Accurate orientation estimation of objects can aid in scene understanding in many applications. In this paper, we consider use cases where passive tags could be deployed to assist radar systems in estimating object orientation. Towards that end, we propose the concept of passive iridescent reflective tags that selectively reflect different wavelengths in different directions. We propose a conceptual tag design based on leaky-wave antennas. We develop a framework for signal modeling and orientation estimation with a multi-tone radar. We analyze the impact of imperfect tag location information, revealing that it minimally impacts orientation estimation accuracy. To reduce estimator complexity, we propose a radiation pointing angle-based estimator with near-optimal performance. We derive its feasible orientation estimation region and show that it depends mainly on the system bandwidth. Monte Carlo simulations validate our analytical results while evincing that the low-complexity estimator achieves near-optimal accuracy and that its feasible orientation estimation region closely matches that of the other estimators. Finally, we show that the optimal number of tones increases with the sensing time under a power budget constraint, multipath effects may be negligible, signal-to-noise ratio gains rise with the number of tones, and many radar antennas can hurt estimation performance when the signal contains very few tones.

Individuals with Opioid Use Disorder (OUD) often struggle to maintain sobriety, with many experiencing relapse within the first year. While medication-assisted treatment (MAT) is among the most effective approaches, access to intensive care is often limited by financial barriers. Mobile health (mHealth) technologies offer a promising, cost-effective alternative by enabling continuous monitoring and timely intervention through tools such as ecological momentary assessments (EMAs), wearable sensors, and smartphone data. In this study, we explore the feasibility of using mHealth data to predict emotions that align with cravings in OUD patients undergoing MAT. Using data collected from EMAs, wearables, smartphone tracking, and surveys, we demonstrate that machine learning models can accurately predict emotional states associated with cravings. These findings highlight the potential of mHealth systems to support individuals with OUD through timely and scalable interventions.

An emerging application of wireless sensing is locating and tracking humans in their living environments, a primitive that can be leveraged in both daily life applications and emergency situations. However, most proposed methods have limited spatial resolution when multiple humans are in close vicinity. The problem becomes exacerbated when there is no line-of-sight path to the humans. In this paper, we consider multi-person localization of humans in close vicinity of each other. We propose the use of synthetic aperture radar that combines both translation and rotation to increase effective aperture size, leveraging small rhythmic changes in the radar range due to human breathing. We experimentally evaluate the proposed algorithm in both line-of-sight and through-wall cases with three to five humans in the scene. Our experimental results show that: (i) larger synthetic apertures due to radar translation improve multi-person localization, e.g., by 1.42× when the aperture size is increased by a factor of 2×, and (ii) rotation can largely compensate for gains provided by translation, e.g., rotating the radar over 360° without changing the aperture size results in 1.22× gains over no rotation. Overall, maximal gains of 2.19× are achieved by rotating and translating over a 2× larger aperture.

Radio Access Networks (RANs) are increasingly softwarized and disaggregated, resulting in diverse and interoperable components running on commodity hardware and compute platforms. In this demo, we present a real-time RAN testing framework, called ETHOS, designed to capture fine-grained metrics on performance, energy consumption, and computational efficiency for O-RAN-compliant systems across a range of compute platforms. ETHOS also interfaces with the RAN stack for real-time controlling to test various scheduling algorithms. ETHOS leverages a shared memory-based inter-process communication (IPC) framework and automates testing through the emulation of diverse user traffic and channel conditions. We deploy ETHOS on the NVIDIA ARC-OTA platform in this demonstration to show its effectiveness. ETHOS is designed to be easily portable to other commercial and open-source RAN software, offering the potential for a unified testing and evaluation framework to accelerate O-RAN research and innovation.

Graphons, as limit objects of dense graph sequences, play a central role in the statistical analysis of network data. However, existing graphon estimation methods often struggle with scalability to large networks and resolution-independent approximation, due to their reliance on estimating latent variables or costly metrics such as the Gromov-Wasserstein distance. In this work, we propose a novel, scalable graphon estimator that directly recovers the graphon via moment matching, leveraging implicit neural representations (INRs). Our approach avoids latent variable modeling by training an INR--mapping coordinates to graphon values--to match empirical subgraph counts (i.e., moments) from observed graphs. This direct estimation mechanism yields a polynomial-time solution and crucially sidesteps the combinatorial complexity of Gromov-Wasserstein optimization. Building on foundational results, we establish a theoretical guarantee: when the observed subgraph motifs sufficiently represent those of the true graphon (a condition met with sufficiently large or numerous graph samples), the estimated graphon achieves a provable upper bound in cut distance from the ground truth. Additionally, we introduce MomentMixup, a data augmentation technique that performs mixup in the moment space to enhance graphon-based learning. Our graphon estimation method achieves strong empirical performance--demonstrating high accuracy on small graphs and superior computational efficiency on large graphs--outperforming state-of-the-art scalable estimators in 75\% of benchmark settings and matching them in the remaining cases. Furthermore, MomentMixup demonstrated improved graph classification accuracy on the majority of our benchmarks.

Using millimeter wave (mmWave) signals for imaging has an important advantage in that they can penetrate through poor environmental conditions such as fog, dust, and smoke that severely degrade optical-based imaging systems. However, mmWave radars, contrary to cameras and LiDARs, suffer from low angular resolution because of small physical apertures and conventional signal processing techniques. Sparse radar imaging, on the other hand, can increase the aperture size while minimizing power consumption and read-out bandwidth. This article presents CoIR, an analysis by synthesis method that leverages the implicit neural network bias in convolutional decoders and compressed sensing to perform high-accuracy sparse radar imaging. The proposed system is data set-agnostic and does not require any auxiliary sensors for training or testing. We introduce a sparse array design that allows for a $5.5\times$5.5× reduction in the number of antenna elements needed compared to conventional MIMO array designs. We demonstrate our system's improved imaging performance over standard mmWave radars and other competitive untrained methods on both simulated and experimental mmWave radar data.

In this paper, we propose a method to repurpose the multi-user MIMO downlink transmission for joint wireless communication and imaging. The key idea is to introduce the concept of virtual users in the communication coverage area and use the existing MUMIMO beamforming methods to jointly beamform towards real and virtual users. The virtual users are placed to complement the locations of actual users, with the objective to illuminate the scene as uniformly as possible. We study a single-parameter tradeoff, introduced by a power split parameter between real and virtual users. We demonstrate via simulated examples that the virtual user concept is effective in providing a scalable imaging and communications performance tradeoff for cases where the real users are clustered in small geographical areas.

Security threats of an IEEE 802.11ax (WiFi 6) system can occur throughout the layers of the protocol stack. Looking at the security aspect of the physical layer and from an attacker's perspective, we present how a spoofing attack can cause an access point to steer the beam dedicated to a legitimate user toward the attacker. More particularly, we propose the BeamSteal attack that takes advantage of the vulnerability of the beamforming feedback (BFF) mechanism. The purpose of the BeamSteal attack is to cause the access point to receive the wrong BFF -information-bearing bits and end up steering the beam toward the attacker instead of the legitimate user. To contrast the harmful effect of the proposed attack, we compare it with two benchmarks, namely the no attack case and the jamming attack case. Our numerical results show that the BeamSteal attack not only degrades the performance of the legitimate user, but also helps the attacker improve its performance significantly.

In this paper, we ask, "Can millimeter-wave (mmWave) radars sense objects not directly illuminated by the radar - for instance, objects located outside the transmit beamwidth, behind occlusions, or placed fully behind the radar?" Traditionally, mmWave radars are limited to sense objects that are directly illuminated by the radar and scatter its signals directly back. In practice, however, radar signals scatter to other intermediate objects in the environment and undergo multiple bounces before being received back at the radar. In this paper, we present Hydra, a framework to explicitly model and exploit multi-bounce paths for sensing. Hydra enables standalone mmWave radars to sense beyond-field-of-view objects without prior knowledge of the environment. We extensively evaluate the localization performance of Hydra with an off-the-shelf mmWave radar in five different environments with everyday objects. Exploiting multi-bounce via Hydra provides 2×-10× improvement in the median beyond-field-of-view localization error over baselines.

The global wireless industry works like clockwork. Every decade, the global community ratifies a new generation of cellular standards relying on advances from the past decades; a similar rhythm drives Wi-Fi standardization. In all standards, cellular and Wi-Fi, a core design principle is that wireless nodescan either transmit or receive in a given frequency band. However, a node cannot simultaneously transmit and receive in the same frequency band due to high self-interference from its own transmitted signal that can drown out the receive signal. As a result, to enable bidirectional communications between nodes, network designs have relied on partitioning time and frequency using a mix of time-division duplex and frequency-division duplex. In short, wireless designs have not considered in-band full duplex (IBFD) as a building block.

In this article, we review the key concepts and the progress in the design of physical-layer aspects of in-band full-duplex (IBFD) communications. One of the fundamental challenges in realizing IBFD is self-interference that can be up to 100 dB stronger than signals of interest. Thus, we start by reviewing state-of-the-art research in self-interference cancellation, addressing both model-based and emerging machine learning-based methods. Then, we turn our attention to new wireless systems with many degrees of freedom for which the traditional IBFD designs do not gracefully scale and, hence, require many innovations to enable IBFD. We provide an extensive review of basic concepts and state of the art in massive multiple-input–multiple-output IBFD. Then, we consider the mmWave band IBFD and review advanced physical-layer architectures. The above review provides the proper context to discuss IBFD innovations and new challenges for sixth-generation networks and beyond, where wireless networks are envisioned to be multifunctional, combining communications with functions such as sensing, cognitive radios, physical-layer security, and wireless power transfer. We conclude this article with a status update on the adoption of IBFD in communication standards.

Social ambiance describes the context in which social interactions happen, and can be measured using speech audio by counting the number of concurrent speakers. This measurement has enabled various mental health tracking and human-centric IoT applications. While on-device Socal Ambiance Measure (SAM) is highly desirable to ensure user privacy and thus facilitate wide adoption of the aforementioned applications, the required computational complexity of state-of-the-art deep neural networks (DNNs) powered SAM solutions stands at odds with the often constrained resources on mobile devices. Furthermore, only limited labeled data is available or practical when it comes to SAM under clinical settings due to various privacy constraints and the required human effort, further challenging the achievable accuracy of on-device SAM solutions. To this end, we propose a dedicated neural architecture search framework for Energy-efficient and Real-time SAM (ERSAM). Specifically, our ERSAM framework can automatically search for DNNs that push forward the achievable accuracy vs. hardware efficiency frontier of mobile SAM solutions. For example, ERSAM-delivered DNNs only consume 40 mW • 12 h energy and 0.05 seconds processing latency for a 5 seconds audio segment on a Pixel 3 phone, while only achieving an error rate of 14.3% on a social ambiance dataset generated by LibriSpeech. We can expect that our ERSAM framework can pave the way for ubiquitous on-device SAM solutions which are in growing demand.

We propose a multiple-input multiple-output (MIMO) detector based on an annealed version of the underdamped Langevin (stochastic) dynamic. Our detector achieves state-of-the-art performance in terms of symbol error rate (SER) while keeping the computational complexity in check. Indeed, our method can be easily tuned to strike the right balance between computational complexity and performance as required by the application at hand. This balance is achieved by tuning hyperparameters that control the length of the simulated Langevin dynamic. Through numerical experiments, we demonstrate that our detector yields lower SER than competing approaches (including learning-based ones) with a lower running time compared to a previously proposed overdamped Langevin-based MIMO detector.

We present a new threat model enabling a passive adversary to infer which overheard packets belong to which transmitters. We call this threat model unsupervised wireless diarization (UWD) where the adversary assigns transmitter identity (label) to received packets in an encrypted wireless network without access to the MAC headers. To demonstrate the feasibility of such an attack, we develop UWDNet, a wireless diarization pipeline comprised of a Siamese neural network to extract embeddings from received packets, a similarity metric to compare embeddings, and unsupervised clustering. We evaluate UWDNet on both synthetic datasets and datasets of real wireless transmissions collected using Rice University's configurable massive MIMO testbed RENEW. Via various experimentation scenarios, our initial results show that UWDNet achieves a diarization accuracy of above 90% on synthetic data of transmitters it has never seen. To push the limits of performance evaluation, we collected a real radio transmissions dataset representing a worst-case (almost pathological) setting where all nodes are co-located. Even in this near-pathological case, UWDNet accuracy is > 60% – well above a random label assignment, indicating the feasibility of unsupervised wireless diarization in real-life scenarios. We also analyzed different factors such as the spatial channel and transmit parameters, which impact diarization accuracy in real-world scenarios.

The capability of nodes to transmit and receive data simultaneously within the same frequency band, referred to as in-band FD, disrupts the conventional assumptions underlying wireless network design. This new feature enhances spectral efficiency and reduces latency, which are essential drivers in advancing next-generation networks. In the past few years, full-duplex (FD) has evolved from being a laboratory idea to being incorporated into telecommunications standards and proof of concepts. From 2010 to 2020, considerable research and development efforts were devoted to advancing FD wireless communications. By 2015, the cable modem industry had already implemented in-band FD technology to establish the DOCSIS 4.0 standard, enabling next-generation cable modems to operate in FD mode. By 2020, FD wireless products started to emerge in the market.

The use of in-band full-duplex (FD) enables nodes to simultaneously transmit and receive on the same frequency band, which challenges the traditional assumption in wireless network design. The full-duplex capability enhances spectral efficiency and decreases latency, which are two key drivers pushing the performance expectations of next-generation mobile networks. In less than ten years, in-band FD has advanced from being demonstrated in research labs to being implemented in standards, presenting new opportunities to utilize its foundational concepts. Some of the most significant opportunities include using FD to enable wireless networks to sense the physical environment, integrate sensing and communication applications, develop integrated access and backhaul solutions, and work with smart signal propagation environments powered by reconfigurable intelligent surfaces. However, these new opportunities also come with new challenges for large-scale commercial deployment of FD technology, such as managing self-interference, combating cross-link interference in multi-cell networks, and coexistence of dynamic time division duplex, subband FD and FD networks.

