State-of-the-art techniques for enhancing robustness of deep networks mostly rely on empirical risk minimization with suitable data augmentation. In this paper, we propose a complementary approach motivated by communication theory, aimed at enhancing the signal-to-noise ratio at the output of a neural network layer via neural competition during learning and inference. In addition to standard empirical risk minimization, neurons compete to sparsely represent layer inputs by maximization of a tilted exponential (TEXP) objective function for the layer. TEXP learning can be interpreted as maximum likelihood estimation of matched filters under a Gaussian model for data noise. Inference in a TEXP layer is accomplished by replacing batch norm by a tilted softmax, which can be interpreted as computation of posterior probabilities for the competing signaling hypotheses represented by each neuron. After providing insights via simplified models, we show, by experimentation on standard image datasets, that TEXP learning and inference enhances robustness against noise and other common corruptions, without requiring data augmentation. Further cumulative gains in robustness against this array of distortions can be obtained by appropriately combining TEXP with data augmentation techniques. The code for all our experiments is available at \url{https://github.com/bhagyapuranik/texp_for_robustness}.

mmWave antenna array calibration is a necessary yet tedious and costly process in manufacturing to capture the non-idealities in phased arrays, in order to obtain codebooks for accurate and stable beam steering. Unfortunately, predefined codebooks provided by manufacturers to steer beams in a given set of directions do not support the arbitrary beam shapes required for various mmWave communication, sensing, and security applications. To create arbitrary beam patterns, one needs to first find the unknown calibration vector for the particular phased array in use. In this paper, we introduce EiCal, a novel zero-shot technique that leverages the beamforming codebook advertised by the manufacturer to extract the calibration vector at zero cost (i.e., with no additional measurements). The key idea is that the unknown desired calibration vector can be obtained via an appropriately designed eigen-decomposition of the given codebook. We experimentally demonstrate the efficacy of EiCal on a 60 GHz mmWave array for two scenarios: angle estimation using compressive pseudorandom beams, and simultaneous steering of beams and nulls. Our results also point to potential simplifications in the calibration process at the manufacturer.

Recent progress in millimeter wave (mmWave) silicon technologies has given rise to a new possibility: digital beamforming for truly massive multiuser (MU-) MIMO. However, there are two key challenges in scaling: packaging a vast array of antennas with the corresponding radio frequency integrated circuits (RFICs), and controlling the complexity of digital signal processing (DSP) for MU detection. In this paper, we show that modular tiled architectures, which simplify the task of RF packaging, also enable significant reduction of the communication and computational burden of DSP for MU-MIMO by utilizing beamspace techniques that take advantage of the sparsity of the mmWave channel. Specifically, we propose and investigate Linear Minimum Mean Squared Error (LMMSE) adaptive MU detection via novel tiled beamspace architectures in which the bulk of the DSP occurs in-place at each tile. The dimensionality reduction and parallelization enabled by such architectures not only reduce the computational burden of inference and training relative to a traditional "full array" baseline, but also significantly reduce the length of the required training period. We consider three different training strategies with differing requirements for computation and inter-tile communication: independent training for each tile, coordinated training across tiles, and hierarchical training based on independent training as a first stage. Simulation results show that these approaches can actually outperform the full array baseline when we limit the length of the training period.

With AI-based decisions playing an increasingly consequential role in our society, for example, in our financial and criminal justice systems, there is a great deal of interest in designing algorithms conforming to application-specific notions of fairness. In this work, we ask a complementary question: can AI-based decisions be designed to dynamically influence the evolution of fairness in our society over the long term? To explore this question, we propose a framework for sequential decision-making aimed at dynamically influencing long-term societal fairness, illustrated via the problem of selecting applicants from a pool consisting of two groups, one of which is under-represented. We consider a dynamic model for the composition of the applicant pool, in which admission of more applicants from a group in a given selection round positively reinforces more candidates from the group to participate in future selection rounds. Under such a model, we show the efficacy of the proposed Fair-Greedy selection policy which systematically trades the sum of the scores of the selected applicants ("greedy'') against the deviation of the proportion of selected applicants belonging to a given group from a target proportion ("fair''). In addition to experimenting on synthetic data, we adapt static real-world datasets on law school candidates and credit lending to simulate the dynamics of the composition of the applicant pool. We prove that the applicant pool composition converges to a target proportion set by the decision-maker when score distributions across the groups are identical.

Speaker recognition, recognizing speaker identities based on voice alone, enables important downstream applications, such as personalization and authentication. Learning speaker representations, in the context of supervised learning, heavily depends on both clean and sufficient labeled data, which is always difficult to acquire. Noisy unlabeled data, on the other hand, also provides valuable information that can be exploited using self-supervised training methods. In this work, we investigate how to pretrain speaker recognition models by leveraging dialogues between customers and smart-speaker devices. However, the supervisory information in such dialogues is inherently noisy, as multiple speakers may speak to a device in the course of the same dialogue. To address this issue, we propose an effective rejection mechanism that selectively learns from dialogues based on their acoustic homogeneity. Both reconstruction-based and contrastive-learning-based self-supervised methods are compared. Experiments demonstrate that the proposed method provides significant performance improvements, superior to earlier work. Dialogue pretraining when combined with the rejection mechanism yields 27.10% equal error rate (EER) reduction in speaker recognition, compared to a model without self-supervised pretraining.

While end-to-end training of Deep Neural Networks (DNNs) yields state of the art performance in an increasing array of applications, it does not provide insight into, or control over, the features being extracted. We report here on a promising neuro-inspired approach to DNNs with sparser and stronger activations. We use standard stochastic gradient training, supplementing the end-to-end discriminative cost function with layer-wise costs promoting Hebbian ("fire together," "wire together") updates for highly active neurons, and anti-Hebbian updates for the remaining neurons. Instead of batch norm, we use divisive normalization of activations (suppressing weak outputs using strong outputs), along with implicit ℓ2normalization of neuronal weights. Experiments with standard image classification tasks on CIFAR-10 demonstrate that, relative to baseline end-to-end trained architectures, our proposed architecture (a) leads to sparser activations (with only a slight compromise on accuracy), (b) exhibits more robustness to noise (without being trained on noisy data), (c) exhibits more robustness to adversarial perturbations (without adversarial training).

Line-of-sight (LoS) millimeter wave (mmWave) multiple-input multiple-output (MIMO) is a promising approach for providing the ultra-high speed point to point links required for wireless backhaul in picocellular networks. The combination of large bandwidth and spatial multiplexing can sustain 100+ Gbps links over 10s to 100s of meters, while the antenna form factors required for providing the necessary spatial degrees of freedom remain small due to the small carrier wavelength. However, the large bandwidth makes the system susceptible to geometric misalignments: relatively small misalignments can cause multi-symbol delay spread across the receiver aperture. Furthermore, as the signaling bandwidth approaches the limits of hardware (i.e., analog-to-digital converter (ADC)) capabilities, the temporal oversampling typically used to overcome intersymbol interference becomes infeasible. In this paper, we investigate an architecture for joint space-time equalization with spatial oversampling by introducing additional receive antennas while maintaining symbol-rate sampling. We consider linear space-time equalization, controlling complexity by employing an adaptive time window at each receiver. We illustrate tradeoffs between the size of the adaptive window and the spatial oversampling factor via analysis and simulation for a 4-stream, 128 Gbps, 100 m LoS link at 130 GHz with both horizontal and vertical misalignment of the 2D planar arrays. For example, we show, via a signal space analysis accompanied by simulation results, that error floors can be avoided via 2X spatial oversampling and a temporal window of 5.

A key goal of next generation networks is to scale hardware design and signal processing algorithms to mmWave and THz arrays with a large number of elements. Imperfect manufacturing and limitations of circuit design introduce variations in the gain and relative phase offset of transmit and receive array elements that must be compensated prior to beam formation for either communication or sensing. We propose a novel method for calibrating large arrays in the field by exploiting the sparsity of the spatial channel. While conventional calibration methods are susceptible to multipath components in the wireless channel, our approach is shown to be robust to multipath interference if the measurements are gathered from a sufficiently diverse set of locations.

Machine learning models are vulnerable to adversarial attacks that can often cause misclassification by introducing small but well designed perturbations. In this paper, we explore, in the setting of classical composite hypothesis testing, a defense strategy based on the generalized likelihood ratio test (GLRT), which jointly estimates the class of interest and the adversarial perturbation. We evaluate the GLRT approach for the special case of binary hypothesis testing in white Gaussian noise under ℓ∞norm-bounded adversarial perturbations, a setting for which a minimax strategy optimizing for the worst-case attack is known. We show that the GLRT approach yields performance competitive with that of the minimax approach under the worst-case attack, while yielding a better robustness-accuracy trade-off under weaker attacks. The GLRT defense is applicable in multi-class settings and generalizes naturally to more complex models for which optimal minimax classifiers are not known.

Deep Neural Networks (DNNs) are vulnerable to adversarial attacks: carefully constructed perturbations to an image can seriously impair classification accuracy, while being imperceptible to humans. The most effective current defense is to train the network using adversarially perturbed examples. In this paper, we investigate a radically different, neuro-inspired defense mechanism, aiming to reject adversarial perturbations before they reach a classifier DNN, using an encoder with characteristics commonly observed in biological vision, followed by a decoder restoring image dimensions that can be cascaded with standard CNN architectures. Unlike adversarial training, all training is based on clean images. Our experiments on the CFAR-10 and a subset of Imagenet datasets show performance competitive with state-of-the-art adversarial training, and point to the promise of bottom-up neuro-inspired techniques for the design of robust neural networks.

Millimeter wave MIMO combines the benefits of compact antenna arrays with a large number of elements and massive bandwidths, so that fully digital beamforming has the potential of supporting a large number of simultaneous users withper userdata rates of multiple gigabits/sec (Gbps). In this paper, we develop an analytical model for the impact of nonlinearities in such a system, and illustrate its utility in providing hardware design guidelines regarding two key challenges: the low available precision of analog-to-digital conversion at high sampling rates, and nonlinearities in ultra-high speed radio frequency (RF) and baseband circuits. We consider linear minimum mean square error (LMMSE) reception for a multiuser MIMO uplink, and provide performance guarantees based on two key concepts: (a) summarization of the impact of per-antenna nonlinearities via a quantity that we term the “intrinsic SNR”, (b) using linear MMSE performance in an ideal system without nonlinearities to bound that in our non-ideal system. For our numerical results, we employ nominal parameters corresponding to outdoor picocells operating at a carrier frequency of 140 GHz, with a data rate of 10 Gbps per user.

This paper investigates the effect of oscillator phase noise on a multiuser millimeter wave (mmWave) massive MIMO uplink as we scale up the number of base station antennas, fixing the load factor, defined as the ratio of the number of simultaneous users to the number of base station antennas. We consider a modular approach in which the base station employs an array of subarrays, or “tiles.” Each tile supports a fixed number of antennas, and can therefore be implemented using a separate radio frequency integrated circuit (RFIC), with synchronization across tiles accomplished by employing a phased locked loop in each tile to synthesize an on-chip oscillator at the carrier frequency by locking on to a common lower frequency reference clock. Assuming linear minimum mean squared error (LMMSE) multiuser detection, we provide an analytical framework that can be used to specify the required power spectral density (PSD) mask for phase noise for a target system performance. Our analysis for the phase noise at the output of the LMMSE receiver indicates two distinct effects: self-noise for each user which is inversely proportional to the number of tiles, and cross-talk between users which is insensitive to the number of tiles, and is proportional to the load factor. These analytical predictions, verified by simulations for a 140 GHz system targeting a per-user data rate of 10 Gbps, show that tiling is a robust approach for scaling. Numerical results for our proposed design approach yield relatively relaxed specifications for phase noise PSD masks.

The vulnerability of deep neural networks to small, adversarially designed perturbations can be attributed to their "excessive linearity." In this paper, we propose a bottom-up strategy for attenuating adversarial perturbations using a nonlinear front end which polarizes and quantizes the data. We observe that ideal polarization can be utilized to completely eliminate perturbations, develop algorithms to learn approximately polarizing bases for data, and investigate the effectiveness of the proposed strategy on the MNIST and Fashion MNIST datasets.

Picocellular architectures are essential for providing the spatial reuse required to satisfy the ever-increasing demand for mobile data. A key deployment challenge is to provide backhaul connections with sufficiently high data rate. Providing wired support (e.g., using optical fiber) to pico base stations deployed opportunistically on lampposts and rooftops is impractical, hence wireless backhaul becomes an attractive approach. A multihop mesh network comprised of directional millimeter (mm) wave links is considered here for this purpose. Such networks are well suited for scaling backhaul data rates due to the abundance of spectrum in the mm wave bands, and the ability to form highly directional, electronically steerable beams. The backhaul design problem is formulated as one of joint routing and resource allocation, accounting for mutual interference across simultaneously active links. A computationally tractable formulation is developed by leveraging the localized nature of interference and the provable existence of a sparse optimal allocation. Numerical results are provided for topologies modeling urban and suburban settings.

Millimeter wave (mmWave) carrier frequencies offer the dual benefits of large bandwidth and physically compact antenna arrays with a large number of elements, so that a large number of simultaneous independently modulated users can be supported with digital beamforming. In this paper, we propose an efficient digital backend design for a wideband mmWave massive MIMO uplink taking full advantage of both features. The challenges in receiver design for a wideband system with a large antenna array include the following: (a) for each user, there is a multi-symbol delay spread across the array, hence the receiver must handle intersymbol as well as multiuser interference; (b) even though we consider channels with a single dominant path, the spatial frequency for a user becomes smeared across the band; (c) the natural strategy of MIMO-OFDM used at lower carrier frequencies is unattractive at mmWave frequencies because the linearity requirements due to large peak-to-average ratio are difficult to meet. In this paper, we introduce a novel technique that combines spatial domain FFT and time domain FFT, together with an interpolation technique for limiting the spread of spatial frequency across the band which significantly reduces the complexity of beamformer weight acquisition. We present simulation results for a system operating at 140 GHz with a bandwidth of 14 GHz and a 256-element horizontally-scanning linear base station array.

A "wireless fingerprint" which exploits hardware imperfections unique to each device is a potentially powerful tool for wireless security. Such a fingerprint should be able to distinguish between devices sending the same message, and should be robust against standard spoofing techniques. Since the information in wireless signals resides in complex baseband, in this paper, we explore the use of neural networks with complex- valued weights to learn fingerprints using supervised learning. We demonstrate that, while there are potential benefits to using sections of the signal beyond just the preamble to learn fingerprints, the network cheats when it can, using information such as transmitter ID (which can be easily spoofed) to artificially inflate performance. We also show that noise augmentation by inserting additional white Gaussian noise can lead to significant performance gains, which indicates that this counter-intuitive strategy helps in learning more robust fingerprints. We provide results for two different wireless protocols, WiFi and ADS-B, demonstrating the effectiveness of the proposed method.

Millimeter (mm) wave picocellular networks are a promising approach for delivering the 1000-fold capacity increase required to keep up with projected demand for wireless data: the available bandwidth is orders of magnitude larger than that in existing cellular systems, and the small carrier wavelength enables the realization of highly directive antenna arrays in compact form factors, thus drastically increasing spatial reuse. In this paper, we carry out an interference analysis for mm-wave picocells in an urban canyon with a dense deployment of base stations. Each base station sector can serve multiple simultaneous users, which implies that both intra- and inter-cell interference must be managed. We propose a cross-layer approach to interference management based on (i) suppressing interference at the physical layer and (ii) managing the residual interference at the medium access control layer. We provide an estimate of network capacity, and establish that 1000-fold increase relative to conventional LTE cellular networks is indeed feasible.

