Localization is a key feature of future Sixth Generation (6G) networks with foreseen accuracy requirements down to the millimeter level, to enable novel applications in the fields of telesurgery, high-precision manufacturing, and others. Currently, such accuracy requirements are only achievable with specialized or highly resource-demanding systems, rendering them impractical for more wide-spread deployment. In this paper, we present the first system that enables low-complexity and low-bandwidth absolute 3D localization with millimeter-level accuracy in generic wireless networks. It performs a carrier phase-based wireless localization refinement of an initial location estimate based on successive location-likelihood optimization across multiple bands. Unlike previous phase unwrapping methods, our solution is one-shot. We evaluate its performance collecting ∼ 350, 000 measurements, showing an improvement of more than one order of magnitude over classical localization techniques. Finally, we will open-source the low-cost, modular FR3 front-end that we developed for the experimental campaign.

The freshness of sensor data is critical for all types of cyber-physical systems. An established measure for quantifying data freshness is the Age-of-Information (AoI), which has been the subject of extensive research. Recently, there has been increased interest in multi-sensor systems: redundant sensors producing samples of the same physical process, sensors such as cameras producing overlapping views, or distributed sensors producing correlated samples. When the information from a particular sensor is outdated, fresh samples from other correlated sensors can be helpful. To quantify the utility of distant but correlated samples, we put forth a two-dimensional (2D) model of AoI that takes into account the sensor distance in an age-equivalent representation. Since we define 2D-AoI as equivalent to AoI, it can be readily linked to existing AoI research, especially on parallel systems. We consider physical phenomena modeled as spatio-temporal processes and derive the 2D-AoI for different Gaussian covariance kernels. For a basic exponential product kernel, we find that spatial distance causes an additive offset of the AoI, while for other kernels the effects of spatial distance are more complex and vary with time. Using our methodology, we evaluate the 2D-AoI of different spatial topologies and sensor densities.

We introduce EgoLife, a project to develop an egocentric life assistant that accompanies and enhances personal efficiency through AI-powered wearable glasses. To lay the foundation for this assistant, we conducted a comprehensive data collection study where six participants lived together for one week, continuously recording their daily activities—including discussions, shopping, cooking, social-izing, and entertainment—using AI glasses for multimodal person-view video references. This effort resulted in EgoLife Dataset, a comprehensive 300-hour egocentric, terpersonal, multiview, and multimodal daily life with intensive annotation. Leveraging this dataset, we troduce EgoLifeQA, a suite of long-context, life-oriented question-answering tasks designed to provide meaningful sistance in daily life by addressing practical questions as recalling past relevant events, monitoring health and offering personalized recommendations.To address the key technical challenges of 1) developing robust visual-audio models for egocentric data, 2) enabling identity recognition, and 3) facilitating long-context question answering over extensive temporal information, we introduce EgoBulter, an integrated system comprising EgoGPT and EgoRAG. EgoGPT is an omni-modal model trained on egocentric datasets, achieving state-of-the-art performance on egocentric video understanding. EgoRAG is a retrieval-based component that supports answering ultra-long-context questions. Our experimental studies verify their working mechanisms and reveal critical factors and bottlenecks, guiding future improvements. By releasing our datasets, models, and benchmarks, we aim to stimulate further research in egocentric AI assistants.

Accurate localization in dense urban areas remains a significant challenge due to the limitations of Global Navigation Satellite Systems (GNSS) in environments with obstacles and reflections, such as urban canyons. While the most recent 3GPP standards offer sophisticated network-centric positioning techniques, their widespread deployment will take time and is hindered by high infrastructure costs and complexity. In this work, we present mm-NOLOC, a UE-centric localization system, designed as a practical fallback when GNSS fails to deliver high accuracy, that leverages the growing deployment of 5G mmWave infrastructure in dense urban areas. Unlike traditional approaches, mm-NOLOC operates independently of 3GPP location support and utilizes only standardized control-plane information collected solely on the UE side – Synchronization Signal Block (SSB) Indices that are mapped to 5G mmWave beam directions – to obtain robust position estimations. To address the uncertainty introduced by urban multipath, mm-NOLOC models the SSB-to-angle relationship as a discrete and multimodal distribution, based on empirical measurements in operational 5G mmWave networks, and uses a particle filter to refine position estimates by integrating probabilistic observations with UE-side motion dynamics. We validate mm-NOLOC through experiments over commercial 5G mmWave deployments, as well as trace-based simulations. Our results show that mm-NOLOC achieves a median localization error below 3 m and a 95th percentile error below 10 m, offering a practical fallback localization solution in urban canyon scenarios for 5G networks without network location support.

Mobile networks are evolving rapidly, with 6G promising unprecedented capabilities in terms of data rates and ultra-low latencies. However, the development of testbed platforms for wireless experimentation has not kept pace. Existing platforms typically offer either end-to-end capabilities with low bandwidth or high bandwidth with limited or no real-time functionality. In this paper, we introduce HELIX, an experimentation platform with 6G scalable real-time capabilities. HELIX integrates a comprehensive physical layer subsystem with multi-numerology support alongside an advanced mixed software-hardware control unit responsible for interacting with the fronthaul network and dynamically configuring the functional split in real time. On the server side, we implement the necessary drivers and routines to enable seamless integration with O-RAN systems, thus facilitating open and end-to-end experimentation. We demonstrate the capabilities of HELIX through a variety of experiments at sub-6 GHz, 28 GHz, and 60 GHz frequencies. Notably, HELIX achieves data rates of up to 1200 Mbps using 256-QAM modulation with over 417 MHz of bandwidth, and end-to-end bidirectional latencies of 500 μs. We show advanced features, including the implementation of Integrated Sensing And Communication (ISAC), and discuss how the platform could be extended to support bandwidths of up to 1670 MHz.

Reconfigurable Intelligent Surfaces (RIS) are a promising technology for creating smart radio environments by controlling wireless propagation. However, several factors hinder the integration of RIS technology into existing cellular networks, including the incompatibility of RIS control interfaces with 5G PHY/MAC procedures for synchronizing radio scheduling decisions and RIS operation, and the cost and energy limitations of passive RIS technology. This paper presents RISENSE, a system for practical RIS integration in cellular networks. First, we propose a novel, low-cost, and low-power RIS design capable of decoding control messages without complex baseband operations or additional RF chains, utilizing a power sensor and a network of microstrip lines and couplers. Second, we design an effective in-band wireless RIS control interface, compatible with 5G PHY/MAC procedures, that embeds amplitude-modulated (AM) RIS control commands directly into standard OFDM-modulated 5G data channels. Finally, we propose a low-overhead protocol that supports swift on-demand RIS re-configurability, making it adaptable to varying channel conditions and user mobility, while minimizing the wastage of 5G OFDM symbols. Our experiments validate the design of RISENSE and our evaluation shows that our system can re-configure a RIS at the same pace as users move, boosting 5G coverage where static or slow RIS controllers cannot.

Joint Communication and Sensing (JCAS) is expected to play a critical role in next-generation wireless networks such as 6G. For complex sensing tasks, such as 3D pose estimation for virtual reality (VR) applications, accurate channel impulse response (CIR) or I/Q samples as well as processing using a neural network is required. Due to the higher bandwidth and antenna array sizes of future wireless networks, it is expected that offloading this data to a remote server for processing would require data rates in the order of 100s of Megabits per second, which is an unreasonable amount of overhead. Therefore it is necessary to preprocess the sensing data locally, and reduce the raw data to useful intermediary features, to mimimize the sensing data transmission overhead, especially when using multiple sensing devices. This paper proposes a method leveraging split inference to distribute neural networks across multiple devices, which achieves high accuracy while addressing the sensing data transfer bottleneck. We evaluate the performance of the proposed method in a VR gaming scenario, where mmWave Wi-Fi signals are used for 3D pose estimation. We show that split inference allows for reducing the communication overhead by three orders of magnitude compared to the centralised approach, while only losing 10% of accuracy. These results pave the way for future work, exploring highly distributed multi-static JCAS as a practical and efficient method of sensing.

The next-generation of mobile networks will increasingly rely on Artificial Intelligence (AI)/Machine Learning (ML) for effective network automation, resource orchestration and management. This translates into performing classification and regression tasks on time series data. Unfortunately, the existing AI/ML models are inherently complex and hard to interpret, which hinders their deployment in production networks. Further, the vast majority of the existing EXplainable Artificial Intelligence (XAI) techniques are either primarily conceived for computer vision and natural language processing and thus fail to provide useful insights.In this paper, we take the research on XAI for time series classification and regression tasks one step further proposing ChronoProf, a new tool that builds on legacy XAI techniques. By creating a linearized version of the original model for different observations, ChronoProf provides insights about the dynamic changes in the model decision-making process across observations and is agnostic to the influence of feature magnitude, which is a key limitation of legacy explainers. Thus, ChronoProf highlights the real influence of model parameters on the output. Our extensive evaluation with real-world mobile traffic traces shows that ChronoProf is able to measure the feature importance, especially in classification tasks where linearized explanations across observations show high consistency.

Interpreting time series predictions from advanced Machine Learning and Deep Learning (ML/DL) models is challenging, as these models often function as black boxes, limiting their applicability in critical domains. To address this, we leverage CHRONOPROF, an Explainable AI (XAI) technique specifically designed for time series data, built upon the SHAP framework. CHRONOPROF improves interpretability by deriving virtual weights from SHAP values, offering a linearized representation of complex model decisions while preserving temporal coherence. However, CHRONOPROF’s complexity can pose challenges for non-expert users. To mitigate this, we developed an interactive dashboard that simplifies interpretation by retrieving stored data and SHAP values to compute and visualize virtual weights along with other representations that are derived from them. This user-friendly interface enables users to explore model behavior across different models and datasets. Ultimately, this innovation facilitates the adoption of CHRONOPROF and fosters trust in AI-driven network operations.

After a rapid deployment worldwide over the past few years, 5G is expected to have reached a mature deployment stage to provide measurable improvement of network performance and user experience over its predecessors. In this study, we aim to assess 5G deployment maturity via three conditions: (1) Does 5G performance remain stable over a long time span (1 year)? (2) Does 5G provide better performance than its predecessor Long-Term Evolution (LTE)? (3) Does the technology offer similar performance across diverse geographic areas and cellular operators? We answer this important question by conducting two year-long measurement campaigns of 5G uplink performance leveraging a custom Android app: one crowd-sourced, cross-sectional campaign spanning 8 major cities in 7 countries and two different continents (Europe and North America), and one controlled campaign focusing on mmWave deployment at a fixed location in the downtown area of Boston, MA. Our datasets show that 5G deployment in major cities appears to have matured, with no major performance improvements observed over a one-year period, but 5G does not provide consistent, superior measurable performance over LTE, especially in terms of latency, and further there exists clear uneven 5G performance across the 8 cities. Our study suggests that, while 5G deployment appears to have stagnated, it is short of delivering its promised performance and user experience gain over its predecessor.

On-device inference offers significant benefits in edge ML systems, such as improved energy efficiency, responsiveness, and privacy, compared to traditional centralized approaches. However, the resource constraints of embedded devices limit their use to simple inference tasks, creating a trade-off between efficiency and capability. In this context, the Hierarchical Inference (HI) system has emerged as a promising solution that augments the capabilities of the local ML by offloading selected samples to an edge server/cloud for remote ML inference. Existing works, primarily based on simulations, demonstrate that HI improves accuracy. However, they fail to account for the latency and energy consumption in real-world deployments, nor do they consider three key heterogeneous components that characterize ML-enabled IoT systems: hardware, network connectivity, and models. To bridge this gap, this paper systematically evaluates HI against standalone on-device inference by analyzing accuracy, latency, and energy trade-offs across five devices and three image classification datasets. Our findings show that, for a given accuracy requirement, the HI approach we designed achieved up to 73% lower latency and up to 77% lower device energy consumption than an on-device inference system. Despite these gains, HI introduces a fixed energy and latency overhead from on-device inference for all samples. To address this, we propose a hybrid system called Early Exit with HI (EE-HI) and demonstrate that, compared to HI, EE-HI reduces the latency up to 59.7% and lowers the device’s energy consumption up to 60.4%. These findings demonstrate the potential of HI and EE-HI to enable more efficient ML in IoT systems.

The ability to perform mobile traffic forecasting effectively with Deep Neural Networks (DNN) is instrumental to optimize resource management in 5G and beyond generation mobile networks. However, despite their capabilities, these Deep Neural Networks (DNN)s often act as complex opaque-boxes with decisions that are difficult to interpret. Even worse, they have proven vulnerable to adversarial attacks which undermine their applicability in production networks. Unfortunately, although existing state-of-the-art EXplainable Artificial Intelligence (XAI) techniques are often demonstrated in computer vision and Natural Language Processing (NLP), they may not fully address the unique challenges posed by spatio-temporal time-series forecasting models. To address these challenges, we introduceDeExpin this paper, a tool that flexibly builds upon legacy EXplainable Artificial Intelligence (XAI) techniques to synthesize compact explanations by making it possible to understand which Base Stations (BSs) are more influential for forecasting from a spatio-temporal perspective. Armed with such knowledge, we run state-of-the-art Adversarial Machine Learning (AML) techniques on those BSs to measure the accuracy degradation of the predictors under adversarial attacks. Our comprehensive evaluation uses real-world mobile traffic datasets and demonstrates that legacy XAI techniques spot different types of vulnerabilities. While Gradient-weighted Class Activation Mapping (GC) is suitable to spot BSs sensitive to moderate/low traffic injection, LayeR-wise backPropagation (LRP) is suitable to identify BSs sensitive to high traffic injection. Under moderate adversarial attacks, the prediction error of the BSs identified as vulnerable can increase by more than 250%.

Forecasting is increasingly considered a fundamental enabler for the management of next-generation mobile networks. While deep neural networks excel at short- and long-term forecasting, their complexity hinders interpretability, a crucial factor for production deployment. The existing EXplainable Artificial Intelligence (XAI) techniques, primarily designed for computer vision and natural language processing, struggle with time series data due to their lack of understanding of temporal characteristics of the input data. In this paper, we take the research on EXplainable Artificial Intelligence (XAI) for time series forecasting one step further by proposingAIChronoLens, a new tool that links legacy XAI explanations with the temporal properties of the input.AIChronoLensallows diving deep into the behavior of time series predictors and spotting, among other aspects, the hidden causes of forecast errors. We show thatAIChronoLens’s output can be utilized for meta-learning to predict when the original time series forecasting model makes errors and fix them in advance, thereby improving the accuracy of the predictors. Extensive evaluations with real-world mobile traffic traces pinpoint model behaviors that would not be possible to identify otherwise and show how model performance can be improved by 32 % upon re-training and by up to 39 % with meta-learning.

Tomorrow’s massive-scale Internet-of-Things (IoT) sensor networks are poised to drive uplink traffic demand, especially in areas of dense deployment. To meet this demand, however, network designers leverage tools that often require accurate estimates of Channel State Information (CSI), which incurs a high overhead and thus reduces network throughput. Furthermore, the overhead generally scales with the number of clients, and so is of special concern in such massive IoT sensor networks. While prior work has used transmissions over one frequency band to predict the channel of another frequency band on the same link, this paper takes the next step in the effort to reduce CSI overhead: predict the CSI of a nearby but distinct link. We proposeCross-Link Channel Prediction(CLCP), a technique that leverages multi-view representation learning to predict the channel response of a large number of users, thereby reducing channel estimation overhead further than previously possible. CLCP’s design is highly practical, exploiting existing transmissions rather than dedicated channel sounding or extra pilot signals. We have implemented CLCP for two different Wi-Fi versions, namely 802.11n and 802.11ax, the latter being the leading candidate for future IoT networks. We evaluate CLCP in two large-scale indoor scenarios involving both line-of-sight and non-line-of-sight transmissions with up to 144 different 802.11ax users. Moreover, we measure its performance with four different channel bandwidths, from 20 MHz up to 160 MHz. Our results show that CLCP provides a 2x throughput gain over baseline and a 30% throughput gain over existing prediction algorithms.

Five years after the initial 5G rollout, several research works have analyzed the performance of operational 5G mmWave networks. However, these measurement studies primarily focus on single-user performance, leaving the sharing and resource allocation policies largely unexplored. In this paper, we fill this gap by conducting the first systematic study, to our best knowledge, of resource allocation policies of current 5G mmWave mobile network deployments through an extensive measurement campaign across four major US cities and two major mobile operators. Our study reveals that resource allocation among multiple flows is strictly governed by the cellular operators and flows are not allowed to compete with each other in a shared queue. Operators employ simple threshold-based policies and often over-allocate resources to new flows with low traffic demands or reserve some capacity for future usage. Interestingly, these policies vary not only among operators but also for a single operator in different cities. We also discuss a number of anomalous behaviors we observe in our experiments across different cities and operators.

This paper undertakes a holistic investigation of two fundamental trade-offs in monostatic OFDM integrated sensing and communication (ISAC) systems, namely, the time-frequency trade-off and the spatial trade-off, originating from the choice of modulation order for random data and the design of beamforming strategies, respectively. To counteract the elevated side-lobe levels induced by varying-amplitude data in high-order QAM signaling, we introduce a novel linear minimum mean-squared-error (LMMSE) estimator. We also provide a rigorous theoretical characterization of side-lobe levels achieved by the proposed LMMSE estimator and two benchmark schemes, proving its superiority for any modulation scheme and SNR level. Moreover, we explore spatial domain trade-offs through two ISAC transmission strategies: concurrent, employing joint beams, and time-sharing, using separate beams for sensing and communications not overlapping in time. Simulations demonstrate improved performance of the LMMSE estimator, especially in detecting weak targets in the presence of strong ones with high-order QAM, consistently yielding more favorable ISAC trade-offs than existing baselines under various modulation schemes, SNR conditions, RCS levels and transmission strategies. Additionally, we present experimental results to validate the effectiveness of the LMMSE estimator in reducing side-lobe levels, based on real-world measurements

Roaming provides users with voice and data connectivity when traveling abroad. This is particularly the case in Europe where the "Roam like Home" policy established by the European Union in 2017 has made roaming affordable. Nonetheless, due to various policies employed by operators, roaming can incur considerable performance penalties as shown in past studies of 3G/4G networks. As 5G provides significantly higher bandwidth, how does roaming affect user-perceived performance? We present, to the best of our knowledge, the first comprehensive and comparative measurement study of commercial 5G in four European countries.Our measurement study is unique in the way it makes it possible to link key 5G mid-band channels and configuration parameters ("policies") used by various operators in these countries with their effect on the observed 5G performance from the network (in particular, the physical and MAC layers) and applications perspectives. Our measurement study not only portrays users’ observed quality of experience when roaming, but also provides guidance to optimize the network configuration and to users and application developers in choosing mobile operators. Moreover, our contribution provides the research community with the largest cross-country roaming 5G dataset to stimulate further research.

