. Performance of this two-step combining scheme is investigated theoretically and by simulations.

A simple and novel method is presented to approximate by the lognormal distribution the probability density function of the sum of correlated lognormal random variables. The method is also shown to work well for approximating the distribution of the sum of lognormal-Rice or Suzuki random variables by the lognormal distribution. The method is based on matching a low-order Gauss-Hermite approximation of the moment-generating function of the sum of random variables with that of a lognormal distribution at a small number of points. Compared with methods available in the literature such as the Fenton-Wilkinson method, Schwartz-Yeh method, and their extensions, the proposed method provides the parametric flexibility to address the inevitable trade-off that needs to be made in approximating different regions of the probability distribution function.

In a pulsed UWB system, either coherent receivers or transmit-reference (TR) receivers can be used to demodulate the signals. For coherent receivers, which are typically based on a Rake stfucture, the large number of resolvable multipaths is a major challenge for channel estimation and receiver complexity. TR receivers can effectively collect energy from all the received multipath components with much smaller complexity, but show reduced performance. Current modulation formats are tuned to either coherent or TR receivers, but cannot interoperate with both of them at the maximum performance possible for each of them. In this paper, we propose an innovative modulation scheme that enables the use of TR and coherent receivers in the same wireless network. The modulation allows demodulation by TR receivers, while at the same time enabling a coherent receiver to fully exploit all available signal energy, and actually to perform slightly better (by 1.8 dB) than a coherent receiver working with conventional BPSK modulation; this additional gain can be interpreted as a coding gain of a trellis-coded modulation scheme. The scheme thus allows the network designers to trade off performance vs. complexity in the receivers, while allowing a uniform signaling (modulation) scheme for all transmitters.

Many channel models for MIMO systems have appeared in the literature. However, with the exception of a few recent results, they are largely focussed on two dimensional (2D) propagation, i.e., propagation in the horizontal plane, and the impact of elevation angle is not considered. The assumption of 2D propagation breaks down when in some propagation environments the elevation angle distribution is significant. Consequently, the estimation of ergodic capacity assuming a 2D channel coefficient alone can lead to erroneous results. In this paper, for cross polarized channels, we define a composite channel model and channel coefficient that takes into account both 2D and 3D propagation. Using this composite channel coefficient we assess the ergodic channel capacity and discuss its sensitivity to a variety of different azimuth and elevation power distributions and other system parameters.

We investigate the diversity gain of MIMO systems with antenna selection in measured propagation channels for wireless personal area networks. We measure both the communication from an access point to a laptop, and between two handheld devices. Both the transmitter and receiver use antenna selection for diversity transmission and reception. We consider a closed-loop system where the transmitter has full channel state information, and analyze a number of different antenna selection algorithms and signal combining methods. We find that line of sight (LOS) and non-line of sight (NLOS) situations have fairly similar behavior, and that different polarizations result in similar SNR gains. We also find that RF-preprocessing of the signals is less effective for the hand held devices scenario than for the access point and laptop scenario. Finally, we compare bulk selection (same antenna subset is used for all frequency sub-channels) to per-tone selection (different antenna subsets can be used for each frequency sub-channel) for a wideband channel.

In this paper we analyze the effects of body shadowing in multiple-input-multiple-output (MIMO) channels used for personal area networks (PANs). We give physical reasoning to why PANs may experience two different types of shadowing, and to support our argument, we present results from a measurement campaign for three different PAN channels with human influence. The campaign is performed using different types of multi-element antenna devices; an access point, a body-worn device and two hand-held devices, conducted over a series of distances between 1 -10 m. For each distance, a number of channel realizations are obtained by moving the antenna devices over a small area, and by rotating the persons holding the devices. The results show that it is suitable to distinguish between body shadowing (due to the rotation of the person holding the device) and shadowing due to surrounding objects (lateral movement). We also present a statistical model where the two types of shadowing are described as separate log-normal processes. Furthermore, we find that body shadowing has a big influence on the capacity of the investigated PAN channels.

In this paper, we present a cluster based analysis of an outdoor-to-indoor Multiple-Input Multiple-Output (MIMO) measurement campaign, and extract model parameters for the COST273 channel model. The measurements were performed at 5.2 GHz for 159 measurement locations in an office building. Multipath component (MPC) parameters have been extracted for these positions using a high-resolution algorithm. We analyze the clustering of MPCs, i.e., grouping together of MPCs with similar DOAs, DODs, and delays. We compare cluster identification by visual inspection to automatic identification by the recently proposed algorithm of Czink et al. In the paper we include results on the intercluster properties such as the distribution of the number of clusters and the cluster powers, as well as intracluster properties such as the angle and delay spreads within the clusters. In particular, we extract parameters for the COST 273 channel model, a standardized generic model for MIMO propagation channels.