Deep neural networks represent the state of the art in machine learning in a growing number of fields, including vision, speech and natural language processing. However, recent work raises important questions about the robustness of such architectures, by showing that it is possible to induce classification errors through tiny, almost imperceptible, perturbations. Vulnerability to such “adversarial attacks”, or “adversarial examples”, has been conjectured to be due to the excessive linearity of deep networks. In this paper, we study this phenomenon in the setting of a linear classifier, and show that it is possible to exploit sparsity in natural data to combat ℓ∞-bounded adversarial perturbations. Specifically, we demonstrate the efficacy of a sparsifying front end via an ensemble averaged analysis, and experimental results for the MNIST handwritten digit database. To the best of our knowledge, this is the first work to show that sparsity provides a theoretically rigorous framework for defense against adversarial attacks.

We consider the problem of optimal precoder design for a multi-input single-output wideband wireless system to maximize two different figures of merit: the total communication capacity and the total received power, subject to individual power constraints on each transmit element. We show that the two optimal precoders satisfy a separation principle that reveals a simple structure for these precoders. We use this separation principle extensively to derive several interesting properties of these two optimal precoders. Some key analytical results are as follows. We show that the power-maximizing precoders must concentrate all their energy in a small number of active channels that cannot exceed the number of input terminals. The capacity-maximizing precoder turns out to be very different from the classical water filling solutions and also very different from the power-maximizing precoders except at asymptotically low SNRs where the power-maximizing precoders also maximize capacity. We also show that the capacity of the wideband system is lower bounded by the sum rate of a multiple-access channel with the same channel gains and power constraints. Finally, the separation principle also yields simple fixed-point algorithms that allow for the efficient numerical computation of the two optimal precoders.

Compressive information acquisition is a natural approach for low-power hardware front ends, since most natural signals are sparse in some basis. Key design questions include the impact of hardware impairments (e.g., nonlinearities) and constraints (e.g., spatially localized computations) on the fidelity of information acquisition. Our goal in this paper is to obtain specific insights into such issues through modeling of a Large Area Electronics (LAE)-based image acquisition system. We show that compressive information acquisition is robust to stochastic nonlinearities, and that appropriately designed spatially localized computations are effective, by evaluating the performance of reconstruction and classification based on the information acquired.

The scaling of analog-to-digital converter (ADC) power consumption with communication bandwidth imposes severe limits on its precision, which significantly impacts receiver performance. In this paper, we consider a “space-time” generalization of the flash architecture by allowing a fixed number of slicers to be dispersed in time (i.e., sampling offset) as well as space (i.e., amplitude), with the goal of investigating its capabilities for analog-to-information conversion (i.e., enabling reliable recovery of digital information, rather than faithful reproduction of the input signal) in the context of channel equalization for binary signaling over a dispersive channel. We first study standard symbol-spaced ADC with severe quantization constraints, estimating the minimum number of slicers needed to avoid error floors. We observe that the performance is sensitive to channel realization and sampling phase, which motivates a more flexible space-time architecture. Using ideas similar to those underlying compressive sensing, we prove that such architectures have no fundamental limitations in theory: randomly dispersing enough one-bit slicers over space and time does provide information sufficient for reliable equalization. We then focus on practical designs for symbol-spaced and fractionally-spaced sampling subject to a constraint on the number of slicers, and propose an algorithm for optimizing slicer thresholds, which significantly improves performance over a standard design.

We consider the problem of multicasting a common message signal from a distributed array of wireless transceivers by beamforming to a set of beam targets, while simultaneously protecting a set of null targets by nullforming to them. We describe a distributed algorithm in which each transmitter iteratively adapts its complex transmit weight using common aggregate feedback messages broadcast by the targets, and the local knowledge of only its own channel gains to the targets. This knowledge can be obtained using reciprocity without any explicit feedback. The algorithm minimizes the mean square error between the complex signal amplitudes at the targets and their desired values. We prove convergence of the algorithm, present geometric interpretations, characterize initializations that lead to minimum total transmit power, and prescribe designs for such initializations. We show that the convergence speed is nondecreasing in the number of transmitters N if a step size parameter is kept constant. For Rayleigh fading channels, as N goes to infinity: 1) convergence can be made arbitrarily fast and 2) beams and nulls can be achieved with vanishing total transmit power even with noise, both with probability one. These results add up to some remarkable scalability properties: the feedback overhead does not grow with the number of transmitters, and with high probability, the algorithm can be configured to converge arbitrarily fast and use vanishingly small total transmit power.

Most prior research in distributed beamforming involves narrowband, frequency nonselective, channels, with the goal of sending a common message from cooperating nodes so that phases of the signals transmitted from the different nodes align at the receiver. The performance metric is the received SNR (directly related to the Shannon capacity for an AWGN channel). This "coherence metric" is maximized when each transmitter compensates its channel phase to the receiver, while transmitting at maximum allowable power. In this paper, we consider the problem of distributed transmit beam-forming over broadband, frequency selective channels, defining the coherence metric as the Shannon capacity, to be maximized subject to a power constraint at each transmitter. OFDM provides a natural decomposition of such channels into narrowband subchannels, hence the problem reduces to determining how each transmitter allocates its power across subchannels. A key technical result is that the optimal solution obeys a separation property that significantly simplifies computation. We show that it differs from classical water-filling due to the per-transmitter power constraints of the distributed beamforming setting. We compare it both structurally and numerically, to a centralized beamforming system with power constraint across transmitters. This is like waterfilling and upper bounds the performance of our setup.

We propose, analyze, and demonstrate an architecture for scalable cooperative reception. In a cluster of N+1 receive nodes, one node is designated as the final receiver, and the N other nodes act as amplify-and-forward relays, which adapt their phases such that the relayed signals add up constructively at the designated receiver. This yields received SNR scaling linearly with N, while avoiding the linear increase in overhead incurred by a direct approach in which received signals are separately quantized and transmitted for centralized processing. By transforming the task of long-distance distributed receive beamforming into one of local distributed transmit beamforming, we can leverage a scalable one-bit feedback algorithm for phase synchronization. We show that time division between the long-distance and local links eliminates the need for explicit frequency synchronization. We provide an analytical framework, whose results closely match Monte Carlo simulations, to evaluate the impact of phase noise due to relaying delay on the performance of the one-bit feedback algorithm. Experimental results from our prototype implementation on software-defined radios demonstrate the expected gains in received signal strength despite significant oscillator drift, and are consistent with results from our analytical framework.

Millimeter (mm) wave picocellular networks are a promising approach for delivering the 1000-fold capacity increase required to keep up with projected demand for wireless data: the available bandwidth is orders of magnitude larger than that in existing cellular systems, and the small carrier wavelength enables the realization of highly directive antenna arrays in compact form factor, thus drastically increasing spatial reuse. In this paper, we carry out an interference analysis for mm wave picocells in an urban canyon, accounting for the geometry associated with the sparse multipath characteristic of this band. While we make some modeling simplifications, our analysis provides a strong indication of the very large capacity, of the order of Terabits/sec per km, provided by such networks, using system bandwidths of the order of a few GHz.

We investigate distributed beamforming from a cluster of N cooperating transmitters to a distant destination over a wideband dispersive channel. Feedback from the destination is critical for enabling this. In order to develop protocols that scale to arbitrarily large numbers of cooperating nodes, we restrict attention to aggregate feedback broadcast from the destination to the entire transmit cluster, rather than per-transmitter channel feedback as in conventional feedback-based MIMO systems. We first show that naive application of a one-bit feedback algorithm developed for narrowband channels to each subcarrier in an OFDM system does achieve beamforming gain on each subcarrier, but results in an effective channel at the destination with severe phase discontinuities across frequency, which is not amenable to standard receive channel estimation algorithms. We then show that it is possible to enforce smoothness of phase across frequency by augmenting the feedback to 2 bits per subcarrier, which enables modeling and estimation of the effective channel as sparse in the time domain. Our preliminary results show that, even when the SNR per node is well below the threshold for reliable demodulation, it is possible to bootstrap using the N-fold power pooling gain obtained from incoherent combining of the signals from multiple transmitters, and to attain a significant fraction of the N2-fold beamforming gain using the proposed algorithm. We also discuss a number of open issues, recognizing that this is only a first step in developing scalable, wideband, distributed MIMO systems.

We consider analog multiband as a means for scaling up the bandwidth for millimeter (mm) wave communication. Analog multiband sidesteps the difficulty of scaling analog-to-digital conversion (ADC) to higher sampling rates by channelizing the available bandwidth in the analog domain into subbands and using existing power-efficient ADCs to digitize each subband. In this paper, we address the issue of efficient channelization into subbands. A direct approach using a bank of mixers with independent frequency synthesizers has several disadvantages, including large power consumption and the potential for oscillator coupling. We explore an alternative approach based on polyphase sampling, using analog discrete Fourier transform (DFT) along with appropriately designed baseband filters. We quantify the inter-subband interference as a function of filter choice, and demonstrate that it can be handled using interference suppression strategies developed in our prior work. A natural comparison of such approaches, where analog channelization is followed by parallel ADCs running at slower rates for each subband, is with time-interleaved ADCs, which also use polyphase samplers, but use parallel sub-ADCs to digitize the entire band. We show that the proposed approach reduces the required dynamic range of the subband ADCs in comparison to the TI-ADC.

Distributed transmit beamforming with N cooperating nodes, each with fixed transmit power, provides a received power scaling with N2, corresponding to a “power pooling” gain of N and a beamforming gain of N. Prior work has shown that the optimal beamforming solution can be attained using a decentralized, iterative algorithm based on one bit (per iteration) feedback broadcast from the receiver to the transmitters. The algorithm is provably convergent in a noiseless setting, and is the basis for several successful prototypes. In this paper, we develop a framework for providing analytical insight into the effect of receiver noise, with the following key question in mind: can we bootstrap the algorithm from the incoherent power-pooled solution to operate in a regime in which the received SNR per node can be made arbitrarily small as we scale up the number of nodes N? Our analytical computations, validated by simulations, yield a somewhat negative answer: while the power-pooling gain guarantees a linear increase in received power with N, the per-node SNR cannot be scaled down with N if we wish to attain a quadratic increase in received power. Specifically, the fraction of the ideal beamforming gain attained using the one-bit algorithm is asymptotically independent of N, and depends only on the per-node SNR. However, the one-bit algorithm provides significant performance gains in practical regimes with a moderate number of cooperating nodes: the per-node SNR required for attaining a substantial fraction of the beamforming gain is low enough (e.g., - 5dB for 65% of the beamforming gain) to provide significant extension in operation regimes, while providing aggregate SNRs which permit reliable communication at high spectral efficiency: for example, starting from -5 dB per-node SNR, we obtain about 11 dB aggregate SNR with 10 cooperating nodes, and 17 dB SNR with 20 cooperating nodes.

Spectral embedding based on the Singular Value Decomposition (SVD) is a widely used preprocessing step in many learning tasks, typically leading to dimensionality reduction by projecting onto a number of dominant singular vectors and rescaling the coordinate axes (by a predefined function of the singular value). However, the number of such vectors required to capture problem structure grows with problem size, and even partial SVD computation becomes a bottleneck. In this paper, we propose a low-complexity it compressive spectral embedding algorithm, which employs random projections and finite order polynomial expansions to compute approximations to SVD-based embedding. For an m times n matrix with T non-zeros, its time complexity is O((T+m+n)log(m+n)), and the embedding dimension is O(log(m+n)), both of which are independent of the number of singular vectors whose effect we wish to capture. To the best of our knowledge, this is the first work to circumvent this dependence on the number of singular vectors for general SVD-based embeddings. The key to sidestepping the SVD is the observation that, for downstream inference tasks such as clustering and classification, we are only interested in using the resulting embedding to evaluate pairwise similarity metrics derived from the euclidean norm, rather than capturing the effect of the underlying matrix on arbitrary vectors as a partial SVD tries to do. Our numerical results on network datasets demonstrate the efficacy of the proposed method, and motivate further exploration of its application to large-scale inference tasks.

We propose an approach to scaling communication bandwidths over dispersive channels that preserves the advantage of DSP-centric receiver design, while sidestepping the difficulty of scaling analog-to-digital conversion (ADC) to higher and higher bandwidths. This is accomplished by channelizing the available bandwidth into contiguous subbands in the analog domain, with the width of a subband chosen so that digitization is possible at reasonable cost and power using existing ADC technology. We illustrate these ideas for multiGigabit indoor mm-wave communication, with GHz bandwidth divided into subbands of width 250-500 MHz. The channel delay spread even after beamforming can be as large as 20 ns, hence the channel seen within subbands is dispersive. Further, the contiguity of subbands and the sloppy analog channelization implies that adjacent subbands interfere with each other. We show that OFDM within subbands is an attractive approach in these settings: the channel dispersion within subbands can be handled with a moderate cyclic prefix, while the inter-band interference manifests itself only on the edge subcarriers. We clarify the structure of the inter-band interference, and show that it is effectively suppressed by adaptive linear Minimum Mean Squared Error (MMSE) techniques for joint detection across adjacent subbands. Our performance evaluation is carried out using channel models developed for the IEEE 802.1 lad 60 GHz standard.

A probabilistic approach to 3-dimensional mapping is proposed that only uses data gathered by GNSS (Global Navigation Satellite System) devices. To accomplish this, the environment is gridded and a physically motivated sensor model is developed that assigns likelihoods of blockage to satellite signals based on their measured SNR (signal-to-noise ratio). It is then shown that the posterior distribution of the map represents a sparse factor graph on which a low complexity implementation of Loopy Belief Propagation can be used for efficient Bayesian estimation. Experimental results are presented which demonstrate our algorithm's ability to coarsely map in three dimensions a corner of a university campus.

As modern communication transceivers scale to multi-Gbps speeds, the power consumption and cost of highresolution, high-speed analog-to-digital converters (ADCs) become a crucial bottleneck in realizing “mostly digital” receiver architectures that leverage Moore's law. This bottleneck could potentially be alleviated by designing analog front ends for the more specific goal of analog-to-information conversion (i.e., preserving the digital information residing in the received signal). As one possible approach towards this goal, we consider a generalization of the standard flash ADC: instead of implementing n bit quantization of a sample by passing it through 2n-1 slicers as in a standard ADC, the slicers are dispersed in time as well as space (i.e., amplitude). Considering BPSK over a dispersive channel, we first show, using ideas similar to those underlying compressive sensing, that randomly dispersing enough one-bit slicers over space and time does provide information sufficient for reliable demodulation over a dispersive channel. We then propose an iterative algorithm for optimizing the design of the sampling times and amplitude thresholds, and provide numerical results showing that the number of slicers can be significantly reduced relative to a conventional flash ADC with comparable bit error rate (BER). These system-level results motivate further investigation, in terms of both circuit and system design, into looking beyond conventional ADC architectures when designing analog front-ends for high-speed communication.

A novel approach is proposed to achieve simultaneous localization and mapping (SLAM) based on the signal-to-noise ratio (SNR) of global navigation satellite system (GNSS) signals. It is assumed that the environment is unknown and that the receiver location measurements (provided by a GNSS receiver) are noisy. The 3D environment map is decomposed into a grid of binary-state cells (occupancy grid) and the receiver locations are approximated by sets of particles. Using a large number of sparsely sampled GNSS SNR measurements and receiver/satellite coordinates (all available from off-the-shelf GNSS receivers), likelihoods of blockage are associated with every receiver-to-satellite beam. The posterior distribution of the map and poses is shown to represent a factor graph, on which Loopy Belief Propagation is used to efficiently estimate the probabilities of each cell being occupied or empty, along with the probability of the particles for each receiver location. Experimental results demonstrate our algorithm's ability to coarsely map (in three dimensions) a corner of a university campus, while also correcting for uncertainties in the location of the GNSS receiver.

Due to frequent non-line-of-sight (NLOS) signal reception, geopositioning using Global Navigation Satellite Systems (GNSS), such as GPS, is unreliable in urban environments, with errors on the order of tens of meters. This poses a major problem for mobile services that benefit from accurate urban localization, such as navigation, geofencing, and hyperlocal advertising applications. Mobile network operators also seek improvements in localization, as government regulators increase handset location accuracy requirements of enhanced 991 service (e911). In our demonstration, we will present the most recent prototype of our urban location improvement technology, called ShadowMaps, which will be shown to accurately track a mobile device in an urban environment, with up to an order of magnitude reduction in GNSS positioning error.