Next-generation mobile networks will increasingly rely on the ability to forecast traffic patterns for resource management. Usually, this translates into forecasting diverse objectives like traffic load, bandwidth, or channel spectrum utilization, measured over time. Among the other techniques, Long-Short Term Memory (LSTM) proved very successful for this task. Unfortunately, the inherent complexity of these models makes them hard to interpret and, thus, hampers their deployment in production networks. To make the problem worsen, EXplainable Artificial Intelligence (XAI) techniques, which are primarily conceived for computer vision and natural language processing, fail to provide useful insights: they are blind to the temporal characteristics of the input and only work well with highly rich semantic data like images or text. In this paper, we take the research on XAI for time series forecasting one step further proposing AIChronoLens, a new tool that links legacy XAI explanations with the temporal properties of the input. In such a way, AIChronoLens makes it possible to dive deep into the model behavior and spot, among other aspects, the hidden cause of errors. Extensive evaluations with real-world mobile traffic traces pinpoint model behaviors that would not be possible to spot otherwise and model performance can increase by 32%.

An established policy for updating systems is zerowait: a source immediately sends a new sample as soon as the sink acknowledges the receipt of the previous one. The rationale of zero-wait is that with instantaneous feedback, the transmission of samples can fully utilize the forward link without ever causing a queue. However, this ideal behavior does not extend to multihop networks and two-way delay. One approach to generalize zero-wait for use in larger networks is message pipelining, where there is a fixed number of samples and acknowledgments $k \geq 1$ in the network at any time. We analyze the peak age-of-information of updating systems with pipelining in multi-hop networks with arbitrarily many queues in the forward and feedback paths. While pipelining improves network utilization, it also increases queuing delays, and the optimal degree k must strike a balance between the two. We show how this depends on the diameter and topology of the network, the presence of bottlenecks, and the statistical distribution of service times. In an a priori unknown and changing network, it is beneficial to adjust the pipelining adaptively. We demonstrate how basic delay-based congestion control can be effectively used to achieve this goal.

Integrated Sensing And Communication (ISAC) systems are expected to perform accurate radar sensing while having minimal impact on communication. Ideally, sensing should only reuse communication resources, especially for spectrum which is contended by many applications. However, this poses a great challenge in that communication systems often operate on narrow subbands with low sensing resolution. Combining contiguous subbands has shown significant resolution gain in active localization. However, multiband ISAC remains unexplored due to communication subbands being highly sparse (non-contiguous) and affected by phase offsets that prevent their aggregation (incoherent). To tackle these problems, we design HiSAC, the first multiband ISAC system that combines diverse subbands across a wide frequency range to achieve super-resolved passive ranging. To solve the non-contiguity and incoherence of subbands, HiSAC combines them progressively, exploiting an anchor propagation path between transmitter and receiver in an optimization problem to achieve phase coherence. HiSAC fully reuses pilot signals in communication systems, applies to different frequencies, and can combine diverse technologies, e.g., 5G-NR and WiGig. We implement HiSAC on an experimental platform in the millimeter-wave unlicensed band and test it on objects and humans. Our results show it enhances the sensing resolution by up to 20 times compared to single-band processing while occupying the same spectrum.

5G in mid-bands has become the dominant deployment of choice in the world. We present - to the best of our knowledge - the first comprehensive and comparative cross-country measurement study of commercial mid-band 5G deployments in Europe and the U.S., filling a gap in the existing 5G measurement studies. We unveil the key 5G mid-band channels and configuration parameters used by various operators in these countries, and identify the major factors that impact the observed 5G performance both from the network (physical layer) perspective as well as the application perspective. We characterize and compare 5G mid-band throughput and latency performance by dissecting the 5G configurations, lower-layer parameters as well as deployment settings. By cross-correlating 5G parameters with the application decision process, we demonstrate how 5G parameters affect application QoE metrics and suggest a simple approach for QoE enhancement. Our study sheds light on how to better configure and optimize 5G mid-band networks, and provides guidance to users and application developers on operator choices and application QoE tuning. We released the datasets and artifacts at https://github.com/SIGCOMM24-5GinMidBands/artifacts.

Reconfigurable Intelligent Surfaces (RISs) have shown great prospects in securing mmWave communication from potential eavesdropping by configuring reflecting elements to strengthen the signal strength at the desired location and creating nulls at potential eavesdropping locations. Acquiring perfect channel information is crucial for optimizing RIS configuration; however, obtaining such information is costly and, as a result, should be performed sparingly. This work studies the impact of the age of channel information on the secrecy performance of a RIS-assisted mmWave network. In particular, we investigate how outdated channel information affects the joint optimization of transmit beamforming and RIS configuration. In our Monte-Carlo simulations, we first identify the factors influencing the aging process of a RIS-assisted mmWave channel in both the near and far fields of the RIS. Subsequently, we examine the impact of channel aging on secrecy capacity and demonstrate that adequate secrecy capacity can still be achieved even when channel information is slightly outdated, thus reducing the need for frequent RIS configuration.

Orthogonal Time Frequency Space (OTFS) modulation is widely recognized as a modulation scheme with advan-tageous properties for both radar and communication waveform designs. In OTFS, information symbols are mapped in the Delay-Doppler (DD) domain leading to reliable communication in high Doppler channels. However, the presence of inverse-discrete Fourier Transform operation in OTFS architecture results in a high Peak-to-Average Power Ratio (PAPR) in the transmit OTFS frames. This paper introduces a novel metric-based symbol pre-distortion algorithm constrained by Error Vector Magnitude (EVM) limits to reduce the PAPR in OTFS modulation. The imposed constraint in the form of EVM limits establishes it as a pragmatic method that adds no side-channel information rather exploits the available EVM limit usually kept in wireless standards. The pre-distortion applied to each symbol/sample in an OTFS frame is determined by the proposed metrics, representing the contribution of each symbol to peak values in the output. The proposed method is simple, flexible, and does not require additional complexity for symbol detection on the receiver end. Our simulation results demonstrate a significant reduction in the PAPR of OTFS blocks for both QPSK and 16-QAM modulation schemes. Furthermore, our proposed Constrained Constellation Shaping scheme exhibits enhanced performance in PAPR reduction as the number of Doppler bins increases for an OTFS frame size.

mmWave communication has come up as the un-explored spectrum for 5G services. With new standards for 5G NR positioning, more off-the-shelf platforms and algorithms are needed to perform indoor positioning. An object can be accurately positioned in a room either by using an angle and a delay estimate or two angle estimates or three delay estimates. We propose an algorithm to jointly estimate the angle of arrival (AoA) and angle of departure (AoD), based only on the received signal strength (RSS). We use mm-FLEX, an experimentation platform developed by IMDEA Networks Institute that can perform realtime signal processing for experimental validation of our proposed algorithm. Codebook-based beampatterns are used with a uniquely placed multi-antenna array setup to enhance the reception of multipath components and we obtain an AoA estimate per receiver thereby overcoming the line-of-sight (LoS) limitation of RSS-based localization systems. We further validate the results from measurements by emulating the setup with a simple ray-tracing approach.

In this work we present RAPID, the first joint communication and radar system based on next-generation IEEE 802.11ay WiFi networks operating in the 60 GHz band. Unlike existing approaches for human sensing at millimeter-wave frequencies, which rely on special-purpose radars, RAPID achieves radar-level sensing accuracy with IEEE 802.11ay access points, thus avoiding the burden of installing ad-hoc sensors. RAPID enables contactless human sensing applications, such as people tracking, Human Activity Recognition (HAR), and person identification without requiring modifications to the standard packet structure. Specifically, we leverage IEEE 802.11ay beam training to accurately localize and track multiple individuals within the same environment. Then, we propose a new way of using beam tracking to extract micro-Doppler signatures from the time-varying Channel Impulse Response (CIR) estimated fromreflectedpackets. Such signatures are fed to a deep learning classifier to perform HAR and person identification. RAPID is implemented on a cutting-edge IEEE 802.11ay-compatible FPGA platform with phased antenna arrays, and evaluated on a large dataset of CIR measurements. It is robust across different environments and subjects, and outperforms state-of-the-art sub-6 GHz WiFi sensing techniques. Using two access points, RAPID reliably tracks multiple subjects, reaching HAR and person identification accuracies of$94\%$and$90\%$, respectively.

Orthogonal time frequency space (OTFS) is a promising alternative to orthogonal frequency-division multiplexing (OFDM) for high-mobility communications. We propose a novel multiple-input multiple-output (MIMO) integrated sensing and communication (ISAC) system based on OTFS modulation. We begin by deriving new sensing and communication signal models for the proposed MIMO-OTFS ISAC system that explicitly capture inter-symbol interference (ISI) and inter-carrier interference (ICI) effects. We then develop a generalized likelihood ratio test (GLRT) based multi-target detection and delay-Doppler-angle estimation algorithm for MIMO-OTFS radar sensing that can simultaneously mitigate and exploit ISI/ICI effects, to prevent target masking and surpass standard unambiguous detection limits in range/velocity. Moreover, considering two operational modes (discovery/track), we propose an adaptive MIMO-OTFS ISAC transmission strategy. For the discovery mode, we introduce the concept of delay-Doppler (DD) multiplexing, enabling omnidirectional probing of the environment and large virtual array at the OTFS radar receiver. For the track mode, we pursue a directional transmission approach and design an OTFS ISAC optimization algorithm in spatial and DD domains, seeking the optimal trade-off between radar signal-to-noise ratio (SNR) and achievable rate. Simulation results verify the effectiveness of the proposed sensing algorithm and reveal valuable insights into OTFS ISAC trade-offs under varying communication channel characteristics.

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

Multiple-Input Multiple-Output (MIMO) systems have been presented for Terahertz (THz) communications to combat high path loss and enable Terabit-per-second (Tbps) links. The lack of experimental MIMO channel measurements, however, has restricted the majority of THz MIMO work to the theoretical realm. This paper presents a novel, first-of-its-kind correlation-based MIMO channel sounder with a timing reference for sub-THz communications, which enables broadband, long-range, and time-varying channel characterization of MIMO systems. Preliminary results from a conference room setting show that 28.5% of the tested scenarios can support spatial multiplexing, and even with beamforming, the capacity outperforms the SISO cases substantially.

While much research effort has been invested in long-range and low-power uplink communication for battery-free IoT networks, current deployments lack a scalable bidirectional communication infrastructure for data collection and processing with battery-free devices. To fill this gap, we introduce Lo W-Fi, a system architecture specifically designed to meet the requirements of battery-free IoT applications. We show the suitability of LoW-Fi for deploying monitoring systems for precision agriculture indoors. This sector is revolutionizing with the installation of smart greenhouses that require the constant monitoring of ambient parameters to ensure optimal conditions for the crops growth. Our system is implemented using commercial off-the-shelf devices, and it works at the intersection of WiFi and LiFi for downlink and RF backscatter for uplink, retaining the advantages of each technology and solving their practical limitations. We evaluate LoW-Fi performance in a real greenhouse, and the experimental results show that it can achieve an uplink (downlink) range of 45m(70m) with 0% BER. The aggregated data rate is up to 4.5 Mb/s.

Machine Learning (ML) is a promising tool to design wireless physical layer (PHY) components. It is particularly interesting for millimeter-wave (mm-wave) frequencies and above, due to the more challenging hardware design and channel environment at these frequencies. Rather than building individual ML-components, in this paper, we design an entire ML-enhanced mm-wave receiver for frequency selective channels. Our ML-receiver jointly optimizes the channel estimation, equalization, phase correction and demapper using Convolutional Neural Networks. We also show that for mm-wave systems, the channel varies significantly even over short timescales, requiring frequent channel measurements, and this situation is exacerbated in mobile scenarios. To tackle this, we propose a new ML-channel estimation approach that refreshes the channel state information using the guard intervals (not intended for channel measurements) that are available for every block of symbols in communication packets. To the best of our knowledge, our ML-receiver is the first work to outperform conventional receivers in general scenarios, with simulation results showing up to 7 dB gains. We also provide an experimental validation of the ML-enhanced receiver with a 60 GHz FPGA-based testbed with phased antenna arrays, which shows a throughput increase by a factor of up to 6 over baseline schemes in mobile scenarios.

The ability to forecast mobile traffic patterns is key to resource management for mobile network operators and planning for local authorities. Several Deep Neural Networks (DNN) have been designed to capture the complex spatio-temporal characteristics of mobile traffic patterns at scale. These models are complex black boxes whose decisions are inherently hard to explain. Even worse, they have proven vulnerable to adversarial attacks which undermine their applicability in production networks. In this paper, we conduct a first in-depth study of the vulnerabilities of DNNs for large-scale mobile traffic forecasting. We propose DeExp, a new tool that leverages EXplainable Artificial Intelligence (XAI) to understand which Base Stations (BSs) are more influential for forecasting from a spatio-temporal perspective. This is challenging as existing XAI techniques are usually applied to computer vision or natural language processing and need to be adapted to the mobile network context. Upon identifying the more influential BSs, we run state-of-the art Adversarial Machine Learning (AML) techniques on those BSs and measure the accuracy degradation of the predictors. Extensive evaluations with real-world mobile traffic traces pinpoint that attacking BSs relevant to the predictor significantly degrades its accuracy across all the scenarios.

Reconfigurable intelligent surfaces (RISs) have great potential to improve the coverage of mmWave networks; however, acquiring perfect channel state information (CSI) of a RIS-enabled mmWave network is very costly and should thus be done infrequently. At the same time, finding an optimal RIS configuration when CSI is outdated is challenging. To this end, this work aims to provide practical insights into the tradeoff between the outdatedness of the CSI and the system performance by using the effective capacity as analytical tool. We consider a RIS-enabled mmWave downlink where the base station (BS) operates under statistical quality-of-service (QoS) constraints. We find a closed-form expression for the effective capacity that incorporates the degree of optimism of packet scheduling and correlation strength between instantaneous and outdated CSI. Moreover, our analysis allows us to find optimal values of the signal-to-interference-plus-noise-ratio (SINR) distribution parameter and their impact on the effective capacity in different network scenarios. Simulation results demonstrate that better effective capacity can be achieved with suboptimal RIS configuration when the channel estimates are known to be outdated. It allows us to design system parameters that guarantee better performance while keeping the complexity and cost associated with channel estimation to a minimum.

With the advancement of the Internet of Things (IoT), many devices will be connected to the Internet, enabling digital twin and smart home applications. However, currently, these IoT devices are operating at lower frequency bands of the wireless spectrum, typically ranging from a few hundred MHz (such as RFID and LoRa) to a few GHz (such as BLE and WiFi). As a result, the current IoT devices not only place a huge strain on these bands, but also cannot benefit from the large bandwidth available in the higher frequencies of the spectrum such as mmWave bands. In this paper, our goal is to bring mmWave technology to existing IoT devices so they can benefit from the advantages this technology offers, such as high network capacity, low interference, and Space Division Multiple Access. To this end, we design mmPlug, a novel plug-and-play module which is simple and energy-efficient. mmPlug can be easily connected to the antenna port of any IoT device, enabling it to operate in the mmWave band. mm-Plug is compatible with different wireless technologies (such as WiFi, Lora, etc.) and does not require any modification to the circuit, firmware or communication protocols of the existing IoT devices. mmPlug achieves this by a novel design which can seamlessly be connected to the antenna port of the IoT device. We have implemented mmPlug on PCB and empirically evaluated its performance. Our results show that mmPlug enables existing IoT devices (such as WiFi and Lora) to operate at mmWave band while achieving accurate localization, uplink and downlink even when they are more than 30 m far from the access point.

The Hierarchical Inference (HI) paradigm has recently emerged as an effective method for balancing inference accuracy, data processing, transmission throughput, and offloading cost. This approach proves particularly efficient in scenarios involving resource-constrained edge devices like micro controller units (MCUs), tasked with executing tinyML inference. Notably, it outperforms strategies such as local inference execution, inference offloading, and split inference (i.e., inference execution distributed between two endpoints). Building upon the HI paradigm, this work explores different techniques aimed at further optimizing inference task execution. We propose three distinct HI approaches and evaluate their utility for image classification.

Tomorrow's massive-scale IoT sensor networks are poised to drive uplink traffic demand, especially in areas of dense deployment. To meet this demand, however, network designers leverage tools that often require accurate estimates of Channel State Information (CSI), which incurs a high overhead and thus reduces network throughput. Furthermore, the overhead generally scales with the number of clients, and so is of special concern in such massive IoT sensor networks. While prior work has used transmissions over one frequency band to predict the channel of another frequency band on the same link, this paper takes the next step in the effort to reduce CSI overhead: predict the CSI of a nearby but distinct link. We propose Cross-Link Channel Prediction (CLCP), a technique that leverages multi-view representation learning to predict the channel response of a large number of users, thereby reducing channel estimation overhead further than previously possible. CLCP's design is highly practical, exploiting existing transmissions rather than dedicated channel sounding or extra pilot signals. We have implemented CLCP for two different Wi-Fi versions, namely 802.11n and 802.11ax, the latter being the leading candidate for future IoT networks. We evaluate CLCP in two large-scale indoor scenarios involving both line-of-sight and non-line-of-sight transmissions with up to 144 different 802.11ax users and four different channel bandwidths, from 20 MHz up to 160 MHz. Our results show that CLCP provides a 2× throughput gain over baseline and a 30% throughput gain over existing prediction algorithms.

Millimeter-Wave (mmWave) WiFi can provide very low latency and multi-Gbps throughput, but real-world deployments usually do not achieve the theoretically feasible performance. One main source of inefficiency is the contention-based random channel access, as it requires omni-directional reception which limits performance. Additionally, carrier sensing at mmWave frequencies is highly unreliable, leading to reduced channel usage. In this paper, we present SIGNalling in the PHY Preamble (SIGNiPHY) for efficient directional communications, a solution that allows to embed user identity in the preamble of data packets. It allows for true early user identification and then immediately steering the beam towards the transmitter while receiving the physical layer preamble. SIGNiPHY enables directional reception in random access mmWave networks, and additionally helps to quickly filter unwanted packets. It does not affect any preamble functions and is backward-compatible with legacy stations. We implement SIGNiPHY on an FPGA-based mmWave testbed and show that it achieves 99.6% decoding accuracy even under very low SINR conditions. We also implement SIGNiPHY in ns-3 to evaluate large networks and show that it achieves throughput gains between 13% and 230% compared to different baseline schemes, due to the lower packet loss rate and improved spatial sharing.