This paper studies the design of signaling waveforms for time-hopping impulse radio (TH-IR) with limits on the power spectral density. Such restrictions are imposed by the spectral mask prescribed by frequency regulators for ultra-wideband (UWB) signals. The "conventional" TH-IR system with pulse-position modulation and time-hopping multiple access gives rise to spectral lines that either violate the regulations, or require a significant power backoff. To remedy this situation, we propose the use of polarity randomization, which eliminates the spectral lines and also leads to a smoothing of the continuous part of the spectrum. We analyze different variants of the polarity randomization, considering short and long randomization sequences, as well as symbol-based or pulse-based randomization. We analyze the effect of this technique on both pulse position modulation and binary phase-shift keying modulation.

Fourth-generation (4G) systems are expected to support data rates of the order of 100 Mb/s in the outdoor environment and 1 Gb/s in the indoor/stationary environment. In order to support such large payloads, the radio physical layer must employ receiver algorithms that provide a significant increase in spectrum efficiency (and, hence, capacity) over current wireless systems. Recently, an explosion of multiple-input-multiple-output (MIMO) studies have appeared with many journals presenting special issues on this subject. This has occurred due to the potential of MIMO to provide a linear increase in capacity with antenna numbers. Environmental considerations and tower loads will often restrict the placing of large antenna spans on base stations (BSs). Similarly, customer device form factors also place a limit on the antenna numbers that can be placed with a mutual spacing of 0.5 wavelength. The use of cross-polarized antennas is widely used in modern cellular installations as it reduces spacing needs and tower loads on BSs. Hence, this approach is also receiving considerable attention in MIMO systems. In order to study and compare various receiver architectures that are based on MIMO techniques, one needs to have an accurate knowledge of the MIMO channel. However, very few studies have appeared that characterize the cross-polarized MIMO channel. Recently, the third-generation partnership standards bodies (3GPP/3GPP2) have defined a cross-polarized channel model for MIMO systems but this model neglects the elevation spectrum. In this paper, we provide a deeper understanding of the channel model for cross-polarized systems for different environments and propose a composite channel impulse model for the cross-polarized channel that takes into account both azimuth and elevation spectrum. We use the resulting channel impulse response to derive closed-form expressions for the spatial correlation. We also present models to describe the dependence of cross-polarization discrimination (XPD) on distance, azimuth and elevation and delay spread. In addition, we study the impact of array width, signal-to-noise ratio, and antenna slant angle on the mutual information (MI) of the system. In particular, we present an analytical model for large system mean mutual information values and consider the impact of elevation spectrum on MI. Finally, the impact of multipath delays on XPD and MI is also explored.

It has been predicted theoretically that for some environments, the capacity of wireless multiple-input multiple-output systems can become very low even for uncorrelated signals; this effect has been termed "keyhole" or "pinhole". In this paper the first unique measurements of this effect are presented. The measurements were performed in a controlled indoor environment that was designed to obtain a keyhole channel. We analyze limitations due to measurement imperfections for measurement-based capacity calculations and keyhole investigations. We further present a bound for the higher eigenmodes as a function of the finite measurement signal-to-noise ratio and multipath component leakage. The bound is compared to the measurement results and shows excellent agreement. Finally, we analyze the envelope distribution and, as expected from theory, it follows a double-Rayleigh distribution

This paper describes the attributes of the COST 259 directional channel model that are applicable for use in the design and implementation of macrocellular mobile and portable radio systems and associated technology. Special care has been taken to model all propagation mechanisms that are currently understood to contribute to the characteristics of practical macrocellular channels and confirm that large scale, small scale, and directional characteristics of implemented models are realistic through their comparison with available measured data. The model that is described makes full use of previously published work, as well as incorporating some new results. It is considered that its implementation should contribute to a too) that can be used for simulations and comparison of different aspects of a large variety of wireless communication systems, including those that exploit the spatial aspects of radio channels, as, for example, through the use of adaptive antenna systems

Ultra-wideband (UWB) communication is a potential technique for future high-speed networks. In this paper, we investigate channel estimation and interference suppression for OFDM based UWB systems. In particular, we modify an existing channel estimation approach for UWB systems and develop an exponential window based approach to estimate correlation of the receive signals for interference suppression. Computer simulation results show that these approaches can be effectively used in OFDM based UWB systems.

The orthogonality factor (OF) quantifies the loss of orthogonality between the signals transmitted simultaneously on a wideband code division multiple access (WCDMA) downlink due to multipath dispersion. It is one of the fundamental parameters that determines the signal-to-interference-plus-noise ratio at the output of a Rake receiver and, consequently, it has a significant impact on downlink system capacity. The OF depends greatly on the delay profile of the multipath channel between the mobile and its serving base station; this profile varies considerably from one location in the cell to another. In this paper, we study how the statistical properties of the small-scale-fading-averaged OF vary over the entire macrocellular area. We use an ensemble of channel profiles at different locations in a cell generated from an implementation of the comprehensive COST259 channel model, which incorporates results from several experimental investigations. We show that the small-scale-fading-averaged OF is itself a random variable whose statistics depend on the mobile's distance from its serving base station. The large observed variance of the OF indicates that using a single value for all users in downlink capacity analyses and simulations, as has been the practice, may lead to erroneous conclusions. Finally, we propose a simple model that closely matches the statistics of the OF as a function of mobile-to-base distance, thus obviating the need to set up the complicated channel model every time OF values are to be generated