Estimation of transmission rates by a malicious user can serve as a stepping stone to further attacks on the network. In this paper, we aim to investigate the general problem of estimating traffic transmission rates in a CSMA/CA network using a class of passive eavesdropping methods. We consider the case where a single eavesdropper passively monitors all active network nodes but cannot observe all packet transmissions due to spatial-reuse collisions. To enable tractable analysis, we first propose an approximate statistical model that can help the eavesdropper estimate transmission rates with partial measurements under spatial reuse. We next consider a class of eavesdroppers that become increasingly more capable, and develop a framework to demonstrate that two classes of eavesdropper capabilities are sufficient to achieve a consistent transmission rate estimator. We provide numerical tests of our proposed estimators under practical network cases using the NS-3 simulator that validate the theoretical results.

We consider the problem of estimating the orientation of a 3D object with the assistance of configurable backscatter tags. We explore the idea of designing tag response codes to improve the accuracy of orientation estimation. To minimize the difference between the true and estimated orientation, we propose two code design criteria. We also derive a lower bound on the worst-case error using Le Cam’s method and provide simulation results for multiple scenarios including line-of-sight only and multipath, comparing the theoretical bounds to those achieved by the designs.

Solving the optimal symbol detection problem in multiple-input multiple-output (MIMO) systems is known to be NP-hard. Hence, the objective of any detector of practical relevance is to get reasonably close to the optimal solution while keeping the computational complexity in check. In this work, we propose a MIMO detector based on an annealed version of Langevin (stochastic) dynamics. More precisely, we define a stochastic dynamical process whose stationary distribution coincides with the posterior distribution of the symbols given our observations. In essence, this allows us to approximate the maximum a posteriori estimator of the transmitted symbols by sampling from the proposed Langevin dynamic. Furthermore, we carefully craft this stochastic dynamic by gradually adding a sequence of noise with decreasing variance to the trajectories, which ensures that the estimated symbols belong to a pre-specified discrete constellation. Based on the proposed MIMO detector, we also design a robust version of the method by unfolding and parameterizing one term– the score of the likelihood– by a neural network. Through numerical experiments in both synthetic and real-world data, we show that our proposed detector yields state-of-the-art symbol error rate performance and the robust version becomes noise-variance agnostic.

Psychomotor retardation in depression has been associated with speech timing changes from dyadic clinical interviews.In this work, we investigate speech timing features from freeliving dyadic interactions.Apart from the possibility of continuous monitoring to complement clinical visits, a study in free-living conditions would also allow inferring sociability features such as dyadic interaction frequency implicated in depression.We adapted a speaker count estimator as a dyadic interaction detector with a specificity of 89.5% and a sensitivity of 86.1% in the DIHARD dataset.Using the detector, we obtained speech timing features from the detected dyadic interactions in multi-day audio recordings of 32 participants comprised of 13 healthy individuals, 11 individuals with depression, and 8 individuals with psychotic disorders.The dyadic interaction frequency increased with depression severity in participants with no or mild depression, indicating a potential diagnostic marker of depression onset.However, the dyadic interaction frequency decreased with increasing depression severity for participants with moderate or severe depression.In terms of speech timing features, the response time had a significant positive correlation with depression severity.Our work shows the potential of dyadic interaction analysis from audio recordings of free-living to obtain markers of depression severity.

We consider the problem of estimating the orientation of a 3D object with the assistance of configurable backscatter tags. We explore the idea of designing tag response codes to improve the accuracy of orientation estimation. To minimize the difference between the true and estimated orientation, we propose two code design criteria. We also derive a lower bound on the worst-case error using Le Cam’s method and provide simulation results for multiple scenarios including perfect and imperfect channel knowledge, comparing the performance of various coding methods against the suggested designs.

In this paper, we take the first step towards quantifying the fundamental performance trade-offs between imaging and communication supported simultaneously using the same network resources. We analyze an uplink system configuration with a full-duplex base station (BS) illuminating an imaging scene while receiving data from a communication user. Our main contributions are two-fold. First, we propose a unified signal space analysis framework based on the degrees of freedom metric to characterize the trade-offs between the two operations in the high signal-to-noise ratio regime. Second, we propose a dual-function joint processing scheme, decode-and-image, that allows the BS to simultaneously form an image of the scene while decoding the uplink user’s data. Our analysis and proposed scheme highlight the benefits of exploiting the uplink signals for imaging, at the cost of increased cooperation between the BS and uplink user. Moreover, our proposed scheme outperforms traditional schemes that enable dual-function operation via spatial or temporal isolation of imaging and communication signals.

Measuring user engagement in interactive tasks can facilitate numerous applications toward optimizing user experience, ranging from eLearning to gaming. However, a significant challenge is the lack of non-contact engagement estimation methods that are robust in unconstrained environments. We present FaceEngage, a non-intrusive engagement estimator leveraging user facial recordings during actual gameplay in naturalistic conditions. Our contributions are three-fold. First, we show the potential of using front-facing videos as training data to build the engagement estimator. We compile FaceEngage Dataset with over 700 picture-in-picture, realisitic, and user-contributed YouTube gaming videos (i.e., with both full-screen game scenes and time-synchronized user facial recordings in subwindows). Second, we develop FaceEngage system, that captures relevant gamer facial features from front-facing recordings to infer task engagement. We implement two FaceEngage pipelines: an estimator trained on user facial motion features inspired by prior psychological works, and a deep learning-enabled estimator. Lastly, we conduct extensive experiments and conclude: (i) certain user facial motion cues (e.g., blink rates, head movements) are engagement-indicative; (ii) our deep learning-enabled FaceEngage pipeline can automatically extract more informative features, outperforming the facial motion feature-based pipeline; (iii) FaceEngage is robust to various video lengths, users/game genres and interpretable. Despite the challenging nature of realistic videos, FaceEngage attains the accuracy of 83.8 percent and leave-one-user-out precision of 79.9 percent, both of which are superior to our face motion-based model.

Limited access to available spectrum motivates new age radio frequency (RF) systems to co-exist and cooperate. In this paper, we consider a joint communication and radar system (labeled Comm-Radar), that is capable of receiving and decoding communication signals while simultaneously enabling radar functionalities. Traditionally, systems that leverage principles of spectrum convergence, deemed each functions to be detrimental to the others. This has led to successive interference cancellations strategies that effectively isolate each of the functionalities. Here, in the context of Comm-Radar processing structure, we show that ambient communication transmissions actually assist in radar estimation. The insight is that communications multipath reflects off radar targets (we denote these comm-target paths) in the environment, and can be processed at the receiver to extract extra information. In this work we characterize the fundamental performance limits of such a joint system.To demonstrate the radar performance gain achievable via the processing of extra comm-target paths, the closed-form Cramér-Rao Bound (CRB) for the estimation of direction, range and velocity of a target is derived, for both the general case, and practical radar and communication signalling schemes. Based on the CRB, joint outer bounds are obtained to characterize the trade off between estimation performance and communication rate. Finally, numerical results verify the advantages of utilizing the target reflections from the communication signal to bolster radar performance.

We take the first step towards understanding the fundamental limits of blind wireless network inference performed by a distributed network of single-antenna adversary nodes. The distributed adversary nodes are assumed to be blind to the protocol parameters as well as the modulation, coding and encryption schemes used by the network being monitored. Focusing on the special case of inferring the channel access probabilities of the monitored nodes, we derive minimax bounds for blind inference. We show that blind inference is possible with similar sample complexity (asymptotically) as non-blind inference given certain network connectivity conditions are satisfied.

Diffuse optical tomography (DOT) is an approach to recover subsurface structures beneath the skin by measuring light propagation beneath the surface. The method is based on optimizing the difference between the images collected and a forward model that accurately represents diffuse photon propagation within a heterogeneous scattering medium. However, to date, most works have used a few source-detector pairs and recover the medium at only a very low resolution. And increasing the resolution requires prohibitive computations/storage. In this work, we present a fast imaging and algorithm for high resolution diffuse optical tomography with a line imaging and illumination system. Key to our approach is a convolution approximation of the forward heterogeneous scattering model that can be inverted to produce deeper than ever before structured beneath the surface. We show that our proposed method can detect reasonably accurate boundaries and relative depth of heterogeneous structures up to a depth of 8 mm below highly scattering medium such as milk. This work can extend the potential of DOT to recover more intricate structures (vessels, tissue, tumors, etc.) beneath the skin for diagnosing many dermatological and cardio-vascular conditions.

In this paper, we consider the problem of estimating the orientation of an object that has been custom-"painted" by an array of backscatter tags. We pose the problem as a coding matrix design with the objective of minimizing orientation estimation error. We show that it is only necessary to consider a subset of code configurations and provide a tractable linear program to find the optimal coding strategy. We provide simulation results using an icosahedral tag arrangement.

Multipath-assisted imaging algorithms have been shown to achieve super-resolution by incorporating multipath information into the imaging pipeline. In this paper, we derive the imaging degrees of freedom for multipath-assisted imaging systems to quantify the amount of super-resolution possible.

We study the sensitivity of multi-user scheduling performance to channel magnitude misreporting in systems with massive antennas. We consider the round-robin scheduler combined with max-min and waterfilling power controls, respectively. We show that user scheduling combined with power allocation, in general, dampens the negative effect of channel misreporting compared to the purely physical layer analysis of channel misreporting without scheduling. We discover several interesting results. First, we observe a periodicity in rate-loss behavior as the number of misreporting users increases. Second, we find that the waterfilling power control is more robust to channel misreporting compared with max-min power control. Third, for homogeneous users with equal average signal-to-noise ratios (SNRs), channel underreporting is harmful but overreporting is beneficial for max-min power control; the opposite impact is found for waterfilling power control. For heterogeneous users with various average SNRs, however, both underreporting and overreporting harm the system for both power control policies, demonstrating the complex interactions across network layers due to channel misreporting.

One fundamental challenge in 5G URLLC is how to optimize massive MIMO systems for achieving low latency and high reliability. A natural design choice to maximize reliability and minimize retransmission is to select the lowest allowed target error rate. However, the overall latency is the sum of queueing latency and retransmission latency, hence choosing the lowest target error rate does not always minimize the overall latency. In this paper, we minimize the overall latency by jointly designing the target error rate and transmission rate adaptation, which leads to a fundamental tradeoff point between queueing and retransmission latency. This design problem can be formulated as a Markov decision process, which is theoretically optimal, but its complexity is prohibitively high for real-system deployments. We managed to develop a low-complexity closed-form policy named Large-arraY Reliability and Rate Control (LYRRC), which is proven to be asymptotically latency-optimal as the number of antennas increases. In LYRRC, the transmission rate is twice of the arrival rate, and the target error rate is a function of the antenna number, arrival rate, and channel estimation error. With simulated and measured channels, our evaluations find LYRRC satisfies the latency and reliability requirements of URLLC in all the tested scenarios.

Millimeter-wave using large-antenna arrays is a key technological component for the future cellular systems, where it is expected that hybrid beamforming along with quantized phase shifters will be used due to their implementation and cost efficiency. In this paper, we investigate the efficacy of full-duplex mmWave communication with hybrid beamforming using low-resolution phase shifters. We assume that the self-interference can be sufficiently cancelled by a combination of propagation domain and digital self-interference techniques, without any analog self-interference cancellation. We formulate the problem of joint self-interference suppression and downlink beamforming as a mixed-integer nonconvex joint optimization problem. We propose LowRes, a near-to-optimal solution using penalty dual decomposition. Numerical results indicate that LowRes using low-resolution phase shifters perform within 3% of the optimal solution that uses infinite phase shifter resolution. Moreover, even a single quantization bit outperforms half-duplex transmissions, respectively by 29% and 10% for both low and high residual self-interference scenarios, and for a wide range of practical antenna to radio-chain ratios. Thus, we conclude that 1-bit phase shifters suffice for full-duplex millimeter-wave communications, without requiring any additional new analog hardware.

With the rise of ever-more sophisticated wearables and sensing technologies, mobile health continues to be an active area of research. However, from a clinical researcher point of view, testing novel use of the mobile health innovations remains a major hurdle, as composing a clinical trial using a combination of technologies still remains in the realm of computer scientists. We take a software-inspired viewpoint of clinical trial designs to design, develop and validate HealthSense to enable expressibility of complex ideas, composability with diverse devices and services while maximally maintaining simplicity for a clinical research user. A key innovation in HealthSense is the concept of a study state manager (SSM) that modifies parameters of the study over time as data accumulates and can trigger external events that affect the participant; this design allows us to implement nearly arbitrary clinical trial designs. The SSM can funnel data streams to custom or third-party cloud processing pipelines and the result can be used to give interventions and modify parameters of the study. HealthSense supports both Android and iOS platforms and is secure, scalable and fully operational. We outline three trials (two with clinical populations) to highlight simplicity, composability, and expressibility of HealthSense.