Mobile network traffic is set to explode in our near future, driven by the growth of bandwidth-hungry media applications. Current capacity solutions, including buying spectrum, WiFi offloading, and LTE picocells, are unlikely to supply the orders-of-magnitude bandwidth increase we need. In this paper, we explore a dramatically different alternative in the form of 60GHz mmwave picocells with highly directional links. While industry is investigating other mmwave bands (e.g. 28GHz to avoid oxygen absorption), we prefer the unlicensed 60GHz band with highly directional, short-range links (~100m). 60GHz links truly reap the spatial reuse benefits of small cells while delivering high per-user data rates and leveraging efforts on indoor 60GHz PHY technology and standards. Using extensive measurements on off-the-shelf 60GHz radios and system-level simulations, we explore the feasibility of 60GHz picocells by characterizing range, attenuation due to reflections, sensitivity to movement and blockage, and interference in typical urban environments. Our results dispel some common myths, and show that there are no fundamental physical barriers to high-capacity 60GHz outdoor picocells. We conclude by identifying open challenges and associated research opportunities.

This paper considers the problem of jointly processing messages received over a forward link from a single distant transmitter to a cooperative receive cluster connected by a local area network with finite available throughput. For N cooperating receivers, ideal distributed receive beamforming with direct exchange of unquantized observations leads to an N-fold gain in signal-to-noise ratio (SNR) for equal-gain additive white Gaussian noise channels, with significant additional gains over fading channels due to diversity. It is shown in this paper that a significant portion of these gains can be obtained simply by exchanging hard decisions among some or all of the nodes in the receive cluster. Mutual information computations and simulations of LDPC-coded systems show that optimal combining of hard decisions tends to perform within 0.5-2 dB of ideal receive beamforming. For the low per-node SNR regime of interest with large receive clusters, asymptotic analysis of a suboptimal combining technique termed "pseudo-beamforming" shows that distributed reception with hard decision exchanges performs within 1-2 dB of ideal receive beamforming.

The orders of magnitude increase in projected demand for wireless cellular data require drastic increases in spatial reuse, with picocells with diameters of the order of 100-200 m supplementing existing macrocells whose diameters are of the order of kilometers. In this paper, we observe that the nature of interference changes fundamentally as we shrink cell size, with near line of sight interference from neighboring picocells seeing significantly smaller path loss exponents than interference in macrocellular environments. Using a propagation model proposed by Franceschetti, which compactly models increased interference in small cells, we show that the network capacity does not scale linearly with the reduction in cell size with standard frequency reuse strategies. Rather, more sophisticated resource sharing strategies based on beamforming and base station cooperation are required to realize the potential of small cells in providing high spectral efficiencies and quasi-deterministic guarantees on availability. Numerical results justifying these conclusions include Chernoff bounds on outage probability for random base station deployment (according to a spatial Poisson process), and simulations for deployment in a regular grid.

We propose and demonstrate a scalable architecture for distributed receive beamforming. In a receive cluster of N + 1 nodes receiving a message from a distant transmitter, N nodes are designated as amplify-and-forward relays and one node is designated as the receiver. The relay nodes apply a phase shift to their received signal and forward it such that their forwarded signals add up constructively at the receiver, with received SNR scaling linearly with N. This approach transforms a distributed receive beamforming problem on the "long link" from transmitter to receive cluster into a distributed transmit beamforming problem on the "short link" from relays to receiver, so that the number of degrees of freedom on the short link need not scale with N. A key simplification relative to distributed transmit beamforming is that, for stable oscillators, relay frequency synchronization is not required. For oscillators with drift, we provide a simple rule of thumb for when explicit frequency synchronization can be avoided. Explicit timing alignment can also be avoided by exploiting the timing of the message received on the long link. That leaves the problem of phase adjustment at the relays, and we employ an algorithm originally invented for distributed transmit beamforming for this purpose, using one bit (per iteration) feedback from the receiver. Experimental results with software-defined radios (whose oscillators have significant drift) demonstrate that the expected gains in received signal strength are obtained with the proposed architecture.

Accurate channel state information (CSI) is essential for attaining beamforming gains in single-user (SU) multiple-input multiple-output (MIMO) and multiplexing gains in multi-user (MU) MIMO wireless communication systems. State-of-the-art limited feedback schemes, which rely on pre-defined codebooks for channel quantization, are only appropriate for a small number of transmit antennas and low feedback overhead. In order to scale informed transmitter schemes to emerging massive MIMO systems with a large number of transmit antennas at the base station, one common approach is to employ time division duplexing (TDD) and to exploit the implicit feedback obtained from channel reciprocity. However, most existing cellular deployments are based on frequency division duplexing (FDD), hence it is of great interest to explore backwards compatible massive MIMO upgrades of such systems. For a fixed feedback rate per antenna, the number of codewords for quantizing the channel grows exponentially with the number of antennas, hence generating feedback based on look-up from a standard vector quantized codebook does not scale. In this paper, we propose noncoherent trellis-coded quantization (NTCQ), whose encoding complexity scales linearly with the number of antennas. The approach exploits the duality between source encoding in a Grassmannian manifold (for finding a vector in the codebook which maximizes beamforming gain) and noncoherent sequence detection (for maximum likelihood decoding subject to uncertainty in the channel gain). Furthermore, since noncoherent detection can be realized near-optimally using a bank of coherent detectors, we obtain a low-complexity implementation of NTCQ encoding using an off-the-shelf Viterbi algorithm applied to standard trellis coded quantization. We also develop advanced NTCQ schemes which utilize various channel properties such as temporal/spatial correlations. Monte Carlo simulation results show the proposed NTCQ and its extensions can achieve near-optimal performance with moderate complexity and feedback overhead.

Analog-to-digital conversion (ADC) is a key bottleneck in scaling DSP-centric receiver architectures to multiGigabit/s speeds. Recent information-theoretic results, obtained under ideal channel conditions (perfect synchronization, no dispersion), indicate that low-precision ADC (1-4 bits) could be a suitable choice for designing such high speed systems. In this work, we study the impact of employing low-precision ADC in a carrier asynchronous system. Specifically, we consider transmission over the block noncoherent additive white Gaussian noise channel, and investigate the achievable performance under low-precision output quantization. We focus attention on an architecture in which the receiver quantizes only the phase of the received signal: this has the advantage of being implementable without automatic gain control, using multiple 1-bit ADCs preceded by analog multipliers. For standard uniform Phase Shift Keying (PSK) modulation, we study the structure of the transition density of the phase-quantized block noncoherent channel. Several results, based on the symmetry inherent in the channel model, are provided to characterize this transition density. Low-complexity procedures for computing the channel information rate, and for block demodulation, are obtained using these results. Numerical computations are performed to compare the performance of quantized and unquantized systems, for different quantization precisions, and different block lengths. With QPSK modulation, it is observed, for example, that for SNR larger than 2-3 dB, 8-bin phase quantization of the received signal recovers about 80-85% of the mutual information attained with unquantized observations, while 12-bin phase quantization recovers more than 90% of the unquantized mutual information. Dithering the constellation is shown to improve the performance in the face of drastic quantization.

We propose and demonstrate a novel architecture for on-the-fly inference while collecting data from sparse sensor networks. In particular, we consider source localization using acoustic sensors dispersed over a large area, with the individual sensors located too far apart for direct connectivity. An Unmanned Aerial Vehicle (UAV) is employed for collecting sensor data, with the UAV route adaptively adjusted based on data from sensors already visited, in order to minimize the time to localize events of interest. The UAV therefore acts as a information-seeking data mule, not only providing connectivity, but also making Bayesian inferences from the data gathered in order to guide its future actions. The system we demonstrate has a modular architecture, comprising efficient algorithms for acoustic signal processing, routing the UAV to the sensors, and source localization. We report on extensive field tests which not only demonstrate the effectiveness of our general approach, but also yield specific practical insights into GPS time synchronization and localization accuracy, acoustic signal and channel characteristics, and the effects of environmental phenomena.

We describe a fully-wireless prototype of distributed transmit beamforming on a software-defined radio platform. Distributed beamforming is a cooperative transmission technique that can achieve orders of magnitude increases in range or energy efficiency of wireless communication systems. However, this technique requires precise synchronization of the radio frequency signal from each transmitter. The significance of our prototype is in demonstrating that this requirement can be satisfied using digital signal processing methods on commodity hardware with low-quality oscillators. Our synchronization approach scales to large numbers of transmitters: each transmitter runs independent algorithms based on periodically transmitted feedback packets from the receiver. A key simplification is the decoupling of the algorithms for frequency locking and beamsteering at each transmitter, even though both processes use the same feedback packets. Frequency locking employs an Extended Kalman filter to track the local oscillator offset between a transmitter and the receiver, using frequency offset measurements based on the feedback packet it waveform, while the phase adjustments for beamsteering are determined using a one-bit feedback algorithm based on the feedback packet it payload. Our prototype demonstrates that distributed transmit beamforming can be incorporated into wireless networks without requiring hardware innovations, and provides open-source building blocks for future research and development.

We present an implementation of distributed transmit beamforming using software-defined radios. The transmit nodes synchronize in carrier frequency using a pilot signal sent by a master node, and employ feedback from the receiver to adjust their carrier phases so as to add up coherently at the receiver. Our implementation advances the state of the art for all-wireless distributed beamforming in two important ways. The first is the implementation of extended Kalman filters for frequency synchronization at the slave nodes, which is shown to be effective despite the high local oscillator (LO) offsets typical of software-defined radios, and the low duty cycle of the pilot transmitted by the master node. The second advance is the implementation of the well-known one bit feedback scheme for phase adjustment using digital feedback from the receiver. We present experimental results that show the efficacy of our implementation for frequency synchronization and beamforming.

Commercial exploitation of the large amounts of unlicensed spectrum available at 60 GHz requires that we take advantage of the low-cost digital signal processing (DSP) made available by Moore's law. A key bottleneck, however, is the cost and power consumption of high-precision analog-to-digital converters (ADCs) at the multiGigabit rates of interest in this band. This makes it difficult, for example, to apply traditional DSP-based approaches to channel dispersion compensation such as time domain equalization or Orthogonal Frequency Division Multiplexing (OFDM), since these are predicated on the availability of full-rate, high-precision samples. In this paper, we investigate the use of analog multitone for sidestepping the ADC bottleneck: transmissions are split into a number of subbands, each of which can be separately sampled at the receiver using a lower rate ADC. For efficient use of spectrum, we do not allow guard bands between adjacent subbands, hence the receiver signal processing must account for intercarrier interference (ICI) across subbands as well as intersymbol interference (ISI) within a subband due to channel dispersion. We illustrate our ideas for short-range (100-200 meters), highly directional, outdoor 60 GHz links, as might be employed for wireless backhaul. Given the large coherence bandwidth of the sparse multipath channels typical of such links that we consider, reliable performance requires spatial diversity, in addition to the beamforming required to close the link. We therefore consider one transmit and two receive antenna arrays, each with 4 × 4 elements. We investigate linear equalization strategies corresponding to different combinations of: (a) combining samples from both arrays/choosing the stronger array and (b) equalizing the subbands independently/jointly. We find that exploiting the spatial diversity completely by combining samples from both arrays is critical for combating fading and inter carrier interference.

In this paper, we propose an algorithm for timing synchronization that attains fundamental bounds derived by Weiss and Weinstein. These bounds state that, in addition to improving with time-bandwidth product and signal-to-noise ratio (SNR), timing accuracy also improves as the carrier frequency gets larger, if the SNR is above a threshold. Our algorithm essentially follows the logic of the Weiss-Weinstein bound, and has the following stages: coarse estimation using time domain samples, fine-grained estimation using a Newton algorithm in the frequency domain, and final refinement to within a small fraction of a carrier cycle. While the results here are of fundamental interest, we are motivated to push the limits of synchronization to enable the tight coordination required for emulating virtual antenna arrays using a collection of cooperating nodes.

A distributed transmit beamforming technique is described for a scenario with two or more transmit nodes and one intended receiver. The protocol includes a measurement epoch, feedback from the intended receiver to the transmit nodes, and a beamforming epoch. The intended receiver tracks the clock and kinematic parameters of the independent transmit nodes and coordinates the transmit nodes by feeding back state predictions which are then used as phase corrections to facilitate passband phase and frequency alignment at the receiver. A three-state dynamic model is developed to describe the stochastic kinematics and clock evolution of each transmit node relative to the frame of the receiver/coordinator. Steady-state analysis techniques are used to analytically predict the tracking performance as well as the beamforming gain as a function of the system parameters. Numerical results show that near-ideal beamforming performance can be achieved if the period between successive observations at the receiver/coordinator is sufficiently small.

We begin by asking whether geographic routing can scale when nodes are mobile; that is, can the overhead involved in tracking node locations be accommodated within the transport capacity of large-scale mobile ad hoc networks (MANETs)? We answer this question in the affirmative by proposing an efficient position publish protocol which fits within the transport capacity and a routing protocol that operates with imperfect information of the destination's location. The routing protocol guarantees, with high probability, routes whose lengths are within a constant “stretch” factor of the shortest path from source to destination. The key idea underlying the scalability of the publish protocol is for each potential destination node to send location updates (with frequency decaying with distance) only to a subset of network nodes, structured as annular regions around it (the natural approach of updating circular regions in distance-dependent fashion does not scale). The routing protocol must then account for the fact that the source and/or relay nodes may not have estimates of the destination's location (or may have stale estimates). Spatial and temporal scaling of protocol parameters are chosen so as to guarantee scalability, route reliability and route stretch.

We present a fully wireless implementation of distributed transmit beamforming using software-defined radios. Distributed beamforming is a cooperative transmission scheme whereby a number of nodes in a wireless network organize themselves into a virtual antenna array and focus their transmission in the direction of the intended receiver, potentially achieving order of magnitude improvements in energy efficiency. The main technical challenge in realizing these gains is in precisely synchronizing the radio frequency signals of the cooperating nodes. This idea has been studied extensively over the past decade, and several techniques and architectures for its practical implementation have been developed. In this work, we demonstrate our recent implementation of distributed beamforming on a standard, open-source, software-defined radio platform, where the low quality oscillators make synchronization particularly challenging. Our demonstration will consist of three cooperating transmitters sending signals that add up constructively at the receiver. Low-rate feedback packets broadcast from the receiver are employed for frequency and phase synchronization at each transmitter in completely distributed fashion.

A fundamental question in multihop wireless network protocol design is how to partition the network's transport capacity among contending flows. A classically "fair” allocation leads to poor throughput performance for all flows because connections that traverse a large number of hops (i.e., long connections) consume a disproportionate share of resources. However, naïvely biasing against longer connections can lead to poor network utilization, because a significantly high fraction of total connections are long in large networks with spatially uniform traffic. While proportional fair allocation provides a significant improvement, we show here that there is a much richer space of resource allocation strategies for introducing a controlled bias against resource-intensive long connections in order to significantly improve the performance of shorter connections. Specifically, mixing strongly biased allocations with fairer allocations leads to efficient network utilization as well as a superior trade-off between flow throughput and fairness. We present an analytical model that offers insight into the impact of a particular resource allocation strategy on network performance, taking into account finite network size and spatial traffic patterns. We point to protocol design options to implement our resource allocation strategies by invoking the connection with the well-studied network utility maximization framework. Our simulation evaluation serves to verify the analytical design prescriptions.