This paper describes a model for mobile radio channels that includes consideration of directions of arrival and is thus suitable for simulations of the performance of wireless systems that use smart antennas. The model is specified for 13 different types of environments, covering macro- micro- and picocells. In this paper, a hierarchy of modeling concepts is described, as well as implementation aspects that are valid for all environments. The model is based on the specification of directional channel impulse response functions, from which the impulse response functions at all antenna elements can be obtained. A layered approach, which distinguishes between external (fixed), large-scale-, and small-scale- parameters allows an efficient parameterization. Different implementation methods, based on either a tapped-delay line or a geometrical model, are described. The paper also derives the transformation between those two approaches. Finally, the concepts of clusters and visibility regions are used to account for large delay and angular spreads that have been measured. In two companion papers, the environment-specific values of the model parameters are explained and justified

We introduce two novel joint radio-frequency (RF)-baseband designs for receivers in a MIMO system with Nttransmit antennas, Nrreceive antennas, but only LrRF chains at the receiver. The joint design introduces an RF pre-processing matrix that processes the signals from the different antennas, and is followed by selection (if necessary), down-conversion, and further processing in the baseband. The schemes are similar to conventional antenna selection in that they use fewer RF chains than antenna elements, but achieve superior performance by exploiting the spatial correlation of the received signals. The first of our proposed designs uses an L times NrRF pre-processing matrix that outputs only L streams followed by baseband signal processing, and, thus, eliminates the need for a selection switch. The second one uses an Nrtimes NrRF pre-processing matrix that outputs Nrstreams and is followed by a switch that selects L streams for baseband signal processing. Both spatial diversity and spatial multiplexing systems are considered and the optimum pre-processing matrices are derived for all cases. To accommodate practical RF design constraints, which prefer a variable phase-shifter-based implementation, a sub-optimal phase approximation is also introduced. Performance better than conventional antenna selection and close to the full complexity receiver is observed in both single cluster and multi-cluster wireless channels. A beam-pattern-based geometric intuition is also developed to illustrate the effectiveness of the optimal solutions

Transmitted-reference (TR) schemes for time-hopping impulse radio (TH-IR) ultra-wideband (UWB) communications allow the use of simple receiver structures that are able to combine energy from different multipath components without channel estimation. A conventional TR receiver consists of a simple delay-and-multiply operation combined with an integrator. On the downside, it shows a performance loss due to non-linear operations on noise terms (generation of noise-noise cross-terms) when forming the decision variable. This paper describes a hybrid receiver structure for UWB communications that reduces these noise-noise cross-terms by first performing a "matched filtering" operation matched to the time-hopping sequence of pulses. The receiver retains most of the simplicity of the conventional TR receiver, but requires an analog correlator for the time-hopping sequence of pulses. The performance the proposed receiver is analyzed in both AWGN and multipath channels. For the AWGN case, the exact expression for the bit error probability is obtained, which takes into account the nonGaussian nature of the noise-noise cross-terms arising in the correlators. For the multipath case, both inter-frame interference and multipath interference from the reference pulse to the data pulse are considered, and approximate closed-form expressions are derived based on the assumption of a large integration interval. Also approximate criteria for optimal integration interval are obtained for the best receiver performance. Simulation studies are presented to analyze the performance of the proposed receiver structure and to confirm the theoretical analysis

The focus of this paper is to derive the symbol error probability (SEP) of a Rake receiver with a limited number of fingers that track the strongest multipath components in a frequency-selective Rayleigh fading channel. We develop an analytical framework that allows the computation of the SEP for nonuniform power dispersion profiles (PDPs) and spreading bandwidth. By transforming the physical Rake receiver with correlated ordered paths into the domain of a 'virtual Rake" receiver with conditionally independent virtual paths, analytical expressions for the SEP are derived in terms of the spreading bandwidth, the channel profile, and the number of combined paths. We show how our analytical results can be used to predict the performance of various Rake architectures in environments with nonuniform PDPs using for example, the channel models defined for the next generation wireless standards. Furthermore, we validate our methodology by comparison to data obtained from channel measurements, showing good agreement with our analytically derived results.

Multipath dispersion leads to the loss of orthogonality between signals transmitted simultaneously on a wide-band code division multiple access (WCDMA) downlink. The orthogonality factor (OF), which models its impact in the link signal-to-interference-plus-noise ratio (SINR) equation, depends - to a large extent - on the power delay profile of the multipath channel between the mobile and its serving base station. We use the comprehensive and general COST259 channel model for urban cellular environments to evaluate the impact on the OF of multipath clusters and distance-dependence of the multipath delay decay time constant, both of which have been observed in several channel measurements. The observed large standard deviation of the OF indicates that using a single value for all users in downlink capacity analyses and simulations, as has been the practice, may lead to erroneous conclusions. We also propose an empirical model to analytically characterize the observed statistics of the OF