Multi-connectivity is considered a key enabler for 5G networks and beyond, aiming to enhance capacity by combining multiple communication links in the same or different bands. Similarly, in cell-free networks all \acpap jointly serve users in the same band, boosting capacity through enhanced spectral efficiency. Both approaches can be very effective in \acmmwave networks by addressing key issues of reliability and robustness due to the multiple simultaneous links. Furthermore, the use of narrow directional beams in \acmmwave spatially separates the signals, allowing for in-band multi-connectivity through local beamtraining. Such in-band multi-connectivity would be an alternative design to traditional cell-free networks that does not rely on phase-coherent processing or centralized methods for interference suppression. The physical layer processing and resource allocation problem then simplifies to a local beamtraining challenge, making these networks easier and simpler to implement and deploy, as any connection just has to train and maintain the local beam. We validate this approach by designing a multi-connectivity \acmmwave network with minimal network synchronization, relying solely on analog beamforming for spatial separation. Our evaluation results demonstrate that in-band multi-connectivity with 4 asynchronous and independent links can provide uninterrupted service even in dense, high-traffic scenarios, compared to up to 20% of service loss in a standard single-connectivity deployment. Distributing the traffic across multiple \acpap also had throughput gains of up to 30%, showing that multi-connectivity \acmmwave networks can provide a high-throughput, reliable and stable service for next-generation applications.

This paper presents the design, implementation and evaluation of waveSLAM, a low-cost mobile robot system that uses the millimetre wave (mmWave) communication devices to enhance the indoor mapping process targeting environments with reduced visibility or glass/mirror walls. A unique feature of waveSLAM is that it only leverages existing Commercial-Off-The-Shelf (COTS) hardware (Lidar and mmWave radios) that are mounted on mobile robots to improve the accurate indoor mapping achieved with optical sensors. The key intuition behind the waveSLAM design is that while the mobile robots moves freely, the mmWave radios can periodically exchange angle and distance estimates between themselves (self-sensing) by bouncing the signal from the environment, thus enabling accurate estimates of the target object/material surface. Our experiments verify that waveSLAM can archive cm-level accuracy with errors below 22 cm and 20° in angle orientation which is compatible with Lidar when building indoor maps.

Thanks to the definition of the new IEEE 802.11bf standard, the development of Wi-Fi sensing applications is gaining momentum in the research community. In this regard, several studies have shown that learning-based approaches that leverage the frequency response of the Wi-Fi channel in the sub-7 GHz bands can reach high accuracy in different classification tasks, such as activity recognition, or person identification. Instead, more fine-grained applications – e.g., human localization and tracking, or respiration and heartbeat monitoring – require implementing model-based approaches to estimate the Wi-Fi multi-path parameters and analyze the time evolution of the paths associated with specific targets (the human body or chest). In this paper, we investigate the performance of six super-resolution algorithms for sub-7 GHz multi-path parameter estimation. Our extensive evaluation indicates that the estimation accuracy that can be achieved through commercial devices allows implementing human localization and tracking strategies but is insufficient to effectively design human vital signs monitoring applications due to the limited frequency and spatial diversity. We pledge to release our implementations for further investigations.1

Mm-wave communications use analog beamforming techniques, which steer the signal energy in a desired direction, to overcome the high path-loss at such frequencies. To determine the direction in which to steer, mm-wave standards such as IEEE 802.11ad specify beam training mechanisms for both access points as well as client stations. However, the overhead of the beam training limits scalability as the density of network deployments increases and mobile devices that require constantre-trainingare supported. We design SPIDER, a low-overhead beam-training mechanism where only access points actively participate in the training and stations perform passive compressive estimation of the angle-of-arrival. To this end, stations carry out phase-coherent measurements by switching through multiple receive beam patterns on a time-scale of tens of nanoseconds when receiving a packet preamble. Since no suitable testbed platforms exist that support such fast antenna reconfiguration, we design a high-performance, full-bandwidth FPGA-based testbed platform for flexible mm-wave experimentation, that we make available as open source. The performance analysis with this testbed shows that our algorithm achieves highly accurate angle estimation used to drive the beam steering decisions and reduces overhead by an order of magnitude compared to IEEE 802.11ad beam training.

A well established method to detect and classify human movements using Millimeter-Wave (mmWave) devices is the time-frequency analysis of the small-scale Doppler effect (termed micro-Doppler) of the different body parts, which requires a regularly spaced and dense sampling of the Channel Impulse Response (CIR). This is currently done in the literature either using special-purpose radar sen-sors, or interrupting communications to transmit dedicated sensing waveforms, entailing high overhead and channel utilization. In this work we present SPARCS, an integrated human sensing and commu-nication solution for mmWave systems. SPARCS is the first method that reconstructs high quality signatures of human movement from irregular and sparse CIR samples, such as the ones obtained during communication traffic patterns. To accomplish this, we formulate the micro-Doppler extraction as a sparse recovery problem, which is critical to enable a smooth integration between communication and sensing. Moreover, if needed, our system can seamlessly inject short CIR estimation fields into the channel whenever communication traffic is absent or insufficient for the micro-Doppler extraction. SPARCS effectively leverages the intrinsic sparsity of the mmWave channel, thus drastically reducing the sensing overhead with re-spect to available approaches. We implemented SPARCS on an IEEE 802.11ay Software Defined Radio (SDR) platform working in the 60 GHz band, collecting standard-compliant CIR traces matching the traffic patterns of real WiFi access points. Our results show that the micro-Doppler signatures obtained by SPARCS enable a typical downstream application such as human activity recognition with more than 7 times lower overhead with respect to existing methods, while achieving better recognition performance.

Millimeter-wave (mmWave) technology is an important element to increase the throughput and reduce latency of future wireless networks. At the same time, its high bandwidth and highly directional antennas allow for unprecedented accuracy in wireless sensing and localization applications. In this paper, we thoroughly analyze mmWave localization and find that it is either extremely accurate or has a very high error, since there is significant mmWave coverage via reflections and even through walls. As a consequence, sub-6 GHz technology can not only provide (coarse) localization where mmWave is not available, but is also critical to decide among multiple candidate antennas and APs for accurate mmWave localization.

Fifth-generation (5G) networks are now entering a stable phase in terms of commercial release. 5G design is flexible to support a diverse range of radio bands (i.e., low-, mid-, and high-band) and application requirements. Since its initial roll-out in 2019, extensive measurements studies have revealed key aspects of commercial 5G deployments (e.g., coverage, signal strength, throughput, latency, handover, and power consumption among the others) for several scenarios (e.g., pedestrian and car mobility, mid-, and high-bands, etc.). In this paper, we take a different angle than previous studies and carry out an in-depth measurement study of 5G in a large public bus transit system in a major European city. For several mobile network operators, we identify how flexible the network deployment is by analyzing Radio Resource Control (RRC) messages, mobility management, and application performance.

5G mmWave is being rapidly deployed by all major mobile operators. With the technology still in its infancy, several early research works analyze the performance of operational 5G mmWave networks. Nonetheless, these measurement studies primarily focus on single-user performance, leaving the sharing and resource allocation policies largely unexplored. In this paper, we fill this gap by conducting the, to our best knowledge, first systematic study of resource allocation policies of current 5G mmWave mobile network deployments through an extensive measurement campaign across four major US cities and two major mobile operators. Our study reveals that resource allocation among multiple flows is strictly governed by the cellular operators and flows are not allowed to compete with each other in a shared queue. Operators employ simple threshold-based policies and often over-allocate resources to new flows with low traffic demands or reserve some capacity for future usage. Interestingly, these policies vary not only among operators but also for a single operator in different cities. We also discuss a number of anomalous behaviors we observed in our experiments across different cities and operators.

Millimeter-wave technology provides the necessary improvements in capacity and performance for the next generation of wireless networks. The new IEEE 802.11ay amendment extends IEEE 802.11ad to offer 100 Gbit/s connectivity in the unlicensed 60 GHz band through technical advancements such as Multiple-Input and Multiple-Output (MIMO), channel bonding and aggregation. Additionally, it offers improvements to the Beamforming Training (BFT) process in order to increase its efficiency and accuracy. One new technique defined by IEEE 802.11ay is Group Beamforming, which allows to simultaneously train all stations, and significantly reduces training overhead, especially in very dense networks. In this paper, we provide an implementation of IEEE 802.11ay in ns-3 and perform, to the best of our knowledge, the first detailed system-level evaluation of the performance of the novel IEEE 802.11ay protocol. We specifically study the performance of Group Beamforming and compare it against the legacy 802.11ad BFT. We explore how different BFT approaches scale in large networks, identify the possible problems and evaluate at how the BFT process influences the performance of the network overall. Our analysis shows that Group Beamforming can outperform the legacy approach, resulting in lower overhead and improved network performance. However, we also found that the Access Point (AP) training is quite vulnerable to interference in dense networks, introducing severe limitations to the performance, especially in large rooms where precise BFT is crucial to maintain the communication link. Therefore, we propose several improvements to Group Beamforming that improve performance and provide robust beamforming even in very dense scenarios.

Future WLAN devices will combine both IEEE 802.11ad and 802.11ac interfaces. The former provides multi-Gbps rates but is susceptible to blockage, whereas the latter is slower but offers reliable connectivity. A fundamental challenge is thus how to combine those complementary technologies, to make the most of the advantages they offer. In this work, we leverage Multipath TCP (MPTCP) to use both interfaces simultaneously in order to achieve a higher overall throughput as well as seamlessly switch to a single interface when the other one fails. We find that standard MPTCP often performs sub-optimally and can yield a throughput much lower than that of single path TCP over the faster of the two interfaces. We analyze the cause of these performance issues in detail and then designMuSher, an agile MPTCP scheduler that allows MPTCP to fully utilize the channel resources available to both interfaces. Our evaluation in realistic scenarios shows thatMuShercan provide a throughput improvement of 50%/130% under WLAN/Internet settings respectively, compared to the default MPTCP scheduler. It further speeds up the recovery of a traffic stream after disruption by a factor of 8x/75x.

We present an extensive experimental evaluation of the performance and power consumption of the 60 GHz IEEE 802.11ad technology on commercial smartphones. We also compare 802.11ad against its main competitors in the 5 GHz band - 802.11ac and, for first time, 802.11ax, on mobile devices. Our performance comparison focuses on two aspects that have not been extensively studied before: (i) dense multi-client and multi-AP topologies and (ii) popular mobile applications under realistic mobility patterns. Our power consumption study covers both non-communicating and communicating modes. We also present the first study of the power saving mode in 802.11ad-enabled smartphones and its impact on performance. Our results show that 802.11ad is better able to address the needs of emerging bandwidth-intensive applications in smartphones than its 5 GHz counterparts. At the same time, we identify several key research directions towards realizing its full potential.

The performance of wireless communication systems is evolving rapidly, making it difficult to build experimentation platforms that meet the hardware requirements of new standards. The bandwidth of current systems ranges from 160 MHz for IEEE 802.11ac/ax to 2 GHz for Millimeter-Wave (mm-wave) IEEE 802.11ad/ay, and they support up to 8 spatial MIMO streams. Mobile 5G and beyond systems have a similarly diverse set of requirements.

WiFi location systems are remarkably accurate, with decimeter-level errors for recent CSI-based systems. However, such high accuracy is achieved under Line-of-Sight (LOS) conditions and with an access point (AP) density that is much higher than that typically found in current deployments that primarily target good coverage. In contrast, when many of the APs within range are in Non-Line-of-Sight (NLOS), the location accuracy degrades drastically. In this paper we present UbiLocate, a WiFi location system that copes well with common AP deployment densities and works ubiquitously, i.e., without excessive degradation under NLOS. UbiLocate demonstrates that meter-level median accuracy NLOS localization is possible through (i) an innovative angle estimator based on a Nelder-Mead search, (ii) a fine-grained time of flight ranging system with nanosecond resolution, and (iii) the accuracy improvements brought about by the increase in bandwidth and number of antennas of IEEE 802.11ac. In combination, they provide superior resolvability of multipath components, significantly improving location accuracy over prior work. We implement our location system on off-the-shelf 802.11ac devices and make the implementation, CSI-extraction tool and custom Fine Timing Measurement design publicly available to the research community. We carry out an extensive performance analysis of our system and show that it outperforms current state-of-the-art location systems by a factor of 2--3, both under LOS and NLOS.

The characterization of user behavior is key to perform traffic analysis, modeling and optimization of network components and protocols. This is especially true for 5G and beyond networks that will heavily rely on machine learning for network optimization. In Base Station (BS) traffic traces, users are uniquely identified by a Radio Network Temporary Identifier (RNTI) assigned to them. RNTIs are not bound to a user but are reused upon expiration of an inactivity timer, whose duration is operator dependent. This implies that, over time, multiple users can be mapped to the same RNTI ID in diverse ways. Hence, when using real-world radio access measurement traces for traffic analysis, distinguishing individual users within the RNTI space is a non-trivial task. In this paper, we provide the first in-depth study of the RNTI allocation process and shed light not only on the setting of the inactivity timer, but also on the relationship of the RNTI allocation scheme and the user characteristics. For this, we collect a large dataset of mobile traffic from multiple BSs of several mobile network operators. The analysis of the decoded control messages of the BS unveils that the RNTI allocation process changes over time depending on the BSs observed load and time of day. We also observe that the RNTI expiration threshold is on the order of minutes, and demonstrate how using thresholds around 10~s that are reported in the vast majority of the literature can bias subsequent analyses. Overall, our work provides an important step towards dependable mobile network trace analysis, and lays more solid foundations to research relying on traffic traces for data-driven analysis and simulation.

Machine learning is a highly promising tool to design the physical layer of wireless communication systems, but its scaling properties for this purpose have not been widely studied. Machine learning algorithms are typically evaluated to learn SISO communications and low modulation orders, whereas current wireless standards use MIMO and high-order modulation schemes to increase capacity. The memory requirements of current Machine learning algorithms for wireless communications increase exponentially with the number of antennas and thus they cannot be used for advanced physical layers and massive MIMO. In this paper, we study the requirements of end-to-end Machine learning models for large-scale MIMO systems, determine the bottlenecks of the architecture, and design different solutions that vastly reduce overhead and allow training higher MIMO and modulation orders. We show that by training the autoencoder in a bit-wise manner, the memory requirements are reduced by several orders of magnitude, which is a critical step for Machine learning-based physical layer design in practical scenarios. Additionally, our design also improves performance over the classical autoencoder for MIMO.

Beyond 5G mobile networks have to support a wide range of performance requirements and unprecedented levels of flexibility. To this end, massive MIMO is a critical technology to improve spectral efficiency and thus scale up network capacity, by increasing the number of antenna elements. This also increases the overhead of Channel State Information (CSI) estimation and obtaining accurate CSI is a fundamental problem in massive MIMO systems. In this paper, we focus on scheduling uplink Sounding Reference Signals (SRSs) that carry pilot symbols for CSI estimation. Under the large number of users and high load that are expected to characterize beyond 5G systems, the limited amount of resources available for SRSs makes the legacy 3GPP periodic allocation scheme largely inefficient. We design TRADER, an SRS resource allocation framework that minimizes the age of channel estimates by taking advantage of machine learning-based short-term traffic forecasts at the base station level. By anticipating traffic bursts, TRADER schedules SRS resources so as to obtain CSI for each user right before the corresponding traffic arrives. Experiments with extensive real-world mobile network traces show that our solution is efficient and robust in high load scenarios: with respect to a round robin schedule of aperiodic SRS, TRADER provides more often CSI within the coherence time (up to 5× for given scenarios), leading to channel gains of up to 2 dB.

The multi-user multiple-input-multiple-output (MU-MIMO) beamforming training (BFT) enables an access point (AP) and multiple stations (STAs) to determine appropriate directional antenna patterns; to this end, the AP transmits multiple action frames to the STAs during the MU-MIMO BFT. However, if the antenna weight vectors (AWVs) are determined to transmit the action frames inefficiently, this could lead to unnecessary transmissions, which could increase the BFT time. To mitigate the signaling overhead, the schemes used in our previous work employed AWVs, which use multiple beams simultaneously to transmit the action frames. Nevertheless, these existing schemes are still adversely affected by redundant transmissions because these schemes overlook the transmit diversity gain obtained from multi-beam concurrent transmission. Therefore, in this study, we propose a novel transmit antenna configuration scheme that mitigates the signaling overhead by considering the transmit diversity of the inter-symbol interference (ISI) channel incurred when multiple beams are used simultaneously. Our proposed scheme determines each candidate AWV using multiple beams and efficiently identifies the STAs within reach of the corresponding multi-beam concurrent transmission. The numerical and simulation results demonstrate that our proposed scheme shortens the BFT time in comparison with existing schemes.

Wireless networks operating in the 60 GHz band have the potential to provide very high throughput but face a number of challenges (e.g., high attenuation, beam training, and coping with mobility) which are widely accepted but often not well understood in practice. Understanding these challenges, and especially their actual impact on consumer-grade hardware is fundamental to fully exploit the high physical layer rates in the 60 GHz band. To this end, we perform an extensive measurement campaign using two commercial off-the-shelf 60 GHz routers in real-world environments. Our results allow us to revisit a range of issues and provide much deeper insights into the reasons for specific performance compared to prior work on performance characterization. Further, our study goes beyond basic link characterization and explores for the first time practical considerations such as coverage and access point deployment. While some of our observations are expected, we also obtain highly surprising insights that challenge the prevailing wisdom in the community. We derive the shortcomings of current commercial 60 GHz devices, and the fundamental problems that remain open on the way to fast and efficient 60 GHz networking.

Millimeter-wave systems not only provide high data rates and low latency, but the very large bandwidth also allows for highly accurate environment sensing. Such properties are extremely useful for smart factory scenarios. At the same time, reusing existing communication links for passive object localization is significantly more challenging than radar-based approaches due to the sparsity of the millimeter-wave multi-path environment and the weakness of the reflected paths compared to the line-of-sight path. In this paper, we explore the passive object localization accuracy that can be achieved with IEEE 802.11ad devices. We use commercial Access Points (APs) whereas the station design is based on a full-bandwidth 802.11ad compatible FPGA-based platform with a phased antenna array. The stations exploit the preamble of the beam training packets of the APs to obtain Channel Impulse Response (CIR) measurements for all antenna patterns. With this, we determine distance and angle information for the different multi-path components in the environment to passively localize a mobile object. We evaluate our system with multiple APs and a moving robot with a metallic surface. Our system operates in real-time and achieves 6.5cm mean error accuracy and sub-meter accuracy in 100% of the cases.