The dominant portion of smartphone traffic is generated by apps that involve human interactivity. Particularly, when human users receive information from a server, they spend a few seconds of information processing before taking an action. The user processing time creates an idle communication period during the app session. Moreover, the generation of the future traffic depends on the service of the current query-response pair. In this paper, we aim at leveraging the properties of such interactions to reap quality-of-experience gains. Existing schedulers, both in practice and theory, are not designed in view of the aforementioned traffic characteristics. Theoretical works predominantly focus on scheduling of traffic that is either generated independently or directly controlled, but not governed by the specific dynamics caused by human interactions. Schedulers in practice, on the other hand, employ round-robin and processor-sharing methods to serve multiple ongoing sessions. We show that neither of these approaches is effective for serving apps that involve human interactivity. Instead, we show that optimal scheduling for interactive traffic is non-randomized over packets, which we call action-based, as it avoids breaking ongoing service of actions in order to align human response times with the service of other actions. Since the design of optimal action-based policy is computationally prohibitive, we develop low-complexity suboptimal action-based policies that are optimal for two ongoing sessions. Our numerical studies based on a real-data trace reveal that our proposed action-based policies can reduce total delay by 22% with respect to packet-based equal processor sharing.

In this paper, we propose a method to reuse the cyclic-prefix (CP) intervals of an ongoing OFDM link, by another link, without degrading the OFDM link performance. We label the link that reuses the CP-intervals as CPLink, as they use only the CP-intervals of the ongoing link labeled, MainLink. We leverage the fact that the most commonly used OFDM receivers discard the samples in CP-intervals to design the CPLink that ensures below-noise-floor interference at every MainLink-receiver. The key contribution in this paper is the design and study of zero-knowledge CPLink, in which the CPLink-transmitter ensures below-noise-floor interference at every MainLink-receiver with no knowledge about the locations or the number of MainLink-receivers. We analytically show that the zero-knowledge CPLink capacity is positive. For LTE frame structure with 20-MHz bandwidth and 2-km cell-radius, we evaluate the CPLink for two kinds of CPLink-receivers: full-duplex base-station and a half-duplex device. Even for the cell-edge CPLink-transmitters, full-duplex zero-knowledge CPLink data rates can be up to 20 Mbps (60 Mbps) when the CP duration is ~7% (25%) of the data-symbol duration. The half-duplex CPLink rate is 5-40 Mbps when the CPLink-transmitter is within 250 m of CPLink-receiver. CPLink capacity is found to increase near-linearly with the CP duration, thus mitigating CP overhead.

Learning from web data is increasingly popular due to abundant free web resources. However, the performance gap between webly supervised learning and traditional supervised learning is still very large, due to the label noise of web data as well as the domain shift between web data and test data. To fill this gap, most existing methods propose to purify or augment web data using instance-level supervision, which generally requires heavy annotation. Instead, we propose to address the label noise and domain shift by using more accessible category-level supervision. In particular, we build our deep probabilistic framework upon variational autoencoder (VAE), in which classification network and VAE can jointly leverage category-level hybrid information. Then, we extend our method for domain adaptation followed by our low-rank refinement strategy. Extensive experiments on three benchmark datasets demonstrate the effectiveness of our proposed method.

Fine-grained image classification, which targets at distinguishing subtle distinctions among various subordinate categories, remains a very difficult task due to the high annotation cost of enormous fine-grained categories. To cope with the scarcity of well-labeled training images, existing works mainly follow two research directions: 1) utilize freely available web images without human annotation; 2) only annotate some fine-grained categories and transfer the knowledge to other fine-grained categories, which falls into the scope of zero-shot learning (ZSL). However, the above two directions have their own drawbacks. For the first direction, the labels of web images are very noisy and the data distribution between web images and test images are considerably different. For the second direction, the performance gap between ZSL and traditional supervised learning is still very large. The drawbacks of the above two directions motivate us to design a new framework which can jointly leverage both web data and auxiliary labeled categories to predict the test categories that are not associated with any well-labeled training images. Comprehensive experiments on three benchmark datasets demonstrate the effectiveness of our proposed framework.

Maintaining reliable millimeter wave (mmWave) connections to many fast-moving mobiles is a key challenge in the theory and practice of 5G systems. In this paper, we develop a new algorithm that can jointly track the beam direction and channel coefficient of mm Wave propagation paths using phased antenna arrays. Despite the significant difficulty in this problem, our algorithm can simultaneously achieve fast tracking speed, high tracking accuracy, and low pilot overhead. In static scenarios, this algorithm can converge to the minimum Cramér-Rao lower bound of beam direction with high probability. Simulations reveal that this algorithm greatly outperforms several existing algorithms. Even at SNRs as low as 5dB, our algorithm is capable of tracking a mobile moving at an angular velocity of 5.45 degrees per second and achieving over 95% of channel capacity with a 32-antenna phased array, by inserting only 10 pilots per second.

In this paper, we investigate the problem of beam alignment in millimeter wave (mmWave) systems, and design an optimal algorithm to reduce the overhead. Specifically, due to directional communications, the transmitter and receiver beams need to be aligned, which incurs high delay overhead since without a priori knowledge of the transmitter/receiver location, the search space spans the entire angular domain. This is further exacerbated under dynamic conditions (e.g., moving vehicles) where the access to the base station (access point) is highly dynamic with intermittent on-off periods, requiring more frequent beam alignment and signal training. To mitigate this issue, we consider an online stochastic optimization formulation where the goal is to maximize the directivity gain (i.e., received energy) of the beam alignment policy within a time period. We exploit the inherent correlation and unimodality properties of the model, and demonstrate that contextual information improves the performance. To this end, we propose an equivalent structured Multi-Armed Bandit model to optimally exploit the exploration-exploitation tradeoff. In contrast to the classical MAB models, the contextual information makes the lower bound on regret (i.e., performance loss compared with an oracle policy) independent of the number of beams. This is a crucial property since the number of all combinations of beam patterns can be large in transceiver antenna arrays, especially in massive MIMO systems. We further provide an asymptotically optimal beam alignment algorithm, and investigate its performance via simulations.

The robustness of system throughput with scheduling is a critical issue. In this paper, we analyze the sensitivity of multi-user scheduling performance to channel misreporting in systems with massive antennas. The main result is that for the round-robin scheduler combined with max-min power control, the channel magnitude misreporting is harmful to the scheduling performance and has a different impact from the purely physical layer analysis. Specifically, for the homogeneous users that have equal average signal-to-noise ratios (SNRs), underreporting is harmful, while overreporting is beneficial to others. In under-reporting, the asymptotic rate loss on others is derived, which is tight when the number of antennas is huge. One interesting observation in our research is that the rate loss “periodically” increases and decreases as the number of misreporters grows. For the heterogeneous users that have various SNRs, both underreporting and overreporting can degrade the scheduler performance. We observe that strong misreporting changes the user grouping decision and hence greatly decreases some users' rates regardless of others gaining rate improvements, while with carefully designed weak misreporting, the scheduling decision keeps fixed and the rate loss on others is shown to grow nearly linearly with the number of misreporters.

In this paper, we study the performance of a full-duplex architecture for large-antenna systems, where analog cancelers can be fewer in number than receive antennas. We study two methods of partial analog self-interference cancellation: the one, where the analog cancelers are assigned to a fixed set of antennas and the other, where analog cancelers are reconfigurable such that they can be dynamically assigned to any receive antennas based on channel conditions. We propose JointNull that jointly optimizes partial analog cancellation and transmit beamforming. With reconfigurable cancelers, JointNull assigns each antenna one of the three roles-full-duplex, half-duplex receive, and half-duplex transmit antennas-and then designs a transmit precoder to suppress self-interference on receive antennas. As a special case of reconfigurable cancellation, fixed partial cancellation method is also addressed. We evaluate JointNull using channel measurements from a 72-antenna array. JointNull achieves close to ideal full-duplex sum rates using very few analog cancelers. For example, JointNull achieves 90% of ideal sum rate using only eight poor quality analog cancelers, each of which can cancel 20 dB of self-interference. When demand is highly downlink biased, JointNull achieves 90% of ideal sum rate with no analog cancelers. Finally, we find that reconfigurable cancelers provide only a marginal gain over fixed assignment; this points to a simpler system design, which uses fixed assignment for partial analog cancellation.

We analyze an inband full-duplex access node that is serving mobile users while simultaneously connecting to a core network over a wireless backhaul link, utilizing the same frequency band for all communication tasks. Such wireless self-backhauling is an intriguing option for the next generation wireless systems since a wired backhaul connection might not be economically viable if the access nodes are deployed densely. In particular, we derive the optimal transmit power allocation for such a system in closed form under quality-of-service (QoS) requirements, which are defined in terms of the minimum data rates for each mobile user. For comparison, the optimal transmit power allocation is solved also for two reference scenarios: a purely half-duplex access node, and a relay-type full-duplex access node. Based on the obtained expressions for the optimal transmit powers, we then show that the systems utilizing a full-duplex capable access node have a fundamental feasibility boundary, meaning that there are circumstances under which the QoS requirements cannot be fulfilled using finite transmit powers. This fundamental feasibility boundary is also derived in closed form. The feasibility boundaries and optimal transmit powers are then numerically evaluated in order to compare the different communication schemes. In general, utilizing the purely full-duplex access node results in the lowest transmit powers for all the communicating parties, although there are some network geometries under which such a system is not capable of reaching the required minimum data rates. In addition, the numerical results indicate that a full-duplex capable access node is best suited for relatively small cells.

A key challenge for frequency-division duplexing (FDD) massive multi-input multi-output (MIMO) is the large overhead in acquiring channel state information (CSI) for transmits beamforming. In this paper, we propose a scalable method called directional training to obtain downlink CSI. Directional training is motivated by two empirical results derived from massive MIMO channel measurements. First, the number of dominant angle-of-arrivals (departures) is much smaller than and nearly independent of the number of base-station antennas. Second, there is a strong correlation between uplink arrival and downlink departure angles even in FDD systems, which leads to the idea of directional training, where a small number of training symbols can be sent to estimate the dominant components of the downlink channel. Therefore, directional training measures much fewer complex coefficients than full-training-based methods, and as a result, compared with full-training, the overall channel acquisition overhead for directional training scales much slower with the number of base-station antennas. We evaluate directional training with extensive experiments with a 64-antenna base-station at two bands separated by approximately 72 MHz. Our results show that directional training-based downlink beamforming outperforms full-training systems by 150% in terms of average spectral efficiency, and loses only 5.3% average spectral efficiency from genie-aided systems.

Authentication of users by exploiting face as a biometric is gaining widespread traction due to recent advances in face detection and recognition algorithms. While face recognition has made rapid advances in its performance, such face-based authentication systems remain vulnerable to biometric presentation attacks. Biometric presentation attacks are varied and the most common attacks include the presentation of a video or photograph on a display device, the presentation of a printed photograph or the presentation of a face mask resembling the user to be authenticated. In this paper, we present PPGSecure, a novel methodology that relies on camera-based physiology measurements to detect and thwart such biometric presentation attacks. PPGSecure uses a photoplethysmogram (PPG), which is an estimate of vital signs from the small color changes in the video observed due to minor pulsatile variations in the volume of blood flowing to the face. We demonstrate that the temporal frequency spectra of the estimated PPG signal for real live individuals are distinctly different than those of presentation attacks and exploit these differences to detect presentation attacks. We demonstrate that PPGSecure achieves significantly better performance than existing state of the art presentation attack detection methods.

We consider a single-cell massive multiple input multiple output full-duplex wireless communication system, where the base-station (BS) is equipped with a large number of antennas. We consider the setup where the single-antenna mobile users operate in half-duplex, while each antenna at the BS is capable of full-duplex transmissions, i.e., it can transmit and receive simultaneously using the same frequency spectrum. The fundamental challenge in this system is intra-cell inter-node interference, generated by the transmissions of uplink users to the receptions at the downlink users. The key operational challenge is estimating and aggregating inter-mobile channel estimates, which can potentially overwhelm any gains from full-duplex operation. In this paper, we propose a scalable and distributed scheme to optimally manage the inter-node interference by utilizing a “one-hop information architecture”. In this architecture, the BS only needs to know the signal-to-interference-plus-noise ratio from the downlink users. Each uplink user needs its own SINR, along with a weighted signal-plus-noise metric from its one-hop neighboring downlink users, which are the downlink users, that it interferes with. The proposed one-hop information architecture does not require any network devices to comprehensively gather the vast inter-node interference channel knowledge, and hence significantly reduces the overhead. Based on the one-hop information architecture, we design a distributed power control algorithm and implement such architecture using overheard feedback information. We show that, in typical asymptotic regimes with many users and antennas, the proposed distributed power control scheme improves the overall network utility and reduces the transmission power of the uplink users.

We study a multi-cell multi-user MIMO full-duplex network, where each base station (BS) has multiple antennas with full-duplex capability supporting single-antenna users with either full-duplex or half-duplex radios. We characterize the up- and downlink ergodic achievable rates for the case of linear precoders and receivers. The rate analysis includes practical constraints, such as imperfect self-interference cancellation, channel estimation error, training overhead, and pilot contamination. We show that the $2\times $ gain of full duplex over half-duplex system remains in the asymptotic regime, where the number of BS antennas grows infinitely large. We numerically evaluate the finite SNR and antenna performance, which reveals that full-duplex networks can use significantly fewer antennas to achieve spectral efficiency gain over the half-duplex counterparts. In addition, the overall full-duplex gains can be achieved under realistic 3GPP multi-cell network settings despite the increased interference introduced in the full-duplex networks.

The feasibility of practical in-band full-duplex radios has recently been demonstrated experimentally. One way to leverage full-duplex in a network setting is to enable three-node full-duplex, where a full-duplex access point (AP) transmits data to one node yet simultaneously receives data from another node. Such three-node full-duplex communication, however, introduces inter-client interference, directly impacting the full-duplex gain. It hence may not always be beneficial to enable three-node full-duplex transmissions. In this paper, we present a distributed full-duplex medium access control (MAC) protocol that allows an AP to adaptively switch between full-duplex and half-duplex modes. We formulate a model that determines the probabilities of full-duplex and half-duplex access so as to maximize the expected network throughput. A MAC protocol is further proposed to enable the AP and clients to contend for either full-duplex or half-duplex transmissions based on their assigned probabilities in a distributed way. Our evaluation shows that, by combining the advantages of centralized probabilistic scheduling and distributed random access, our design improves the overall throughput by 1.53 times, on average, as compared with the greedy downlink-uplink client pairing.