This paper describes a comprehensive statistical model for UWB propagation channels that is valid for a frequency range from 3-10 GHz. It is based on measurements and simulations in the following environments: residential indoor, office indoor, built-up outdoor, industrial indoor, farm environments, and body area networks. The model is independent of the used antennas. It includes the frequency dependence of the pathloss, as well as several generalizations of the Saleh-Valenzuela model, like mixed Poisson times of arrival and delay dependent cluster decay constants. The model can thus be used for realistic performance assessment of UWB systems. It was accepted by the IEEE 802.15.4a working group (WG) as standard model for evaluation of UWB system proposals

We propose a modified layered structure for multiple-input multiple-output (MIMO) systems, where the layer detection order is fixed and the data rate for each layer is allocated based on the detection order and channel statistics. With Gaussian approximation of layer capacities, we derive the optimum data rate allocation and the amount of backoff from mean layer capacity is proportional to the standard deviation of the layer capacity. The minimum overall outage probability of a layered system is uniquely determined by the normalized capacity margin. We then investigate how to select the total information rate to maximize effective throughput. Simulation results show significant performance improvement with the proposed algorithm, and the performance gap between layered structure and the channel capacity diminishes with increasing ergodicity within each codeword.

We consider a time-hopping impulse-radio system that uses transmitted-reference pulses for implicit channel estimation and equalization. A hybrid receiver structure first performs a filtering matched to the hopping sequence, and a subsequent correlation of the data pulses with the reference pulses. We analyze the performance of such a system both in AWGN and in multipath. For the AWGN case, we give exact expressions for the bit error probability that take into account the non-Gaussian nature of the noise-noise crossterms arising in the correlators. For the multipath case, we analyze inter-frame interference, as well as multipath interference from the reference pulse to the data pulse, providing dosed-form equations in the limit of a large number of multipath components.

In this paper, we present the (to our knowledge) first measurement results for ultra-wideband channels in industrial environments, i.e., a factory hall. The measurements are done with virtual arrays, which allows analysis of the small-scale fading statistics, as well as a directional analysis. We find that there is dense multipath scattering due to the abundance of metallic scatterers in the considered environment. Multiple scatterer clusters can be identified both in the delay and the angular domain. Typical rms delay spreads lie between 30 ns for LOS scenarios and 40 ns for NLOS scenarios. For non-LOS scenarios at large distances, the maximum of the power delay profile is observed some 40 ns after the arrival of the first multipath components. We also draw conclusions about the behavior of typical UWB system designs in the measured channels.

For a multiple input multiple output system, antenna selection reduces complexity at the expense of performance. In this paper, we propose two novel RF pre-processing architectures that significantly improve the performance of antenna selection, while marginally increasing the complexity. These architectures introduce an RF pre-processing matrix, M, that multiplies the vector of incoming signals prior to downconversion. The elements of M use only the knowledge of the channel statistics. In the first architecture, M outputs a reduced number of streams - an explicit selection algorithm is therefore not required. In the second architecture, the number of output streams equals the number of input streams, and the reduction of the number of streams is achieved by a selection switch that uses instantaneous channel state information. We show that the optimal pre-processing receiver projects the received signal along the eigenvectors of the correlation matrix. In a correlated channel, both these architectures significantly outperform conventional antenna selection. We also develop a beam-pattern based intuition and compare the performance of our scheme to other RF preprocessing schemes previously proposed in the literature.

In time hopping impulse radio, N/sub f/ pulses of duration T/sub c/ are transmitted for each symbol. This gives rise to two types of processing gain: (i) pulse combining gain, which is a factor N/sub f/, and (ii) pulse spreading gain, which is N/sub c/ = T/sub f//T/sub c/, where T/sub f/ is the mean interval between two subsequent pulses. This paper investigates the trade-off between these two types of processing gain with and without random polarity codes in the presence of timing jitter. Approximate expressions for bit error probability are derived for both coded and uncoded systems over additive white Gaussian noise channels and are used as the criterion to choose optimal N/sub f/ and N/sub c/ values. The effects of timing jitter and multiple access interference on the selection of optimal system parameters are explained through theoretical analysis. Simulation studies support the theoretical results.

We investigate the effect of symbol spreading on the performance of ultrawideband systems based on multiband OFDM. Ultrawideband channels offer the possibility of very high frequency diversity, as the used bandwidth is much larger than the coherence bandwidth of the channel. In multiband-OFDM, frequency diversity stems from (i) forward error correction coding, which distributes the symbols associated with one bit among different tones, and (ii) interleaving, which distributes the symbols among different employed frequency bands. We show that for weak (rate 3/4) error correcting codes, the achieved diversity order is insufficient. However, the use of Walsh-Hadamard spreading gives additional diversity within each frequency band and greatly improves the performance. Furthermore, we propose a novel scheme for grouping OFDM tones before the spreading is performed. This grouping gives additional flexibility in the system design.