Millimeter-wave (mm-wave) communications, as any other emerging technology, require suitable experimentation platforms that allow validation and field tests in both academic and industry research environments. Existing platforms for mm-wave systems are based on Commercial-Off-The-Shelf (COTS) devices or expensive proprietary hardware platforms. In this paper we propose a mixed software-hardware testbed for mm-wave experimentation using Software Defined Radio (SDR) devices. Specifically, we design and implement the hardware processing blocks required to decode the preamble of frames that follow the structure of IEEE 802. Had compliant frames, working at a scaled-down bandwidth, along with their integration in X310 USRP devices using the RFNoC framework and 60GHz transceivers. The testbed is validated for different indoor channels with real-time Channel Impulse Response (CIR) measurements. The design exploits the maximum bandwidth for X310 devices while leaving enough FPGA logic space (≍ 60%), for further upgrades and extension of the system.

Inertial sensors embedded in mobile devices, such as accelerometers and gyroscopes, have shown great potential to study human motion. In this paper, we propose to estimate the device movement without any access to physical inertial sensors in the mobile. Our idea is to infer the movements of the mobile through radio measurements, a concept we call “virtual inertial sensors”. We propose a method for estimating the rotation of a user that uses only WiFi Fine Time Measurements (FTM) to infer the rotation speed. We evaluate and demonstrate the proposed approach with experiments, using commodity 802.11ac Access Point (AP)s for Channel State Information (CSI) and FTMs measurements, and a Google Pixel 3 smartphone as mobile terminal. While FTM works with only one single antenna, it achieves better performance than a CSI-based estimator that exploits four antennas and multiple sub-carriers at the AP, but is limited by the typical one single WiFi antenna at the smartphone side. Together with walking speed estimation of a user, we envision that virtual inertial sensors can be leveraged by location systems and sensing mechanisms, including 5G, to improve localization accuracy, infer user behavior, and design better and more secure communication.

Millimeter-Wave (mm-wave) technology is increasingly being considered for mobile devices and use cases such as vehicular communication. This requires suitable experimentation platforms to support systems-oriented research to tackle the multitude of problems and challenges of mm-wave communications in such environments. To this end, we introduce mm-FLEX, a flexible and modular open platform with real-time signal processing capabilities that supports a bandwidth of 2 GHz and is compatible with mm-wave standard requirements. mm-FLEX integrates an FPGA-based baseband processor with full-duplex capabilities together with mm-wave RF front-ends and phased antenna arrays that are fully configurable from the processor in real-time. To demonstrate the capabilities of mm-FLEX, we implement a scalable, ultra-fast beam alignment mechanism for IEEE 802.11ad systems. It is based on compressive estimation of the signal's angle-of-arrival by means of switching through multiple receive beam patterns on a nano-second time-scale while receiving a packet preamble. Our implementation is open source and is made publicly available to the research community.

Machine learning is a highly promising tool to design the physical layer of wireless communication systems, but it usually requires that a channel model is known. As data rates increase and wireless transceivers become more complex, the wireless channel, hardware imperfections, and their interactions become more difficult to model and compensate explicitly. New machine learning schemes for the physical layer do not require an explicit model but implicitly learn the end-to-end link including channel characteristics and non-linearities of the system directly from the training data.

Event-based vision fosters a new way of sensing reality. Event-based cameras work radically differently compared to legacy frame-based cameras because they continuously measure brightness changes at a per-pixel granularity (i.e., events) rather than snapshots of intensity measurements (i.e., frames). Event-based cameras are applied in robotics and augmented and virtual reality applications due to their properties of low-latency, high temporal resolution and dynamic range. For example, they greatly improve unmanned aerial vehicle (UAV) navigation and collision avoidance. While event-based vision is currently restricted to local devices, in the near future applications involving distributed systems will gain momentum, such as the coordination of swarms of UAVs or robots. However, the network traffic characteristics of event-based vision systems are largely unexplored. In this paper, we aim to fill this gap by providing the first study of network traffic generated by event-based cameras. To this end, we employ publicly available data sets and experimentally study properties like the impact of packet/event losses on typical computer vision operations like tracking, and the implications of medium access under contention. We find that complex scenes that incur a high event generation rate are more robust against packet loss due to transmission errors or wireless contention. Conversely, packet loss or delay are more harmful to tracking and visualization operations when the event generation rate is small.

Millimeter wave (mmWave) communication not only provides ultra-high speed radio access but is also ideally suited for efficient and flexible wireless backhauling. Specifically for dense deployments, a mmWave macro base station (MBS) that serves a large number of mmWave micro base stations (μBSs) is much more cost effective than legacy cellular architectures which connect μBSs to the core network through fibers. In addition, μBSs can cooperate with each other by acting as relay nodes. The directional nature of mmWave communication allows for spatial reuse, even in the presence of interference, which can be exploited to optimize mmWave wireless backhaul performance. The optimization opportunistically prioritizes the use of good connections at the MBS and further leverages compact and concurrent transmissions between μBS. Relays and directional antennas speed up communication, but increase the complexity of the scheduling problem. In this work, we study the mmWave backhaul scheduling problem and derive an MILP formulation for it as well as upper and lower bounds. We prove that the problem is NP-hard and can be approximated, but only if interference is negligible. By means of numerical simulations, we compare theoretical results with heuristics in small system sizes. Results validate the analysis and demonstrate the high performance of our heuristics in realistic cellular settings.

Next-generation wireless networks promise to provide extremely high data rates, especially exploiting the so-called millimeter-wave frequency range. Gaining information from spectrum usage is becoming important to provide smart adaptation capabilities to future network protocol stacks. Issues such as deafness, misaligned antennas, or blockage may severely impact network performance, and their identification is crucial. Despite the complexity of full analytical models, machine learning techniques are progressively being considered to improve spectrum usage at higher layers. In this paper, we design a signal processing technique that uses narrowband physical layer energy traces, obtained from one or multiple channel sniffers. The proposed technique utilizes a combination of template matching and an Explicit Duration Hidden Markov Model (EDHMM) to correctly classify frames, while coping with the non-stationarity of the traces. This leads to a protocol level monitor that does not need to decode the channel at the physical layer, but just infers the type of packets that are exchanged based on sub-sampled energy traces. The performance of this framework is evaluated using off-the-shelf mm-wave wireless devices, quantifying its detection performance in the presence of one or multiple sniffers, and assessing the impact of physical layer parameters such as noise power and signal levels.

Millimeter-wave (mmWave) communication is a promising technology to cope with the exponential increase in 5G data traffic. Such networks typically require a very dense deployment of base stations. A subset of those, so-called macro base stations, feature high-bandwidth connection to the core network, while relay base stations are connected wirelessly. To reduce cost and increase flexibility, wireless backhauling is needed to connect both macro to relay as well as relay to relay base stations. The characteristics of mmWave communication mandates new paradigms for routing and scheduling. The paper investigates scheduling algorithms under different interference models. To showcase the scheduling methods, we study the maximum throughput fair scheduling problem. Yet the proposed algorithms can be easily extended to other problems. For a full-duplex network under the no interference model, we propose an efficient polynomial-time scheduling method, the schedule-oriented optimization. Further, we prove that the problem is NP-hard if we assume pairwise link interference model or half-duplex radios. Fractional weighted coloring based approximation algorithms are proposed for these NP-hard cases. Moreover, the approximation algorithm parallel data stream scheduling is proposed for the case of half-duplex network under the no interference model. It has better approximation ratio than the fractional weighted coloring based algorithms and even attains the optimal solution for the special case of uniform orthogonal backhaul networks.

Millimeter-wave (mm-wave) bands will play an essential role in 5G mobile networks in supporting the increasing demand for higher data rates. Communications at mm-wave frequencies pose unique challenges. The high propagation loss and unfavorable atmospheric absorption make the channel quality highly variable - short communication ranges and blockage through obstacles may prevent communication altogether. The use of directional antennas helps to achieve higher communication ranges and provides better spatial reuse and lower interference compared to omni-directional communications. At the same time, this introduces the problem of beam misalignment. Mm-wave research has primarily focused on the PHY and MAC layers, whereas the transport layer aspects of mm-wave systems require further attention. In this article, we analyze the behavior of TCP in mm-wave networks and study its impact on system-level performance. Through extensive simulations, we show the effect of different types of blockages on the behavior of the congestion control in the presence of handovers, and when small, medium and long flows coexist. Protocols like CUBIC that target high throughput benefit significantly when jointly optimizing link layers buffers and timeouts. While the optimization fosters prompt reaction to short-term blockages, the performance of such protocols significantly decreases when obstacles degrade the channel quality for longer time periods. Hybrid-designs like TCP YeAH are more robust to blockage, but fail to recover quickly and to ramp up to the link capacity after timeouts.

Future millimeter-wave networks will support very high densities of devices and access points. This vastly increases the overhead required for access point selection and beam training. Fortunately, the quasi-optical properties of millimeter-wave channels make location-based network optimization a highly promising technique to reduce control overhead in such millimeter-wave WLANs. In this paper, we extract channel state information from off-the-shelf routers, we use it to design a high accuracy location system, and then show how location information enables the optimization of network operations. The resulting scheme, named LEAP, can predict blockage, optimize access point association, and select the most suitable antenna beam patterns while significantly reducing the beam training overhead. We show that compared to standard state-of-the-art 802.11ad systems, LEAP's location driven management greatly improves network performance and link stability.

Future WLAN devices will combine both IEEE 802.11ad and 802.11ac interfaces. The former provides multi-Gbps rates but is susceptible to blockage, whereas the latter is slower but offers reliable connectivity. A fundamental challenge is thus how to combine those complementary technologies, to make the most of the advantages they offer. In this work, we explore leveraging Multipath TCP (MPTCP) to use both interfaces simultaneously in order to achieve a higher overall throughput as well as seamlessly switching to a single interface when the other one fails. We find that standard MPTCP often performs sub-optimally and may even yield a throughput much lower than that of single path TCP over the faster of the two interfaces. We analyze the cause of these performance issues in detail and then design MuSher, an agile MPTCP scheduler that allows MPTCP to fully utilize the channel resources available to both interfaces. Our evaluation in realistic scenarios shows that MuSher provides a throughput improvement of up to 1.5x/2.3x and speeds up the recovery of a traffic stream, after disruption, by a factor of up to 8x/75x, under WLAN/Internet settings respectively, compared to the default MPTCP scheduler.

Precise localization is becoming an integral part of mobile network architectures, not only to provide location-based services but also to optimize the operation of the network itself through suitable context information. Location systems are of particular importance for indoor settings where GPS may be unavailable. While upcoming 5G systems will provide improved location accuracy, for a long time to come many areas will only have LTE coverage, and ubiquitous localization will thus also have to rely on LTE technology. To evaluate the location accuracy that can be achieved with current mobile systems, we implement a localization algorithm in a standard-compliant LTE testbed based on software-defined radios. We assess the localization accuracy in representative indoor scenarios. Despite a bandwidth of only 20MHz, the results show a good median error around 2m, but significantly larger errors may occur in non-line-of-sight cases. Nevertheless, the accuracy is sufficient for a range of potential applications.

Both IEEE 802.15.3c and 802.11ad use the 60 GHz band for high datarate Wireless Personal/Local Area Network applications. These millimeter-wave communications use very directional antennas since the small wavelength allows to integrate many small antenna elements to form a beamforming antenna array, enabling very high spatial reuse as can be found in dense indoor and IoT settings. However, earlier work shows that current mmWave systems are not as directional as theory would suggest, with significant interference that may prevent spatial reuse. In this work, we propose a centralized system that allows the network to carry out the beamtraining process not only to maximize signal power, but also taking into account other stations in order to minimize interference. This system is designed to work with unmodified clients. We implement and validate our system on commercial off-the-shelf 60 GHz hardware, achieving an average throughput gain of 24.67% for TCP traffic, and up to a twofold throughput gain in specific cases.

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

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

Millimeter-wave (mmWave) communications have emerged as one of the most promising options to vastly increase wireless data rates due to the high bandwidth they offer. Given the high path loss at mmWave frequencies, such systems require directional antennas to achieve a good communication range. Thus, the communicating devices need to align the beam directions of their mmWave antennas. Due to the high penetration loss, the paths between the antennas also need to be free of blocking obstacles. This makes an efficient and reliable operation of mmWave networks in dynamic environments very challenging. At the same time, the directionality reduces interference and allows to scale these networks to much higher access point and device densities. In this paper, we discuss the above-mentioned challenges and present techniques that allow mmWave networks to scale to high-density deployments, to adapt to dynamic and mobile environments, and to consistently achieve high data rates. This includes learning the environment to find different propagation paths, reacting timely to channel impairments such as blockage, and integrating mmWave networks with networks operating at a lower frequency for robustness. A key ingredient to enable these forms of adaptivity is the use of location information. Such mechanisms then turn a collection of very-high-speed but brittle mmWave links into an efficient, low-latency, and reliable network.

Understanding the gap between theory and practice of 60 GHz networks is crucial. Theoretical models typically focus on the physical layer only and assume ideal hardware components. However, such assumptions do not hold in practice. For instance, the cost-effective design of commercial off-the-shelf products often results in hardware impairments, such as highly irregular beam shapes. Further, mobility and frame aggregation have a strong impact throughout the protocol stack. Taking these effects into account when designing 60 GHz networking mechanisms is critical. In this paper, we characterize the aforementioned effects and contribute a comprehensive dataset of real-world traces that capture them. Among other traces, our dataset includes measurements of actual beam patterns, the impact of the cases of the devices on propagation, exhaustive measurements on the degradation of link performance in case of mobility, and the behavior of aggregation at the physical layer. Our dataset is the most comprehensive study of this kind to date. As a result, our work allows researchers to take the aforementioned hardware impairments into account, which paves the way for comparable and realistic evaluations of 60 GHz networking mechanisms in the community.

Millimeter-wave (mm-wave) location systems not only provide accurate positioning for location-based services but can also help optimize network operations, for example, through location-driven beam steering and access point association. In this paper, we design and evaluate localization schemes that exploit the characteristics of mm-wave communication systems. We propose two range-free algorithms belonging to the broad classes of triangulation and angle difference of arrival. The schemes work both with multiple anchors and with as few as a single anchor, under the only assumption that the floor plan and the positions of the mm-wave access points are known. Moreover, they are designed to be lightweight so that even computationally-constrained devices can run them. We evaluate our proposed algorithms against two benchmark approaches based on fingerprinting and angles of arrival, respectively. Our results, obtained both by means of simulations and through measurements involving commercial 60-GHz mm-wave devices, show that sub-meter accuracy is achieved in most of the cases, even in the presence of only a single access point. The availability of multiple access points substantially improves the localization accuracy, especially for large indoor spaces.

The very large bandwidth available in the 60 GHz band allows, in principle, to design highly accurate positioning systems. Integrating such systems with standard protocols (e.g., IEEE 802.11ad) is crucial for the deployment of location-based services, but it is also challenging and limits the design choices. Another key problem is that consumer-grade 60 GHz hardware only provides coarse channel state information, and has highly irregular beam shapes due to its cost-efficient design. In this paper, we explore the location accuracy that can be achieved using such hardware, without modifying the 802.11ad standard. We consider a typical 802.11ad indoor network with multiple access points (APs). Each AP collects the coarse signal-to-noise ratio of the directional beacons that clients transmit periodically. Given the irregular beam shapes, the challenge is to relate each beacon to a set of transmission angles that allows to triangulate a user. We design a location system based on particle filters along with linear programming and Fourier analysis. We implement and evaluate our algorithm on commercial off-the-shelf 802.11ad hardware in an office scenario with mobile human blockage. Despite the strong limitations of the hardware, our system operates in real-time and achieves sub-meter accuracy in 70% of the cases.

Mobile applications outsource their cloud infrastructure deployment and content delivery to cloud computing services and content delivery networks. Studying how these services, which we collectively denote Cloud Service Providers (CSPs), perform over Mobile Network Operators (MNOs) is crucial to understanding some of the performance limitations of today's mobile apps. To that end, we perform the first empirical study of the complex dynamics between applications, MNOs and CSPs. First, we use real mobile app traffic traces that we gathered through a global crowdsourcing campaign to identify the most prevalent CSPs supporting today's mobile Internet. Then, we investigate how well these services interconnect with major European MNOs at a topological level, and measure their performance over European MNO networks through a month-long measurement campaign on the MONROE mobile broadband testbed. We discover that the top 6 most prevalent CSPs are used by 85 % of apps, and observe significant differences in their performance across different MNOs due to the nature of their services, peering relationships with MNOs, and deployment strategies. We also find that CSP performance in MNOs is affected by inflated path length, roaming, and presence of middleboxes, but not influenced by the choice of DNS resolver.

Millimeter wave (mmWave) communications are an essential component of 5G-and-beyond ultra-dense Gbit/s wireless networks, but also pose significant challenges related to the communication environment. Especially beam-training and tracking, device association, and fast handovers for highly directional mmWave links may potentially incur a high overhead. At the same time, such mechanisms would benefit greatly from accurate knowledge about the environment and device locations that can be provided through simultaneous localization and mapping (SLAM) algorithms. In this paper we tackle the above issues by proposing CLAM, a distributed mmWave SLAM algorithm that works with no initial information about the network deployment or the environment, and achieves low computational complexity thanks to a fundamental reformulation of the angle-differences-of-arrival mm Wave anchor location estimation problem. All information required by CLAM is collected by a mmWave device thanks to beam training and tracking mechanisms inherent to mm Wave networks, at no additional overhead. Our results show that CLAM achieves submeter accuracy in the great majority of cases. These results are validated via an extensive experimental measurement campaign carried out with 60-GHz mmWave hardware.