Many popular smartphone apps involve human interaction through the entire session; e.g. apps for web browsing, making reservations and online gaming. In this work, we characterize bi-directional interactive app traffic in the timescale of seconds, that is shaped by the human interaction. We collect and analyze a dataset comprising 1500 interactive app sessions. The combined uplink and downlink traffic bursts are the outcome of user-server interactions, which we label as actions. Within each action, we discover high correlation between the number of uplink and downlink packets reaching 0.98. Our study reveals that the distribution of action duration and interarrival can be approximated with exponential or gamma distributions. The analysis provides insights on the temporal characteristics of bi-directional packet bursts associated with actions, during an app session. We show that action-based service at access points (APs), where actions constitute the service units rather than packets, can reduce service delay by 50%.

In most wireless networks, nodes have only limited local information about the state of the network, which includes connectivity and channel state information. With limited local information about the network, each node's knowledge is mismatched; therefore, they must make distributed decisions. In this paper, we pose the following question: If every node has network state information only about a small neighborhood, how and when should nodes choose to transmit? While link scheduling answers the above question for point-to-point physical layers that are designed for an interference-avoidance paradigm, we look for answers in cases when interference can be embraced by advanced PHY-layer design, as suggested by results in network information theory. To make progress on this challenging problem, we propose a constructive distributed algorithm that achieves rates higher than link scheduling based on interference avoidance, especially if each node knows more than one hop of network state information. We compare our new aggressive algorithm to a conservative algorithm we have presented in a 2013 conference paper. Both algorithms schedule subnetworks such that each subnetwork can employ advanced interference-embracing coding schemes to achieve higher rates. Our innovation is in the identification, selection, and scheduling of subnetworks, especially when subnetworks are larger than a single link.

Multiple transmitting antennas can considerably increase the downlink spectral efficiency by beamforming to multiple users at the same time. However, multiuser beamforming requires channel state information (CSI) at the transmitter, which leads to training overhead and reduces overall achievable spectral efficiency. In this paper, we propose and analyze a sequential beamforming strategy that utilizes full-duplex base station to implement downlink data transmission concurrently with CSI acquisition via in-band closed or open loop training. Our results demonstrate that full-duplex capability can improve the spectral efficiency of uni-directional traffic, by leveraging it to reduce the control overhead of CSI estimation. In moderate SNR regimes, we analytically derive tight approximations for the optimal training duration and characterize the associated respective spectral efficiency. We further characterize the enhanced multiplexing gain performance in the high SNR regime. In both regimes, the performance of the proposed full-duplex strategy is compared with the half-duplex counterpart to quantify spectral efficiency improvement. With experimental data and 3-D channel model from 3GPP, in a 1.4 MHz 8 × 8 system LTE system with the block length of 500 symbols, the proposed strategy attains a spectral efficiency improvement of 130% and 8% with closed and open loop training, respectively.

In this paper, we present and study a digital-controlled method, called SoftNull, to enable full-duplex in many-antenna systems. Unlike most designs that rely on analog cancelers to suppress self-interference, SoftNull relies on digital transmit beamforming to reduce self-interference. SoftNull does not attempt to perfectly null self-interference, but instead seeks to reduce self-interference sufficiently to prevent swamping the receiver's dynamic range. Residual self-interference is then cancelled digitally by the receiver. We evaluate the performance of SoftNull using measurements from a 72-element antenna array in both indoor and outdoor environments. We find that SoftNull can significantly outperform half-duplex for small cells operating in the many-antenna regime, where the number of antennas is many more than the number of users served simultaneously.

The feasibility of practical in-band full-duplex radios has recently been demonstrated experimentally. One way to leverage full-duplex in a network setting is to enable three-node full-duplex, where a full-duplex access point (AP) transmits data to one node yet simultaneously receives data from another node. Such three-node full-duplex communication however introduces inter-client interference, directly impacting the full-duplex gain. It hence may not always be beneficial to enable three-node full-duplex transmissions. In this paper, we present a distributed full-duplex medium access control (MAC) protocol that allows an AP to adaptively switch between full-duplex and half-duplex modes. We formulate a model that determines the probabilities of full-duplex and half-duplex access so as to maximize the expected network throughput. A MAC protocol is further proposed to enable the AP and clients to contend for either full-duplex or half-duplex transmissions based on their assigned probabilities in a distributed way. Our evaluation shows that, by combining the advantages of centralized probabilistic scheduling and distributed random access, our design improves the overall throughput by 3.16× and 1.44×, on average, as compared to half-duplex 802.11 and greedy downlink-uplink client pairing.

mHealth, which involves using smartphones as a tool for healthcare monitoring and delivery, continues to gain traction worldwide. As a result, new pilot programs and clinical trials continue to be launched to establish clinical evidence. However, the continual need for software changes between iterations of the trial creates a lengthy time loop between clinical researchers requesting a change and software developers implementing the requested change. We propose a new methodology for performing clinical trials, called ScaleMed. The ScaleMed methodology involves decoupling low-level app functionality from high-level trial-related operational parameters, a common software development practice. By utilizing a centralized website that allows clinical researchers to rapidly update these trial parameters, iterative clinical trials have the potential to move at a much faster rate than in current practice. We demonstrate an example of the ScaleMed methodology applied to an ongoing mental health trial (codenamed Lucy). In addition, we conducted a separate trial on the usability of the ScaleMed-enabled Lucy platform and found a predominantly positive response from the potential clinical users. In our limited trials, we showed that the time to make changes to app parameters was cut from weeks to less than a minute.

When a multi-antenna (MIMO) base-station operates in full-duplex mode, multiple uplink and downlink streams can be supported simultaneously in the same frequency band. However, the inter-mobile interference from uplink streams to the downlink streams can limit the system performance. In this paper, we first characterize the degrees-of-freedom of a multiuser MIMO (MU-MIMO) full-duplex network with half-duplex mobile clients, and derive the regimes where the inter-mobile interference can be mitigated to yield significant gains over the half-duplex counterpart. The achievability is based on interference alignment and requires full channel-state information at the transmitter (CSIT). Next, we study the case with partial CSIT where only the base-station acquires downlink channel values to avoid collecting network-wide CSIT at all transmitters in the system. We show that the key to achieving the sum degrees-of-freedom upper bound with only partial CSIT is the ability of the base-station to switch antenna modes that can be realized via reconfigurable antennas.

In this paper (J. Bai and A. Sabharwal, “Distributed Full-Duplex via Wireless Side- Channels: Bounds and Protocols,” IEEE Trans. Wireless Commun., vol. 12, no. 8, pp. 4162–4173, Aug. 2013), there are revisions to theorems and formulas that are presented.

Recent advances in signal processing have demonstrated in-band full-duplex capability at WiFi ranges. In addition to simultaneous two-way exchange between two nodes, full-duplex access points can potentially support simultaneous uplink and downlink flows. However, the atomic three-node topology, which allows simultaneous uplink and downlink, leads to inter-client interference. In this paper, we propose a random-access medium access control protocol using distributed power control to manage inter-client interference in wireless networks with full-duplex-capable access points that serve half-duplex clients. Our key contributions are two-fold. First, we identify the regimes in which power control provides sum throughput gains for the three-node atomic topology, with one uplink flow and one downlink flow. Second, we develop and benchmark PoCMAC, a full 802.11-based protocol that allows distributed selection of a three-node topology. The proposed MAC protocol is shown to achieve higher capacity as compared to an equivalent half-duplex counterpart, while maintaining similar fairness characteristics in single contention domain networks. We carried out extensive simulations and software-defined radio-based experiments to evaluate the performance of the proposed MAC protocol, which is shown to achieve a significant improvement over its half-duplex counterpart in terms of throughput performance.

The challenge of full-duplex wireless communication is self-interference received directly from the transmit antennas and backscattered from nearby objects. Spatial isolation of the receive antennas from the transmit antennas can mitigate self-interference, but may cause the spatial resources of the channel to be under-utilized, sacrificing spatial multiplexing performance. We present an analysis of spatial isolation of self-interference for full-duplex base stations, which leverages the antenna-theorybased channel model of Poon et. al.We characterize the scattering conditions under which spatial isolation can enable a degrees-of-freedom gain over half-duplex, and show that the gain is inversely proportional to the overlap between the backscattering intervals (set angles of departure/arrival of backscattered self-interference), and the forward scattering intervals (set of angles of departure/arrival to/from the intended users).

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

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

Recent research results have demonstrated the feasibility of full-duplex wireless communication for short-range links. Although the focus of the previous works has been active cancellation of the self-interference signal, a majority of the overall self-interference suppression is often due to passive suppression, i.e., isolation of the transmit and receive antennas. We present a measurement-based study of the capabilities and limitations of three key mechanisms for passive self-interference suppression: directional isolation, absorptive shielding, and cross-polarization. The study demonstrates that more than 70 dB of passive suppression can be achieved in certain environments, but also establishes two results on the limitations of passive suppression: (1) environmental reflections limit the amount of passive suppression that can be achieved, and (2) passive suppression, in general, increases the frequency selectivity of the residual self-interference signal. These results suggest two design implications: (1) deployments of full-duplex infrastructure nodes should minimize near-antenna reflectors, and (2) active cancellation in concatenation with passive suppression should employ higher-order filters or per-subcarrier cancellation.

One of the main bottlenecks in practical full-duplex systems is the oscillator phase noise, which bounds the possible cancellable self-interference power. In this paper, a digital-domain self-interference cancellation scheme for full-duplex orthogonal frequency division multiplexing systems is proposed. The proposed scheme increases the amount of cancellable self-interference power by suppressing the effect of both transmitter and receiver oscillator phase noise. The proposed scheme consists of two main phases, an estimation phase and a cancellation phase. In the estimation phase, the minimum mean square error estimator is used to jointly estimate the transmitter and receiver phase noise associated with the incoming self-interference signal. In the cancellation phase, the estimated phase noise is used to suppress the intercarrier interference caused by the phase noise associated with the incoming self-interference signal. The performance of the proposed scheme is numerically investigated under different operating conditions. It is demonstrated that the proposed scheme could achieve up to 9 dB more self-interference cancellation than the existing digital-domain cancellation schemes that ignore the intercarrier interference suppression.

In most wireless networks, nodes have only limited local information about the network state, which includes connectivity and channel state information. With limited local information about the network, each node's knowledge is mismatched, therefore they must make distributed decisions. In this paper, we pose the following question - if every node has network state information only about a small neighborhood, how and when should nodes choose to transmit? While scheduling answers the above question for point-to-point physical layers which are designed for an interference-avoidance paradigm, we look for answers in cases when interference can be embraced by advanced code design, as suggested by results in network information theory. To make progress on this challenging problem, we propose a distributed algorithm which achieves rates higher than interference-avoidance based link scheduling, especially if each node knows more than one hop of network state information.

Dirty paper codes are a powerful tool for combating known interference. However, there is a significant difference between knowing the transmitted interference sequence and knowing the received interference sequence, especially when the channel modifying the interference is uncertain. We present an upper bound on the capacity of a compound vector dirty paper channel where although an additive Gaussian sequence is known to the transmitter, the channel matrix between the interferer and receiver is uncertain but known to lie within a bounded set. Our bound is tighter than previous bounds in the low-SIR regime for the scalar version of the compound dirty paper channel and employs a construction that focuses on the relationship between the dimension of the message-bearing signal and the dimension of the additive state sequence. Additionally, a bound on the high-SNR behavior of the system is established.

Feasibility of full-duplex opens up the possibility of applying it to cellular networks to operate uplink and downlink simultaneously for multiple users. However, simultaneous operation of uplink and downlink poses a new challenge of intra-cell inter-node interference. In this paper, we identify scenarios where inter-node interference can be managed to provide significant gain in degrees of freedom over the conventional half-duplex cellular design.

We study the limits of reliable communication in two-user interference channels where each of the two transmitters knows a different subset of the four channel gains characterizing the network state. In order to systematically analyze this problem, we introduce two concepts. First, we define a local view model for network state information at each transmitter, wherein each transmitter knows only a subset of the four channel gains. This subset may be mismatched from that of the other transmitter, limiting the ability of the two transmitters to coordinate transmission decisions. Second, we define a notion of maximal rate regions achievable by transmission policies that dominate time-division multiplexing (TDM). Specifically, these “TDM-dominating capacity regions” characterize rates achievable when transmission schemes must, for every possible realization of network state, achieve rates at least as good as what can be achieved through TDM. We consider a set of seven symmetric local views based on an assumption that each transmitter uses the same mechanism to gather its local view. For five out of the seven local views, we show that TDM is sufficient to achieve the full TDM-dominating capacity region for the linear deterministic interference channel. For these five local views, our result implies that no single policy can achieve a rate point outside the TDM region without inducing sub-TDM performance in another network state. The common traits shared by the two remaining local views (those with better performance than TDM) are: first, each transmitter knows its outgoing interference channel gain, and second, there exists at least one channel gain known to both transmitters. For these two local views, transmitters can use their knowledge to achieve opportunistic rate gains beyond TDM. Using the relationship between the linear deterministic channel and the Gaussian channel, we extend our conclusions to bounded gap characterizations of the TDM-dominating capacity region for the Gaussian interference channel with local views.