The performance of a binary phase shift keyed random time-hopping impulse radio system with pulse-based polarity randomization is analyzed. The effects of multiple access interference are investigated for both chip-synchronous and asynchronous systems. It is shown that the performance of a chip-synchronous system is the same as that for the symbol-synchronous case studied in E. Fisher and H. V. Poor (Oct. 2-4, 2002). The asynchronous system is modelled as a chip-synchronous system with uniformly distributed timing jitter on the transmitted pulses of interfering users. This extends the analytical technique developed for the chip-synchronous case to the asynchronous case. An approximate closed-form expression for the probability of error, expressed in terms of the autocorrelation function of the transmitted pulse, is derived for the asynchronous case. The analysis shows that the chip-synchronous assumption can result in over-estimating the error probability, and hence that the system design based on this approximation will he on the safe side. The degree of over-estimation mainly depends on the autocorrelation function of the UWB pulse and signal-to-interference-plus-noise-ratio (SIR) of the system. Simulations studies support this approximate analysis.

The capacity of downlink cellular multiple-input multiple-output (MIMO) systems, where co-channel interference is the dominant channel impairment, is investigated in this paper, mainly from a signal-processing perspective. Turbo space-time multiuser detection (ST MUD) is employed for intracell communications and is shown to closely approach the ultimate capacity limits in Gaussian ambient noise for an isolated cell. Then, it is combined with various multiuser detection methods for combating intercell interference. Among various multiuser detection techniques examined, linear minimum-mean-square-error (MMSE) MUD and successive interference cancellation are shown to be feasible and effective. Based on these two multiuser detection schemes, one of which may outperform the other for different settings, an adaptive detection scheme is developed, which together with a Turbo ST MUD structure offers substantial performance gain over the well-known V-BLAST techniques with coding in this interference-limited cellular environment. The obtained multiuser capacity is excellent in the high to medium signal-to-interference ratio scenario. Nonetheless, numerical results also indicate that a further increase in system complexity, using base-station cooperation, could lead to further significant increases of the system capacity. The asymptotic multicell MIMO capacity with linear MMSE MUD preprocessing is also derived, and this analysis agrees well with the simulation results.

In this paper, reverse link capacity of a signal-to-interference ratio (SIR) based power-controlled direct-sequence code-division multiple-access (DS-CDMA) system, with the use of an antenna array and a Rake receiver in a multiple cell environment, is investigated. Both transmit and receive beamforming in the reverse link are considered. Instead of using tedious iterative methods, reverse link user capacity represented by a simple closed-form expression is derived, which relates to the number of antennas, the number of Rake receiver fingers, a target SIR, and the processing gain. The most efficient distribution of antenna elements between the base stations and the mobile stations to maximize the user capacity is observed through the numerical results, which also show significant capacity improvement by increasing the number of the antennas and Rake receiver fingers.

This paper addresses the antenna subset selection in multiple antenna systems with full diversity transmission, for both correlated and uncorrelated channels. To reduce the severe performance degradation of traditional selection/combining schemes, we propose to embed phase-shift-only operations in the RF chains before selection. The resulting system shows a significant advantage in utilizing the multiple antenna diversity under almost any channel condition while incurring only a small hardware overhead. With the optimum phase shifter design given in analytical form, our analysis shows that with more than two branches allowed for selection, the new scheme can achieve the same SNR gain as the full-complexity MRC (maximum-ratio-combining). Even when only one branch is allowed, the performance is still well above the conventional selection scheme and near optimum.

We consider an ultra-wide bandwidth system using reduced-complexity RAKE receivers, which are based on either selective (called SRake) or partial (called PRake) combining of a subset of the available resolved multipath components. We investigate the influence of the spreading bandwidth on the system performance using the two considered types of RAKE receivers. We show that, for a fix number of RAKE fingers and a fix transmit power, there is an optimum bandwidth. This optimal bandwidth increases with the number of RAKE fingers, and is higher for an SRake than for a Prake. We also investigate the effects of the fading statistics (Rayleigh or Nakagami) on the optimal spreading bandwidth. We find that the optimal spreading bandwidth is approximately the same for both types of fading, but that the actual performance of an SRake can be better or worse in Rayleigh fading (compared to Nakagami), depending on the spreading bandwidth and the number of fingers.

We address the antenna subset selection problem in spatially correlated MIMO channels. To reduce the severe performance degradation of the traditional antenna selection scheme in correlated channels, we propose to embed DFT operations in the RF chains. The resulting system shows a significant advantage both for diversity schemes and for the capacity of spatial multiplexing, while requiring only a minor hardware overhead.

In this paper, we investigate the design of pulse shaping UK filters for impulse radio ultrawideband (UWB) communications systems. The goal of the shaping is to meet an arbitrary spectrum mask, e.g., the mask mandated by the FCC for UWB emissions. Compared with classical FIR filter designs, the current problem introduces three new challenges: (1) it is minimax with quadratic constraints, (2) a single-sided distortion function is used, (3) delay positions are treated as tuning parameters. We first approach this problem by constructing a least-squares approximation to the minimax problem where the optimization over delays can be easily solved. With the LS solution serving as an initialization, nonlinear optimization techniques are employed to fine tune the solutions.