We investigate spatial interference statistics for multigigabit outdoor mesh networks operating in the unlicensed 60-GHz “millimeter (mm) wave” band. The links in such networks are highly directional: Because of the small carrier wavelength (an order of magnitude smaller than those for existing cellular and wireless local area networks), narrow beams are essential for overcoming higher path loss and can be implemented using compact electronically steerable antenna arrays. Directionality drastically reduces interference, but it also leads to “deafness,” making implicit coordination using carrier sense infeasible. In this paper, we make a quantitative case for rethinking medium access control (MAC) design in such settings. Unlike existing MAC protocols for omnidirectional networks, where the focus is on interference management, we contend that MAC design for 60-GHz mesh networks can essentially ignore interference and must focus instead on the challenge of scheduling half-duplex transmissions with deaf neighbors. Our main contribution is an analytical framework for estimating the collision probability in such networks as a function of the antenna patterns and the density of simultaneously transmitting nodes. The numerical results from our interference analysis show that highly directional links can indeed be modeled as pseudowired, in that the collision probability is small even with a significant density of transmitters. Furthermore, simulation of a rudimentary directional slotted Aloha protocol shows that packet losses due to failed coordination are an order of magnitude higher than those due to collisions, confirming our analytical results and highlighting the need for more sophisticated coordination mechanisms.

Recent work has shown that, despite the minimal information provided by a binary proximity sensor, a network of these sensors can provide remarkably good target tracking performance. In this article, we examine the performance of such a sensor network for tracking multiple targets. We begin with geometric arguments that address the problem of counting the number of distinct targets, given a snapshot of the sensor readings. We provide necessary and sufficient criteria for an accurate target count in a one-dimensional setting, and provide a greedy algorithm that determines the minimum number of targets that is consistent with the sensor readings. While these combinatorial arguments bring out the difficulty of target counting based on sensor readings at a given time, they leave open the possibility of accurate counting and tracking by exploiting the evolution of the sensor readings over time. To this end, we develop a particle filtering algorithm based on a cost function that penalizes changes in velocity. An extensive set of simulations, as well as experiments with passive infrared sensors, are reported. We conclude that, despite the combinatorial complexity of target counting, probabilistic approaches based on fairly generic models of trajectories yield respectable tracking performance.

In this paper, we investigate spatial multiplexing at millimeter (mm) wave carrier frequencies for short-range indoor applications by quantifying fundamental limits in line-of-sight (LOS) environments and then investigating performance in the presence of multipath and LOS blockage. Our contributions are summarized as follows. For linear arrays with constrained form factor, an asymptotic analysis based on the properties of prolate spheroidal wave functions shows that a sparse array producing a spatially uncorrelated channel matrix effectively provides the maximum number of spatial degrees of freedom in a LOS environment, although substantial beamforming gains can be obtained by using denser arrays. This motivates our proposed mm-wave MIMO architecture, which utilizes arrays of subarrays to provide both directivity and spatial multiplexing gains. System performance is evaluated in a simulated indoor environment using a ray-tracing model that incorporates multipath effects and potential LOS blockage. Eigenmode transmission with waterfilling power allocation serves as a performance benchmark, and is compared to the simpler scheme of beamsteering transmission with MMSE reception and a fixed signal constellation. Our numerical results provide insight into the spatial variations of attainable capacity within a room, and the combinations of beamsteering and spatial multiplexing used in different scenarios.

As the date rates and bandwidths of communication systems scale up, the cost and power consumption of high-precision (e.g., 8-12 bits) analog-to- digital converters (ADCs) become prohibitive. One possible approach to relieve this bottleneck is to redesign communication systems with the starting assumption that the receiver employs ADCs with drastically reduced precision (e.g., 1-4 bits). Encouraging results from information- theoretic analysis in idealized settings prompt a detailed investigation of receiver signal processing algorithms when ADC precision is reduced. In this paper, we investigate the problem of automatic gain control (AGC) for pulse amplitude modulation (PAM) signaling over the AWGN channel, with the goal being to align the ADC thresholds with the maximum likelihood (ML) decision regions. The approach is to apply a variable gain to the ADC input, fixing the ADC thresholds, with the gain being determined by estimating the signal amplitude from the quantized ADC output. We consider a blind approach in which the ML estimate for the signal amplitude is obtained based on the quantized samples corresponding to an unknown symbol sequence. We obtain good performance, in terms of both channel capacity and uncoded bit error rate, at low to moderate SNR, but the performance can actually degrade as SNR increases due to the increased sensitivity of the ML estimator in this regime. However, we demonstrate that the addition of a random Gaussian dither, with power optimized to minimize the normalized mean squared error of the ML estimate, yields performance close to that of ideal AGC over the entire range of SNR of interest.

We consider the problem of estimating the impulse response of a dispersive channel when the channel output is sampled using a low-precision analog-to-digital converter (ADC). While traditional channel estimation techniques require about 6 bits of ADC precision to approach full-precision performance, we are motivated by applications to multiGigabit communication, where we may be forced to use much lower precision (e.g., 1-3 bits) due to considerations of cost, power, and technological feasibility. We show that, even with such low ADC precision, it is possible to attain near full-precision performance using closed-loop estimation, where the ADC input is dithered and scaled. The dither signal is obtained using linear feedback based on the Minimum Mean Squared Error (MMSE) criterion. The dither feedback coefficients and the scaling gains are computed offline using Monte Carlo simulations based on a statistical model for the channel taps, and are found to work well over wide range of channel variations.

We propose and investigate a deterministic traveling wave model for the progress of epidemic routing in disconnected mobile ad hoc networks. In epidemic routing, broadcast or unicast is achieved by exploiting mobility: message-carrying nodes "infect" non message-carrying nodes when they come within communication range of them. Early probabilistic analyses of epidemic routing follow a "well-mixed" model which ignores the spatial distribution of the infected nodes, and hence do not provide good performance estimates unless the node density is very low. More recent work has pointed out that the infection exhibits wave-like characteristics, but does not provide a detailed model of the wave propagation. In this paper, we model message propagation using a reaction-diffusion partial differential equation that has a traveling wave solution, and show that the performance predictions made by the model closely match simulations in regimes where the well- mixed model breaks down. In particular, we show that well-mixed models are generally overly optimistic in regard to the scaling of the message delivery delay with problem parameters such as communication range, node density, and total area. In contrast to prior work, our model provides insight into the spatial distribution of the "infection," and reveals that the performance is sensitive to the geometry of the deployment region, not just its area.

Multi-gigabit outdoor mesh networks operating in the unlicensed 60 GHz "millimeter (mm) wave" band, offer the possibility of a quickly deployable broadband extension of the Internet. We consider mesh nodes with electronically steerable antenna arrays, with both the transmitter and receiver synthesizing narrow beams that compensate for the higher path loss at mm-wave frequencies, achieving ranges on the order of 100 meters using the relatively low transmit powers attainable with low-cost silicon implementations. Such highly directional networking differs from WiFi networks at lower carrier frequencies in two ways that have a crucial impact on protocol design: (1) directionality drastically reduces spatial interference, so that pseudowired link abstractions form an excellent basis for protocol design; (2) directionality induces deafness, which makes medium access control (MAC) based on carrier sensing infeasible. Interference analysis in our prior work shows that, in such a setting, coordination between transmitters and receivers, rather than interference management, becomes the key MAC performance bottleneck. However, the question of whether such coordination can be achieved in a distributed fashion while achieving high medium utilization, was left open. In this paper, we answer this question in the affirmative, presenting a distributed MAC protocol that employs memory to achieve approximate time division multiplexed (TDM) schedules without explicit coordination or resource allocation. The efficacy of the protocol is demonstrated via packet level simulations, while a Markov chain fixed-point analysis provides insight into the effect of parameter choices.

Realization of all-digital baseband receiver processing for multi-Gigabit communication requires analog-to-digital converters (ADCs) of sufficient rate and output resolution. A promising architecture for this purpose is the time-interleaved ADC (TI-ADC), in which several "sub-ADCs" are employed in parallel. However, the timing mismatch between the sub-ADCs, if left uncompensated, leads to error floors in receiver performance. Standard linear digital mismatch compensation (e.g., based on the zero-forcing criterion) requires a number of taps that increases with the desired resolution. In this paper, we show that oversampling provides a scalable (in the number of sub-ADCs and in the desired resolution) approach to mismatch compensation, allowing elimination of mismatch-induced error floors at reasonable complexity. While the structure of the interference due to mismatch is different from that due to a dispersive channel, there is a strong analogy between the role of oversampling for mismatch compensation and for channel equalization. We illustrate the efficacy of the proposed mismatch compensation techniques for an OFDM receiver.

Recent work has shown that mesh networks based on short-range outdoor millimeter (mm) wave links in the unlicensed 60 GHz band are a promising approach to providing an easily deployable broadband infrastructure. In this paper, we investigate the robustness of such links, focusing in particular on the effect of multipath fading resulting from reflections from the ground and building walls for a lamppost deployment of mm wave nodes. Our ray tracing based model shows that, while only a small number of paths are significant for the highly directional links considered, they can cause significant fluctuations in the received signal strength. Our simulations show that 10-20 dB fades below the benchmark of free space propagation can occur quite easily (e.g., 5-15% of the time, averaging across typical deployment scenarios), and that the received power is extremely sensitive to small variations in geometry (e.g., altering the position of the antenna by 1 cm can reduce the received power as much as 46.7 dB). We also demonstrate, however, that extremely robust performance can be obtained by employing multiple antennas at appropriately chosen separations, using standard space-time communications strategies such as transmit precoding (when the transmitter knows the channel) and space-time coding (when the transmitter does not know the channel).

Analog-to-digital converters (ADCs) with high sampling rates and output resolution are required for the design of mostly digital transceivers in emerging multi-Gigabit communication systems. A promising approach is to use a time-interleaved (TI) architecture with slower sub-ADCs in parallel, but mismatch among the sub-ADCs, if left uncompensated, can cause error floors in receiver performance. Conventional mismatch compensation schemes typically have complexity (in terms of number of multiplications) that increases with the desired resolution at the output of the TI-ADC. In this paper, we investigate an alternative approach, in which mismatch and channel dispersion are compensated jointly, with the performance metric being overall link reliability rather than ADC performance. For an OFDM system, we characterize the structure of mismatch-induced interference, and demonstrate the efficacy of a frequency-domain interference suppression scheme whose complexity is independent of constellation size (which determines the desired resolution). Numerical results from computer simulation and from experiments on a hardware prototype show that the performance with the proposed joint mismatch and channel compensation technique is close to that without mismatch. While the proposed technique works with offline estimates of mismatch parameters, we provide an iterative, online method for joint estimation of mismatch and channel parameters which leverages the training overhead already available in communication signals.

In matrix embedding (ME)-based steganography, the host coefficients are minimally perturbed such that the transmitted bits fall in a coset of a linear code, with the syndrome conveying the hidden bits. The corresponding embedding distortion and vulnerability to steganalysis are significantly less than that of conventional quantization index modulation (QIM)-based hiding. However, ME is less robust to attacks, with a single host bit error leading to multiple decoding errors for the hidden bits. In this paper, we employ the ME-RA scheme, a combination of ME-based hiding with powerful repeat accumulate (RA) codes for error correction, to address this problem. A key contribution of this paper is to compute log likelihood ratios for RA decoding, taking into account the many-to-one mapping between the host coefficients and an encoded bit, for ME. To reduce detectability, we hide in randomized blocks, as in the recently proposed Yet Another Steganographic Scheme (YASS), replacing the QIM-based embedding in YASS by the proposed ME-RA scheme. We also show that the embedding performance can be improved by employing punctured RA codes. Through experiments based on a couple of thousand images, we show that for the same embedded data rate and a moderate attack level, the proposed ME-based method results in a lower detection rate than that obtained for QIM-based YASS.

The concept of distributed transmit beamforming is implicit in many key results of network information theory. However, its implementation in a wireless network involves the fundamental challenge of ensuring phase coherence of the radio frequency signals from the different transmitters in the presence of unknown phase offsets between the transmitters and unknown channel gains from the transmitters to the receiver. In this paper, it is shown that such phase alignment can be achieved using distributed adaptation by the transmitters with minimal feedback from the receiver. Specifically, each transmitter independently makes a small random adjustment to its phase at each iteration, while the receiver broadcasts a single bit of feedback, indicating whether the signal-to-noise ratio (SNR) improved or worsened after the current iteration. The transmitters keep the ¿good¿ phase adjustments and discard the ¿bad¿ ones, thus implementing a distributed ascent algorithm. It is shown that, for a broad class of distributions for the random phase adjustments, this procedure leads to asymptotic phase coherence with probability one. A simple analytical model, borrowing ideas from statistical mechanics, is used to characterize the progress of the algorithm, and to provide guidance on parameter choices. This analytical model is based on a conjecture on the distribution of the received phases when the number of transmitters becomes large. Finally, the proposed system is shown to be scalable: the random phase perturbations can be chosen such that the convergence time is linear in the number of collaborating nodes.

Motivated by the paradigm of event-based monitoring, which can potentially alleviate the inherent bandwidth and energy constraints associated with wireless sensor networks, we consider the problem of joint coding of correlated sources under a cost criterion that is appropriately conditioned on event occurrences. The underlying premise is that individual sensors only have access to partial information and, in general, cannot reliably detect events. Hence, sensors optimally compress and transmit the data to a fusion center, so as to minimize the expected distortion in segments containing events. In this work, we derive and demonstrate the approach in the setting of entropy constrained distributed vector quantizer design,using a modified distortion criterion that appropriately accounts for the joint statistics of the events and the observation data. Simulation results show significant gains over conventional design as well as existing heuristic based methods, and provide experimental evidence to support the promise of our approach.

Spatial multiplexing for millimeter (mm) wave line of sight (LOS) links potentially enables data rates of the order of 10-100 Gbps. Most prior work in this area has focused on uniform transmit and receive arrays, for which it is known that the spatial responses seen by different transmitters can be made orthogonal by choosing the antenna spacing appropriately as a function of range and wavelength. In this paper, we show that variations in range can cause significant degradation in performance for such uniformly spaced arrays optimized for a given range, due to the appearance of high correlations between the spatial responses for different transmitters (and hence rank deficiency in the MIMO channel matrix) as a function of range. We then demonstrate that optimized nonuniform arrays alleviate this problem by keeping correlations between spatial responses small over a significantly larger set of ranges than is possible with uniform spacing.

Digital transmission of uncompressed high-definition video is challenging because of its high data rate and its extreme sensitivity to bit errors. In this paper we propose a simple error correction scheme to reduce the bit error effects in the video at the receiver end of a wireless channel. Our scheme uses the large amount of spatial redundancy already present in uncompressed HD video data to provide an extra layer of protection in addition to that provided by channel coding. Thus, our method requires no change to the video signal being transmitted and is compatible with any existing solution for HD video transmission. Using simulations over a range of byte error rates, we show that our scheme effectively reduces the number of visible artifacts because of uncorrected channel errors, and provides approximately 7 dB improvement in peak signal to noise ratio.

We investigate an architecture for multi-Gigabit outdoor mesh networks operating in the unlicensed 60 GHz "millimeter (mm) wave" band. In this band, the use of narrow beams is essential for attaining the required link ranges in order to overcome the higher path loss at mm wave carrier frequencies. However, highly directional links make standard MAC methods for interference management, such as carrier sense multiple access, which rely on neighboring nodes hearing each other, become inapplicable. In this paper, we study the extent to which we can reduce, or even dispense with, interference management, by exploiting the reduction in interference due to the narrow beamwidths and the oxygen absorption characteristic of the 60 GHz band. We provide a probabilistic analysis of the interference incurred due to uncoordinated transmissions, and show that, for the parameters considered, the links in the network can be thought of as pseudo-wired. That is, interference can essentially be ignored in MAC design, and the challenge is to schedule half-duplex transmissions in the face of the "deafness" resulting from highly directional links. We provide preliminary simulation results to validate our approach.

We consider communication over the block noncoherent AWGN channel with low-precision Analog-to-Digital Converters (ADCs) at the receiver. For standard uniform Phase Shift Keying (PSK) modulation, we investigate the performance of a receiver architecture that quantizesonlythephaseof the received signal; this has the advantage of beingimplementablewithoutautomaticgaincontrol, using multiple 1-bit ADCs preceded by analog multipliers. We study the structure of the transition density of the resulting channel model. Several results, based on the symmetry inherent in the channel, are provided to characterize this transition density. A low complexity procedure for computing the channel capacity is obtained using these results. Numerical capacity computations for QPSK show that 8-bin phase quantization of the received signal recovers more than 80-85% of the capacity attained with unquantized observations, while 12-bin phase quantization recovers above 90-95% of the unquantized capacity. Dithering the constellation is shown to improve the performance in the face of drastic quantization.