In this paper, we study the impact of different channel output feedback architectures on the capacity of the two-user interference channel. For a two-user interference channel, a feedback link can exist between receivers and transmitters in nine canonical architectures (see Fig. 3 ), ranging from only one feedback link to four feedback links. We derive the exact capacity region for the symmetric deterministic interference channel and the constant-gap capacity region for the symmetric Gaussian interference channel for all of the nine architectures. We show that for a linear deterministic symmetric interference channel, in the weak interference regime, all models of feedback, except the one, which has only one of the receivers feeding back to its own transmitter, have the identical capacity region. When only one of the receivers feeds back to its own transmitter, the capacity region is a strict subset of the capacity region of the rest of the feedback models in the weak interference regime. However, the sum-capacity of all feedback models is identical in the weak interference regime. Moreover, in the strong interference regime, all models of feedback with at least one of the receivers feeding back to its own transmitter have the identical sum-capacity. For the Gaussian interference channel, the results of the linear deterministic model follow, where capacity is replaced with approximate capacity.

In this paper, we analytically study the regime in which practical full-duplex systems can achieve larger rates than an equivalent half-duplex systems. The key challenge in practical full-duplex systems is uncancelled self-interference signal, which is caused by a combination of hardware and implementation imperfections. Thus, we first present a signal model which captures the effect of significant impairments such as oscillator phase noise, low-noise amplifier noise figure, mixer noise, and analog-to-digital converter quantization noise. Using the detailed signal model, we study the rate gain region, which is defined as the region of received signal-of-interest strength where full-duplex systems outperform half-duplex systems in terms of achievable rate. The rate gain region is derived as a piecewise linear approximation in log-domain, and numerical results show that the approximation closely matches the exact region. Our analysis shows that when phase noise dominates mixer and quantization noise, full-duplex systems can use either active analog cancellation or baseband digital cancellation to achieve near-identical rate gain regions. Finally, as a design example, we numerically investigate the full-duplex system performance and rate gain region in typical indoor environments for practical wireless applications.

In this paper, we study a three-node full-duplex network, where a base station is engaged in simultaneous up- and downlink communication in the same frequency band with two half-duplex mobile nodes. To reduce the impact of inter-node interference between the two mobile nodes on the system capacity, we study how an orthogonal side-channel between the two mobile nodes can be leveraged to achieve full-duplex-like multiplexing gains. We propose and characterize the achievable rates of four distributed full-duplex schemes, labeled bin-and-cancel, compress-and-cancel, estimate-and-cancel and decode-and-cancel. Of the four, bin-and-cancel is shown to achieve within 1 bit/s/Hz of the capacity region for all values of channel parameters. In contrast, the other three schemes achieve the near-optimal performance only in certain regimes of channel values. Asymptotic multiplexing gains of all proposed schemes are derived to show that the side-channel is extremely effective in regimes where inter-node interference has the highest impact.

Full duplex communication requires nodes to cancel their own signal which appears as an interference at their receive antennas. Recent work has experimentally demonstrated the feasibility of full duplex communications using software radios. In this paper, we address capacity comparisons when the total amount of analog radio hardware is bounded. Under this constraint, it is not immediately clear if one should use these radios to perform full-duplex self-interference cancellation or use the radios to give additional MIMO multiplexing advantage. We find that repurposing radios for cancellation, instead of using all of them for half-duplex over-the-air transmission, can be beneficial since the resulting full-duplex system performs better in some practical SNR regimes and almost always outperforms half duplex in symmetric degrees-of-freedom (large SNR regime).

In scenarios where devices are too small to support MIMO antenna arrays, symbol-level cooperation may be used to pool the resources of distributed single-antenna devices to create a virtual MIMO antenna array. We address design fundamentals for distributed cooperative protocols where relays have an incomplete view of network information. A key issue in distributed networks is potential loss in spatial reuse due to the increased radio footprint of flows with cooperative relays. Hence, local gains from cooperation have to balance against network level losses. By using a novel binary network model that simplifies the space over which cooperative protocols must be designed, we develop a mechanism for the systematic and computational development of cooperative protocols as functions of the amount of network state information available at relay nodes. Through extensive network analysis and simulations, we demonstrate the successful application of this method to a series of protocols that span a range of network information availability at cooperative relays.

Due to the large size of wireless networks, it is often impractical for nodes to track changes in the complete network state. As a result, nodes have to make distributed decisions about their transmission and reception parameters based on their local view of the network. In this paper, we characterize the impact of distributed decisions on the global network performance in terms of achievable sum rates. We first formalize the concept of local view by proposing a protocol abstraction using the concept of local message passing. In the proposed protocol, nodes forward information about the network state to other neighboring nodes, thereby allowing network-state information to trickle to all the nodes. The protocol proceeds in rounds, where all transmitters send a message followed by a message by all receivers. The number of rounds then provides a natural metric to quantify the extent of local information at each node. We next study two network connectivities, Z-channel, and a three-user double Z-channel. In each case, we characterize achievable sum rate with partial message passing leading to two main results. First, in many cases, nodes can make distributed decisions with only local information about the network and can still achieve the same sum capacity as can be attained with global information irrespective of the actual channel gains. We label such schemes as universally optimal. Second, for the case of three-user double Z-channel, we show that universal optimality is not achievable if the per node information is below a threshold. In fact, distributed decisions can lead to unbounded losses compared to full information case for some channel gains.

High-speed wireless network interfaces are among the most power-hungry components on mobile systems. This is particularly true for multiple-input-multiple-output (MIMO) network interfaces which use multiple RF chains simultaneously. In this paper, we present a novel power management solution for MIMO network interfaces on mobile systems, called antenna management. The key idea is to adaptively disable a subset of antennas and their RF chains to reduce circuit power consumption, when the capacity improvement of using a large number of antennas is small. Antenna management judiciously determines the number of active antennas to minimize energy per bit while satisfying the data rate requirement. This work provides both theoretical framework and system design of antenna management. We first present an algorithm that efficiently solves the problem of minimizing energy per bit and, then offer its 802.11n-compliant system designs. We employ both Matlab-based simulation and prototype-based experiment to validate the energy efficiency benefit of antenna management. The results show that antenna management can achieve 21% one-end energy per bit reduction to the front end of the MIMO network interface, compared to a static MIMO configuration that keeps all antennas active.

We study a two-user cognitive channel, where the primary flow is sporadic, cannot be re-designed and operating below its link capacity. To study the impact of primary traffic uncertainty, we propose a block activity model that captures the random on-off periods of primary's transmissions. Each block in the model can be split into parallel Gaussian-mixture channels, such that each channel resembles a multiple user channel (MAC) from the point of view of the secondary user. The secondary senses the current state of the primary at the start of each block. We show that the optimal power transmitted depends on the sensed state and the optimal power profile is paranoid, i.e. either growing or decaying in power as a function of time. We show that such a scheme achieves capacity when there is no noise in the sensing. The optimal transmission for the secondary performs rate splitting and follows a layered water-filling power allocation for each parallel channel to achieve capacity. The secondary rate approaches a genie-aided scheme for large block-lengths. Additionally, if the fraction of time primary uses the channel tends to one, the paranoid scheme and the genie-aided upper bound get arbitrarily close to a no-sensing scheme.

We present an experiment-based characterization of passive suppression and active self-interference cancellation mechanisms in full-duplex wireless communication systems. In particular, we consider passive suppression due to antenna separation at the same node, and active cancellation in analog and/or digital domain. First, we show that the average amount of cancellation increases for active cancellation techniques as the received self-interference power increases. Our characterization of the average cancellation as a function of the self-interference power allows us to show that for a constant signal-to-interference ratio at the receiver antenna (before any active cancellation is applied), the rate of a full-duplex link increases as the self-interference power increases. Second, we show that applying digital cancellation after analog cancellation can sometimes increase the self-interference, and thus digital cancellation is more effective when applied selectively based on measured suppression values. Third, we complete our study of the impact of self-interference cancellation mechanisms by characterizing the probability distribution of the self-interference channel before and after cancellation.

In this work, we report the first study of an important realization of directional communication, beamforming, on mobile devices. We first demonstrate that beamforming is already feasible on mobile devices in terms of form factor, device mobility and power efficiency. Surprisingly, we show that by making an increasingly profitable tradeoff between transmit and circuit power, beamforming with state-of-the-art integrated CMOS implementations can be more power-efficient than its single antenna counterpart. We then investigate the optimal way of using beamforming in terms of device power efficiency, by allowing a dynamic number of active antennas. We propose a simple yet effective solution, BeamAdapt, which allows each mobile client in a network to individually identify the optimal number of active antennas with guaranteed convergence and close-to-optimal performance. We finally report a WARP-based prototype of BeamAdapt and experimentally demonstrate its effectiveness in realistic environments, and then complement the prototype-based experiments with Qualnet-based simulation of a large-scale network. Our results show that BeamAdapt with four antennas can reduce the power consumption of mobile clients by more than half compared to a single antenna, while maintaining a required network throughput.

We propose a quantizer design method for practical half-duplex estimate-and-forward (EF) relaying. First, we identify the regime in which EF relaying yields substantial gains where the SNR is low and the relay-destination link is strong. Then we discover design simplifications that reduce complexity with little loss in the above regime. For relay quantizer design, we first consider mean-squared distortion minimization. To illustrate the unsuitability of the approach, we present an example with AWGN links and a BPSK source where the quantizer with worst mean squared distortion in a given set maximizes achievable rate. A distortion-minimizing quantizer attempts to preserve the received signal at the relay. The quantizer should instead preserve source information. In information theoretical terms, the quantizer should maximize the mutual information between the source transmission and the quantizer output conditioned on the side information at the destination subject to a rate constraint. The above conclusion, derived from information theoretical principles, is then translated to a quantizer design method for the low SNR regime. Using LDPC codes of blocklength 100000, BPSK modulation, and quantizers designed using the proposed criterion, we observe performance less than a decibel away from the achievable rate at a BER of 10-4.

“If we know more, we can achieve more.” This adage also applies to communication networks, where more information about the network state translates into higher sum-rates. In this paper, we formalize this increase of sum-rate with increased knowledge of the network state. The knowledge of network state is measured in terms of the number of hops,h, of information available to each transmitter and is labeled ash-local view. To understand how much capacity is lost due to limited information, we propose to use the metric of normalized sum-capacity, which is theh-local view sum-capacity divided by global-view sum capacity. For the cases of one and two-local view, we characterize the normalized sum-capacity for many classes of deterministic and Gaussian interference networks. In many cases, a scheduling scheme called maximal independent graph scheduling is shown to achieve normalized sum-capacity. We also show that its generalization for 1-local view, labeled coded set scheduling, achieves normalized sum-capacity in some cases where its uncoded counterpart fails to do so.

Most communication systems use some form of feedback, often related to channel state information. In this paper, we study diversity multiplexing tradeoff for both frequency division duplex (FDD) and time division duplex (TDD) systems, when both receiver and transmitter knowledge about the channel is noisy and potentially mismatched. For FDD systems, we first extend the achievable tradeoff region for 1.5 rounds of message passing to get higher diversity compared to the best known scheme, in the regime of higher multiplexing gains. We then break the mold of all current channel state based protocols by using multiple rounds of conferencing to extract more bits about the actual channel. This iterative refinement of the channel increases the diversity order with every round of communication. The protocols are on-demand in nature, using high powers for training and feedback only when the channel is in poor states. The key result is that the diversity multiplexing tradeoff with perfect training and$K$levels of perfect feedback can be achieved, even when there are errors in training the receiver and errors in the feedback link, with a multiround protocol which has$K$rounds of training and$K-1$rounds of binary feedback. The above result can be viewed as a generalization of Zheng and Tse, and Aggarwal and Sabharwal, where the result was shown to hold for$K=1$and$K=2$, respectively. For TDD systems, we also develop new achievable strategies with multiple rounds of communication between the transmitter and the receiver, which use the reciprocity of the forward and the feedback channel. The multiround TDD protocol achieves a diversity-multiplexing tradeoff which uniformly dominates its FDD counterparts, where no channel reciprocity is available.

We present five axioms for fairness measures in resource allocation. A family of fairness measures satisfying the axioms is constructed. Special cases of this family include ¿-fairness, Jain's index, and entropy. Properties of fairness measures satisfying the axioms are proven, including Schur-concavity. Among the engineering implications is a generalized Jain's index that tunes the resolution of fairness measure, a new understanding of ¿-fair utility functions, and an interpretation of "larger ¿ is more fair". We also construct an alternative set of axioms to capture system efficiency and feasibility constraints.

In distributed wireless networks, nodes often do not have access to complete network information (e.g. network topology, channel gains, etc.). As a result, they have to execute their transmission and reception strategies with partial information about the network, in a distributed fashion. Thus, the key question is how good are the distributed decisions in comparison to the optimal decisions based on full network knowledge. In this paper, we formalize the concept of partial-information sum-capacity by defining normalized sum-capacity, which is defined as the maximum achievable fraction of full-information sum-capacity with a given amount of partial information. We then examine four deterministic networks, multiple access, multiuser Z-channel chain, one-to-many and many-to-one interference channel, and characterize the normalized sum-capacity. For each network, two cases of partial network information are analyzed: (a) each transmitter only knows the channel gains to its receiver, and (b) transmitters knows the channel gains of all links which are no more than two hops away. Quite interestingly, we show that in all eight cases (4 networks × 2 forms of partial information), the normalized sum-capacity is achieved by scheduling subnetworks for which there exist a universally optimal distributed strategy with the available partial information. Furthermore, we show that while actual sum-capacity is not known in all cases, normalized sum-capacity can be in fact be exactly characterized.

Inspired by distributed resource allocation problems in dynamic topology networks, we initiate the study of distributed consensus with finite messaging passing. We first find a sufficient condition on the network graph for which no distributed protocol can guarantee a conflict-free allocation after R rounds of message passing. Secondly we fully characterize the conflict minimizing zero-round protocol for path graphs, namely random allocation, which partitions the graph into small conflict groups. Thirdly, we enumerate all one-round protocols for path graphs and show that the best one further partitions each of the smaller groups. Finally, we show that the number of conflicts decrease to zero as the number of available resources increase.