Millimeter-wave (mmWave) communication is a promising technology to cope with the expected exponential increase in data traffic in 5G networks. mmWave networks typically require a very dense deployment of mmWave base stations (mmBS). To reduce cost and increase flexibility, wireless backhauling is needed to connect the mmBSs. The characteristics of mmWave communication, and specifically its high directionality, imply new requirements for efficient routing and scheduling paradigms. We propose an efficient scheduling method, so-called schedule-oriented optimization, based on matching theory that optimizes QoS metrics jointly with routing. It is capable of solving any scheduling problem that can be formulated as a linear program whose variables are link times and QoS metrics. As an example of the schedule-oriented optimization, we show the optimal solution of the maximum throughput fair scheduling (MTFS). Practically, the optimal scheduling can be obtained even for networks with over 200 mmBSs. To further increase the runtime performance, we propose an efficient edge-coloring based approximation algorithm with provable performance bound. It achieves over 80% of the optimal max-min throughput and runs 5 to 100 times faster than the optimal algorithm in practice. Finally, we extend the optimal and approximation algorithms for the cases of multi-RF-chain mmBSs and integrated backhaul and access networks.

Beamforming is vital to overcome the high attenuation in wireless millimeter-wave networks. It enables nodes to steer their antennas in the direction of communication. To cope with complexity and overhead, the IEEE 802.11ad standard uses a sector codebook with distinct steering directions. In current off-the-shelf devices, we find codebooks with generic pre-defined beam patterns. While this approach is simple and robust, the antenna modules that are typically deployed in such devices are capable of generating much more precise antenna beams. In this paper, we adaptively adjust the sector codebook of IEEE 802.11ad devices to optimize the transmit beam patterns for the current channel. To achieve this, we propose a mechanism to extract full channel state information (CSI) regarding phase and magnitude from coarse signal strength readings on off-the-shelf IEEE 802.11ad devices. Since such devices do not expose the CSI directly, we generate a codebook with phase-shifted probing beams that enables us to obtain the CSI by combining strategically selected magnitude measurements. Using this CSI, transmitters dynamically compute a transmit beam pattern that maximizes the signal strength at the receiver. Thereby, we automatically exploit reflectors in the environment and improve the received signal quality. Our implementation of this mechanism on off-the-shelf devices demonstrates that adaptive codebook optimization achieves a significantly higher throughput of about a factor of two in typical real-world scenarios.

802.11ad links provide data rates up to 6.7 Gbps but remain highly susceptible to blockage and mobility. On the other hand, legacy 802.11ac/n links yield much lower rates but are robust even under dynamic scenarios. In this work, we explore using Multipath TCP (MPTCP) to engage both 802.11ad and 802.11ac interfaces simultaneously for performance speed-up and improved reliability. We show that vanilla MPTCP achieves these goals under static conditions but often results in performance worse than using the faster interface alone under dynamic scenarios. We then design and implement AMuSe, a new MPTCP scheduler that allows MPTCP to perform near-optimally under all scenarios.

Wireless networks operating in the 60 GHz band have the potential to provide very high throughput but face a number of challenges (e.g., high attenuation, beam training, and coping with mobility) which are widely accepted but often not well understood in practice. Understanding these challenges, and especially their actual impact on consumer-grade hardware is fundamental to fully exploit the high physical layer rates in the 60 GHz band. To this end, we perform an extensive measurement campaign using two commercial off-the-shelf 60 GHz routers in practical real-world environments. Our study is centered around two fundamental adaptation mechanisms in 60 GHz networks-beam training and rate control- whose interactions are key for performance. Understanding these interactions allows us to revisit a range of issues and provide much deeper insights into the reasons for specific performance compared to prior work on performance characterization. Further, our study goes beyond basic link characterization and explores for the first time practical considerations such as coverage and access point deployment. While some of our observations are expected, we also obtain highly surprising insights that challenge the prevailing wisdom in the community.

Milimeter-wave links can provide GBit/s data rates but are highly susceptible to blockage. In case a direct line-of-sight communication path becomes blocked, communication via a reflected path may allow to maintain connectivity. A common approach is to switch to such an alternative path whenever the first path becomes blocked. However, this requires detecting the blockage and then reconfiguring the transceiver to use the new path which incurs latency. For traffic with strict latency or reliability requirements, or in highly dynamic environments where path switching would be frequent, using both paths concurrently can be more beneficial. In this paper, we consider using multiple paths and dividing the transmission power over those paths, instead of path switching. We propose an algorithm to allocate power among the different mmWave communication paths to overcome link blockage under randomly distributed obstacles. The power allocation algorithm is based on analysis of the blockage probabilities of the direct and reflected paths using geometric probability, to statistically maximize the overall capacity of the path between two nodes. We evaluate the performance of the proposed algorithm via simulation for various wireless environments.

The use of directional antennas in millimeter-wave communication promises high spatial reuse at multi-gigabit-per-second data rates in dense wireless networks. Existing work studies such networks using commercial hardware but is limited to individual links. Moreover, such hardware typically allows for little or no control of the lower layers of the protocol stack. In this paper, We study the performance of dense millimeterwave deployments featuring up to eight stations. To this end, we use a practical IEEE 802.11ad millimeter-wave testbed that allows access to the lower layer parameters of each station. This enables us to analyze the impact of these parameters on upper layer performance. We study, for first time to our best knowledge, issues such as the impact of channel contention on the buffer size at the transport layer, the effect of frame aggregation, and the efficiency of spatial sharing. Our results show that using large buffer sizes with TCP is harmful due to channel contention despite the multi-gigabit-per-second data rates. Further, frame aggregation is only beneficial up to a certain level due to higher error rates for large frames. Finally, we also study delay, showing that the regular beacon transmission time can degrade performance.

Anticipatory networking is a recent branch of network optimization based on prediction of the system state. Our work specifically tackles prediction-driven resource allocation for mobile networks. While some anticipatory networking concepts have been proposed in the literature, understanding of the potential real world gains is so far very limited. Future mobile networks will likely integrate such mechanisms, and thus it is of paramount importance to understand the actual performance improvements and in which scenarios they can be realized. Analyzing a month of LTE control channel information collected in four locations, we show how anticipatory networking can enhance current LTE networks. First, we propose a comprehensive optimization framework encompassing different forecasting solutions. Then, we provide a thorough analysis of the aggregated network traffic and the contributions of individual users. In particular, we show that predictable traffic accounts for more than 95 percent of the total traffic volume and that simple prediction and optimization techniques allow network operators to save 50 percent of the resources and/or on average more than double the offered data rate in our data set.

Millimeter-wave devices must use highly directional antennas to achieve GBit/s data rates over reasonable distances due to the high path loss. As a consequence, it is important to precisely align the antenna beams between sender and receiver. Even minor movement or rotation of a device can result in beam misalignment and thus a strong performance degradation. Existing work as well as standards such as IEEE 802.11ad tackle this issue by means of antenna sector probing. This comes at the expense of a significant overhead, which may significantly reduce the performance of millimeter-wave communication, particularly in mobile scenarios. In this paper, we present a mechanism that can track both movement and rotation of 60 GHz mobile devices with zero overhead. To this end, we transmit part of the preamble of each packet using a multi-lobe beampattern. Our approach does not require any additional control messages and is backward compatible with 802.11ad. We implement our scheme on a 60 GHz testbed using phased antenna arrays, and show that we reduce the angle error to less than 5° in most cases. We also perform simulations to validate our approach in a wide range of scenarios, achieving up to 2x throughput gain.

Achieving data-rates of multiple Gbps in 60 GHz millimeter-wave (mm-wave) communication systems requires efficient beam-steering algorithms. To find the optimal steering direction on IEEE 802.11ad compatible devices, state-of-the-art approaches sweep through all predefined antenna sectors. Recently, much more efficient alternatives, such as compressive path tracking, have been proposed, which scale well even with arrays with thousands of antenna elements. However, such have not yet been integrated into consumer devices. In this work, we adapt compressive path tracking for sector selection in off-the-shelf IEEE 802.1 lad devices. In contrast to existing solutions, our compressive sector selection tolerates the imperfections of low-cost hardware, tracks beam directions in 3D and does not rely on pseudo-random beams. We implement our protocol on a commodity router, the TP-Link Talon AD7200, by modifying the sector sweep algorithm in the IEEE 802.11ad chip's firmware. In particular, we modify the firmware to obtain the signal strength of received frames and to select custom sectors. Using this extension, we precisely measure the device's sector patterns. We then select the best sector based on the measured patterns and sweep only through a subset of probing sectors. Our results demonstrate, that our protocol outperforms the existing sector sweep, increases stability, and speeds up the sector selection by factor 2.3.

Millimeter wave (mm-wave) communication is a topic of intensive recent study, as it allows to significantly boost data rates of future 5G networks. In this paper, we focus on a mm-wave system consisting of a single Access Point (AP) and two User Equipments (UEs), where one UE requires high throughput, while the other is characterized by a low latency demand. Given that setup, we aim at optimally allocating the available AP hardware resources for the beam training phase and data communication, in order to efficiently serve both UEs via hybrid analog-digital beamforming. We evaluate an optimization framework with the objective to maximize the expected rate of one UE, for a given latency constraint set by the other UE. The optimal data rates are illustrated for different latency constraints and for different strategies of exploiting the full RF chain set at the AP side. We observe that our proposed access schemes outperform the basic TDMA approach by up to 22 %.

Device localization is a highly important functionality for a range of applications. It is particularly beneficial in mmWave networks, where it can be used to reduce the beam training overhead and anticipate handovers between access points. In this paper, we present JADE, an algorithm that estimates the location of a mobile user in an indoor space without any knowledge about the surrounding environment (floor plan, location of walls and presence of reflective surfaces) or about the location and number of access points available therein. JADE leverages the beam procedure used in pre-standard and commercial mmWave equipment to estimate the angle-of-arrival of multipath components of the signal sent by visible access points. This information is then employed to localize the mobile user, estimate the position of access points and finally form a map of the environment. No radar-like ranging operations are required for this. Our results demonstrate that JADE can localize a user with sub-meter accuracy in the broad majority of the cases, and that it even outperforms localization algorithms that require full knowledge of the environment and access point positions.

In order to cope with the severe path loss, millimeter-wave (mm-wave) systems exploit highly directional communication. As a consequence, even a slight beam mis-alignment between two communicating devices (for example, due to mobility) can generate a significant signal drop. This leads to frequent invocations of time-consuming mechanisms for beam re-alignment, which deteriorate system performance. In this paper, we propose smart beam training and tracking strategies for fast mm-wave link establishment and maintenance under node mobility. We leverage the ability of hybrid analog-digital transceivers to collect channel information from multiple spatial directions simultaneously and formulate a probabilistic optimization problem to model the temporal evolution of the mm-wave channel under mobility. In addition, we present for the first time a beam tracking algorithm that extracts information needed to update the steering directions directly from data packets, without the need for spatial scanning during the ongoing data transmission. Simulation results, obtained by a custom simulator based on ray tracing, demonstrate the ability of our beam training/tracking strategies to keep the communication rate only 10% below the optimal bound. Compared to the state of the art, our approach provides a 40% to 150% rate increase, yet requires lower complexity hardware.

This work conducts one of the first experimental studies of Multipath TCP (MPTCP) in dual-band 60 GHz/5 GHz WLANs using off-the-shelf hardware. We consider both uncoupled and different coupled congestion control algorithms, compare their performance and their potential to improve throughput over single path TCP, and uncover their limitations. In contrast to a recent study that reports reduced throughput with MPTCP compared to single path TCP over 60 GHz, our results show that significant performance improvements are possible, especially in the case of uncoupled congestion control. On the other hand, performance gains with coupled congestion control are lower as these algorithms often fail to fully utilize the capacity of both paths simultaneously. We also observe a pathological case that can lead to significantly reduced throughput with MPTCP regardless of the congestion control algorithm.

Due to high propagation loss and directivity, mmWave links are very susceptible to obstacles blocking the direct line-of-sight path for communication. In this case, indirect communication via a relay may help to circumvent the blockage. In this paper, we propose a two-hop relay selection algorithm for mmWave communications. For the relay selection, we analyze the probability that an indirect path is available given that the direct path is blocked trough geometric analysis. We then choose the most promising node among neighbors as relay. The analysis shows that the probability of an indirect path is a function of the obstacle density as well as the location of relay nodes. When the density is low, the correlation between the direct path and an indirect path is dominant, i.e., the angle between the direct path and the path to relay should be large, whereas the blockage probability of an indirect path becomes more dominant as the density increases, i.e., relay links should not be too long. The probability analysis also allows to decide an initial antenna angle for beam-training in mobile mmWave environments. Through numerical studies, we verify our analytical results.

Carrier aggregation increases the throughput of LTE mobile networks by aggregating bandwidth at different frequencies. The theoretical effectiveness of carrier aggregation has been widely studied in the literature and operators claim it substantially increases the network data rate. However, to the best of our knowledge no practical evaluation of the performance of carrier aggregation has been performed using real traffic data. We perform a thorough measurement-based study to assess how carrier aggregation improves the service offered in selected locations in Madrid, Spain. Although the best results for data rate boost are in line with the operator claims, we find that resource usage in aggregated bands is frequently suboptimal, and higher data rates could be achieved by simply overloading a single band. On the other hand, the quality of service achieved by users effectively exploiting carrier aggregation could not be achieved without it.

The analysis of real mobile traffic traces is helpful to understand usage patterns of cellular networks. In particular, mobile data may be used for network optimization and management in terms of radio resources, network planning, energy saving, for instance. However, real network data from the operators is often difficult to be accessed, due to legal and privacy issues. In this paper, we overcome the lack of network information using a LTE sniffer capable of decoding the unencrypted LTE control channel and we present a temporal and spatial analysis of the recorded traces. Moreover, we present a methodology to derive a stochastic characterization for the daily variation of the LTE traffic. The proposed model is based on a discrete-time Markov chain and is compared with the real traces. Results show that, with a limited number of states, our model presents a high level of accuracy in terms of first and second order statistics.

The directional nature of communication in millimeter-wave bands suggests that the frequency selectivity of the channel may be limited due to the absence of reflected paths. However, our measurement studies show that reflections often cause significant frequency selectivity in practical scenarios, which we can exploit to increase the otherwise limited range of 60 GHz networks. Specifically, we measure real-world indoor 60 GHz channels with a bandwidth of 2 GHz, and study their behavior with respect to techniques such as bitloading, subcarrier switch-off, and waterfilling. To this end, we consider Orthogonal Frequency-Division Multiplexing (OFDM) as defined in the IEEE 802.11ad standard and show that in contrast to common belief, these techniques are highly beneficial in millimeter-wave networks. We analyze this in practice for both horn antennas as well as an electronically-steerable phased antenna array. Most importantly, our practical results demonstrate that for the specific case of the 60 GHz band, this selectivity allows for a range extension of up to 50%. Hence, our approach enables us to alleviate one of the main limitations of millimeter-wave networks.

Recently, spectrum optimization solutions require mobile phones to obtain precise, accurate and fine-grained estimates of the radio link data rate. In particular, the effectiveness of anticipatory schemes depends on the granularity of these measurements. In this paper we use a reliable LTE control channel sniffer (OWL) to extensively compare mobile phone measurements against exact LTE radio link data rates. We also provide a detailed study of latencies measured on mobile phones, the sniffer, and a server to which the phone is connected. In this study, we show that mobile phones can accurately (if slightly biased) estimate the physical radio link data rate. We highlight the differences among measurements obtained using different mobile phones, communication technologies and protocols.

The lower layer characteristics of 60 GHz wireless networks impact the entire protocol stack. This includes effects such as sudden link failure due to blockage, high packet loss due to deafness, or suboptimal transmit rates due to antenna misalignment. To understand the resulting behaviors at the upper layers, it is key to have access to lower layer information. At the time of writing, 60 GHz experimentation hardware is either limited in terms of bandwidth, real-time capability, or does not support a full stack. In contrast, commercial off-the-shelf (COTS) hardware covers all of the above features but is a black box regarding the lower layers. Our demo exploits hidden monitoring capabilities of a certain COTS device which has become popular in the 60 GHz research community. The demo allows conference attendees to interact with 60 GHz links and observe how this impacts lower layer parameters. Moreover, the demo shows how such interactions relate to the performance at the upper layers.

The millimeter-wave technology is emerging as an alternative to legacy 2.4/5 GHz WiFi, offering multi-Gigabit throughput. While a lot of attention has been paid recently to analyzing the performance of the 60 GHz technology and adapting it for indoor WLAN usage, the power consumption aspect has largely been neglected. Given that mobile devices are the next target for 60 GHz, any discussion about this technology is incomplete without considering power consumption. In this work, we present the first, to our best knowledge, detailed study of the power consumption of 60 GHz commodity devices. We evaluate the power and energy consumption of two standard-compliant 60 GHz wireless adapters in different operating states and under a number of different configurations. We also compare our results against 802.11ac and discuss power-performance tradeoffs for the two technologies.

Troubleshooting millimeter-wave (mm-wave) wireless networks is complex due to the directionality of the communication. Issues such as deafness, misaligned antennas, or blockage may severely impact network performance, and identifying them is crucial to improve network deployments. To this end, access to lower-layer information is important. However, commercial off-the-shelf mm-wave wireless devices typically do not provide such information. Even if they would, detecting effects such as deafness based on information of a single node that forms part of the network is typically hard. In this paper, we present the design and evaluation of an external sniffing device that can infer the aforementioned performance issues only using narrowband physical layer energy traces. Our sniffer does not need to decode any data, resulting in a simple but effective approach which also preserves privacy and works on encrypted networks. Our key contribution is a machine learning framework which enables automated energy trace analysis while coping with the non-stationarity of the traces. We evaluate its performance in practice using off-the-shelf wireless devices operating in the 60 GHz band. Our results show that the above framework correctly infers physical layer events in virtually all cases, thus providing valuable information to troubleshoot issues in mm-wave networks.

Millimeter-wave (mm-wave) communication systems can provide much higher data rates than systems operating at lower frequencies, but achieving such rates over sufficiently large distances requires highly directional beamforming at both the transmitter and receiver. These antenna beams have to be aligned very precisely in order to obtain sufficient link margin. In this paper, we first propose a parallel-adaptive beam training protocol, which significantly accelerates the link establishment between mm-wave devices by exploiting the ability of hybrid analog-digital beamforming antennas to scan multiple spatial sectors simultaneously. Second, we deal with practical constraints of the mm-wave transceivers and design a novel greedy geometric algorithm to synthesize sector beam patterns featuring configurable beamwidth and multi-beam radiation as required by the proposed beam training protocol. These multi-beam patterns are then also used for concurrent data communication over multiple paths, in case several suitable directions are found during the beam training. Simulation results show that our algorithm is able to shape antenna patterns very close to those attained by a fully digital beamforming architecture, yet requires lower complexity hardware compared with the state-of-the-art solutions. Exploiting such multi-beam antenna patterns, our parallel beam training protocol can provide up to 82% effective rate increase and 70% search time decrease compared with existing sequential protocols. The acceleration of the beam training phase shifts the optimum balance between the search overhead and the achieved directivity gain so that the best performance is reached with a training load 30% to 60% lower than that of the sequential beam training.