We present a cross-layer modeling and design approach for multiGigabit indoor wireless personal area networks (WPANs) utilizing the unlicensed millimeter (mm) wave spectrum in the 60 GHz band. Our approach accounts for the following two characteristics that sharply distinguish mm wave networking from that at lower carrier frequencies. First, mm wave links are inherently directional: directivity is required to overcome the higher path loss at smaller wavelengths, and it is feasible with compact, low-cost circuit board antenna arrays. Second, indoor mm wave links are highly susceptible to blockage because of the limited ability to diffract around obstacles such as the human body and furniture. We develop a diffraction-based model to determine network link connectivity as a function of the locations of stationary and moving obstacles. For a centralized WPAN controlled by an access point, it is shown that multihop communication, with the introduction of a small number of relay nodes, is effective in maintaining network connectivity in scenarios where single-hop communication would suffer unacceptable outages. The proposed multihop MAC protocol accounts for the fact that every link in the WPAN is highly directional, and is shown, using packet level simulations, to maintain high network utilization with low overhead.

As communication systems scale up in speed and bandwidth, the cost and power consumption of high-precision (e.g., 8-12 bits) analog-to-digital conversion (ADC) becomes the limiting factor in modern transceiver architectures based on digital signal processing. In this work, we explore the impact of lowering the precision of the ADC on the performance of the communication link. Specifically, we evaluate the communication limits imposed by low-precision ADC (e.g., 1-3 bits) for transmission over the real discrete-time additive white Gaussian noise (AWGN) channel, under an average power constraint on the input. For an ADC with K quantization bins (i.e., a precision of log2K bits), we show that the input distribution need not have any more than K+1 mass points to achieve the channel capacity. For 2-bin (1-bit) symmetric quantization, this result is tightened to show that binary antipodal signaling is optimum for any signal-to- noise ratio (SNR). For multi-bit quantization, a dual formulation of the channel capacity problem is used to obtain tight upper bounds on the capacity. The cutting-plane algorithm is employed to compute the capacity numerically, and the results obtained are used to make the following encouraging observations : (a) up to a moderately high SNR of 20 dB, 2-3 bit quantization results in only 10-20% reduction of spectral efficiency compared to unquantized observations, (b) standard equiprobable pulse amplitude modulated input with quantizer thresholds set to implement maximum likelihood hard decisions is asymptotically optimum at high SNR, and works well at low to moderate SNRs as well.

We explore fundamental performance limits of tracking a target in a two-dimensional field of binary proximity sensors, and design algorithms that attain those limits while providing minimal descriptions of the estimated target trajectory. Using geometric and probabilistic analysis of an idealized model, we prove that the achievable spatial resolution in localizing a target's trajectory is of the order of 1/ρ R , where R is the sensing radius and ρ is the sensor density per unit area. We provide a geometric algorithm for computing an economical (in descriptive complexity) piecewise linear path that approximates the trajectory within this fundamental limit of accuracy. We employ analogies between binary sensing and sampling theory to contend that only a “lowpass” approximation of the trajectory is attainable, and explore the implications of this observation for estimating the target's velocity. We also consider nonideal sensing, employing particle filters to average over noisy sensor observations, and geometric geometric postprocessing of the particle filter output to provide an economical piecewise linear description of the trajectory. In addition to simulation results validating our approaches for both idealized and nonideal sensing, we report on lab-scale experiments using motes with acoustic sensors.

Time-interleaved (TI) analog-to-digital converters (ADCs) are a promising architecture for realizing the highspeed ADCs required to implement "mostly digital" receivers for emerging multiGigabit communication systems. Mismatch between the parallel ADCs comprising a TI-ADC is a fundamental performance bottleneck. While there exist mismatch correction techniques for generic applications of ADC, we illustrate in this paper that mismatch compensation can be subsumed within the overall receiver design for communication applications. We consider an orthogonal frequency division multiplexing (OFDM) link with TI-ADC used after downconversion. We show that mismatch results in frequency selective interference across subcarriers that can significantly degrade the performance of a standard OFDM receiver. However, this performance degradation can be alleviated significantly by joint channel and mismatch estimation and compensation, leveraging already available training or pilot sequences. This eliminates the necessity for dedicated hardware for mismatch correction. Specifically, we present an algorithm for estimating the channel gains and the mismatch, followed by low-complexity linear equalization to suppress the inter-subcarrier interference resulting from the mismatch. Our simulations show that the error floor due to mismatch-generated interference can be eliminated, permitting bandwidth-efficient operation with large constellations.

Our recently introduced JPEG steganographic method called yet another steganographic scheme (YASS) can resist blind steganalysis by embedding data in the discrete cosine transform (DCT) domain in randomly chosen image blocks. To maximize the embedding rate for a given image and a specified attack channel, the redundancy factor used by the repeat- accumulate (RA) code based error correction framework in YASS is optimally chosen by the encoder. An efficient method is suggested for the decoder to accurately compute this redundancy factor. We also show experimentally which DCT coefficients are better suited for hiding and detection under various attacks. The effectiveness of YASS for robust steganography is demonstrated for certain attacks.

Throughput performance of multihop wireless networks is governed by how the network's transport capacity (in bit-meters per second) is partitioned among different network flows. Max-min fair allocation leads to poor throughput performance for all flows because connections traversing a large number of hops consume a disproportionate share of resources. While proportional fair allocation provides a significant improvement, we point out here that there is a much richer space of resource allocation strategies for introducing a controlled bias against resource-intensive long connections in order to significantly improve the performance of shorter connections. We present an analytical model that gives insight into the impact of a particular resource allocation strategy on network performance, in a manner that captures the effect of finite network size and spatial traffic patterns. Our simulation results demonstrate that it is possible to provide significantly better performance to shorter connections than max-min fair or proportional fair resource allocations, with minimal impact on the performance of long connections, using mixed bias strategies blending "fair" allocations with a strong bias against long connections.

We investigate the fundamental performance limits of target localization, where a network of sensors observe and cooperatively estimate the 2D location of a target. Taking a general view of a sensor as any device whose observations depend statistically on target position, we consider the binary hypothesis testing problem of choosing between the correct target location and an incorrect location at a distance r from it, given the outputs of all sensors in the network. By considering a random placement of sensors in an infinitely large sensing area, we obtain upper and lower bounds on the error probability of this hypothesis testing problem. The error bounds depend only on the type of sensor and are independent of the detailed geometry of the sensor network deployment. This provides a compact comparison of the localization performance of sensors whose characteristics might differ widely (e.g., received signal strength, proximity and time of arrival sensors). Also the bounds decrease exponentially with the density of sensors, and the rate of decrease is shown to have a simple geometric interpretation.

We investigate the limits of communication over the discrete-time additive white Gaussian noise (AWGN) channel, when the channel output is quantized using a small number of bits. We first provide a proof of our recent conjecture on the optimality of a discrete input distribution in this scenario. Specifically, we show that for any given output quantizer choice with K quantization bins (i.e., a precision of log2K bits), the input distribution, under an average power constraint, need not have any more than K + 1 mass points to achieve the channel capacity. The cutting-plane algorithm is employed to compute this capacity and to generate optimum input distributions. Numerical optimization over the choice of the quantizer is then performed (for 2-bit and 3-bit symmetric quantization), and the results we obtain show that the loss due to low-precision output quantization, which is small at low signal-to-noise ratio (SNR) as expected, can be quite acceptable even for moderate to high SNR values. For example, at SNRs up to 20 dB, 2-3 bit quantization achieves 80-90% of the capacity achievable using infinite-precision quantization.

We propose and investigate a minimalistic model for distributed detection using a sensor network. The signal of interest is a priori unknown. When a signal is present, sensors receive scaled, delayed and noisy versions of it, and signal presence is decided solely based on the correlation between sensor observations. We obtain encouraging performance results for both centralized and distributed detection, and subsequent centralized signal estimation. We observe that temporal alignment of sensor observations prior to combining is the bottleneck that determines the SNR threshold after which these methods work well. For ideal temporal alignment, detection performance improves exponentially with the number of sensors.

We investigate a new approach to uplink communications in wideband outdoor cellular systems that can take advantage of multiple antennas at the base station in a scalable manner, while eliminating or minimizing overhead for channel estimation. The proposed techniques, which focus on exploiting correlated channels with the use of closely spaced antenna arrays, are applicable to emerging Orthogonal Frequency Division Multiplexing (OFDM) based Wireless Metropolitan Area Network (WMAN) systems, such as those based on the IEEE 802.16/20 standards. Outdoor channels frequently have a small number of dominant spatial modes, which can be learned from overhead-free estimation of the spatial covariance matrix by averaging across subcarriers. We describe an eigenbeamforming receiver which projects the received signal along the dominant spatial modes, yielding a beamforming gain that scales up with the number of receive elements and a diversity level depending on the number of dominant spatial modes. Shannon limits are first computed for block fading approximations to time- and frequency-selective channels. The suboptimal noncoherent diversity-combining receiver is shown to approach these limits, with linear complexity in the number dominant modes. Further, for dealing with spatially non-white interfering signals, adaptive suppression techniques are shown to mitigate strong interference with minimal training overhead.

We develop bandwidth efficient radio transceivers, using amplitude/phase modulations, for frequency non-selective channels whose time variations are typical of outdoor mobile wireless systems. The transceiver is noncoherent, neither requiring pilots for channel estimation and tracking nor assuming prior channel knowledge on the part of the receiver. Serial concatenation of a binary outer channel code with an inner differential modulation code provides a turbo structure that, along with the channel memory, is exploited for joint iterative channel and data estimation. While prior work on noncoherent communication mainly focuses on PSK alphabets, we consider a moderate to high SNR regime in which amplitude/phase constellations are more efficient. First, the complexity of block noncoherent demodulation is reduced to a level that is comparable to coherent receivers. Then, a tool for choosing the constellation and bit-to-symbol mapping is developed by adapting extrinsic information transfer (EXIT) charts for noncoherent demodulation. The recommended constellations differ significantly from standard coherent channel constellations, and from prior recommendations for uncoded noncoherent systems. The analysis shows that standard convolutional codes are nearly optimal when paired with differential amplitude/phase modulation.

We present practical approaches for steganography that can provide improved security by closely matching the second-order statistics of the host rather than just the marginal distribution. The methods are based on the framework of statistical restoration, wherein a fraction of the host symbols available for hiding is actually used to restore the statistics; thus reducing the rate, but providing security against steganalysis. We establish correspondence between steganography and the earth-mover's distance (EMD), a popular distance metric used in computer vision applications. The EMD framework can be used to define the optimum flow (modifications) of the host symbols for compensation. This formulation is used for image steganography by restoring the second-order statistics of the blockwise discrete cosine transform (DCT) coefficients. Some practical limitations of this approach (such as computational complexity and difficulty in dealing with overlapping coefficient pairs) are noted, and a new method is proposed that alleviates these deficiencies by identifying the coefficients to modify based on a local compensation criterion. Experimental results on several thousand natural images demonstrate the utility of the presented methods.

Calibration of sub-converter mismatches is a challenging task for high-performance time-interleaved analog-to-digital converters (TIADC). Presently known blind correction methods can remove static gain and sampling time mismatches. However, actual subconverters significantly deviate from a simple gain-timing model, and the resulting modeling error strictly limits maximum output signal-to-noise ratio achievable. Generalized mismatch modeling is, therefore, necessary to break the limitation of gain-timing model. In this paper, we propose a blind method for correcting generalized mismatch errors for M=2 TIADC, which is the first in the literature to the authors' knowledge. Cyclostationary spectral analysis shows that unique identification is possible in most practical cases. Simulation results show significant performance improvement by the proposed generalized correction method.

We examine the Shannon limits of communication systems when the precision of the analog-to-digital conversion (ADC) at the receiver is constrained. ADC is costly and power- hungry at high speeds, hence ADC precision is expected to be a limiting factor in the performance of receivers which are heavily based on digital signal processing. In this paper, we consider transmission over an ideal discrete-time real baseband additive white Gaussian noise (AWGN) channel, and provide capacity results when the receiver ADC employs a small number of bits, generalizing our prior work on one bit ADC. We show that dithered ADC does not increase capacity, and hence restrict attention to deterministic quantizers. To compute the capacity, we use a dual formulation of the channel capacity problem, which is attractive due to the discrete nature of the output alphabet. The numerical results we obtain strongly support our conjecture that the optimal input distribution is discrete, and has at most one mass point in each quantizer interval. This implies, for example, that it is not possible to support large alphabets such as 64-QAM using 2-bit quantization on the I and Q channels, at least with symbol rate sampling.

The 7 GHz of unlicensed spectrum in the 60 GHz band offers the potential for multiGigabit indoor wireless personal area networking (WPAN). With recent advances in the speed of silicon (CMOS and SiGe) processes, low-cost transceiver realizations in this "millimeter (mm) wave" band are within reach. However, mm wave communication links are more fragile than those at lower frequencies (e.g., 2.4 or 5 GHz) because of larger propagation losses and reduced diffraction around obstacles. On the other hand, directional antennas that provide directivity gains and reduction in delay spread are far easier to implement at mm-scale wavelengths. In this paper, we present a cross-layer modeling methodology and a novel multihop medium access control (MAC) architecture for efficient utilization of 60 GHz spectrum, taking into account the preceding physical characteristics. We propose an in-room WPAN architecture in which every link is constrained to be directional, for improved power efficiency (due to directivity gains) and simplicity of implementation (due to reduced delay spread). We develop an elementary diffraction-based model to determine network link connectivity, and define a multihop MAC protocol that accounts for directional transmission/reception, procedures for topology discovery and recovery from link blockages.

Recent work has shown that, despite the minimal information provided by a binary proximity sensor, a network of such sensors can provide remarkably good target tracking performance. In this paper, we examine the performance of such a sensor network for tracking multiple targets. We begin with geometric arguments that address the problem of counting the number of distinct targets, given a snapshot of the sensor readings. We provide necessary and sufficient criteria for an accurate target count in a one-dimensional setting, and provide a greedy algorithm that determines the minimum number of targets that is consistent with the sensor readings. While these combinatorial arguments bring out the difficulty of target counting based on sensor readings at a given time, they leave open the possibility of accurate counting and tracking by exploiting the evolution of the sensor readings across time. To this end, we develop a particle filtering algorithm based on a cost function that penalizes changes in velocity. An extensive set of simulations, as well as experiments with passive infrared sensors, are reported. We conclude that, despite the combinatorial complexity of target counting, probabilistic approaches based on fairly generic models for the trajectories yield respectable tracking performance.

Energy efficient communication is a fundamental problem in wireless ad-hoc and sensor networks. In this paper, we explore the feasibility of a distributed beamforming approach to this problem, with a cluster of distributed transmitters emulating a centralized antenna array so as to transmit a common message signal coherently to a distant base station. The potential SNR gains from beamforming are well-known. However, realizing these gains requires synchronization of the individual carrier signals in phase and frequency. In this paper we show that a large fraction of the beamforming gains can be realised even with imperfect synchronization corresponding to phase errors with moderately large variance. We present a master-slave architecture where a designated master transmitter coordinates the synchronization of other (slave) transmitters for beamforming. We observe that the transmitters can achieve distributed beamforming with minimal coordination with the base station using channel reciprocity. Thus, inexpensive local coordination with a master transmitter makes the expensive communication with a distant base station receiver more efficient. However, the duplexing constraints of the wireless channel place a fundamental limitation on the achievable accuracy of synchronization. We present a stochastic analysis that demonstrates the robustness of beamforming gains with imperfect synchronization, and demonstrate a tradeoff between synchronization overhead and beamforming gains. We also present simulation results for the phase errors that validate the analysis

In this paper, we present a framework for the design of steganographic schemes that can provide provable security by achieving zero Kullback-Leibler divergence between the cover and the stego signal distributions, while hiding at high rates. The approach is to reserve a number of host symbols for statistical restoration: host statistics perturbed by data embedding are restored by suitably modifying the symbols from the reserved set. A dynamic embedding approach is proposed, which avoids hiding in low probability regions of the host distribution. The framework is applied to design practical schemes for image steganography, which are evaluated using supervised learning on a set of about 1000 natural images. For the presented JPEG steganography scheme, it is seen that the detector is indeed reduced to random guessing.