In a two-user interference channel, there are four possible feedback paths - two from each receiver to the transmitters. This leads to 16 possible models of feedback. In this paper, we derive the sum capacity of two user deterministic interference channel for all sixteen cases. We find that whenever any of the direct link feedback from a receiver to its own transmitter is present, the sum-capacity is the same as when all four feedback links are present. Further when no direct link feedback is present, the sum capacity with one cross-link feedback and two cross-links of feedback is the same. This sum-capacity is the same as the sum-capacity when there is no feedback except in the regime of interference in which both interfering links are weaker than both the direct-links in which case the sum-capacity is the same as sum-capacity of the feedback model with all four feedback links.

Wireless access is known to be power-hungry for mobile devices. A key reason is that devices radiate power in all directions and much of this power will not reach the destination. To address this waste, we present BeamSwitch, a multi-antenna system designed to realize directional communication efficiently. Unlike power-hungry and expensive beamforming, BeamSwitch requires only one transceiver. We provide an 802.11-compliant design and prototype of BeamSwitch. Our measurements show that with three passive directional antennas, BeamSwitch reduces the power consumption of a commercial 802.11 adapter by up to 20% and provide better quality under diverse propagation environments and extreme rotation.

We report a first-of-its-kind realization of directional transmission for smartphone-like mobile devices using multiple passive directional antennas, supported by only one RF chain. The key is a multi-antenna system (MiDAS) and its antenna selection methods that judiciously select the right antenna for transmission. It is grounded by two measurement-driven studies regarding 1) how smartphones rotate during wireless usage in the field and 2) how orientation and rotation impact the performance of directional antennas under various propagation environments.

You-Chiun Wang, Tomoaki Ohtsuki, Athanasios (Thanos) Vasilakos, Ashutosh Sabharwal, Yuh-Shyan Chen, Yu-Chee Tseng; Algorithms, Protocols and Future Applica

Most communication systems use some form of feedback, often related to channel state information. The common models used in analyses either assume perfect channel state information at the receiver and/or noiseless state feedback links. However, in practical systems, neither is the channel estimate known perfectly at the receiver and nor is the feedback link perfect. In this paper, we study the achievable diversity multiplexing tradeoff using i.i.d. Gaussian codebooks, considering the errors in training the receiver and the errors in the feedback link for frequency division duplex (FDD) systems, where the forward and the feedback are independent multiple input multiple output (MIMO) channels. Our key result is that the maximum diversity order with one-bit of feedback information is identical to systems with more feedback bits. Thus, asymptotically inSNR, more than one bit of feedback does not improve the system performance at constant rates. Furthermore, the one-bit diversity-multiplexing performance is identical to the system which has perfect channel state information at the receiver along with noiseless feedback link. This achievability uses novel concepts of power controlled feedback and training, which naturally surface when we consider imperfect channel estimation and noisy feedback links. In the process of evaluating the proposed training and feedback protocols, we find an asymptotic expression for the joint probability of theSNRexponents of eigenvalues of the actual channel and the estimated channel which may be of independent interest.

The problem of time-varying channel estimation in multihop sensor networks is examined. Two relay processing methods are explored: amplify-and-forward and encode-and-forward. Bounds on the end-to-end distortion for all internode channel estimates are computed for these two relay processing schemes. Performance is analyzed via the asymptotic limit of the decay rate of the end-to-end distortion with respect to SNR at high SNR. It is also established that asymptotically in SNR, amplify-and-forward can outperform encode-and-forward and in fact can achieve the maximum possible distortion exponent (distortion decay rate) order of unity. Linear and many-to-one topologies are then examined and it is shown that orthogonal access in the many-to-one network is optimal.

We show that efficient implementation of codebook-based beamforming Multiple Input Single Output (MISO) systems with good performance is feasible in the presence of channel-induced imperfections (due to imperfect channel estimate and feedback delay) and implementation-induced imperfections (due to real-world radio hardware effects). To present our results, we adopt a mixed approach of analytical, simulation, and experimental evaluation. Our analytical and simulation results take into account channel-induced imperfections but do not take into account implementation-induced imperfections (which are difficult to model in a tractable way). Thus, we complement these results with experimental results that do take into account both channel and implementation-induced imperfections. This mixed approach provides a more complete picture of expected performance. As part of our study we develop a framework for Average Error Vector Magnitude Squared (AEVMS)-based analysis of beamforming MISO systems which facilitates comparison of analytical, simulation, and experimental results on the same scale. In addition, AEVMS allows fair comparison of experimental results obtained from different wireless testbeds. We derive novel expressions for the AEVMS of beamforming MISO systems and show how the AEVMS relates to important system characteristics like the diversity gain, coding gain, and error floor.

In this paper, we examine how the precision of network topology knowledge impacts the achievable degree of max-min fairness. We focus on time-division multiple access (TDMA) networks, and employ a model based on physical-layer events that sufficiently describes the topology effects with respect to TDMA. Using Jain's fairness index, our key contribution is a characterization of the fairness loss resulting from allocation of resources (in our case time-divisions) based on imprecise knowledge of topology. We find loss is more pronounced when a single link has low signal-to-noise ratio (SNR); i.e. links which have poor throughput also make the allocation more unfair. Conversely, our analysis suggests that if the relative error in estimating link qualities is identical for all links in the network, no one link dominates the fairness loss.

In distributed wireless networks, nodes often do not know the topology (network size, connectivity and the channel gains) of the network. Thus, they cannot compute their own maximum transmission rate and appropriate transmission scheme. In this paper, we address the inter-related problems of learning the network and the associated best achievable rates. To make progress, we will focus on K-user deterministic interference networks. First, we propose a message passing algorithm which allows nodes to incrementally learn the network topology. In each round of message passing, nodes forward what they believe is the new information to their neighbors and thus the network topology information trickles via broadcasts. Next, we consider two special examples of Z-channel and double-Z interference network and determine the sum-rate points with incomplete network information at different nodes. We show that the sum-rate point can in fact be achieved with less than full information at all the nodes but in general, less network information implies reduced set of achievable rates. In order to analyze the performance of a double-Z interference network with limited information, we find the capacity region of a deterministic double-Z interference network with full information, which is of independent interest.

We present the theoretical foundation, implementation, and experimental evaluation of a novel power-saving mechanism for wireless transmission from multiple-input multiple-output (MIMO) transceivers, called RF chain management. RF chain management seeks to minimize the energy per bit for MIMO transmission, via adaptively choosing the optimal RF chain configuration, and satisfies the minimum data rate requirement at the same time. Our simulation shows that up to 45% and averagely 23% energy per bit reduction can be achieved. We have also built a prototype based on the WARP platform, and our experimental results have proved the feasibility of RF chain management in real systems and under realistic channels.

In this demo we present the Wireless Open-Access Research Platform for Networks (WARPnet), a research testbed aimed at performing experiments at the network level. The platform is designed to support not only conventional research areas but also new modalities like cooperative coding and network coding. It is based on the Virtex-4 FPGA, which provides the resources to implement novel MIMO physical and MAC layer algorithms. Additionally, the platform adds an orthogonal wireless backdoor network that allows remote programming, control and monitoring of the nodes.

In this poster, we present a protocol to enable secondary users to harness residual capacity in a frequency band with primary users. The key contribution of the proposed protocol is that it does not require any knowledge of primary network traffic or topology.

We consider multiple-antenna communication systems in Rayleigh fading channel, where the transmitter does not know the channel coefficients and the receiver has only an estimate of them. We further assume that the transmitter and receiver know the statistics of the estimation error. We refer to this system as partially coherent system, for which we derive the expressions for the optimal detector and study the constellation design problem. Since the Chernoff bound on pairwise error probability of the partially coherent systems appears to be intractable, we use Stein's Lemma to propose a design criterion based on Kullback-Leibler (KL) distance between conditional distributions. Using the KL-based design criterion, we construct constellations for multiple-antenna systems which can be demodulated in the presence of channel estimation errors. The proposed constellations are multi-level, with multi-dimensional spherical constellations at each level. We show that these new constellations provide significant performance improvement over the conventional single-antenna PSK and QAM constellations, and multiple-antenna techniques such as Bell Lab's Space-Time (BLAST) architecture and orthogonal transmit diversity (OTD) schemes, when the estimation variance is comparable to the reciprocal of the signal-to-noise ratio.

In this paper, we present an information theoretic study of a decentralized multiple access channel (MAC), where users are allowed to autonomously change their data rates, transmission powers, and channel codes, independently from other users in the system. We introduce a policy-based access mechanism, where each user is allocated a set of code-books, with various data rates and powers. Successful reception of all users is guaranteed, provided that each of the users adheres to its assigned access policy, when selecting its date rate and power. We completely characterize the capacity region for such policy-based access in the low power regime, for both single and multi-antenna (MIMO) systems, and we show that nonorthogonal policies outperform orthogonal ones. We also show that known MIMO capacity scaling laws, which apply in single user systems, carry over into framework of decentralized MAC.

Previous work has shown that CSMA-based protocols such as the IEEE 802.11 can yield flow starvation in multi-hop wireless networks. While prior protocol designs alleviated such starvation by utilizing MIMO, they require global synchronization to temporally align transmissions and require per-packet distribution of the complete channel state information. In this paper, we experimentally show that MIMO networks based on pre-802.11 n medium access, the state-of-the-art for asynchronous MIMO CSMA, worsen starvation as compared to SISO networks. Consequently, we design an asynchronous MIMO MAC protocol that counters starvation. We show that randomized and non-greedy antenna allocation coupled with local residual capacity estimation results in previously-starving nodes capturing a fair share of system resources while simultaneously exploiting throughput gains available to multi-antenna systems.

In multiuser systems, the downlink capacity to different users is often different due to the near-far effect. We capture this asymmetry in the feedback link by introducing an asymmetric feedback model where different users get different amount of channel feedback from the base-station. Then, we derive the outage probability for the maximum-likelihood receiver, allowing us to study the impact of feedback asymmetry on multiuser performance. Interestingly, we discover that introducing systematic asymmetry in feedback can be beneficial in some cases, where only one user can adapt its power/rate to provide systemwide performance equivalent to that obtained by global feedback to all users.

In this paper, we study the effect of feedback channel noise on the diversity-multiplexing tradeoff in multiuser MIMO systems using quantized feedback, where each user has m transmit antennas and the base-station receiver has n antennas. We derive an achievable tradeoff and use it to show that in SNR-symmetric channels, a single bit of imperfect feedback is sufficient to double the maximum diversity order to 2 mn compared to when there is no feedback (maximum is mn at multiplexing gain of zero). Further, additional feedback bits do not increase this maximum diversity order beyond 2 mn. Finally, the above diversity order gain of mn over non-feedback systems can also be achieved for higher multiplexing gains, albeit requiring more than one bit of feedback.

In this work, we analyze the diversity-multiplexing performance of a MIMO multiple access wireless system with non-cooperating transmitters. Each of the transmitters and receiver use noisy and mismatched versions of the channel estimate to implement decentralized power control. While accounting for the resources consumed in training, we show that with relatively simple power control, regardless of the number of transmitters, we can achieve double the maximum diversity order of a system with no instantaneous channel state information at the transmitters. Intuitively, the gain can be attributed to using temporal degrees of freedom enabled by power control without coding over multiple coherence intervals.

Channel state feedback at the transmitter is extensively used to increase the reliability of wireless transmissions. In multiuser systems, the downlink capacity to different users is often different due to the near-far effect. We capture this asymmetry by introducing an asymmetric feedback model where different users get a different amount of feedback from the base station. First, we derive the outage probability for the optimum maximum-likelihood receiver which forms an upper bound on the diversity-multiplexing performance. This is accompanied by the conditions under which these bounds can be achieved. Second, we analyze the performance of two popular suboptimal receivers: the spatial decorrelator and the successive interference cancellation receiver. As a special case, when there is no asymmetry, the performance matches feedback-based single-user performance in many scenarios.

In this correspondence, we study design of transceiver antenna arrays and its impact on spectral efficiency of low-power systems. Our primary motivation is construction of practical and portable multi-antenna configurations with a very small and a-priori fixed volume for placing antennas. Using spectral efficiency as a target metric for array optimization, we show that any array configuration, transmit or receive, can be characterized via a parameter that we interpret as "effective degrees of freedom." For any array configuration, effective degrees of freedom describes an equivalent uncorrelated array, which results in the same low-power behavior of spectral efficiency. Joint optimization of transmit and receive antenna configurations decouples into maximizing effective degrees of freedom for transmitter and receiver separately. To achieve this goal, we introduce and study a theoretical benchmark of "limiting degrees of freedom," which is the least upper bound on effective degrees of freedom, evaluated over all configurations with finite number of antennas. Limiting degrees of freedom therefore describes the best possible performance for any transceiver array which confines its elements inside a given space. We compute a closed-form expression for limiting degrees of freedom of a circular geometry. Finally, we present numerical procedure and examples for designing linear and square arrays with nonuniform spacing, which typically exhibit significant spectral efficiency gains over uniform arrays.

In this paper, we analyze the asymptotic performance of multiple antenna channels where the transmitter has finite bit channel state information. Using the diversity multiplexing tradeoff to characterize the system performance, we demonstrate that channel feedback can fundamentally change the system behavior. Even one-bit of information can increase the diversity order of the system compared to the system with no transmitter information. In addition, as the amount of channel information at the transmitter increases, the diversity order for each multiplexing gain increases and goes to infinity for perfect transmitter information. The major reason for diversity order gain is in temporal power control, which adapts the power control strategy based on the average channel conditions of the channel.