Wireless multicasting suffers from the problem that the transmit rate is usually determined by the receiver with the worst channel conditions. Composite or adaptive beamforming allows using beamforming patterns that trade-off antenna gains between receivers, which can be used to overcome this problem. A common solution for wireless multicast with beamforming is to select the pattern that maximizes the minimum rate among all receivers. However, when using opportunistic multicast to transmit a finite number of packets to all receivers-the finite horizon problem-this is no longer optimal. Instead, the optimum beamforming pattern depends on instantaneous channel conditions as well as the number of received packets at each receiver. We formulate the finite horizon multicast beamforming problem as a dynamic programming problem to obtain an optimal solution. We further design a heuristic that has sufficiently low complexity to be implementable in practice. To deal with imperfect feedback, and in particular feedback delay, we extend the algorithm to work with estimated state and channel information. We show through extensive simulations that our algorithms significantly outperform prior solutions.

Millimeter wave (mmWave) wireless technologies are expected to become key enablers of multi-gigabit wireless access in next-generation cellular and local area networks. Due to unfavorable radio propagation, mmWave systems will exploit large-scale MIMO and adaptive antenna arrays at both the transmitter and receiver to realize sufficient link margin. Unfortunately, power and cost requirements in mmWave radio frontends make the use of fully-digital beamforming very challenging. In this paper, we focus on hybrid analog-digital beamforming and address two relevant aspects of the initial access procedure at mmWave frequencies. First, we propose a beam training protocol which effectively accelerates the link establishment by exploiting the ability of mobile users to simultaneously receive from multiple directions. Second, we deal with practical constraints of mmWave transceivers and propose a novel, geometric approach to synthesize multi-beamwidth beam patterns that can be leveraged for simultaneous multi-direction scanning. Simulation results show that the proposed hybrid codebooks are able to shape beam patterns very close to those attained by a fully-digital beamforming architecture, yet require lower complexity hardware compared with the state of the art. Furthermore, the reduced duration of the beam training phase, in turn enabled by the multi-beam characteristics of our hybrid codebooks, provides a 25% to 70% increase in spectral efficiency compared to existing sequential scanning strategies.

Achieving multi-gigabit per second data rates, millimeter wave communication promises to accommodate future and current demands for very high speed wireless data transmission. However, the mandatory use of directional antennas brings significant challenges for the design of efficient MAC layer mechanisms. In particular, IEEE 802.11ad for the 60 GHz band lacks omni-directional transmissions and carrier sensing. This prevents stations from overhearing the actions of other stations, the so called “deafness” problem, which substantially impairs the efficiency and fairness of CSMA/CA medium access. Most existing solutions to this problem depend on properties of lower frequency bands and thus do not apply to 60 GHz. In this paper, we propose a dual-band MAC protocol combining 60 GHz communication with co-existing 5 GHz interfaces. By broadcasting control messages on 5 GHz frequencies, we solve the deafness problem and can use the 60 GHz band exclusively for high rate data transmission. While our approach occupies air time on the 5 GHz band for control messages, it does achieve a net throughput gain (over both bands) of up to 65.3% compared to IEEE 802.11ad. In addition, our simulation results show an improvement of MAC fairness of up to 42.8% over IEEE 802.11ad.

In this paper, we target single-anchor localization schemes for millimeter wave (MMW) systems. The schemes are designed to be lightweight, so that even computationally-constrained devices can support them. We identify the main propagation properties of MMW signals that have an impact on localization and design three algorithms that exploit these, namely a triangulation-validation procedure, an angle difference-of-arrival approach, and a scheme based on location fingerprinting. We evaluate the algorithms by means of simulations, and draw conclusions on their robustness. We then validate our results via measurements involving commercial pre- standard 60- GHz MMW hardware. Our experiments confirm that, by relying only on a single anchor and without requiring complex signal processing at the receiver, the algorithms can localize a node with high probability, and in many cases with sub-meter accuracy. We conclude by discussing how these algorithms complement each other in terms of robustness and localization success probability.

mmMAGIC (Millimetre-Wave Based Mobile Radio Access Network for Fifth Generation Integrated Communications) is an EU funded 5G-PPP project, whose overall objective is to design and pre-develop a mobile radio access technology (RAT) operating in the 6–100 GHz range, capable of impacting standards and other relevant fora. The focus of the project is on extreme Mobile Broadband, which is expected to drive the 5G requirements for massive increase in capacity and data-rates. This paper elaborates on some 5G key research areas such as: identification of the most compelling use-cases and Key Performance Indicators (KPIs) for future 5G systems, advantages and challenges of millimeter-wave (mmWave) technologies, channel measurements and channel modeling, network architecture; and the design of a new mobile radio interface including multi-node and multi-antenna transceiver architecture.

The impact of frame aggregation on wireless network performance increases dramatically with higher data rates. The key problem is that the transmission time of packets decreases while the medium access, preamble and packet header overhead remain the same. Recent 802.11 standards address this issue using frame aggregation, i.e., grouping multiple data frames in a single transmission to reduce the overhead. This already provides substantial efficiency gains in networks operating in the 2.4 GHz and 5 GHz bands, and for future 60 GHz networks such as 802.11ad, gains are even more pronounced due to the order-of-magnitude higher data rates. In 802.11ad, frame aggregation becomes crucial to achieve the multi-gbps data rates that are possible in theory, since medium access overhead can be 20x larger than the time required to transmit a single packet. While frame aggregation is essential, it very much depends on the traffic patterns present in the wireless network, and a node may not always have enough packets in the transmit queue to achieve a sufficiently large aggregated frame size. In this paper, we investigate in which case nodes should wait to construct a larger aggregated packet before starting the channel access procedure. We present a simple waiting policy for the uplink case that either waits for a minimum number of packets or for a maximum amount of time, whichever comes first. For the downlink case, we utilize a maximum weight scheduling policy with a maximum waiting time. Our results show that both policies significantly improve medium utilization, thus increasing throughput and reducing end-to-end delay.

Due to the directionality of transmissions in millimeter wave (mm-wave) networks, wireless stations are usually unable to overhear when other stations access the channel. This makes it hard to design efficient distributed beam coordination and scheduling mechanisms. At the same time, centralized schemes only perform well in relatively simple, static scenarios. In practical settings where links have different channel qualities and in the context of relaying or in-band backhauling, centrally coordinating all stations becomes difficult. In this paper, we propose a low complexity, decentralized, learning-based scheduling algorithm for mm-wave networks that handles heterogeneous link rates and packet sizes efficiently. Compared to state-of-the-art slotted channel access for mm-wave networks, the proposed mechanism achieves throughput gains of up to a factor of 8 in single-hop scenarios and end-to-end throughput improvements of up to a factor of 1.6 in multi-hop topologies.

Mobile data traffic is increasing rapidly and wireless spectrum is becoming a more and more scarce resource. This makes it highly important to operate mobile networks efficiently. In this paper we are proposing a novel lightweight measurement technique that can be used as a basis for advanced resource optimization algorithms to be run on mobile phones. Our main idea leverages an original packet dispersion based technique to estimate per user capacity. This allows passive measurements by just sampling the existing mobile traffic. Our technique is able to efficiently filter outliers introduced by mobile network schedulers and phone hardware. In order to asses and verify our measurement technique, we apply it to a diverse dataset generated by both extensive simulations and a week-long measurement campaign spanning two cities in two countries, different radio technologies, and covering all times of the day. The results demonstrate that our technique is effective even if it is provided only with a small fraction of the exchanged packets of a flow. The only requirement for the input data is that it should consist of a few consecutive packets that are gathered periodically. This makes the measurement algorithm a good candidate for inclusion in OS libraries to allow for advanced resource optimization and application-level traffic scheduling, based on current and predicted future user capacity.

We propose an algorithm for opportunistic multicasting in wireless networks. Whereas prior multicast rate adaptation schemes primarily optimize long-term throughput, we investigate the finite horizon problem where a fixed number of packets has to be transmitted to a set of wireless receivers in the shortest amount of time-a common problem, e.g., for software updates or video multicast. In the finite horizon problem, the optimum rate critically depends on the recent reception history of the receivers and requires a fine balance between maximizing overall throughput and equalizing individual receiver throughput. We formulate a dynamic programming algorithm that optimally solves this problem. We then develop two low complexity heuristics that perform close to the optimal solution and are suitable for practical online scheduling. We further analyze the performance of our algorithms by means of simulation. They substantially outperform existing solutions based on throughput maximization or favoring the user with the worst channel, and we obtain a 30 percent performance improvement over the former and a 120 percent improvement over the latter in scenarios with Rayleigh fading. We further analyze the performance of the schemes under imperfect state information and observe an even higher improvement over the benchmark schemes.

Mobile applications such as VoIP, (live) gaming, or video streaming have diverse QoS requirements ranging from low delay to high throughput. The optimization of the network quality experienced by end-users requires detailed knowledge of the expected network performance. Also, the achieved service quality is affected by a number of factors, including network operator and available technologies. However, most studies measuring the cellular network do not consider the performance implications of network configuration and management. To this end, this paper reports about an extensive data set of cellular network measurements, focused on analyzing root causes of mobile network performance variability. Measurements conducted on a 4G cellular network in Germany show that management and configuration decisions have a substantial impact on the performance. Specifically, it is observed that the association of mobile devices to a point of presence (PoP) within the operator's network can influence the end-to-end performance by a large extent. Given the collected data, a model predicting the PoP assignment and its resulting RTT leveraging Markov chain and machine learning approaches is developed. RTT increases of 58% to 73% compared to the optimum performance are observed in more than 57% of the measurements. Measurements of the response and page load times of popular websites lead to similar results, namely, a median increase of 40% between the worst and the best performing PoP.

Mobile applications such as VoIP, (live) gaming, or video streaming have diverse QoS requirements ranging from low delay to high throughput. The optimization of the network quality experienced by end-users requires detailed knowledge of the expected network performance. Also, the achieved service quality is affected by a number of factors, including network operator and available technologies. However, most studies focusing on measuring the cellular network do not consider the performance implications of network configuration and management. To this end, this paper reports about an extensive data set of cellular network measurements, focused on analyzing root causes of mobile network performance variability. Measurements conducted over four weeks in a 4G cellular network in Germany show that management and configuration decisions have a substantial impact on the performance. Specifically, it is observed that the association of mobile devices to a Point of Presence (PoP) within the operator's network can influence the end-to-end RTT by a large extent. Given the collected data a model predicting the PoP assignment and its resulting RTT leveraging Markov Chain and machine learning approaches is developed. RTT increases of 58% to 73% compared to the optimum performance are observed in more than 57% of the measurements.

The performance of current consumer-grade devices for 60 GHz wireless networks is limited. While such networks promise both high data rates and uncomplicated spatial reuse, we find that commercially available devices based on the WiHD and WiGig standards may suffer from their cost-effective design. Very similar mechanisms are used in upcoming devices based on the IEEE 802.11ad standard. Hence, understanding them well is crucial to improve the efficiency and performance of next generation millimeter wave networks. In this paper, we present the first in-depth beamforming, interference, and frame level protocol analysis of off-the-shelf millimeter wave systems with phased antenna arrays. We focus on (a) the interference due to the lack of directionality of consumer-grade antennas, and (b) the degree of data aggregation of current devices. Regarding (a), our beam pattern measurements show strong side lobes that challenge the common conception of high spatial reuse in 60 GHz networks. We also show that reflections in realistic settings worsen this effect. Further, we measure weak directionality when beamforming towards the boundary of the transmission area of an antenna array. Regarding (b), we observe that devices only aggregate data if connections require high bandwidth, thus increasing medium usage time otherwise.

Millimeter-wave communication achieves multi-Gbps data rates via highly directional beamforming to overcome pathloss and provide the desired SNR. Unfortunately, establishing communication with sufficiently narrow beamwidth to obtain the necessary link budget is a high overhead procedure in which the search space scales with device mobility and the product of the sender-receiver beam resolution. In this paper, we design, implement, and experimentally evaluate Blind Beam Steering (BBS) a novel architecture and algorithm that removes in-band overhead for directional mm-Wave link establishment. Our system architecture couples mm-Wave and legacy 2.4/5 GHz bands using out-of-band direction inference to establish (overhead-free) multi-Gbps mm-Wave communication. Further, BBS evaluates direction estimates retrieved from passively overheard 2.4/5 GHz frames to assure highest mm-Wave link quality on unobstructed direct paths. By removing in-band overhead, we leverage mm-Wave's very high throughput capabilities, beam-width scalability and provide robustness to mobility. We demonstrate that BBS achieves 97.8% accuracy estimating direction between pairing nodes using at least 5 detection band antennas. Further, BBS successfully detects unobstructed direct path conditions with an accuracy of 96.5% and reduces the IEEE 802.11ad beamforming training overhead by 81%.

Mobile network operators are expected to face significant traffic increase in the upcoming years. One alternative method is to intelligently move transmissions to times of network underutilization, either on 3G/4G or by offloading to WiFi. Video content, predicted by Cisco to constitute 69% of mobile traffic, offers the greatest potential for offloading. To this end, the demonstrated app strives to relieve the mobile network in a two ways. First, long-term prefetching of promising videos based on posts from the user's Online Social Network feed is performed. The knowledge about which video is likely being requested in the near future offers the opportunity to schedule the transmission according to its probability of being watched. Second, the approach is complemented with short-term prefetching, which is used whenever a content could not be downloaded by long-term prefetching. In this case, resources are optimized so as to maximize the communication efficiency while preserving the quality of service. The demonstrated app considers the smartphone's observed cellular network history to optimize the mobile throughput. A customized video player implements both the long-term and short-term prefetching. It reduces both the load on mobile networks, decreases playback pausing events and hereby achieves a high QoE. Thus, the player addresses both the operators' and the users' needs.

The exponential growth of media streaming traffic will have a strong impact on the bandwidth consumption of the future wireless infrastructure. One key challenge is to deliver services taking into account the stringent requirements of mobile video streaming, e.g., the users' expected Quality-of-Service. Admission control and resource allocation can strongly benefit from the use of anticipatory information such as the prediction of future user's demand and expected channel gain. In this paper, we use this information to formulate an optimal admission control scheme that maximizes the number of accepted users into the system with the constraint that not only the current but also the expected demand of all users must be satisfied. Together with the optimal set of accepted users, the optimal resource scheduling is derived. In order to have a solution that can be computed in a reasonable time, we propose a low complexity heuristic. Numerical results show the performance of the proposed scheme with respect to the state of the art.

Mobile data traffic is increasing rapidly and wireless spectrum is becoming a more and more scarce resource. This makes it highly important to operate the mobile network efficiently. In this paper we are proposing a novel lightweight measurement technique that can be used as a basis for advanced resource optimization algorithms to be run on mobile phones. Our main idea leverages an original packet dispersion based, technique to estimate both per user capacity and asymptotic dispersion rate. This allows passive measurements using only existing mobile traffic. Our technique is able to efficiently filter outliers introduced by mobile network schedulers. In order to verify the feasibility of our measurement technique, we run a week-long measurement campaign spanning two cities in two countries, different radio technologies, and covering all times of the day. The campaign demonstrates that our technique is effective even if it is provided only with a small fraction of the exchanged packets of a flow. The only requirement for the input data is that it should consist of a few consecutive packets that are gathered periodically. This makes the measurement algorithm a good candidate for inclusion in OS libraries to allow for advanced resource optimization and application-level traffic scheduling, based on current and predicted future user capacity.

A highly interesting trend in mobile network optimization is to exploit knowledge of future network capacity to allow mobile terminals to prefetch data when signal quality is high and to refrain from communication when signal quality is low. While this approach offers remarkable benefits, it relies on the availability of a reliable forecast of system conditions. This paper focuses on the reliability of simple prediction techniques and their impact on resource allocation algorithms. In addition, we propose ICARO, a resource allocation technique that is robust to prediction uncertainties. The algorithm combines autoregressive filtering and statistical models for short, medium, and long term forecasting. We validate our approach by means of an extensive simulation campaign based on real measurement data collected in Berlin. We show that our solution performs close to an omniscient optimizer and outperforms a limited horizon omniscient optimizer by 10 - 15%. Our solution provides up to 30% saving of system resources compared to a simple solution that always maintains a full buffer and is close to optimal in terms of buffer under-run time.

Multilayer video streaming allows to provide different video qualities to a group of multicast receivers with heterogeneous receive rates. The number of layers received (and thus the receive rate) determines the quality of the decoded video stream. For such layered multicast streaming, network coding provides higher capacity than multicast routing. Network coding can be performed within a layer or across layers, and in general, interlayer coding outperforms intralayer coding. An optimal solution to a network-coded layered multicast problem may require decoding of the network code at interior nodes to extract information to be forwarded. However, decoding consumes resources and introduces delay, which is particularly undesirable at interior nodes (the routers) of the network. In this paper, we thus focus on the interlayer network coding problem without decoding at interior nodes. We show that the problem is NP-hard and propose a heuristic algorithm for rate allocation and coding based on the Edmonds-Karp maximum flow algorithm. We prove that our algorithm ensures decodability of the information received and provides some fairness properties. Finally, we perform extensive simulations and show that our algorithm may even outperform other heuristics that do require decoding at interior nodes.

Distributed opportunistic scheduling (DOS) techniques have been recently proposed for improving the throughput performance of wireless networks. With DOS, each station contends for the channel with a certain access probability. If a contention is successful, the station measures the channel conditions and transmits in case the channel quality is above a certain threshold. Otherwise, the station does not use the transmission opportunity, allowing all stations to recontend. A key challenge with DOS is to design a distributed algorithm that optimally adjusts the access probability and the threshold of each station. To address this challenge, in this paper, we first compute the configuration of these two parameters that jointly optimizes throughput performance in terms of proportional fairness. Then, we propose an adaptive algorithm based on control theory that converges to the desired point of operation. Finally, we conduct a control theoretic analysis of the algorithm to find a setting for its parameters that provides a good tradeoff between stability and speed of convergence. Simulation results validate the design of our mechanism and confirm its advantages over previous works.