In steganography (the hiding of data into innocuous covers for secret communication) it is difficult to estimate how much data can be hidden while still remaining undetectable. To measure the inherent detectability of steganography, Cachin suggested the ϵ-secure measure, where ϵ is the Kullback Leibler (K-L) divergence between the cover distribution and the distribution after hiding. At zero divergence, an optimal statistical detector can do no better than guessing; the data is undetectable. The hider's key question then is, what hiding rate can be used while maintaining zero divergence? Though work has been done on the theoretical capacity of steganography, it is often difficult to use these results in practice. We therefore examine the limits of a practical scheme known to allow embedding with zero-divergence. This scheme is independent of the embedding algorithm and therefore can be generically applied to find an achievable secure hiding rate for arbitrary cover distributions.

We report experimental verification of the previously proposed blind method of correcting gain and timing mismatches for a two-channel time-interleaved analog-to-digital converter (TIADC). The experimental setup allows for two different M=2 TIADC configurations with 14-bit resolution and 200-MHz overall sampling rates. Mismatch parameters are estimated by the blind algorithm with representative narrowband and wideband signals. The spurious-free dynamic range (SFDR) performance is then evaluated by using sinusoids. We discuss performance gain, as well as the limitations of the proposed blind algorithm when applied to real world analog-to-digital (A/D) converters.

We explore fundamental performance limits of tracking a target in a two-dimensional field of binary proximity sensors, and design algorithms that attain those limits. In particular, using geometric and probabilistic analysis of an idealized model, we prove that the achievable spatial resolution Δ in localizing a target's trajectory is of the order of 1overρ R, where R is the sensing radius and ρ is the sensor density per unit area. Using an Occam's razor approach, we then design a geometric algorithm for computing an economical (in descriptive complexity) piecewise linear path that approximates the trajectory within this fundamental limit of accuracy. We employ analogies between binary sensing and sampling theory to contend that only a "lowpass" approximation of the trajectory is attainable, and explore the implications of this obervation for estimating the target's velocity.We show through simulation the effectiveness of the geometric algorithm in tracking both the trajectory and the velocity of the target for idealized models. For non-ideal sensors exhibiting sensing errors, the geometric algorithm can yield poor performance. We show that non-idealities can be handled well using a particle filter based approach, and that geometric post-processing of the output of the Particle Filter algorithm yields an economical path description as in the idealized setting. Finally, we report on our lab-scale experiments using motes with acoustic sensors to validate our theoretical and simulation results.

We consider optimization of the capacity of a multi-input single-output wideband cellular "downlink," in which the base station has estimates of the statistics of the spatial channel. Our main focus is on orthogonal frequency-division multiplexed (OFDM) systems, although some of our results apply to single-carrier systems, as well. Prior work has shown that estimates of the channel spatial covariance can be obtained without overhead for both frequency-division duplex (FDD) and time-division duplex (TDD) systems by suitably averaging uplink measurements. In this paper, we investigate the benefits of supplementing this "free" covariance feedback with mean feedback, where the latter refers to estimates of the spatial channel realization in each subcarrier. Mean feedback can be obtained using reciprocity for TDD systems, and requires explicit feedback for FDD systems. We first devise strategies for using both covariance and mean feedback, mainly restricting attention to beamforming, which is optimal or near-optimal for many outdoor channels with narrow spatial spread. Second, since mean feedback degrades rapidly with feedback delay for mobile channels, we develop quantitative rules of thumb regarding the accuracy required for the mean feedback to be a useful supplement to the already available, and robust, covariance feedback. Our results validate the following intuition: the accuracy requirements for mean feedback to be useful are more relaxed for channels with larger spatial spread, or for a larger number of transmit elements.

Print-scan resilient data hiding finds important applications in document security and image copyright protection. This paper proposes methods to hide information into images that achieve robustness against printing and scanning with blind decoding. The selective embedding in low frequencies scheme hides information in the magnitude of selected low-frequency discrete Fourier transform coefficients. The differential quantization index modulation scheme embeds information in the phase spectrum of images by quantizing the difference in phase of adjacent frequency locations. A significant contribution of this paper is analytical and experimental modeling of the print-scan process, which forms the basis of the proposed embedding schemes. A novel approach for estimating the rotation undergone by the image during the scanning process is also proposed, which specifically exploits the knowledge of the digital halftoning scheme employed by the printer. Using the proposed methods, several hundred information bits can be embedded into images with perfect recovery against the print-scan operation. Moreover, the hidden images also survive several other attacks, such as Gaussian or median filtering, scaling or aspect ratio change, heavy JPEG compression, and rows and/or columns removal

The difficult task of steganalysis, or the detection of the presence of hidden data, can be greatly aided by exploiting the correlations inherent in typical host or cover signals. In particular, several effective image steganalysis techniques are based on the strong interpixel dependencies exhibited by natural images. Thus, existing theoretical benchmarks based on independent and identically distributed (i.i.d.) models for the cover data underestimate attainable steganalysis performance and, hence, overestimate the security of the steganography technique used for hiding the data. In this paper, we investigate detection-theoretic performance benchmarks for steganalysis when the cover data are modeled as a Markov chain. The main application explored here is steganalysis of data hidden in images. While the Markov chain model does not completely capture the spatial dependencies, it provides an analytically tractable framework whose predictions are consistent with the performance of practical steganalysis algorithms that account for spatial dependencies. Numerical results are provided for image steganalysis of spread-spectrum and perturbed quantization data hiding.

Recent work has shown that cross-layer optimization of the physical layer and medium access control for a wireless collision channel, based on a receiver with adaptive multiuser detection capability, is capable of providing significantly better performance than classical Aloha. The basic features of such a system are multipacket reception (MPR) capability, and the ability (with high probability) to estimate the number of contending users even when the packets are not successfully received. We provide an analytical model that includes these features, and use it to derive methods for backlog estimation and stabilization. Two classes of users are considered: high priority users with quality of service (QoS) requirements, who must succeed within a deadline with a specified probability; and low priority users whose throughput we wish to maximize, while maintaining the QoS for high priority users, and keeping the overall system stable. We obtain contention policies that ensure QoS and stability, based on backlog estimates obtained by extending Rivest's pseudo-Bayesian technique for classical Aloha. The channel throughput and the achievable QoS is characterized as a function of the arrival rates for high and low priority users. Finally, we apply these methods to simulations of a system employing differential minimum mean squared error (DMMSE) adaptive multiuser detection, and find that the analytical model provides accurate guidelines for design and performance predictions.

We investigate data hiding techniques that attempt to defeat steganalysis by restoring the statistics of the composite image to resemble that of the cover. The approach is to reserve a number of host symbols for statistical restoration: host statistics perturbed by data embedding are restored by suitably modifying the symbols from the reserved set. While statistical restoration has broad applicability to a variety of hiding methods, we illustrate our ideas here for quantization index modulation (QIM) based hiding. We propose a method for significantly reducing the detectability of QIM, while preserving its robustness to attacks. We next use the framework of statistical restoration to develop a method to combat steganalysis techniques which detect block-DCT embedding by evaluating the increase in blockiness of the image due to hiding. Numerical results demonstrating the efficacy of these techniques are provided.

We consider an imaging sensor net, in which a stationary collector node employs imaging techniques for localization and data collection from sensor nodes without geolocation or inter-networking capabilities. Sensors that are near enough to an event of interest become active. We consider a dense sensor field, in which multiple sensors are activated by the same event. The collector scans the sensor field with a directional antenna. Active sensors which fall in the beam electronically reflect the collector's beacon, thus creating a radar-like geometry. The collector processes the observations obtained in multiple beams to obtain maximum likelihood estimates of the locations of events of interest

Recent work has shown that large gains in communication capacity are achievable by distributed beamforming in sensor networks. The principal challenge in realizing these gains in practice, is in synchronizing the carrier signal of individual sensors in such a way that they combine coherently at the intended receiver. In this paper, we provide a scalable mechanism for achieving phase synchronization in completely distributed fashion, based only on feedback regarding the power of the net received signal. Insight into the workings of the protocol is obtained from a simple theoretical model that provides accurate performance estimates

Pilot symbol assisted modulation (PSAM) is a standard approach for transceiver design for time-varying channels, with channel estimates obtained from pilot symbols being employed for coherent demodulation of the data symbols. In this paper, we show that PSAM schemes can be improved by adapting the coded modulation strategy at the sender to the quality of the channel measurement at the receiver, without requiring any channel feedback from the receiver. We consider performance in terms of achievable rate for binary signaling schemes. The transmitter employs interleaved codes, with data symbols coded according to their distance from the nearest pilot symbols. Symbols far away from pilot symbols encounter poorer channel measurements at the receiver and are therefore coded with lower rate codes, while symbols close to pilot symbols benefit from recent channel measurements and are coded with higher rate codes. The performance benefits from this approach are quantified in the context of binary signaling over time-varying Rayleigh fading channels described by a Gauss-Markov model. The spacing of the pilot symbols is optimized to maximize the mutual information between input and output in this setting. Causal and noncausal channel estimators of varying complexity and delay are considered. It is shown that, by appropriate optimization for the spacing between consecutive pilot symbols, the adaptive coding techniques proposed can improve achievable rate, without any feedback from the receiver to the sender. Moreover, channel estimation based on the two closest pilot symbols is generally close to optimal.

The linear minimum mean-squared error (MMSE) criterion is known to provide adaptive algorithms for interference suppression in direct-sequence (DS) code-division multiple-access (CDMA) systems. However, standard MMSE adaptation is not robust to fast fading, being unable to compensate for rapid channel variations. In this paper, we provide a framework for deriving robust adaptive algorithms in this setting based on a new differential MMSE (DMMSE) criterion, which is a constrained optimization problem in which the quantity to be tracked is the ratio of the data appearing in two successive observation intervals. When applied to a DS-CDMA system with short spreading waveforms (i.e., with period equal to the symbol interval) operating over a flat-fading channel, the DMMSE criterion avoids tracking the fades, exploiting the negligible variation of the fading gain over two consecutive symbols. For frequency-selective fading, the DMMSE criterion is extended to provide a new eigenrake receiver which provides interference suppression and diversity combining without requiring explicit information regarding the desired user's propagation channel.

The bidirectional arbitrated decision-feedback equalizer (BAD), which has bit-error rate performance between a decision-feedback equalizer (DFE) and maximum a posteriori (MAP) detection, is presented. The computational complexity of the BAD algorithm is linear in the channel length, which is the same as that of the DFE, and significantly lower than the exponential complexity of the MAP detector. While the relative performance of BAD to those of the DFE and the MAP detector depends on the specific channel model, for an error probability of 10/sup -2/, the performance of BAD is typically 1-2 dB better than that of the DFE, and within 1 dB of the performance of MAP detection.

We consider space-time communication for a cellular downlink in which the base station (BS) transmitter has multiple antennas, while the mobile receiver has one or two. Only a subset of the available antennas at the BS are used for transmission, thus reducing the number of RF chains and the complexity of the baseband signal processing. It is assumed that the BS does not know the instantaneous downlink channel realization, but has estimates of the covariance of the space-time channel: covariance information can be easily obtained in wideband systems by averaging uplink channel measurements over frequency. Based on the optimization of a lower bound for capacity, we are able to provide rules of thumb for antenna selection as a function of the physical channel characteristics and the number of receive antennas. This procedure is shown to be optimal or near-optimal (relative to exhaustive computation of the best antenna subset) at moderate SNR.

We propose an equalization algorithm that employs multiple decision-feedback equalizers (DFE)s operating in different directions and arbitration among the outputs of these equalizers to mitigate the effects of two-dimensional intersymbol interference (ISI). The multi-directional arbitrated DFE (MAD) exploits directional diversity to reduce the effects of error-propagation while maintaining complexity on the same order as a DFE. Simulation results show that, when four DFEs are used, the MAD algorithm can achieve substantial gains over a single DFE, including gains of over 10 dB at 10/sup -2/ BER for simulations in this paper.

This paper proposes a method to hide information into images that achieves robustness against printing and scanning with blind decoding. A significant contribution of this paper is a technique to estimate and undo rotation. The method is based on the fact that laser printers use an ordered digital halftoning algorithm for printing. Using the proposed hiding method, several hundred information bits can be embedded into 512/spl times/512 images with perfect recovery against the print-scan operation. Moreover, the hidden images also survive other attacks such as Gaussian or median filtering, scaling or aspect ratio change, heavy JPEG compression, and rows and/or columns removal.

Quantization index modulation (QIM) techniques have been gaining popularity in the data hiding community because of their robustness and information-theoretic optimally against a large class of attacks. In this paper, we consider detecting the presence of QIM hidden data, which is an important consideration when data hiding is used for covert communication, or steganography. For a given host distribution, we are able to quantify detectability compactly in terms of a parameter related to the robustness of the hiding scheme to attacks. Using detection theory we show that QIM quickly transitions from easily detectable to virtually undetectable as this parameter varies. We also obtain performance benchmarks for QIM hiding in images, indicating that a scheme designed to be robust to say a moderate degree of JPEG compression, should be easily detectable. While practical application of detection theory to images is difficult because of statistical variations across images, we employ supervised learning to show that standard QIM schemes for images are indeed quite easily detectable. However, it remains an open issue as to whether it is possible to devise QIM variants that are less vulnerable to steganalysis.

Most approaches to sensor data collection in the literature are based on a multihop wireless relay between sensor nodes forming an ad hoc network to reach a remote data-processing destination. In this paper, we propose an alternative Virtual Radar paradigm, which, in its most rudimentary form, is implementable with sensor nodes without networking capabilities. We introduce this concept for a simple setting in which each sensor only has one bit of information to send (e.g., indicating whether the level of a certain chemical has crossed a threshold). "Active" sensors (those which have one to send) respond to a beacon sent by the collector node, precisely timed with a trigger sequence in the beacon. The collector node uses a modified version of synthetic aperture radar processing to obtain an "image" of the activity in the sensor network.

Energy efficient transfer of data from sensors is a fundamental problem in sensor networks. In this paper, we propose a distributed beamforming approach to this problem, with a cluster of sensors emulating a centralized antenna array. While it is well-known that beamforming can provide large performance gains, such gains presuppose not only accurate knowledge of the channel, but also time and phase synchronization at the transmitter. We propose explicit methods for achieving such synchronization in a distributed fashion, and analyze the effects of various sources of coordination error on the attained performance. We find that, as long as the error in range measurements or placement of the sensor nodes is within a fraction of a carrier wavelength, the proposed distributed beamforming strategies achieve most of the gains available from a centralized beamformer.

In this paper, we propose an alternative approach, which enables drastic simplification of sensor node functionality, by moving the complexity to a special collector node. The collector node collects multiple snapshots of the activity in the sensor field, and initiates collection at every snapshot by sending a beacon to the sensor nodes. The collector node uses the received signal over multiple snapshots to obtain an "image" of the activity in the sensor field. Nodes that hear this beacon and have activity to report, respond, timing their response with a "starts transmission sequence" in the beacon, thus creating a radar-like geometry. This virtual radar paradigm, therefore approaches event localization in sensor networks.

This paper focuses on optimizing the capacity of a cellular "downlink" in which the base station (BS) is equipped with multiple antennas, while the mobile has a single antenna. The BS has access to both the first and second order statistics of the channel, obtained from mean feedback and covariance feedback, respectively. Our results apply to wideband orthogonal frequency division multiplexing (OFDM) systems, where accurate covariance feedback is obtained without overhead by averaging uplink measurements. Mean feedback is obtained from uplink measurements using reciprocity for time division duplex (TDD) systems, and would require explicit feedback for frequency division duplex (FDD) systems. Since mean feedback degrades rapidly with feedback delay for mobile channels, and requires feedback overhead for FDD systems, our purpose is to quantify the tradeoffs in using covariance feedback alone versus using both covariance and mean feedback.