In this work, we propose and analyze a two-way training system that exploits the fact that most wireless nodes are capable of both transmitting and receiving signals. That is, the underlying channel is by design a two-way channel, even if only one of the nodes has data to communicate. For half-duplex nodes with one antenna at the source node and M antennas at the destination node, we show that a novel training system can double the maximum diversity order of the system. The key departure from existing work is that (a) all resources used to obtain channel information at the source and destination are accounted for, and (b) channel estimates at the source and destination are mismatched and noisy. We further derive the full diversity-multiplexing tradeoff which demonstrates a diversity order gain at almost all multiplexing gains due to information at the source, even when that information is based on noisy estimates and subjected to full resource accounting.

We propose Low Density Parity Check (LDPC) code designs for the half-duplex relay channel. Our designs are based on the information theoretic random coding scheme for decode-and-forward relaying. The source transmission is decoded with the help of side information in the form of additional parity bits from the relay. We derive the exact relationships that the component LDPC code profiles in the relay coding scheme must satisfy. These relationships act as constraints for the density evolution algorithm which is used to search for good relay code profiles. To speed up optimization, we outline a Gaussian approximation of density evolution for the relay channel. The asymptotic noise thresholds of the discovered relay code profiles are a fraction of a decibel away from the achievable lower bound for decode-and-forward relaying. With random component LDPC codes, the overall relay coding scheme performs within 1.2 dB of the theoretical limit.

In this correspondence, a relay rate-constrained variation of the three-node relay channel is introduced and studied. In the rate-constrained relay channel, the relay cannot reliably decode or encode beyond a rate constraint R macr. A cut-set upper bound, and rate-constrained variations of decode-and-forward and estimate-and-forward protocol are derived. It is observed that relative performance of the unconstrained protocols does not predict relative performance of the constrained protocols; that is, estimate-and-forward almost always offers superior performance for small rate constraints. A new metric, denoted as the relay efficiency, measures the slope at which the relay channel rate increases over the relay-less channel. While the cut-set upper bound has a relay efficiency of one for the Gaussian relay channel at R macr = 0, the two new protocols do not. Finally, it is shown that an effective relay rate constraint can be computed for general relay protocols. To demonstrate this concept, a Markovian amplify-and-forward protocol, in which the relay does not perform any explicit encoding or decoding, is examined and compared to the introduced rate-constrained protocols.

A new class of abstract sensor networks is introduced and analyzed. The object of the sensing is the inter-node channel. Examples of systems which seek to sense the channel include: underwater sonar, radar and optics based atmospheric sensor networks. In these networks, the sensing and communication tasks share the bandwidth resource in addition to the sharing of power resources as has been conventionally studied in the sensor network framework. Bounds on distortion tradeoffs are developed for various protocols, such as source coding analogues of decode-and-forward and amplify-and-forward and simple topologies such as two-hop networks (three node system).

We propose a practical coding scheme for half-duplex estimate-and-forward relaying. The proposed construction is guided by the information theoretic coding scheme for the estimate-and-forward relay protocol. Our construction incorporates several design features to reduce receiver complexity without compromising performance. Observing that the relaying gain is significant only at low SNRs, we use binary LDPC codes in the source broadcast phase of relaying. Estimation is performed by entropy constrained scalar quantization of the received signal at the relay. Finally, a procedure similar to maximal ratio combining is used to aggregate direct and relayed signals at the destination. An important practical advantage of our scheme is that it does not require source-relay symbol synchronization. The codes outperform direct and two-hop channel capacities, as well as decode-and-forward relaying when the relay-destination link is strong

Transmitter side information enables techniques such as beamforming, power control, and rate control in fading channels. It is commonly accepted in the literature that the addition of transmitter information (CSIT) to receiver information (CSIR) provides better performance than receiver information alone. In this work, we examine the performance of a symmetric, single-input, multiple-output (SIMO) channel in which CSIT is acquired through the use of training symbols, and we have a genie-aided receiver. We give a closed form expression for outage probability at high SNR while accounting for the resources consumed by training. We also analyze the diversity-multiplexing tradeoff and find that, though the diversity falls far below that of systems with perfect CSIT, it is still sufficiently superior to that achieved by CSIR-only systems to justify the cost of training. We show that, at zero multiplexing, transmitter training doubles the diversity order of a CSIR-only system and offers nonzero diversity at all achievable multiplexing gains

A joint sensing/communication problem is considered for sensor networks. Herein, the channel(s) between a source and destination are the parameters to be sensed and communicated over the network. Lower bounds on the end-to-end distortion are developed for a multihop, linear network. Internode communication is assumed to be done via an encode-and-forward approach. For a many-to-one topology with two hops, data aggregation and time-division communication approaches are compared. Asymptotic in the SNR, it is shown that a time-division approach is superior

In this paper, we consider practical methods to approach the theoretical performance limits in the fading relay channel under different assumptions of transmitter channel knowledge. Specifically, we consider two degrees of transmitter channel knowledge: 1) perfect feedback is available and power control is employed and 2) no channel state knowledge is available at the transmitters and only spatial power allocation is possible. First, when perfect feedback is available, the optimal power control policy determines the ultimate limits of performance for constant rate transmission in the slow fading environment. However, in practice, perfect channel knowledge is not possible at the transmitters due to the finite capacity of the feedback links. We find practical methods to approach this performance limit through the use of power control with finite rate feedback. The finite-rate feedback results are shown for the low-complexity, full-diversity amplify-and-forward (AF) protocol. Interestingly, we see that only a few feedback bits are needed to achieve most of the gains of the optimal perfect feedback power control algorithm. Second, we consider the performance limit when the transmitters have no channel state knowledge and derive the optimal spatial power allocation between the source and relay for a given sum power constraint for the AF protocol. For most practical cases of interest, equal power allocation between the source and relay is shown to be nearly optimal. Our work suggests that there is minimal power savings from using spatial power allocation at the transmitters. To obtain large performance improvements over constant power transmission, it is imperative to have feedback for each realization of the channel state to allow for temporal power control.

We present a procedure for communication power optimization in a network of randomly distributed sensors with an observer (data collector) moving on a fixed path. The key challenge in using a mobile observer is that it remains within communication range of any sensor for a brief duration, and inability to transfer data in this duration leads to data loss. We establish that the process of data collection can be modeled by a queue with deadlines, where arrivals correspond to the observer entering the range of a sensor and a missed deadline means data loss. The queuing model is then used to identify the combination of system parameters that ensures adequate data collection with minimum power. The results obtained from the queuing analogy take a simple form in the asymptotic regime of dense sensor networks. Additionally, for sensor networks that cannot tolerate data loss, we derive a tight bound on minimum sensor separation that ensures that no data will be lost on account of mobility. We present two examples to illustrate our results, from which it is seen that power reduction by two orders of magnitude or more is typical relative to a static sensor network. The scenarios chosen for power comparisons also provide guidelines on the choice of path, if such a choice is available.

We consider a multiple antenna system with finite rate feedback, in which the quantized channel state information at the transmitter is used solely for temporal power control. We show that similar to systems without feedback, the tradeoff between diversity order and multiplexing gain exists. However, unlike the systems with feedback that apply both rate and power control, systems with only power control are unable of achieving non-zero diversity order at the maximum multiplexing gain. The analysis is based on asymptotic behavior of the distribution of order statistics of the eigenvalues of channel matrix, which is a key step in evaluating the diversity order.

In this paper, we study the impact of delay constraints on the throughput of a queued multiple-access system. We model the channel as a collision channel with carrier sense to capture the inherent information sharing due to broadcast nature of the wireless channels. Since the queue state information is unknown to other nodes and a delay-bounded communication is desired, we show that a fraction of throughput is lost as protocol overhead. More importantly, we show that there is a tradeoff between protocol overhead and queuing delay; larger delays allowing smaller overheads. In addition, we show that larger network loads can also be used to reduce protocol overhead, which is in direct contrast of the behavior exhibited by commonly used medium access protocols

In this paper, the impact of limited resources on achievable rates in relay channels is investigated. Resource limitation is modeled (as previously proposed) by (Rmacr) which constrains the rate at which a relay can reliably decode or encode data. With this constraint in hand, the achievable rates for two commonly studied protocols, decode-and-forward and estimate-and-forward are derived and_compared. In particular, the case of severe resource limitation (Rmacr rarr 0) is considered. While decode-and-forward is the superior protocol for most relay locations (topologies) under no rate constraints, estimate-and forward almost always offers superior performance when relaying with strong rate constraints

The IEEE 802.11 wireless media standard supports multiple frequency channels as well as multiple data rates at the physical (PHY) layer. In this paper, we introduce the multi-channel opportunistic auto rate (MOAR), an enhanced MAC protocol for multi-channel and multi-rate IEEE 802.11 enabled wireless ad hoc networks to opportunistically exploit the presence of frequency diversity (in the form of multiple frequency channels). The key mechanism of MOAR is that if the signal to noise ratio on the current channel is not favorable, mobile nodes can opportunistically skip to better quality frequency channels enabling data transmission at a higher rate. As channel separation for IEEE 802.11 is greater than the coherence bandwidth, different channels experience independent fading and hence there is a high probability that the skipping nodes will find better channel conditions on one of the other frequency channels. Each skip comes at the cost of resources spent in channel measurement since channel quality of different channels is not known a priori. Consequently, we devise an optimal skipping rule for MOAR which maps the channel conditions at the PHY layer to a MAC rule which allows each node to determine its optimum number of skips based on average channel conditions. Finally, we perform an extensive set of ns-2 simulations to evaluate the performance of MOAR and the impact of such factors as location distribution, channel conditions and error in channel measurements on the throughput gains offered by MOAR.

In this paper, we present a framework for the design of minimal power schedulers that satisfy average packet delay bounds for multiple users in a Gaussian wireless broadcast channel. We completely characterize the achievable region in the multidimensional delay-power space, and present schedulers that achieve the boundary regions. The optimal schedulers minimize the transmission power by jointly allocating rate and power to the different users based on various buffer and channel conditions. Finally, we also present low complexity scheduler designs that have near optimal performance.

In this paper, we analyze the outage probability of a single user system with multiple antennas at the transmitter, single antenna at the receiver, and finite rate feedback power control. The optimum power control is complex and the analysis is not tractable. Hence we propose a sub-optimal power allocation scheme, with very low computational complexity, which is asymptotically optimum. Analyzing the proposed algorithm we show that the diversity order can potentially be increased unboundedly by increasing the feedback rate and without increasing number of transmit or receive antennas. We find a closed form approximation to this diversity-like gain at large SNRs, as a function of number of transmit antennas, number of quantization levels, and average available SNR. Simulation results confirm the validity of the analysis.

The packet outage probability for fading channels can be significantly reduced by exploiting queuing delay and transmitter channel information is demonstrated in this paper. Queuing delay gain is conceptually similar to delay diversity, but at a packet time-scale instead of symbol time-scale. First, a lower bound on outage probability assuming full channel state information at the transmitter (CSIT) is computed and then simple outage minimizing transmission policies which adapt the rate and power of the transmitted signal based jointly on buffer occupancy and channel conditions is constructed . We demonstrate that the rate of decrease of outage with increasing transmitter channel information is higher for larger delays. We also address the closely coupled problem of designing a practical feedback channel which supplies the CSIT.

In this paper, packet scheduling with maximum delay constraints is considered with the objective to minimize average transmit power over Gaussian channels. The main emphasis is on deriving robust schedulers which do not rely on the knowledge of the source arrival process. Towards that end, we first show that all schedulers (robust or otherwise) which guarantee a maximum queuing delay for each packet are equivalent to a time-varying linear filter. Using the connection between filtering and scheduling, we study the design of optimal power minimizing robust schedulers. Two cases, motivated by filtering connection, are studied in detail. First, a time-invariant robust scheduler is presented and its performance is completely characterized. Second, we present the optimal time-varying robust scheduler, and show that it has a very intuitive time water-filling structure. We also present upper and lower bounds on the performance of power-minimizing schedulers as a function of delay constraints. The new results form an important step towards understanding of the packet time-scale interactions between physical layer metric of power and network layer metric of delay

The main goal of this paper is to show that multi-hop single-user communication achieves the per node transport capacity of Θ(ln N N) in homogeneous sensor networks, making it order-optimal. Our contributions in this paper are three-fold. First, we construct a route-discovery and scheduling scheme based on spatial TDMA for sensor networks. Second, we show that our schedule achieves a per node transport capacity of Θ(lnN N), the same as that achievable by beamforming. Third, we compare multi-hop communication and beamforming based methods in terms of the network power consumption required to attain a fixed throughput. Based on our power calculations, we conclude that if the channel attenuation is above a certain threshold (which we calculate), then multi-hop communication performs better, whereas below the threshold, beamforming is preferable.

Motivated by limited computational resources in sensor nodes, the impact of complexity constraints on the communication efficiency of sensor networks is studied. A single-parameter characterization of processing limitation of nodes in sensor networks is invoked. Specifically, the relaying nodes are assumed to donate only a small part of their total processor time to relay other nodes information. The amount of donated processor time is modelled by the node's ability to decode a channel code reliably at given rate R. Focusing on a four node network, with two relays, prior work for a complexity constrained single relay network is built upon. In the proposed coding scheme, the transmitter sends a broadcast code such that the relays decode only the coarse information, and assist the receiver in removing ambiguity only in that information. Via numerical examples, the impact of different power constraints in the system, ranging from per node power bound to network wide power constraint is explored. As the complexity bound R increases, the proposed scheme becomes identical to the recently proposed achievable rate by Gupta & Kumar (2003). Both discrete memoryless and Gaussian channels are considered.