Interference alignment (IA) is often considered in the spatial domain in combination with MIMO systems. In contrast, aligning interference in the frequency domain among multiple subcarriers can also benefit single-antenna OFDM-based access networks. It allows for flexible operation on a per-subcarrier basis. We investigate the gains achievable by frequency IA in practice for a scenario with multiple access points and clients. Previous work is predominantly theoretical and focuses on idealized cases where all nodes have the same average signal-to-noise ratio (SNR). On the contrary, in practical networks, nodes typically have heterogeneous SNRs depending on channel conditions, which might have a significant impact on IA performance. We tackle this problem by designing mechanisms that adaptively choose which nodes shall perform IA on which subcarriers depending on current channel conditions. We implement and validate our approach on software-defined radios. To the best of our knowledge, this is the first practical implementation of IA in the frequency domain. Our measurements show that (1) frequency IA is feasible in practice, and (2) choosing appropriate nodes and subcarriers overcomes the main limitations due to heterogeneous SNRs. Our mechanisms enable IA in scenarios where it would be infeasible otherwise, achieving throughput gains close to the 33% theoretical maximum.

Wireless multicasting suffers from the problem that the transmit rate is usually determined by the receiver with the worst channel. Composite or adaptive beamforming allows using beamforming patterns that trade off antenna gains between receivers. A common solution for wireless multicast with beamforming is to select the pattern that maximizes the minimum rate among all receivers (for a given transmit power). However, when using opportunistic multicast to transmit a finite number of packets to all receivers - the finite horizon problem - this is no longer optimal. Instead, the optimum beamforming pattern depends on instantaneous channel conditions as well as the number of received packets at each receiver. We formulate the finite horizon multicast beamforming problem as a dynamic programming problem to obtain the optimal solution. We further design a heuristic that has sufficiently low complexity to be implementable in practice and show through extensive simulations that our algorithm significantly outperforms prior solutions.

Dense deployment of small cells is an important, emerging trend to enable future cellular networks to cope with growing traffic demand. However, this reduces the number of users per cell and thus opportunistic scheduling gain. We propose a base station-driven energy-aware approach to exploit user-user communication to increase the opportunistic gain. We use tools from stochastic Lyapunov optimization to determine the optimal scheduling policy subject to a constraint on energy consumption for user-user communication. Our simulation results show that with a large energy budget, packet transfer delay is reduced by up to 70%. The bulk of the performance improvement can be achieved with only a small increase in energy consumption, where 60% of the improvement is achieved at only 20% of the additional energy consumption. Further, we evaluate our algorithm using realistic video traffic traces and show that frame loss ratio is reduced by 90% and PSNR is improved by 4dB.

To meet the increasing demand for wireless capacity, future networks are likely to consist of dense layouts of small cells. The number of users in each cell is thus reduced, which results in diminished gains from opportunistic scheduling, particularly under dynamic traffic loads. We propose a user-initiated base station (BS)-transparent traffic spreading approach that leverages user-user communication to increase BS scheduling flexibility. The proposed scheme can increase opportunistic gain and improve user performance. For a specified tradeoff between performance and power expenditure, we characterize the optimal policy by modeling the system as a Markov decision process and also present a heuristic algorithm that yields significant performance gains. Our simulations show that, in the performance-centric case, average file transfer delays are lowered by up to 20% even in homogeneous scenarios and up to 50% with heterogeneous users. Further, we show that the bulk of the performance improvement can be achieved with a small increase in power expenditure, e.g., in an energy-sensitive case, up to 78% of the performance improvement can be typically achieved at only 20% of the power expenditure of the performance-centric case.

OFDM based wireless communication systems split the available frequency band into so-called sub-carriers, and data is transmitted on each of these sub-carriers in parallel. With frequency selective fading, sub-carriers may experience different channel qualities. Thus, choosing a different modulation and coding scheme (MCS) per sub-carrier improves performance. However, this comes at an increase in transceiver complexity and no current wireless system adapts the MCS at such a fine granularity. Some OFDMA based systems such as LTE allow to adapt the MCS per user, whereas wireless local area networks as specified by IEEE 802.11 use the same MCS on every sub-carrier. The performance of such wireless systems that use a single MCS in a frequency selective fading channel can be significantly improved through Sub-Carrier Switch Off (SSO), a simple but powerful alternative to adaptive MCS. SSO deactivates weak sub-carriers that excessively raise the error probability to improve the overall throughput. In this paper, we implement and test SSO in a software-defined radio testbed based on the Wireless Open Access Research Platform (WARP). We present a novel light-weight method for selecting the sub-carriers to be switched off based on the per-sub-carrier channel quality. The results we obtain from our measurements indicate that throughput increases of up to 250% are possible and thus SSO is a highly promising and very low complexity mechanism for future wireless local area networks.

Distributed opportunistic scheduling (DOS) is inherently more difficult than conventional opportunistic scheduling due to the absence of a central entity that knows the channel state of all stations. With DOS, stations use random access to contend for the channel and, upon winning a contention, they measure the channel conditions. After measuring the channel conditions, a station only transmits if the channel quality is good; otherwise, it gives up the transmission opportunity. The distributed nature of DOS makes it vulnerable to selfish users: By deviating from the protocol and using more transmission opportunities, a selfish user can gain a greater share of wireless resources at the expense of “well-behaved” users. In this paper, we address the problem of selfishness in DOS from a game-theoretic standpoint. We propose an algorithm that satisfies the following properties: 1) When all stations implement the algorithm, the wireless network is driven to the optimal point of operation; and 2) one or more selfish stations cannot obtain any gain by deviating from the algorithm. The key idea of the algorithm is to react to a selfish station by using a more aggressive configuration that (indirectly) punishes this station. We build on multivariable control theory to design a mechanism for punishment that is sufficiently severe to prevent selfish behavior, yet not so severe as to render the system unstable. We conduct a game-theoretic analysis based on repeated games to show the algorithm's effectiveness against selfish stations. These results are confirmed by extensive simulations.

ABSTRACT We present a system for real‐time traffic support in infrastructure andad hocIEEE 802.11 networks. The proposed elastic MAC (E‐MAC) protocol provides a distributed transmission schedule for stations with real‐time traffic requirements, while allowing a seamless coexistence with standard IEEE 802.11 clients, protecting best‐effort 802.11 traffic from starvation by means of admission control policies. Our scheduling decisions are based on an ‘elastic’ transmission opportunity (TXOP) assignment which allows for efficient wireless resource usage: whenever a real‐time station does not use the assigned TXOP, the other real‐time stations can take over the unused access opportunity, thus preventing the well‐known inefficiencies of static time division multiple access (TDMA) schemes. Unlike other TDMA‐based solutions for 802.11, E‐MAC does not require a tight synchronization among the participating clients, thus allowing its implementation on commodity WLAN hardwareviaminor software changes at the client side, and no changes at the access points (APs). We studied the performance of our mechanismvians‐2 simulations and a mathematical model, showing that it outperforms IEEE 802.11e in terms of throughput, delay, and jitter. We finally provide a proof of concept through the results obtained in a real testbed where we implemented the E‐MAC protocol. Copyright © 2011 John Wiley & Sons, Ltd.

Distributed Opportunistic Scheduling (DOS) techniques have been recently proposed to improve the throughput performance of wireless networks. With DOS, each station contends for the channel with a certain access probability. If a contention is successful, the station measures the channel conditions and transmits in case the channel quality is above a certain threshold. Otherwise, the station does not use the transmission opportunity, allowing all stations to recontend. A key challenge with DOS is to design a distributed algorithm that optimally adjusts the access probability and the threshold of each station. To address this challenge, in this paper we first compute the configuration of these two parameters that jointly optimizes throughput performance in terms of proportional fairness. Then, we propose an adaptive algorithm based on control theory that converges to the desired point of operation. Finally, we conduct a control theoretic analysis of the algorithm to find a setting for its parameters that provides a good tradeoff between stability and speed of convergence. Simulation results validate the design of the proposed algorithm and confirm its advantages over previous proposals.

Multi-layer video streaming allows to provide different video qualities to a group of multicast receivers with heterogeneous receive rates. The number of layers received determines the quality of the decoded video stream. For such layered multicast streaming, network coding provides higher capacity than multicast routing. Network coding can be performed within a layer (intra-layer) or across layers (inter-layer), and in general inter-layer coding outperforms intra-layer coding. An optimal solution to a network coded layered multicast problem may require decoding of the network code at interior nodes to extract information to be forwarded. However, decoding consumes resources and introduces delay, which is particularly undesirable at interior nodes (the routers) of the network. In this paper, we thus focus on the inter-layer network coding problem without decoding at interior nodes. We propose a heuristic algorithm for rate allocation and code assignment based on the Edmonds-Karp maximum flow algorithm and perform simulations that show that our algorithm may even outperform other heuristics that do require decoding at interior nodes.

The multiple-access relay channel with two sources, a single relay, and one destination is considered. Under the assumption of noisy source-relay links causing the relay to be unable to decode without error, we propose a framework for designing one- and two-dimensional quantizers for quantizing the soft information at the relay. These quantizers are mutual-information preserving. Simulation results show a) that mutual-information preserving quantization schemes outperform techniques in which the soft information is forwarded in an analog fashion to the destination, b) that two-dimensional quantization outperforms one-dimensional quantization for source-relay links of different quality, and c) that diversity order of two can be gained in block Rayleigh fading channels by having the relay adaptively select a two-dimensional quantizer from a fixed set of quantizers shared with the destination, depending on the channel state on the source-relay links.

Video streaming is considered as one of the most important and challenging applications for next generation cellular networks. Current infrastructures are not prepared to deal with the increasing amount of video traffic. The current Internet, and in particular the mobile Internet, was not designed with video requirements in mind and, as a consequence, its architecture is very inefficient for handling video traffic. Enhancements are needed to cater for improved Quality of Experience (QoE) and improved reliability in a mobile network. In this paper we design a novel dynamic transport architecture for next generation mobile networks adapted to video service requirements. Its main novelty is the transport optimization of video delivery that is achieved through a QoE oriented redesign of networking mechanisms as well as the integration of Content Delivery Networks (CDN) techniques.

Exploiting base station cooperation in wireless mobile access networks leads to benefits in wireless transmission capacity, inter-cell interference management, and cell edge user experience. The clustering of cooperative Base Station (BS) sets, necessary for achieving the desired wireless performance, poses several challenges in the backhaul architecture design. This paper addresses the problem of placing and connecting controller/processing nodes within the backhaul infrastructure, which coordinate and/or process signals of cooperating base stations. We formulated a Mixed Integer Linear Programm (MILP) for this problem and a heuristic algorithm that approximates the optimal solution. While the heuristic's solution quality is close to the optimum, the runtime and memory requirements are multiple orders of magnitude lower compared to solving the MILP. This advantage allows to use the proposed heuristic either for backhaul/core network pre-planning or for on-the-fly network reconfiguration during ongoing mobile network operation.

The orthogonal multiple-access relay channel with two sources is considered. The goal of this paper is to show the applicability and effectiveness of a previously introduced quantize-and-forward scheme to a more realistic channel and system model, including orthogonal frequency division multiple access and multipath fading channels. Simulation results are provided to demonstrate the gain of quantize-and-forward relayed communication as opposed to the point-to-point links without the relay.

The full degree of freedom in mobile systems heavily depends on the energy provided by the mobile phone's batteries. Their capacity is in general limited and for sure not keeping pace as the mobile devices are crammed up with new functionalities. The discrepancy of Moore's law, offering twice the processing power at least each second year, and the development in batteries, which did not even double over the last decade, makes a shift in researchers' way of designing networks, protocols, and the mobile device itself. The bottleneck to take care of in the design process of mobile systems is not only the wireless data rate, but even more the energy limitation as the customers ask for new energy-hungry services, e.g., requiring faster connections or even multiple air interfaces, and longer standby or operational times of their mobile devices at the same time. In this survey, the energy consuming entities of a mobile device such as wireless air interfaces, display, mp3 player and others are measured and compared. The presented measurement results allow the reader to understand what the energy hungry parts of a mobile device are and use those findings for the design of future mobile protocols and applications. All results presented in this work and further results are made public on our web page [2].

The source coding rate allocation problem for the orthogonal multiple-access relay channel with M users and compress-and-forward at the relay is addressed. In case of Gaussian codebooks at the sources and Gaussian channels, we show that the sum-rate-optimal assignment of source coding rate at the relay is given by waterfilling. For general modulation alphabets at the sources and finite-alphabet discrete memoryless channels, the source coding rate allocation problem is formulated using the information bottleneck method, based on which we appropriately modify a standard cutting-plane algorithm to numerically compute an optimal source coding rate vector at the relay.

Broadcast video streaming represents an important service which will be provided by 4G networks. The current video compression in use, the H.264/AVC (Advance Video Coding), is capable of providing good video quality at substantially lower bit rates than previous video compression standards. The scalable extension of AVC, SVC (Scalable Video Coding), enables a source to transmit a video composed of multiple quality, temporal and spatial sub-streams. The flexibility offered by SVC is appealing for broadcast streaming services, where heterogeneous devices and sets of users are supported. SVC combined with MAC scheduling and the flexibility offered by different modulation and coding schemes provide a novel solution for broadcast streaming services. In this paper, we propose a generic optimization framework to evaluate the performance of the current standard used for streaming services, AVC, and its scalable extension SVC, in terms of wireless utilization, storage requirements and perceived video quality for a single-site transmission scenario. This framework can be implemented on mobile multimedia broadcasting systems, such as ISDB-Tmm [1]. ISDB-Tmm currently considers AVC as video codec, but can be extended to support SVC as well. Selected simulations show the benefits provided by using SVC for broadcast video streaming in a single cell network making use of a flexible MAC layer scheduling. Our study is then extended to a multi-cell network, discussing the issues related to practical deployments.

This paper addresses the fairness of Medium Access Control (MAC) protocols that are capable of handling interference on the physical layer to a varying extent. The variation addressed is thereby not based on different physical layer techniques applied, but on differing attribute levels of the same physical layer technique. This can e.g., be a varying number of antennas at each node in case of Multiple Input Multiple Output (MIMO). Fair contention resolution in such protocols becomes a trade off between on the one hand avoiding a permanent overwhelming of low level devices with interference, and on the other hand blocking of all spatial reuse by these low level devices. The latter one would restrain the better equipped terminals from taking advantage of their excellent equipment. In order to balance both aspects, we propose the Fair and Adaptive Contention Resolution Algorithm (FACRA). The algorithm is fully distributed and requires only the exchange of a single parameter value between competing terminals. We show by means of simulations that the algorithm significantly increases the spatial reuse by avoiding frequent blocking by low level devices, without disadvantaging these devices significantly.

In wireless ad hoc networks, multiple access interference is the limiting factor for the overall system performance. The lack of any central control unit necessitates a careful joint design of both, the physical and the MAC layer. Two promising technologies that address this problem are power control based cross-layer design and multiuser detection based cross-layer design. While approaches in the first category suppress the interference by power control at the transmitter side, approaches in the second cancel interference at the receiver with a technique called multiuser detection. In this paper, we compare two cross-layer approaches that are representative of power control based cross-layer design and multiuser detection based cross-layer design, respectively. We separately investigate both, the solely PHY performance of the proposed solutions, and the gains achieved by the appropriate MAC design, in terms of overall throughput. Thus, we gain insight into the interaction between both layers that reflects the quality of the cross-layer designs. We test the performance in medium as well as high traffic load scenarios. Based on the obtained results, we show that applying power control in cross-layer designs can in dense traffic scenarios even lead to a worse performance than no cross-layer design at all. Thus, we conclude that in general MUD-based cross-layer design is the better alternative.

Thanks to its in-network drop-based adaptation capabilities, H.264 Scalable Video Coding is perceived as an effective approach for delivering video over networks characterized by sudden large bandwidth fluctuations, such as Wireless LANs. Performance may be boosted by the adoption of application-aware/cross-layer schedulers devised to intelligently drop video data units (NALUs), so that i) decoding dependencies are preserved, and ii) the quality perceived by the end users is maximized. In this paper, we provide a theoretical formulation of a QoE utility-optimal cross-layer scheduling problem for H.264 SVC downlink delivery over WLANs. We show that, because of the unique characteristics of the WLAN MAC operation, this problem significantly differs from related approaches proposed for scheduled wireless technologies, especially when the WLAN carries background traffic in the uplink direction. From these theoretical insights, we derive, design, implement and experimentally assess a simple practical scheduling algorithm, whose performance is very close to the optimal solution.

Interference management is one of the most critical operations for CSMA/CA protocols. In this paper we investigate how specific PHY-layer operations, designed to mitigate the effect of interference, can cause the radio-receiver circuitry to over-estimate the channel occupation status, thus unnecessarily limiting the overall spatial reuse or impeding the synchronization to incoming frames. We individuated the root of this problem in the surprising effectiveness of the overall packet detection procedure of WLAN PHY, which allows for the demodulation of strongly attenuated 802.11 frames. The transmit spectrum mask for the IEEE 802.11 standard allows limited power leakage over adjacent channels. Such a low power signal from adjacent channels nowadays can be correctly recognized by recent OFDM/CCK receive circuitry, which has an important impact on WLAN performance. A receiver may miss the reception of packets destined to it, whenever a frame capture of a transmission on a partially overlapping channel occurs. A similar effect occurs in the transmit phase. In carrier sense mode, the OFDM/CCK receiver of the WLAN device is capable of synchronizing to the preamble of low SNR signals, thus unnecessarily deferring the channel access in case of transmissions on partially overlapping channels.

In this paper we consider practical dissemination algorithms exploiting network coding for data broadcasting in ad hoc wireless networks. For an efficient design, we analyze issues related to the use of network coding in realistic network scenarios. In detail, we quantify the impact of random access schemes, as used by IEEE 802.11, on the performance of network coding. In such scenarios, deadlock situations may occur where the delivery process stops and some of the nodes never gather the required packets. To tackle this problem, we propose a proactive mechanism (called proactive network coding) which adapts its transmission schedule according to the decoding status of neighboring nodes. This scheme can detect when nodes need additional packets in order to decode and acts accordingly. We finally investigate the behavior of network coding schemes in multi-rate environments, where we propose a distributed heuristic approach for the selection of data rates.