In this paper, we investigate wideband space-time communication on the uplink of an outdoor cellular system, in which the base station is equipped with N antennas and the mobile has a single antenna. We assume noncoherent reception at the base station, which incurs significantly less overhead than pilot-based estimation of the space-time channel from each mobile to the base station. Noncoherent communication techniques are particularly well suited to outdoor cellular systems for which channel time variations are significant due to mobility at vehicular speeds

We provide a compact characterization of outage rates for a wideband wireless communication system whose parameters are chosen to model an outdoor cellular downlink. The base station transmitter is equipped with an antenna array, while the mobile receiver has a single antenna. Our analysis quantifies the effects of frequency and spatial diversity for measurement-based channel models available in the literature. Design prescriptions based on our framework would apply, for example, to fourth-generation cellular systems using orthogonal frequency-division multiplexing. Our information-theoretic computations yield the following findings. Complex models typically employed in simulations can be replaced by simple, bandwidth-dependent, tap-delay-line models without loss of accuracy. The spectral efficiency (i.e., the achievable rate, divided by the bandwidth) is well approximated as a Gaussian random variable, so that it is only necessary to specify its mean and variance in order to compute the outage rates. We provide analytical formulas for the mean and variance as a function of the space-time channel model, and verify that the resulting outage rates match closely with simulation. For a wide class of outdoor channels, the mean spectral efficiency depends only on the spatial diversity, while the variance depends on the spatial and frequency diversity via a product. Our definitions of frequency and spatial diversity have physically motivated interpretations, and do not rely on high signal-to-noise ratio asymptotics, as in prior work.

Information-theoretic analyses for data hiding prescribe embedding the hidden data in the choice of quantizer for the host data. In this paper, we propose practical realizations of this prescription for data hiding in images, with a view to hiding large volumes of data with low perceptual degradation. The hidden data can be recovered reliably under attacks, such as compression and limited amounts of image tampering and image resizing. The three main findings are as follows. 1) In order to limit perceivable distortion while hiding large amounts of data, hiding schemes must use image-adaptive criteria in addition to statistical criteria based on information theory. 2) The use of local criteria to choose where to hide data can potentially cause desynchronization of the encoder and decoder. This synchronization problem is solved by the use of powerful, but simple-to-implement, erasures and errors correcting codes, which also provide robustness against a variety of attacks. 3) For simplicity, scalar quantization-based hiding is employed, even though information-theoretic guidelines prescribe vector quantization-based methods. However, an information-theoretic analysis for an idealized model is provided to show that scalar quantization-based hiding incurs approximately only a 2-dB penalty in terms of resilience to attack.

We consider wideband communication (e.g., using orthogonal frequency-division multiplexed (OFDM) systems) over a typical cellular "downlink," in which both the base station and the mobile may have multiple antennas, but the number of antennas at the mobile is assumed to be small. Implicit channel feedback can play a powerful role in such systems, especially for outdoor channels, which typically exhibit narrow spatial spreads. A summary of our findings is as follows: a) Implicit channel feedback regarding the covariance matrix for the downlink space-time channel can be obtained, without any power or bandwidth overhead, by suitably averaging uplink channel measurements across frequency. Since this approach relies on statistical reciprocity, it applies to both time-division duplex (TDD) and frequency-division duplex (FDD) systems. Using such covariance feedback yields significantly better performance at lower complexity than conventional space-time or space-frequency codes, which do not employ feedback; b) We provide guidelines for optimizing antenna spacing in systems with covariance feedback. Theoretical investigation of a hypothetical system with completely controllable channel eigenvalues shows that the optimal number of channel eigenmodes is roughly matched to the (small) number of receive antenna elements. Thus, while antenna elements in conventional systems without feedback should be spaced far apart in order to ensure uncorrelated responses, the optimal antenna spacing with covariance feedback is much smaller, thereby concentrating the channel energy into a small number of eigenmodes.

It is well known that independent and identically distributed Gaussian inputs, scaled appropriately based on the signal-to-noise ratio (SNR), achieve capacity on the additive white Gaussian noise (AWGN) channel at all values of SNR. In this correspondence, we consider the question of whether such good input distributions exist for frequency-nonselective Rayleigh-fading channels, assuming that neither the transmitter nor the receiver has a priori knowledge of the fading coefficients. In this noncoherent regime, for a Gauss-Markov model of the fading channel, we obtain explicit mutual information bounds for the Gaussian input distribution. The fact that Gaussian input generates bounded mutual information motivates the search for better choices of fixed input distributions for high-rate transmission over rapidly varying channels. Necessary and sufficient conditions are derived for characterizing such distributions for the worst case scenario of memoryless fading, using the criterion that the mutual information is unbounded as the SNR gets large. Examples of both discrete and continuous distributions that satisfy these conditions are given. A family of fixed input distributions with mutual information growth rate of O((loglogSNR)/sup 1-u/), u>0 are constructed. It is also proved that there does not exist a single fixed-input distribution that achieves the optimal mutual information growth rate of loglogSNR.

We consider space-time communication over a typical cellular "downlink" based on orthogonal frequency division multiplexing (OFDM), in which the base station may have several antenna elements, while the mobile has 1 or 2 antenna elements. A summary of our findings is as follows: (a) Implicit channel feedback regarding the covariance matrix for the downlink space-time channel can be obtained, without any power or bandwidth overhead, by suitably averaging uplink channel measurements across frequency. Since this approach relies on statistical reciprocity, it applies to both time division duplex (TDD) and frequency division duplex (FDD) systems. The covariance feedback can be used to obtain better performance on the downlink, at lower encoding and decoding complexity, compared to standard space-time coding (which does not employ channel feedback). (b) The conventional design without channel feedback is to space the transmit antennas far enough apart so as to ensure uncorrelated responses. However, when implicit feedback is available, much better performance is obtained with significantly smaller antenna spacing, optimized such that the number of dominant eigenmodes of the channel matches the number of receive antenna elements.

We consider the problem of hiding images in images. In addition to the usual design constraints such as imperceptible host degradation and robustness in presence of variety of attacks, we impose the condition that the quality of the recovered signature image should be better if the attack is milder. We present a simple hybrid analog-digital hiding technique for this purpose. The signature image is compressed efficiently (using JPEG) into a sequence of bits, which is hidden using a previously proposed digital hiding scheme. The residual error between the original and compressed signature image is then hidden using an analog hiding scheme. The results show (perceptual as well as mean-square error) improvement as the attack becomes milder.

In this paper we consider a hypothesis testing approach for detection of hiding in the least significant bit (LSB). This steganalysis problem is a composite hypothesis testing problem. We state a regularity condition on the image histogram, which reduces this problem to a simple hypothesis testing problem. We then develop a number of simple practical tests based on the estimation of the optimal log likelihood ratio statistic. We show that our tests significantly outperform Stegdetect, a popular hypothesis test available in the literature. Our approach also leads to good estimates of the hiding rate.

The paper contains a systematic investigation of practical coding strategies for noncoherent communication over fading channels, guided by explicit comparisons with information-theoretic benchmarks. Noncoherent reception is interpreted as joint data and channel estimation, assuming that the channel is time varying and a priori unknown. We consider iterative decoding for a serial concatenation of a standard binary outer channel code with an inner modulation code amenable to noncoherent detection. For an information rate of about 1/2 bit per channel use, the proposed scheme, using a quaternary phase-shift keying (QPSK) alphabet, provides performance within 1.6-1.7 dB of Shannon capacity for the block fading channel, and is about 2.5-3 dB superior to standard differential demodulation in conjunction with an outer channel code. We also provide capacity computations for noncoherent communication using standard phase-shift keying (PSK) and quadrature amplitude modulation (QAM) alphabets; comparing these with the capacity with unconstrained input provides guidance as to the choice of constellation as a function of the signal-to-noise ratio. These results imply that QPSK suffices to approach the unconstrained capacity for the relatively low information and fading rates considered in our performance evaluations, but that QAM is superior to PSK for higher information or fading rates, motivating further research into efficient noncoherent coded modulation with QAM alphabets.

Weighted fair queueing (WFQ)-based packet scheduling schemes require processing at line speeds for tag computation and tag sorting. This requirement presents a bottleneck for their implementation at high transmission speeds. We propose an alternative and lower complexity approach to packet scheduling, based on modifications of the classical round-robin scheduler. Contrary to conventional belief, we show that appropriate modifications of the weighted round-robin (WRR) service discipline can, in fact, provide tight fairness properties and efficient delay guarantees to multiple sessions. Two such modifications are described: 1) list-based round robin, in which the server visits different sessions according to a precomputed list which is designed to obtain the desirable scheduling properties; 2) multiclass round robin, a version of hierarchical round robin with controls designed for good scheduling properties. The schemes considered are compared with well-known WFQ schemes and with deficit round robin (a credit-based WRR), on the basis of desirable properties such as bandwidth guarantees, fairness in excess bandwidth sharing, worst-case fairness, and efficiency of latency (delay guarantee) tuning. The scheduling schemes proposed and analyzed here operate with fixed packet sizes, and hence can be used in applications such as cell scheduling in ATM networks, time-slot scheduling on wireless links as in GPRS air interface, etc. A credit-based extension of the proposed schemes to handle variable packet sizes is also possible.

Information-theoretic analyses for data hiding prescribe embedding the hidden data in the choice of quantizer for the host data. In this paper, we consider a suboptimal implementation of this prescription, with a view to hiding high volumes of data in images with low perceptual degradation. Our two main findings are as follows: (a) In order to limit perceptual distortion while hiding large amounts of data, the hiding scheme must use perceptual criteria in addition to information-theoretic guidelines. (b) By focusing on “benign” JPEG compression attacks, we are able to attain very high volumes of embedded data, comparable to information-theoretic capacity estimates for the more malicious Additive White Gaussian Noise (AWGN) attack channel, using relatively simple embedding techniques.

This paper considers noncoherent communication over a frequency-nonselective channel in which the time-varying channel gain is unknown a priori, but is approximately constant over a coherence interval. Unless the coherence interval is large, coherent communication, which requires explicit channel estimation and tracking prior to detection, incurs training overhead which may be excessive, especially for multiple-antenna communication. In contrast, noncoherent detection may be viewed as a generalized likelihood ratio test (GLRT) which jointly estimates the channel and the data, and hence does not require separate training. The main results in this paper are as follows. (1) We develop a "signal space" criterion for signal and code design for noncoherent communication, in terms of the distances of signal points from the decision boundaries. (2) The noncoherent metric thus obtained is used to guide the design of signals for noncoherent communication that are based on amplitude/phase constellations. These are significantly more efficient than conventional differential phase-shift keying (PSK), especially at high signal-to-noise ratio (SNR). Also, known results on the high-SNR performance of multiple-symbol demodulation of differential PSK are easily inferred from the noncoherent metric. (3) The GLRT interpretation is used to obtain near-optimal low-complexity implementations of noncoherent block demodulation. In particular, this gives an implementation of multiple symbol demodulation of differential PSK, which is of linear complexity (in the block length) and whose degradation from the exact, exponential complexity, implementation can be made as small as desired.

Quality of service (QoS) provisioning in wireless networks involves accounting for the statistical fluctuations in the wireless channel quality, in addition to the traffic variability of interest in a purely wireline setting. We consider providing QoS to packetized, delay-constrained (real-time) applications over a Rayleigh-faded wireless downlink. Since the wireless medium is prone to high error rates with typically correlated errors, it is essential to use some kind of link-layer error-recovery mechanism to provide the desired level of reliability. We call this procedure of converting a link with frequent and correlated errors into a near-lossless packet pipe "link shaping." The link-shaping scheme considered in this paper exploits the natural interleaving provided by packet-by-packet transmissions to different mobiles to break up the error correlations due to Rayleigh fading and employs forward error correction (FEC) coding on the interleaved data. In addition to considering static (peak-rate) bandwidth sharing as in conventional wireless downlinks, we propose mechanisms for statistical multiplexing of traffic, which lead to substantial capacity gains. For example, for 13 kb/s voice sources over a 1-Mb/s link, we obtain a two-fold capacity gain over static (peak-rate) bandwidth allocation.

This paper considers the problem of multiuser detection for a system in which each user employs nonlinear modulation, with an emphasis on noncoherent detection techniques which do not require knowledge of the users' channel parameters at the receiver. Our goals are to gain fundamental insight into the capabilities of multiuser detection in such a setting, and to provide practical algorithms that perform better than conventional matched-filter reception. We begin by providing fundamental performance benchmarks by considering coherent maximum-likelihood (ML) detection, which requires knowledge of the users' channel parameters, as well as noncoherent detection, formulated in a non-Bayesian generalized likelihood ratio test (GLRT) framework. The asymptotic performance of each detector, as the noise level vanishes, is characterized, yielding simple geometric criteria for near-far resistance. In general, both the ML and GLRT detectors have complexity which is exponential in the number of users. We, therefore, propose the more practical sequential decision projection (SDP) detector which has complexity which is quadratic in the number of users. It is shown that the SDP detector has nonzero asymptotic efficiency if the users' powers are suitably disparate.

The use of channel feedback from receiver to transmitter is standard in wireline communications. While knowledge of the channel at the transmitter would produce similar benefits for wireless communications as well, the generation of reliable channel feedback is complicated by the rapid time variations of the channel for mobile applications. The purpose of this paper is to provide an information-theoretic perspective on optimum transmitter strategies, and the gains obtained by employing them, for systems with transmit antenna arrays and imperfect channel feedback. The spatial channel, given the feedback, is modeled as a complex Gaussian random vector. Two extreme cases are considered: mean feedback, in which the channel side information resides in the mean of the distribution, with the covariance modeled as white, and covariance feedback, in which the channel is assumed to be varying too rapidly to track its mean, so that the mean is set to zero, and the information regarding the relative geometry of the propagation paths is captured by a nonwhite covariance matrix. In both cases, the optimum transmission strategies, maximizing the information transfer rate, are determined as a solution to simple numerical optimization problems. For both feedback models, our numerical results indicate that, when there is a moderate disparity between the strengths of different paths from the transmitter to the receiver, it is nearly optimal to employ the simple beamforming strategy of transmitting all available power in the direction which the feedback indicates is the strongest.

With the growth in Internet access services over networks with asymmetric links such as asymmetric digital subscriber line (ADSL) and cable-based access networks, it becomes crucial to evaluate the performance of TCP/IP over systems in which the bottleneck link speed on the reverse path is considerably slower than that on the forward path. In this paper, we provide guidelines for designing network control mechanisms for supporting TCP/IP. We determine the throughput as a function of buffering, round-trip times, and normalized asymmetry (defined as the ratio of the transmission time of acknowledgment (ACK) in the reverse path to that of data packets in the forward path). We identify three modes of operation which are dependent on the forward buffer size and the normalized asymmetry, and determine the conditions under which the forward link is fully utilized. We also show that drop-from-front discarding of ACKs on the reverse link provides performance advantages over other drop mechanisms in use. Asymmetry increases the TCP already high sensitivity to random packet losses that occur on a time scale faster than the connection round-trip time. We generalize the by-now well-known relation relating the square root of the random loss probability to obtained TCP throughput, originally derived considering only data path congestion. Specifically, random loss leads to significant throughput deterioration when the product of the loss probability, the normalized asymmetry and the square of the bandwidth delay product is large. Congestion in the reverse path adds considerably to TCP unfairness when multiple connections share the reverse bottleneck link. We show how such problems can be alleviated by per-connection buffer and bandwidth allocation on the reverse path.