In this paper a new achievable rate region for the user cooperation channel is derived which exceeds the best-known result for this channel. Since the user cooperation channel includes many other known channels as a special case, the new rate region provides improved achievable rates for these cases. The most notable example is that of the Gaussian relay channel, for which we present a new closed form inner bound higher than the only known result for many channel conditions.

We consider multiaccess problem in low power systems, where we allow each user to select its own data rate and transmit power locally and independently from other users. Here, every user has a set of low power codebooks, labeled a "policy," which accommodates a range of small spectral efficiencies, while treating instantaneous data rates of other users as an unknown compound parameter. Even with such coarse user coordination, multiuser detection enables a system, which is superior to any classic orthogonal division system. First we fully characterise the set of achievable policies, after which we demonstrate that in multiantenna systems, policies are be viewed as awarding protected receiver spatial dimensions to each user.

In this paper, we study minimal power transmission of bursty sources over wireless channels with constraints on mean queuing delay. The power minimizing schedulers adapt power and rate of transmission based on the queue and channel state. We show that packet scheduling based on queue state can be used to trade queuing delay with transmission power, even on additive white Gaussian noise (AWGN) channels. Our extensive simulations show that small increases in average delay can lead to substantial savings in transmission power, thereby providing another avenue for mobile devices to save on battery power. We propose a low-complexity scheduler that has near-optimal performance. We also construct a variable-rate quadrature amplitude modulation (QAM)-based transmission scheme to show the benefits of the proposed formulation in a practical communication system. Power optimal schedulers with absolute packet delay constraints are also studied and their performance is evaluated via simulations.

In this paper, we derive the capacity of the Gaussian degraded 'cheap' relay channel, consisting of nodes using 'cheap' radios operating in TDD mode when transmitting and sending in the same frequency band. The TDD model captures a practical limitation of most of the RF radios used in commercial wireless systems. The proof of achievability relies on a combination of superposition encoding and list decoding, while the converse is derived using the min-cut max-flow theorem for networks with 'cheap' nodes (previously derived by the authors). Even with 'cheap' radios, our capacity analysis shows that cooperative coding is beneficial and has a capacity advantage over direct transmission.

We consider the problem of digital communication in a Rayleigh flat-fading environment using a multiple-antenna system, when the channel state information is available neither at the transmitter nor at the receiver. It is known that at high signal-to-noise ratio (SNR), or when the coherence interval is much larger than the number of transmit antennas, a constellation of unitary matrices can achieve the capacity of the noncoherent system. However, at low SNR, high spectral efficiencies, or for small values of coherence interval, the unitary constellations lose their optimality and fail to provide an acceptable performance. In this work, inspired by the Stein's lemma, we propose to use the Kullback-Leibler (KL) distance between conditional distributions to design space-time constellations for noncoherent communication. In fast fading, i.e., when the coherence interval is equal to one symbol period and the unitary construction provides only one signal point, the new design criterion results in pulse amplitude modulation (PAM)-type constellations with unequal spacing between constellation points. We also show that in this case, the new design criterion is equivalent to design criteria based on the exact pairwise error probability and the Chernoff information. When the coherence interval is larger than the number of transmit antennas, the resulting constellations overlap with the unitary constellations at high SNR, but at low SNR they have a multilevel structure and show significant performance improvement over unitary constellations of the same size. The performance improvement becomes especially more significant when an appropriately designed outer code or multiple receive antennas are used. This property, together with the facts that the proposed constellations eliminate the need for training sequences and are most suitable for low SNR, makes them a good candidate for uplink communication in wireless systems.

We study a multiple-antenna system where the transmitter is equipped with quantized information about instantaneous channel realizations. Assuming that the transmitter uses the quantized information for beamforming, we derive a universal lower bound on the outage probability for any finite set of beamformers. The universal lower bound provides a concise characterization of the gain with each additional bit of feedback information regarding the channel. Using the bound, it is shown that finite information systems approach the perfect information case as (t-1)2/sup -B/t-1/, where B is the number of feedback bits and t is the number of transmit antennas. The geometrical bounding technique, used in the proof of the lower bound, also leads to a design criterion for good beamformers, whose outage performance approaches the lower bound. The design criterion minimizes the maximum inner product between any two beamforming vectors in the beamformer codebook, and is equivalent to the problem of designing unitary space-time codes under certain conditions. Finally, we show that good beamformers are good packings of two-dimensional subspaces in a 2t-dimensional real Grassmannian manifold with chordal distance as the metric.

In this paper, we study communication in wireless networks where there is no central control available, like wireless ad hoc and sensor networks. We derive the outage probability of multi-hop communication and study it in detail for a simple linear topology. The results indicate that an appropriate choice of route can lead to large performance gains over alternate routes. In the second part of the paper, we consider the problem of estimating outage-minimizing routes using channel measurements. We show that finding the optimal routes reliably requires many independent channel measurements, which is equivalent to sending multiple route discovery requests. The large overhead in route discovery can prove to be counter-productive in networks which transmit information at the same time-scale as the mobility of nodes.

The IEEE 802.11 wireless media access standard supports multiple data rates at the physical layer. Moreover, various auto rate adaptation mechanisms at the medium access layer have been proposed to utilize this multi-rate capability by automatically adapting the transmission rate to best match the channel conditions. In this paper, we introduce the Opportunistic Auto Rate (OAR) protocol to better exploit durations of high-quality channels conditions. The key mechanism of the OAR protocol is to opportunistically send multiple back-to-back data packets whenever the channel quality is good. As channel coherence times typically exceed multiple packet transmission times for both mobile and non-mobile users, OAR achieves significant throughput gains as compared to state-of-the-art auto-rate adaptation mechanisms. Moreover, over longer time scales, OAR ensures that all nodes are granted channel access for the same time-shares as achieved by single-rate IEEE 802.11. We describe mechanisms to implement OAR on top of any existing auto-rate adaptation scheme in a nearly IEEE 802.11 compliant manner. We also analytically study OAR and characterize the gains in throughput as a function of the channel conditions. Finally, we perform an extensive set of ns-2 simulations to study the impact of such factors as node velocity, channel conditions, and topology on the throughput of OAR.

Wireless emph ad hoc networks based on the IEEE 802.11 protocol can incur severe unfairness even in simple topologies. In particular, two topological properties that we define in a graph-theoretic framework and refer to as information asymmetry and perceived collisions result in significant performance degradations and unfairness. In this paper, we present the design and analysis of Distributed Wireless Ordering Protocol (DWOP), a distributed scheduling and media access algorithm targeted towards ensuring that packets access the medium in an order defined by an ideal reference scheduler such as FIFO, Virtual Clock, or Earliest Deadline First. In this way, DWOP enables QoS differentiation as well as fairness when combined with TCP. Our key technique is piggybacking head-of-line packet priorities in IEEE 802.11 control messages so that nodes can assess the relative priority of their own queued packets. With a graph-theoretic problem formulation, we design DWOP to achieve the exact reference ordering in fully connected graphs, and to have well-characterized deviations from the reference order in more complex topologies. A simple theoretical model indicates that the scheme attains rapid convergence for newly arriving nodes, and extensive simulations indicate that nearly exact reference ordering can be achieved, even in complex asymmetric and perceived collision topologies.

Multiple antenna transmission and reception have been shown to significantly increase the achievable data rates of wireless systems. However, most of the existing analysis assumes perfect or no channel information at the receiver and transmitter. The performance gap between these extreme channel assumptions is large and most practical systems lie in between. Therefore, it is important to analyze multiple antenna systems in the presence of partial channel information. We upper bound the outage probability performance of multiple antenna systems with preamble-based channel estimation and quantized feedback. We design causal feedback and power control schemes to minimize this upper bound on outage probability. We consider the following practical issues in our analysis and design: (1) the channel information is imperfect both at the receiver and at the transmitter and (2) part of the total available resources for the system need to be used for estimation and feedback. Our results demonstrate that for block fading channels, sending a periodic preamble and causally receiving channel state information via a feedback channel can lead to substantial gains in the outage performance over any nonfeedback scheme. Most of the gains achieved by perfect feedback can be achieved by very few bits of feedback. Furthermore, it is demonstrated that these outage probability gains can be translated into improvements in frame error rate performance of systems using space-time codes. Thus, implementing a power control, even at the cost of reduced spectral resources for the forward channel is beneficial for block fading channels.

We study delay and rate constrained transmission of bursty traffic over wireless channels. We characterize the minimum power requirements via bounds for both single user and multiuser downlink problems, using a class of randomized first-come first-served policies. We show that a larger tolerable delay leads to power reduction, even for single-user Gaussian channels; a source coding interpretation is offered for the result. Further, we show that traffic with maximum-delay constraints requires more power than the same traffic with average-delay constraints.

We consider uplink scheduling for bursty traffic. We characterize the achievable rate region for Gaussian multiple access in terms of minimum required powers, with a constraint on average transmission delay for all users. We show that delay and rate constrained, power minimizing schemes perform scheduling accompanied with power control. Further, for the class of randomized stationary schedulers, it is shown that the achievable region is a convex polytope. We highlight that power requirements of a user can be reduced by either allowing additional delay (time scheduling gain) or increasing the power of another user (multiuser power exchange). Results are presented for two user additive white Gaussian noise channel and can be extended to finite state fading channels.

Providing quality of service in random access multi-hop wireless networks requires support from both medium access and packet scheduling algorithms. However, due to the distributed nature of ad hoc networks, nodes may not be able to determine the next packet that would be transmitted in a (hypothetical) centralized and ideal dynamic priority scheduler. In this paper, we develop two mechanisms for QoS communication in multi-hop wireless networks. First, we devise distributed priority scheduling a technique that piggybacks the priority tag of a node's head-of-line packet onto handshake and data packets; e.g., RTS/DATA packets in IEEE 802.11. By monitoring transmitted packets, each node maintains a scheduling table which is used to assess the node's priority level relative to other nodes. We then incorporate this scheduling table into existing IEEE 802.11 priority back-off schemes to approximate the idealized schedule. Second, we observe that congestion, link errors, and the random nature of medium access prohibit an exact realization of the ideal schedule. Consequently, we devise a scheduling scheme termedmulti-hop coordinationso that downstream nodes can increase a packet's relative priority to make up for excessive delays incurred upstream. We next develop a simple analytical model to quantitatively explore these two mechanisms. In the former case, we study the impact of the probability of overhearing another packet's priority index on the scheme's ability to achieve the ideal schedule. In the latter case, we explore the role of multi-hop coordination in increasing the probability that a packet satisfies its end-to-end QoS target. Finally, we perform a set of ns-2 simulations to study the scheme's performance under more realistic conditions.

Minimum-mean squared error (MMSE) receivers are designed and analyzed for multiple data rate direct-sequence code-division multiple-access (DS-CDMA) systems. The inherent cyclostationarity of the DS-CDMA signal is exploited to construct receivers for asynchronous multipath channels. Multiple- and single-bandwidth access are treated for both single and multicarrier scenarios. In general, the optimal receiver is periodically time-varying. When the period of the optimal receiver is large, suboptimal receivers are proposed to achieve a lower complexity implementation; the receivers are designed as a function of the cyclic statistics of the signals. In multiple chipping rate systems, the complexity of receivers for smaller bandwidth users can also be controlled by changing their front-end filter bandwidth. The effect of front-end filter bandwidth on receiver performance and system capacity is quantified for a variable chipping rate system. Analysis and simulation show that significant performance gains are realized by the periodically time-varying MMSE receivers over their time-invariant counterparts.

Cell sectorization is used extensively to increase the capacity of cellular systems; however, inter-sector interference caused by non-ideal sector antennas hinders full realization of sectorization benefits. We propose a new technique for sector beam synthesis, using phased antenna arrays, to minimize inter-sector interference, or equivalently, to maximize the beam efficiency. A peak-to-peak ripple constraint on the power density inside the sector leads to a non-convex optimization task; a reparametrization results in an easily solved convex fractional linear program. The proposed procedure accommodates non-isotropic antenna elements, arbitrary element spacing, and arbitrary sector boundaries, but is limited to uniformly spaced linear (or planar) arrays. Finally, the proposed method is used to predict antenna array size requirements for a DS-CDMA cellular system.

Detection methods for multiple rate direct sequence code division multiple access (DS-CDMA) signalling are addressed. Attention is focused on the development of receivers based on the minimum mean squared error (MMSE). Due to the cyclostationarity of the multirate signal, a representation of the multirate signal in terms of the Fourier basis is possible. This expansion facilitates construction of the MMSE receivers. Both variable spreading gain as well as variable chipping rate DS-CDMA access schemes are considered. For the multiple chipping rate system, the effect of the front-end filter bandwidth on the receiver performance is studied. Simulation results are provided to compare the performance of the proposed receivers.

We present an adaptive compression and resampling technique using wavelet packet shrinkage. The motivating application is surface digitization for computer-aided manufacturing. The objective is to improve estimation accuracy using a sparse and irregular, but more accurate, resampling of the data. We provide modified thresholds for wavepacket shrinkage, heuristics for choosing the resampling grid, and a mean estimator using measured data. Computed results illustrate application of the proposed technique.

In most estimation and design problems, there exists more than one solution that satisfies all constraints. In this paper, we address the problem of estimating the complete set of feasible solutions. Multiple feasible solutions are frequently encountered in signal restoration, image reconstruction, array processing, system identification and filter design. An estimate of the size of the feasibility set can be utilized to quantitatively evaluate inclusion and effectiveness of added constraints. Further, set estimation can be used to determine a null feasibility set. We compute ellipsoidal approximations to the set of feasible solutions using a new ellipsoid algorithm and the method of analytic centers. Both algorithms admit multiple convex constraint sets with ease. Also, the algorithms provide a solution which is guaranteed to be in the interior of the feasibility set.