In this paper we look at the problem of accurately reconstructing distributed signals through the collection of a small number of samples at a data gathering point. The techniques that we exploit to do so are Compressive Sensing (CS) and Principal Component Analysis (PCA). PCA is used to find transformations that sparsify the signal, which are required for CS to retrieve, with good approximation, the original signal from a small number of samples. Our approach dynamically adapts to non-stationary real world signals through the online estimation of their correlation properties in space and time; these are then exploited by PCA to derive the transformations for CS. The approach is tunable and robust, independent of the specific routing protocol in use and able to substantially outperform standard data collection schemes. The effectiveness of our recovery algorithm, in terms of number of transmissions in the network vs reconstruction error, is demonstrated for synthetic as well as for real world signals which we gathered from an actual wireless sensor network (WSN) deployment. We stress that our solution is not limited to WSNs, but can be readily applied to other types of network infrastructures that require the online approximation of large and distributed data sets.

A network with two sources, one relay, and one destination is considered. Under the assumption of noisy source- relay links causing the relay to be unable to decode without error, we propose a quantizer design framework where the quantizer jointly compresses the soft information available for both sources at the relay. The quantizer design is based on the information bottleneck method using the notion of relevant information as an optimization criterion.

Motivated by streaming applications with stringent delay constraints, we consider the design of online network coding algorithms with timely delivery guarantees. Assuming that the sender is providing the same data to multiple receivers over independent packet erasure channels, we focus on the case of perfect feedback and heterogeneous erasure probabilities. Based on a general analytical framework for evaluating the decoding delay, we show that existing ARQ schemes fail to ensure that receivers with weak channels are able to recover from packet losses within reasonable time. To overcome this problem, we re-define the encoding rules in order to break the chains of linear combinations that cannot be decoded after one of the packets is lost. Our results show that sending uncoded packets at key times ensures that all the receivers are able to meet specific delay requirements with very high probability.

This paper describes the H.264 scalable video coding streaming evaluation framework (SVEF). This is the first open-source framework for experimental assessment of H.264 scalable video coding (SVC) delivery over real networks. Effectively adapting of the transport of an H.264 SVC stream to time-varying, bandwidth constrained, and loss prone networks is an important research area. However, very little experimental work has been performed due to the unavailability of real-time H.264 SVC players, the limitations of existing decoding software libraries when challenged with network-imparied received SVC streams (e.g., affected by random loss of Network Abstraction Layer Units - NALUs), and the lack of solutions for SVC streaming support. SVEF overcomes these issues by developing missing components and by integrating them in a hybrid online/offline experimental framework. We believe SVEF will be of significant help to the research community interested in experimentally benchmarking their own proposed SVC adaptation approaches and delivery mechanisms. As a proof-of-concept of SVEF, we provide the experimental performance evaluation of an SVC cross-layer in-network scheduler in a wireless LAN hot spot scenario.

In this paper we investigate the interaction of channel code and modulation (i.e., the bitrate) of wireless transmissions and application layer forward error correction in the context of media streaming. To this end, we design a multimedia delivery protocol based on systematic network coding with partial feedback for reliable broadcast streaming over WLANs. Differently from other work, our approach takes into account the trade-off between (i) exploiting feedback-based encoded retransmissions at the multimedia broadcast source to compensate for the lack of MAC-layer error recovery mechanisms, and (ii) the efficient use of the wireless channel resources as given by the selected modulation scheme and the amount of feedback messages. The proposed protocol is tailored to the delay/packet loss rate demands of multimedia traffic and uses a combination of modulation rate and a forward error correction overhead which jointly leads to a minimal channel usage.

Interference limits throughput in wireless networks. To protect themselves against interference, many wireless protocols create areas around receivers in which no node is allowed to transmit. If such an exclusion area is small, more transmissions can proceed simultaneously but observe higher interference, creating a tradeoff between network capacity and link capacity.

We consider a wireless relay network where two sources transmit independent information on mutually orthogonal channels to a common destination with the help of one relay. Based on the expression for the achievable rate in such a network for compress-and-forward relaying without Wyner-Ziv coding, we design mutual-information preserving quantizers for compression at the relay. In the proposed relaying scheme, both the relay and the destination share a fixed set of quantizers, among which the relay selects a suitable one depending on the channel quality on the source-relay links for compression of its received values. Simulations performed in Rayleigh block fading channels reveal that full diversity order of two can be achieved using that scheme. We also comment on the size of the quantizer set and the associated signaling overhead.

Network coding is a technique which is particularly suitable for the dissemination of data in distributed ad hoc networks. The definition of a mathematical model that describes the interactions among nodes and, in particular, their relationship in terms of buffer subspaces is still an open and challenging problem. The contribution of this paper is an analysis of the relationship between the network topology and the subspace overlap among nodes. This analysis can be used to establish criteria for the design of packet combination policies in diverse networking scenarios. Differently from previous studies, we will explicitly take the overlap among subspaces into account through a framework comprising networks with fixed as well as mobile nodes.

In this paper we consider a server sending a scalable video stream over a wireless channel to an end-user. We design a fast rate adaptation algorithm which chooses the right transmission policy (video data and associated unequal error protection) based on channel feedback. Our algorithm can be easily implemented as a smart scheduling module at the server side, which adapts its packet selection rules based on the total available channel rate and losses experienced by the end-user. Our simulation results show the good performance of our algorithm compared to the optimal transmission policy, for a wide range of channel conditions. We also identify scenarios that fully benefit from scalable encoding augmented by unequal error protection, compared to traditional bitstream-switching methods based on single stream transmission protected by FEC.

Cooperative relaying has been shown to provide diversity gains which can significantly improve the packet error rate (PER) in wireless transmissions. In ad hoc wireless routing where packets may travel over a number of hops before reaching the destination, hop-wise cooperative relaying may severely reduce network capacity. This approach was mainly addressed in literature so far. In this paper, we efficiently apply cooperative relaying along a complete path and over multiple hops at the same time. We use information from the routing layer to improve the medium access control (MAC) layer's performance. Simulations and testbed implementation show appealing gains through diversity resulting in up to 66% better PER performance and up to 148% goodput increase compared to conventional approaches.

Network coding is a highly efficient data dissemination mechanism for wireless networks. Since network coded information can only be recovered after delivering a sufficient number of coded packets, the resulting decoding delay can become problematic for delay-sensitive applications such as real-time media streaming. Motivated by this observation, we consider several algorithms that minimize the decoding delay and analyze their performance by means of simulation. The algorithms differ both in the required information about the state of the neighborspsila buffers and in the way this knowledge is used to decide which packets to combine through coding operations. Our results show that a greedy algorithm, whose encodings maximize the number of nodes at which a coded packet is immediately decodable significantly outperforms existing network coding protocols.

Opportunistic Relaying (OR) and Selection Decode-and-Forward (SDF) cooperation protocols can both substantially improve the performance of wireless networks but fundamentally differ in utilized redundancy, relays, and channel knowledge. To analyze when SDF or OR improves error and data rate, we (1) derive their general outage probability and capacity for arbitrary relay configurations, (2) systematically benchmark both approaches in two-hop configurations, (3) study how often beneficial configurations occur in large networks, and, finally, condition our capacity results by this occurrence probability. Our results clearly show that OR maximizes the outage capacity at high acceptable error rate while SDF succeeds if a low error rate is required. SDF performs best if even the relays can cooperate among themselves, supported frequently in networks with more than three neighbors. Consequently, cooperating relays, adapting between OR and SDF, and joining these two approaches should be the focus of future protocol design. To this end, our paper provides a theoretical basis, adaptation rules, and design guidelines.

In this paper a cooperative retransmission scheme for 802.11 WLANs is proposed. The mechanism allows stations with good channel to the access point to retransmit packets on behalf of other stations with worse channel. The cooperative partner selection is carried out in a completely opportunistic and distributed way that is compatible with 802.11. Simulations performed in a realistic WLAN scenario with adaptive modulation show that the system provides considerable gains in terms of goodput and packet error rate in comparison with legacy 802.11.

In recent years, network coding has proved to be an efficient tool to disseminate data through a network. A number of practical schemes have been proposed to implement network coding also in wireless environments. Most of them are based on reactive and probabilistic random network coding and their effectiveness has been investigated under the assumption of idealized network conditions. However, recent work has shown that the benefits of such strategies decrease when applied in realistic network environments. In this paper, we propose an algorithm to efficiently disseminate data through network coding in realistic wireless networks by using a proactive approach, named ProNC. We develop a distributed and self-adaptable protocol which substantially increases the performance of network coding in practical scenarios and achieves full reliability with both low protocol overhead and low delay. We show the effectiveness of ProNC via ns-2 simulations and compare it with previously proposed schemes.

The present paper investigates practical algorithms to efficiently exploit random network coding for data delivery in multi-hop wireless networks. In the past few years, a great deal of work has been carried out to derive analytical results about network coding. However, only recently have researchers started to utilize the theoretical findings in practical settings. Network coding is a new paradigm for data delivery which proved to be very efficient. It is particularly suitable for wireless networks due to the inherent broadcast nature of the channel. Even though previous work dealt with practical schemes exploiting these new techniques, many issues concerning the coexistence of network coding and channel access mechanisms are still unsolved. In addition, it is still unclear how packets should be combined in order to get the highest benefits in terms of throughput, delay, and energy efficiency. Our work presents an accurate investigation of these aspects. In particular, we couple several MAC and scheduling schemes together with different network coding strategies, and compare them via extensive ns2 simulation. Finally, we propose a new timing strategy for the combination of data packets in random network coding.

Wireless routing based on an embedding of the connectivity graph is a very promising technique to overcome shortcomings of geographic routing and topology-based routing. This is of particular interest when either absolute coordinates for geographic routing are unavailable or when they poorly reflect the underlying connectivity in the network. We focus on dynamic networks induced by time-varying fading and mobility. This requires that the embedding is stable over time, whereas the focus of most existing embedding algorithms is on low distortion of single realizations of a graph. We develop a beacon-based distributed embedding algorithm that requires little control overhead, produces low distortion embeddings, and is stable. We also show that a low-dimensional embedding suffices, since at a sufficiently large scale, wireless connectivity graphs are dictated by geometry. The stability of the embedding allows us to combine geo-routing on the embedding with last encounter routing (LER) for node lookup, further reducing the control overhead. Our routing algorithm avoids dead ends through randomized greedy forwarding. We demonstrate through extensive simulations that our combined embedding and routing scheme outperforms existing algorithms.

To overcome the lack of strict QoS guarantees in existing hotspots, in this paper we present a system for realtime traffic support in 802.11 networks that works in either infrastructure or ad-hoc mode. The proposed mechanism, called elastic MAC (E-MAC) protocol, helps stations with real-time traffic to organize and establish a transmission schedule in a distributed manner, while coexisting with standard 802.11 stations. This distributed scheduling guarantees very short delays and a minimum reserved data rate for real-time stations, while protecting best-effort 802.11 traffic from starvation. Another feature of our mechanism is time-slot reuse, which improves the network efficiency by allowing other real-time stations to take over unused slots (e.g., in case of using voice codecs with silence suppression). We evaluate the performance of our system using a testbed implementation, ns-2 simulations and a mathematical model, and show how it outperforms other QoS schemes (e.g., 802.11e) in terms of throughput, delay, and jitter.

A major problem in tracking objects in sensor networks is trading off update traffic and timeliness of the data that is available to a monitoring site. Typically, either all objects regularly update some central registry with their location information, or the monitoring instance floods the network with a request when it needs information for a particular object. More sophisticated approaches use a P2P-like distributed storage structure on top of geographic routing. The applicability of the latter is limited to certain topologies, and having separate storage and routing algorithms reduces efficiency. In this paper, we present a different solution which is based on the scalable source routing (SSR) protocol. SSR is a network layer routing protocol that has been inspired by distributed hash tables (DHT). It provides key-based routing in large networks of resource-limited devices such as sensor networks. We argue that this approach is more suitable for object tracking in sensor networks because it evenly spreads the updates over the whole network without being limited to a particular network topology. We support our argument with extensive simulations.

Mobile object tracking is the process of tracking objects which are moving constantly in their environment and thus change their location and context. We present in this paper a decentralized data management solution based on peer-to-peer (P2P) concepts and in particular a distributed hash table (DHT). Sensor network nodes run a DHT system, to which tracked objects or sensors insert their data about the objects. This distributed data storage can be queried by the application according to the DHT principles in order to retrieve the current status of an object without stability or bottleneck problems. Moreover, for scalability, our concept comprises a two tier architecture where we separate local tracking systems, which deal with frequent updates in a constrained local environment, and global tracking. In this way a second tier DHT based P2P system interconnects all local P2P systems for a global query resolution.

Abstract — We show that network coding allows to realize en-ergy savings in a wireless ad-hoc network, when each node of the network is a source that wants to transmit information to all other nodes. Energy efficiency directly affects battery life and thus is a critical design parameter for wireless networks. We propose an implementable method for performing network coding in such a setting. We analyze theoretical cases in detail, and use the insights gained to propose a practical, fully distributed method for realistic wireless ad-hoc scenarios. We address practical issues such as setting the forwarding factor, managing generations, and impact of transmission range. We use theoretical analysis and packet level simulation. I.

We consider the design of the MAC layer for low power, low data-rate, and impulse-radio ultra-wide band (IR-UWB) networks. In such networks, the primary concern is energy consumption rather than rate efficiency. We explore several dimensions such as power control, rate adaptation, mutual exclusion, slotted versus non-slotted operation, power saving modes and interference mitigation. We analyze the effect of these design choices on the energy consumption and rate efficiency. We use a method of energy quanta for computing the energy consumption. We find that for both cases, the optimal operation is non-coordinated and with no power control. Sources should send at their maximum power and not pay attention to neighboring nodes. However, sources should constantly adapt their transmission rate to the level of interference

Protocols for beamforming antennas usually direct the beam toward the respective communication partner. This requires significant coordination between nodes and results in frequent changes of the beam direction. In this paper, we present much simpler algorithms that instead aim at improving connectivity and robustness of routing. A node computes the optimal beam direction using aggregate information about its neighborhood such as the number of neighbors in each beam direction. We analyze the performance of such algorithms in terms of number of paths to a destination, mutual interference, and route lifetime in mobile networks, and show that they are a promising alternative to existing beamforming schemes

No abstract available.

Abstract Due to environmental concerns and strict constraints on interference imposed on other networks, the radiated power of emerging pervasive wireless networks needs to be strictly limited, yet without sacrificing acceptable data rates. Pulsed time‐hopping ultra‐wideband (TH‐UWB) is a radio technology that has the potential to satisfy this requirement. Although TH‐UWB is a multi‐user radio technology, non‐zero cross‐correlation between TH sequences, time‐asynchronicity between sources and a multipath channel environment make it sensitive to strong interferers and near‐far scenarios. While most protocols manage interference and multiple‐access through power control or mutual exclusion, we base our design on rate control, a relatively unexplored dimension for multiple‐access and interference management. We further take an advantage of the nature of pulsed TH‐UWB to propose an interference mitigation scheme that alleviates the need for an exclusion scheme. A source is always allowed to send and continuously adapts its channel code (hence its rate) to the interference experienced at the destination. In contrast to power control or exclusion, our MAC layer is local to sender and receiver, and does not need coordination among neighbors not involved in the transmission. We show by simulation that we achieve a significant increase in the network throughput compared to alternative designs. Copyright © 2005 John Wiley & Sons, Ltd.

We present a joint PHY/MAC architecture (DCC-MAC) for 802.15.4a-like networks based on PPM-UWB. Unlike traditional approaches, it fully utilizes the specific nature of UWB to achieve high rates at low protocol complexity. It is the first MAC protocol that adapts the channel code (and thus the bit rate) to interference from concurrent transmissions instead of enforcing exclusion. In order to avoid a complex mutual exclusion protocol at the MAC layer, we propose an interference mitigation scheme. The scheme is based on a modification of the physical layer that cancels much of the interfering energy, in particular from nearby interferers. We further use dynamic channel coding to combat the remaining interference. Sources constantly adjust their channel codes to the level of interference and send incremental redundancy as required. Contention between sources sending to the same destination is solved by a "private MAC" protocol that involves only the nodes that want to talk to the same destination. The private MAC does not use any common channel; this avoids the issues of hidden and exposed terminals altogether. We show by simulation that our MAC protocol fully satisfies the application requirements of 802.15.4a in terms of link lengths, rates and mobility. We further show that it achieves a significant increase in network throughput, compared to traditional MAC protocols like 802.15.4, that are separated from the physical layer.

In this paper we present a TCP-friendly congestion control scheme for non-adaptable flows. The main characteristic of these flows is that their data rate is determined by an application and cannot be adapted to the current congestion situation of the network. Typical examples of non-adaptable flows are those produced by networked computer games or live audio and video transmissions where adaptation of the quality is not possible (e.g., since it is already at the lowest possible quality level). We propose to perform congestion control for non-adaptable flows by suspending them at appropriate times so that the aggregation of multiple non-adaptable flows behaves in a TCP-friendly manner. The decision whether or not a flow is to be suspended is based on random experiments. In order to allocate probabilities for these experiments, the data rate of the non-adaptable flow is compared to the rate that a TCP flow would achieve under the same conditions. We present a detailed discussion of the proposed scheme and evaluate it through extensive simulation with the network simulator ns-2.

In this paper we introduce TFMCC, an equation-based multicast congestion control mechanism that extends the TCP-friendly TFRC protocol from the unicast to the multicast domain. The key challenges in the design of TFMCC lie in scalable round-trip time measurements, appropriate feedback suppression, and in ensuring that feedback delays in the control loop do not adversely affect fairness towards competing flows. A major contribution is the feedback mechanism, the key component of end-to-end multicast congestion control schemes. We improve upon the well-known approach of using exponentially weighted random timers by biasing feedback in favor of low-rate receivers while still preventing a response implosion. We evaluate the design using simulation, and demonstrate that TFMCC is both TCP-friendly and scales well to multicast groups with thousands of receivers. We also investigate TFMCC's weaknesses and scaling limits to provide guidance as to application domains for which it is well suited.

This paper proposes a mechanism for equation-based congestion control for unicast traffic. Most best-effort traffic in the current Internet is well-served by the dominant transport protocol, TCP. However, traffic such as best-effort unicast streaming multimedia could find use for a TCP-friendly congestion control mechanism that refrains from reducing the sending rate in half in response to a single packet drop. With our mechanism, the sender explicitly adjusts its sending rate as a function of the measured rate of loss events, where a loss event consists of one or more packets dropped within a single round-trip time. We use both simulations and experiments over the Internet to explore performance.