The performance of a near-far-resistant, finite-complexity, minimum mean squared error (MMSE) linear detector for demodulating direct sequence (DS) code-division multiple access (CDMA) signals is studied, assuming that the users are assigned random signature sequences. We obtain tight upper and lower bounds on the expected near-far resistance of the MMSE detector, averaged over signature sequences and delays, as a function of the processing gain and the number of users. Since the MMSE detector is optimally near-far-resistant, these bounds apply to any multiuser detector that uses the same observation interval and sampling rate. The lower bound on near-far resistance implies that, even without power control, linear multiuser detection provides near-far-resistant performance for a number of users that grows linearly with the processing gain.

This paper considers the problem of supporting TCP, the Internet data transport protocol, over a lossy wireless link whose quality varies over time. In order to prevent throughput degradation, it is necessary to "hide" the losses and the time variations of the wireless link from TCP. A number of solutions to this problem have been proposed in previous studies, but their performance was studied on a purely experimental basis. This paper presents an approximate analysis, validated by computer simulations, for TCP performance over wireless links. The analysis provides the basis for a systematic approach to supporting TCP over wireless links. The specific case of a Rayleigh-faded wireless link and automatic repeat request-based link-layer recovery is considered for the purpose of illustration. The numerical results presented for this case show that a simple solution, that of using an appropriately designed link-layer error-recovery scheme, prevents excessive deterioration of TCP throughput on wireless links.

End-to-end data transport protocols have two main functions: error recovery and congestion control. The information required by the sender to perform these functions is provided by acknowledgements (ACKs) from the receiver. The Internet transport protocol, TCP/IP, uses cumulative acknowledgements (CACKs), which provide a robust but minimal mechanism for error recovery which is inadequate for heterogeneous networks with random loss. Furthermore, TCP's congestion control mechanism is based on counting ACKs, and is therefore vulnerable to loss of ACKs on the reverse path, particularly when the latter may be slower than the forward path, as in asymmetric networks. The contributions of this paper are as follows:(a) We show that a simple enhancement of CACK provides sufficient information for end-to-end congestion control. We term this ACK format total ACKs (TACKs).(b) We devise a novel ACK format that uses TACKs for congestion control, and negative ACKs (NACKs) for efficient error recovery. Typically, the main concern with NACKs is that of robustness to ACK loss, and we address this using an implementation that provides enough redundancy to provide such robustness.(c) We use the TACK+NACK acknowledgement format as the basis for a new transport protocol that provides efficient error recovery and dynamic congestion control. The protocol provides large performance gains over TCP in an environment with random loss, and is robust against loss of ACKs in the reverse path. In particular, the protocol gives high throughput upto a designed level of random loss, independent of the bandwidth-delay product. This is in contrast to TCP, whose throughput deteriorates drastically if the random loss probability is higher than the inverse square of the bandwidth-delay product.

Direct sequence (DS) code division multiple access (CDMA) is a promising technology for wireless environments with multiple simultaneous transmissions because of several features: asynchronous multiple access, robustness to frequency selective fading, and multipath combining. The capacity of DS-CDMA systems is interference-limited and can therefore be increased by techniques that suppress interference. In this paper, we present developments in interference suppression using blind adaptive receivers that do not receive knowledge of the signal waveforms and propagation channels of the interference, and that require a minimal amount of information about the desired signal. The framework considered generalizes naturally to include additional capabilities such as receive antenna diversity. The most powerful application of the methods described here is for linearly modulated CDMA systems with short spreading waveforms (i.e., spreading waveforms with period equal to the symbol interval), for which they provide substantial performance gains over conventional reception. Implications for future system design due to the restriction of short spreading waveforms and directions for further investigation are discussed.

It has been shown that minimum-mean-squared-error (MMSE) demodulators are effective means of interference suppression in code division multiple-access (CDMA) systems. The MMSE demodulator can be implemented adaptively using an initial training sequence, followed by decision-directed adaptation. This requires that the symbol-level timing of the desired user be known prior to training. We remove this requirement by providing a method for timing acquisition in which the output of the acquisition process is a near-far-resistant demodulator which automatically accounts for the delays and amplitudes of both the desired signal and the interference without explicitly estimating these parameters. The only requirements are a training sequence for the desired user and a finite uncertainty regarding the symbol timing. The latter condition can be realized by using a periodic training sequence even if the absolute timing uncertainty is arbitrarily large.

With the envisaged growth in Internet access services over networks with asymmetric links such as asymmetric digital subscriber line (ADSL) and hybrid fiber coax (HFC), it becomes crucial to evaluate the performance of window-based protocols over systems in which the reverse link is considerably slower than the forward link. Even if the actual bandwidth asymmetry is moderate, high effective asymmetries can result because of bidirectional traffic. Our objective is to determine, whether TCP/IP performs reasonably in a setting in which the reverse link is the primary bottleneck. Our main results are as follows. (1) For both the prevalent Tahoe version with Fast Retransmit and the Reno version of TCP we determine the throughput as a function of buffering, round-trip times and normalized asymmetry (taken to be the ratio of the transmission time of ACKs in the reverse path to that of data packets in the forward path). We identify three modes of operation which are dependent on the forward buffer sizes and the normalized asymmetry. (2) Asymmetry increases the TCP's already high sensitivity to random packet losses that might be caused by transient bursts in real-time traffic. Specifically, random loss leads to significant throughput deterioration when the product of the loss probability, the asymmetry and the square of the bandwidth delay product is large. (3) Congestion in the reverse path adds considerably to the TCP's unfairness when multiple connections share the reverse link. Link bandwidth sharing is unfair even for connections with identical round-trip times and hence use of per connection buffer allocation on the reverse path appears essential.

This paper examines the performance of TCP/IP, the Internet data transport protocol, over wide-area networks (WANs) in which data traffic could coexist with real-time traffic such as voice and video. Specifically, we attempt to develop a basic understanding, using analysis and simulation, of the properties of TCP/IP in a regime where: (1) the bandwidth-delay product of the network is high compared to the buffering in the network and (2) packets may incur random loss (e.g., due to transient congestion caused by fluctuations in real-time traffic, or wireless links in the path of the connection). The following key results are obtained. First, random loss leads to significant throughput deterioration when the product of the loss probability and the square of the bandwidth-delay product is larger than one. Second, for multiple connections sharing a bottleneck link, TCP is grossly unfair toward connections with higher round-trip delays. This means that a simple first in first out (FIFO) queueing discipline might not suffice for data traffic in WANs. Finally, while the Reno version of TCP produces less bursty traffic than the original Tahoe version, it is less robust than the latter when successive losses are closely spaced. We conclude by indicating modifications that may be required both at the transport and network layers to provide good end-to-end performance over high-speed WANs.

The major limitation on the performance and capacity of direct-sequence code division multiple access (CDMA) is the multiple-access interference due to simultaneous transmissions. The signal processing for near-far resistant multiuser detectors typically follows a bank of matched filters or correlators (sampled at the symbol rate to produce sufficient statistics for demodulation) corresponding to all simultaneous transmissions. Due to the asynchronism among the transmissions, implementation of any near-far resistant demodulator using such a front end requires an infinite memory. However, an alternative front end which consists of a chip matched filter sampled at a multiple of the chip rate, coupled with the use of a finite observation interval for the demodulation of each symbol, can be used to obtain finite memory, near-far resistant, methods for demodulation and timing acquisition for asynchronous CDMA systems. We discuss the design of interference suppression schemes based on this front end.

No abstract available.

Acquisition of synchronism is considered for DS/SS CDMA systems. For large systems with large timing uncertainties, it has been shown previously that acquisition in the presence of multiple-access interference may impose a significant limitation on capacity. This leads the authors to consider a system in which timing uncertainties are relatively small and to propose an acquisition scheme which exploits this to reduce complexity and acquisition overhead. The proposal may be appropriate for a microcellular environment for personal communications in which CDMA packet transmission is employed for both voice and data. Packetized transmission would imply that the overhead available for acquisition is small, and the large number of microcells would restrict the cost of the acquisition scheme used in the receiver in each microcell. The acquisition time required for a simple serial search scheme may therefore be unacceptably large. On the other hand, while acquisition using a passive matched filter is fast, the filter length required for reliable acquisition is liable to be excessive in terms of cost and complexity. Motivated by these considerations, the authors propose a two-stage acquisition scheme which employs a short programmable matched filter for initial detection, followed by a correlator for verification. Numerical results based on an approximate analysis of acquisition performance in the presence of multiple access interference are employed to compare the scheme with conventional acquisition schemes.>

The decorrelating detector and the linear minimum mean-square error (MMSE) detector are known to be effective strategies to counter the presence of multiuser interference in code-division multiple-access channels; in particular, those multiuser detectors provide optimum near-far resistance. When training data sequences are available, the MMSE multiuser detector can be implemented adaptively without knowledge of signature waveforms or received amplitudes. This paper introduces an adaptive multiuser detector which converges (for any initialization) to the MMSE detector without requiring training sequences. This blind multiuser detector requires no more knowledge than does the conventional single-user receiver: the desired user's signature waveform and its timing. The proposed blind multiuser detector is made robust with respect to imprecise knowledge of the received signature waveform of the user of interest.>

In personal communications applications, users communicate via wireless with a wireline network. The wireline network tracks the current location of the user, and can therefore route messages to a user regardless of the user's location. In addition to its impact on signaling within the wireline network, mobility tracking requires the expenditure of wireless resources as well, including the power consumption of the portable units carried by the users and the radio bandwidth used for registration and paging. Ideally, the mobility tracking scheme used for each user should depend on the user's call and mobility pattern, so the standard approach, in which all cells in a registration area are paged when a call arrives, may be wasteful of wireless resources. In order to conserve these resources, the network must have the capability to page selectively within a registration area, and the user must announce his or her location more frequently. We propose and analyze a simple model that captures this additional flexibility. Dynamic programming is used to determine an optimal announcing strategy for each user. Numerical results for a simple one-dimensional mobility model show that the optimal scheme may provide significant savings when compared to the standard approach even when the latter is optimized by suitably choosing the registration area size on a per-user basis. Ongoing research includes computing numerical results for more complicated mobility models and determining how existing system designs might be modified to incorporate our approach.

In personal communications applications, users communicate via wireless with a wireline network. The wireline network tracks the current location of the user, and can therefore route messages to a user regardless of the user's location. In addition to its impact on signaling within the wireline network, mobility tracking requires the expenditure of wireless resources as well, including the power consumption of the portable units carried by the users and the radio bandwidth used for registration and paging. Ideally, the mobility tracking scheme used for each user should depend on the user's call and mobility pattern, so that the current registration area approach (which ignores such information) may be wasteful of wireless resources under certain circumstances. In the paper, the authors provide a model and an optimization algorithm based on dynamic programming for choosing the mobility tracking scheme on a per-user basis. While illustrative results are provided for a simple one-dimensional mobility model, the approach is shown to be applicable to a very general class of problems.>

We consider interference suppression for direct-sequence spread-spectrum code-division multiple-access (CDMA) systems using the minimum mean squared error (MMSE) performance criterion. The conventional matched filter receiver suffers from the near-far problem, and requires strict power control (typically involving feedback from receiver to transmitter) for acceptable performance. Multiuser detection schemes previously proposed mitigate the near-far problem, but are complex and require explicit knowledge or estimates of the interference parameters. In this paper, we present and analyze several new MMSE interference suppression schemes, which have the advantage of being near-far resistant (to varying degrees, depending on their complexity), and can be implemented adaptively when interference parameters are unknown and/or time-varying, Numerical results are provided that show that these schemes offer significant performance gains relative to the matched filter receiver. We conclude that MMSE detectors can alleviate the need for stringent power control. In CDMA systems, and may be a practical alternative to the matched filter receiver.>

We consider binary orthogonal signaling over a nonselective Rician-fading channel with additive white Gaussian noise. The received signal over such a channel may have both a specular component and a scatter (Rayleigh-faded) component. If there is only a scatter component, the noncoherent receiver is optimal. If there is only a specular component, the optimal receiver is the coherent receiver. In general, the optimal receiver for a Rician channel depends on the strengths of the two signal components and the noise density, and the set of possible optimal receivers is infinite. We consider a system in which the noncoherent receiver and the coherent receiver are employed in a parallel configuration for a symbol-by-symbol demodulation of the received signal. Each sequence of transmitted symbols produces a sequence at the output of each of the parallel receivers. The task of identifying which of these received sequences is a more reliable reproduction of the transmitted sequence is the data verification problem. In this paper, we show that data verification can be accomplished by combining side information from the demodulators with a suitable error-control coding scheme. The resulting system is a universal receiver that provides good performance over the entire range of channel parameters. In particular, the universal receiver performs better than the traditional noncoherent receiver.>

The purpose of this paper is to illustrate the issues involved in designing the demodulator portion of a universal receiver for unknown or time-varying channels by means of a specific example. We consider the class of nonselective Rician fading channels with additive white Gaussian noise. The optimal receiver for a Rician channel depends on the parameters of the channel, and the collection of optimal receivers for channels in the class of interest forms an infinite receiver class. We find a finite number of receivers in this receiver class with the property that, regardless of the parameters of the channel in effect, at least one of these receivers provides a symbol error probability that is within a specified deviation from the optimal symbol error probability for the channel. These receivers are then used in parallel to perform a symbol-by-symbol demodulation of the received signal. The receiver output that gives the most reliable reproduction of the transmitted sequence is identified by means of a data verification mechanism. The resulting system is a universal receiver. Methods for data verification are developed in other papers. In this paper, we develop an algorithm for finding the required finite set of receivers. Typical issues, such as the tradeoff between the number of parallel receivers and the allowed deviation from optimality, are discussed.>

The effect of multiple-access interference on the acquisition of direct-sequence spread-spectrum (DS/SS) signals is studied. A passive matched filter approach is used, and the acquisition window length, which is the length of the matched filter, determines the complexity of the acquisition scheme. The acquisition-based capacity of a DS/SS system is defined to be the maximum number of simultaneous transmissions permissible while maintaining acceptable acquisition performance. The performance of the acquisition scheme is evaluated for large acquisition window length, and the asymptotic analysis yields the capacity as a function of the acquisition window length. If this length is linearly related to the processed gain, the acquisition-based capacity is smaller than that obtained by consideration of post-acquisition criteria only (e.g., bit-error probability for the demodulated signal). The results indicate the relative importance of the acquisition problem and suggest directions for further research.>

The problem of communication over a channel with unknown characteristics is addressed. The true channel is from a known set of channels, but the transmitter and receiver do not know which of these channels is actually in effect. The goal of a universal receiver is to provide nearly optimal demodulation regardless of the channel that is actually in effect. A parallel receiver implementation is proposed for a universal scheme to cope with such uncertainty. The parallel system consists of a finite number of receivers with the property that, for each channel in the set, the performance of at least one of the receivers will be within a specified performance range. Data verification is accomplished by an appropriate coding system. Sufficient conditions for the existence of such a universal receiver for a prescribed set of channels are established, procedures are outlined for the receiver design, and an example is given to illustrate the applicability of the theory. For M-ary signaling it is shown that, from an information-theoretic viewpoint, the data verification can be achieved at no extra cost by use of the intrinsic side information that is provided by an appropriate coding scheme that also provides error correction.>

The multiple-access capability of asynchronous frequency-hop packet-radio networks is analyzed. The only interference considered is multiple-access interference, and perfect side information is assumed. Bounds on the probability of error for unslotted systems are developed based on the distributions of the maximum and minimum interference levels over the duration of a given packet, and these are employed to develop corresponding bounds on the throughput. The idealized model makes possible the derivation of asymptotic results showing the convergence of these bounds for high traffic levels. The asymptotic performance of the system is seen to be the same as that of the corresponding slotted system. Results for the maximum asymptotic throughput are also obtained. These results show that the asymptotic sum capacity of the channel can be attained using Reed-Solomon coding. All these results are valid for either fixed or exponentially distributed packet lengths. The results indicate that the performance of frequency-hop networks is insensitive both to the distribution of packet lengths and to whether or not transmissions are slotted. It also demonstrates the efficacy of Reed-Solomon coding in combating multiple-access interference.>