We consider the error floor of binary frequency-shift keying (FSK) due to intersymbol interference in time-dispersive mobile radio channels, with a limiter-discriminator-integrator detector. The errors are caused by bursts in the instantaneous frequency. We propose and verify that a nonlinear frequency discriminator can achieve zero error floor for pure FSK by clipping off these bursts. For filtered FSK, the error floor is not completely removed, but strongly reduced - typically by one or two orders of magnitude. The tighter the filtering, the less effective this nonlinearity is. The nonlinear discriminator can also be used in conjunction with adaptive sampling.

We analyze the clustering of scatterers in mobile radio channels, i.e, the fact that scatterers are usually not located uniformly in the whole coverage area, but tend to occur in clusters. While this has been recognized for some time, a realistic model for this phenomenon has been lacking up to now. We first analyze measurements to extract the distribution of the number of observed clusters. We then present a model that reflects not only this distribution, but also reproduces the appearance and disappearance of clusters as the mobile station moves through the cell. Our approach has been adopted as an important part of the COST259 directional channel model, a standard model for directional mobile radio channels. Finally, we discuss the implications of the model for the system performance of CDMA and SDMA systems.

We evaluate the link performance of an ultra-wide band (UWB) system using reduced-complexity RAKE receivers, which are based on either partial combining (called PRAKE) or selective combining (called SRAKE). The first is suboptimum and combines the first arriving multipath components, while the second combines the strongest multipath components. We use a statistical tapped-delay-line channel model that is based on extensive measurement campaigns, and reflects both small-scale and large-scale variations of the channel. Through semi-analytical evaluations of the bit error probability, we show that the simpler PRAKE receiver is almost as good as the SRAKE even for a small number of fingers. We show how this behavior can be related to the channel characteristics.

We study the MIMO wireless channel, and derive a generic channel model that we believe has the ability to explain all important effects, including (i) interdependency of directions-of-arrival and directions-of-departure, (ii) large delay and angle dispersion by propagation via far clusters, (iii) rank reduction of the transfer function matrix. We point out the relevant propagation effects, and propose a geometry-based model that includes the relevant effects. The required parameters for the complete definition of the model are enumerated, and typical simulation results for urban macrocells are presented.

We analyze the channel capacity of multiple-input multiple-output (MIMO) systems in frequency selective channels, with channel knowledge at the transmitter side. An optimum transmission scheme uses Shannon's principle of water filling jointly in the frequency and the spatial domain. However, we show that the largest gain by using water filling resides in the spatial domain, and that the capacity gain from the frequency domain is very small for MIMO systems. We quantify the gain for a theoretical Rayleigh fading channel model and for microcellular channels as measured in an enclosed courtyard.

In a paper by Dai and Molisch (see Proc. IEEE VTC 2002 Spring, Birmingham, AL, May 2002), space-time layered architectures (BLAST) and turbo coding/processing techniques, which are effective for single-link transmission, are combined with multiuser detection (MUD) methods for combating intercell interference. It is shown that, depending on the channel configuration, linear MMSE or successive interference cancellation (SIC) can be the preferable MUD method. Based on this fact, an adaptive scheme is developed. T/sup h/e downlink capacity of interference-limited multiple-input multiple-output (MIMO) cellular systems is investigated. It is found that the obtained MUD capacity is excellent in high to medium SIR scenario, but still deteriorates in strong interference environments, leaving ample room for possible improvement through other techniques. The performance of the proposed adaptive MUD scheme in a standard cellular environment, both in the non-line-of-sight (NLOS) and the line-of-sight (LOS) case, is simulated, and the advantages over the standard V-BLAST scheme are quantified.

We consider the application of multiuser detection to the downlink of interference-limited multiple-input multiple-output (MIMO) cellular systems. MIMO systems have been shown to yield a tremendous capacity for a single link with white Gaussian noise. In a cellular environment, there will often be co-channel interference from other cells, which becomes the dominating channel impairment. Here space-time layered architectures and turbo processing techniques are combined with multiuser detection techniques for combating intercell interference. Among various multiuser detection methods examined, linear MMSE and successive interference cancellation have been shown to be feasible and effective. Based on these two multiuser detection schemes, one of which may outperform the other for different settings, an adaptive detection scheme is developed.

We consider the propagation in macro- and microcellular environments for systems with multiple antennas at both transmitter and receiver. We consider the important propagation effects that lead to (i) interdependency of directions-of-arrival and directions-of-departure, (ii) large delay and angle dispersion by propagation via far clusters, (iii) rank reduction of the transfer function matrix. We propose a generic channel model that reflects those propagation effects, and propose different parameterizations of that model for macro- and microcellular environments. Cumulative distribution functions of the information-theoretic capacity derived from that model quantify the impact of the model on system design.

We propose a new method for the simulation of microcellular propagation channels, which includes both deterministic and stochastic aspects. First, the geometrical layouts of "typical" deployment areas, called "Virtual Cell Deployment Areas" (VCDA), as well as routes of the mobile station (MS) through the VCDA, are defined. Then, the delays and directions-of-arrival of multipath clusters can be traced by geometrical means. Finally, the small-scale fading within the clusters is modelled stochastically. This approach was adopted by the COST259 DCM, the standard model for directional wireless channels.

We establish a statistical model for the ultra-wide bandwidth (UWB) indoor channel based on an extensive measurement campaign in a typical modern office building with 2-ns delay resolution. The approach is based on the investigation of the statistical properties of the multipath profiles measured in different rooms over a finely spaced measurement grid. The analysis leads to the formulation of a stochastic tapped-delay-line (STDL) model of the UWB indoor channel. The averaged power delay profile can be well-modeled by a single exponential decay with a statistically distributed decay constant. The small-scale statistics of path energy gains follow Gamma distributions whose parameters m are truncated Gaussian variables with mean values and standard deviations decreasing with delay. The total received energy experiences a lognormal shadowing around the mean energy given by the path-loss power law. We also find that the correlation between multipath components is negligible. Finally, we propose an implementation of the STDL model and give a comparison between the experimental data and the simulation results.

We measure the capacity of multiple-input multiple-output radio systems in microcellular environments. We use a new data evaluation method that allows to evaluate the cumulative distribution function of the capacity from a single measurement. This method is based on an extraction of the parameters of the multipath components and, thereafter, a synthetic variation of their phases. In the analyzed environments, we find capacities to be about 30% smaller than would be anticipated from an idealized model. In addition, the frequency selectivity of the channel makes the CDF of the capacity steeper and, thus, increases the outage capacity, compared with the frequency-flat case, but the influence on the mean capacity is small.

We present a statistical analysis of wideband three-dimensional channel measurements at base station locations in an urban environment. Plots of the received energy over azimuth, elevation, and delay planes suggest that the incident waves group to clusters in most measured transmitter positions. A super-resolution algorithm (Unitary ESPRIT) allows one to resolve individual multipath components in such clusters and hence enables a detailed statistical analysis of the propagation properties. The origins of clusters-sometimes even individual multipath components-such as street apertures, large buildings, roof edges, or building corners can be localized on the city map. Street guided propagation dominates most of the scenarios (78%-97% of the total received power), while quasi-line-of-sight over-the-rooftop components are weak(3%-13% of the total received power). For this measurement campaign, in 90% of the cases, 75% of the total received power is concentrated in the two strongest clusters, but only 55% in the strongest one. Our analysis yields an exponential decay of power with 8.9 dB//spl mu/s, and a standard deviation of the log-normally distributed deviations from the exponential of 9.0 dB. The power of cross-polarized components is 8 dB below copolarized ones on average (vertical transmission).

We show that measurements of time-varying mobile radio channels obtained with uncalibrated correlative channel sounders are affected by four different types of systematic errors (commutation, pulse-compression, aliasing, and misinterpretation error). We analyze these errors and provide upper error bounds that are formulated in terms of channel and sounder parameters. Based on these error bounds, we provide guidelines for a judicious choice of important sounder parameters. Computer simulations using a simple two-path channel illustrate our theoretical results. Finally, we show how our results can be used to assess the accuracy of measured channel data.

At Bell Laboratories, he studied indoor microwave propagation and modeling, packet reservation multiple access for wireless systems and optical WDM networks.He became Manager, Voice Research Department, at Motorola Codex, involved in the implementation integrated voice and data packet systems.On returning to Bell Laboratories he led a multi-disciplinary team to create a software tool for Wireless System Engineering (WiSE), now in widespread use in Lucent Technologies.He received the Distinguished Member of Technical Staff award and is Director of the Wireless Communications Research Department.He is interested in microwave propagation measurements and models, intelligent antennas, third generation wireless system and space time systems achieving high capacities using transmit and receive antenna arrays.He has published over eighty papers and has twelve patents.He is a Fellow of the IEEE. He is editor for the IEEE Transactions on Communications and the IEEE Transactions on Wireless.

We consider the capacity of multiple-input-multiple-output (MIMO) systems with reduced complexity. One link end uses all available antennas, while the other chooses the "best" L out of N antennas. As "best", we use those antennas that maximize capacity. We derive an upper bound on the capacity that can be expressed as the sum of the logarithms of ordered chi-squared variables. This bound is then evaluated analytically, and compared to results from Monte Carlo simulations. As long as L is at least as large as the number of antennas at the other link end, the achieved capacity is close to the capacity of a full-complexity system. We demonstrate, for example, that for L=3, N=8 at the receiver, and 3 antennas at the transmitter, the capacity of the reduced-complexity scheme is 20 bits/s/Hz compared to 23 bits/s/Hz of a full-complexity scheme.

Intercarrier interference can be a dominant source of errors in OFDM-systems, requiring some sort of equalization. We show that serial and parallel interference cancellation techniques, which were originally developed for CDMA multiuser techniques, can be used for OFDM equalization and are much more efficient than a brute force inversion of the channel matrix. Maximum excess delays of up to 70% of the OFDM block duration can be handled without loss of performance.

We develop an analytical framework to quantify the effects of the spreading bandwidth on spread spectrum systems operating in multipath environments with arbitrary power delay profile. The focus of the paper is to characterize the symbol error probability (SEP) performance of a RAKE receiver tracking the L strongest multipath components in frequency-selective Rayleigh fading. By transforming the physical RAKE receiver with correlated ordered paths into the domain of a "virtual RAKE" receiver with conditionally independent virtual paths, analytical expressions for the SEP are derived in terms of the spreading bandwidth, multipath spread of the channel and the number of combined paths.

We introduce a computationally efficient OFDM transmission technique that does not require a cyclic prefix and thus provides high spectral efficiency. The intersymbol interference (ISI) due to time dispersion of the channel is combated by ISI cancellation. We show that the inversion of the channel matrix for interchannel interference (ICI) cancellation can be done by the "operator perturbation technique" (OPT) much more efficiently than by conventional equation solvers. We supplement the OPT by blind techniques and hence reduce the error floor to zero for frequency-selective channels. The expense for a 2 Mbit/s transmission is about 500 MFlops. The technique can also deal with time-varying channels in a straightforward way.

We consider the error floor of coherently and differentially detected PAM (pulse amplitude modulation) in time-dispersive fading channels; PAM includes PSK (phase shift keying) and QAM as special cases. We introduce a new Zak (1968) transform based method for computing the error floor. This method can be applied to arbitrary modulation formats and a very general class of time-dispersive channels. We prove that unfiltered BPSK exhibits no error floor when the sampling phase is chosen adaptively and (for coherent detection) the basis pulse satisfies a symmetry condition. For filtered BPSK there is an error floor, which depends on the filter bandwidth. For higher-order modulations, there is always an error floor; it can be attributed to I-Q crosstalk.

A new approach to multicarrier digital communication over time-varying, frequency selective fading channels is presented. We propose a transmission signal set whose basic structure is similar to standard orthogonal frequency division multiple access (OFDM)-setups, i.e., a system of functions generated by time and frequency-shifted versions of a pulse-like prototype function known as a Weyl-Heisenberg (WH) system. Unlike previous OFDM studies, however, which are restricted to the case of orthonormal pulses, we consider nonorthogonal pulses that are adapted to realistically available a priori knowledge of the channel. Perfect transmultiplexing in the case of an ideal channel is incorporated as a mathematical side-constraint. We derive the expected intersymbol/interchannel interference of such a nonorthogonal FDM (NOFDM) system under the assumption of a wide-sense stationary uncorrelated scattering (WSSUS) channel. Based on this result, we compare OFDM and NOFDM schemes with regard to robustness against delay/Doppler spread.

We analyze how multipath effects namely errors due to channel-induced intersymbol interference, can be reduced by frequency-discriminator detection for FSK. We first introduce a new mathematical technique that can be considered as generalization of FM noise-click theory, and thus allows insights into the error mechanisms. Building on those insights, we propose and verify that a nonlinear frequency discriminator can achieve zero error floor for pure binary FSK and drastically reduced the error floor for filtered FSK by clipping off the ISI-induced FM clicks. The stronger the filtering (before or after the frequency discriminator), the more the beneficial effect of the nonlinearity is reduced. Finally, we show that increasing the modulation index decreases the error floor, especially in the presence of frequency shifts. Our results suggest the design of improved receivers that are less sensitive to ISI.

We analyze the error floor of unequalized personal communications systems using minimum-shift keying (MSK) with training-sequence-based adaptive sampling in a two-delay fading channel. We include Gaussian filtering of the input, finite accuracy in determining the optimum sampling time, and filtering at the receiver. We prove that for pure MSK and low time dispersion, training-sequence-based adaptive sampling can completely avoid errors caused by intersymbol interference. The actual errors are caused by "secondary" effects (filtering and finite-resolution sampling) in conjunction with the channel time dispersion. Errors occur if the normalized phasor of the channel impulse response falls into certain error regions; the computation of these regions gives physical insights into the error mechanism and allows a highly efficient computation of the average bit-error rate (BER). The average BER varies as K/spl middot/(S/T)/sup 2/, where S is the RMS delay spread and T is the bit length. The proportionality constant K depends on the Gaussian filtering in the transmitter, the receiver filtering, and the amount of oversampling. The BER can be orders of magnitude lower than for the (quasi-) fixed sampling case, in which timing is derived solely based on the channel delay power profile. For two-branch diversity reception, we show that the BER is proportional to (S/T)/sup 4/.

We give an overview of the work of COST 231 on the "error floor", i.e. errors introduced by the time dispersion of the mobile radio channel. The error floor depends mainly on the RMS delay spread S and on the sampling scheme; the error probability is in general K/spl middot/(S/T)/sup 2/, where T is the bit length. For fixed sampling, the proportionality constant K is about 0.5, and depends only weakly on the filters. For adaptive sampling, K is one or more orders of magnitude smaller, depending strongly on the filters in the system. Errors occur in a "bursty" way in the fading dips.

This paper presents a new concept for direction of arrival (DOA) estimation and signal reconstruction from the output signals of an equispaced array. The proposed single snapshot algorithm, which is based on a direct data matrix only, utilizes the concept of virtual imaged arrays to reach the theoretical limit of antenna elements for resolution of a given number of coherent paths. Extensive simulations of our new and some well known algorithms show equal performance of these types whereas our new algorithm excels by reducing computational and hardware complexity.