Millimeter-wave transceivers use large antenna arrays to form narrow high-directional beams and overcome severe attenuation. Narrow beams require large signaling overhead to be aligned if no prior information about beam directions is available. Moreover, beams drift with time due to user mobility and may need to be realigned. Beam tracking is commonly used to keep the beams tightly coupled and eliminate the overhead associated with realignment. Hence, with periodic measurements, beams are adjusted before they lose alignment. We propose a model where the receiver adjusts beam direction “continuously” over each physical-layer sample according to a carefully calculated estimate of the continuous variation of the beams. In our approach, the change of direction is updated using the rate variation prediction of beam angles via three different solutions. Our approach incurs no additional overhead in pilots, yet, the performance of beam tracking is improved significantly. Numerical results reveal an SNR enhancement associated with reducing the MSE of the beam directions. In addition, our approach reduces the pilot overhead by 60% and up to 87% while achieving a similar total tracking duration as the state-of-the-art.

Millimeter-wave (mmWave) spectrum promises high throughput links for next-generation wireless agricultural networks, which will be characterized by teams of autonomous ground vehicles, unmanned aerial vehicles (UAVs), and connected agricultural machinery. However, channel models at mmWave frequencies in agricultural environments remain elusive. Moreover, due to the dynamic crop growth behavior, agricultural field channels bear notable distinctions from urban and rural macrocellular network channels. In this work, the most extensive agricultural field experiments on the mmWave spectrum are reported and a channel model is developed to characterize the large-scale path loss, coherence bandwidth, and link quality under the effect of various environmental factors. In particular, this study investigates the effects of wind on signal-to-noise ratio, and the diffuse scattering of electromagnetic waves due to near-canopy propagation at different crop growth stages. Accordingly, (1) during the growing season, the crop canopy surface acts as a “new ground”. This new ground creates multipath components and results in a higher path loss exponent, which is correlated with the relative height between the crop canopy surface and the radios, (2) An increase of 4 m/s in gust speed results in a half-power drop (3-dB SNR degradation) due to beam misalignment and increased scattering, (3) the channel coherence bandwidth increases as the water content in the crop decreases, and (4) the beam-level spatial consistency allows for micro-mobility support for agricultural robotic applications. It is also shown that the impacts of humidity and water vapor on the mmWave channel are insignificant in the absence of rain and irrigation. Such characteristics are fundamental for designing advanced channel estimation and signal processing algorithms in advanced agricultural Internet-of-Things solutions. The extensive experiment dataset is made public for future reproducible research (https://ieeedataport.org/documents/mmwave-farm-channel-modelingwireless-agricultural-networks-broadband-millimeter-wave).

Large MIMO transceivers are integral components of next-generation wireless networks. However, for such systems to be practical, their channel estimation process needs to be fast and reliable. Although several solutions for fast estimation of sparse channels do exist, there is still a gap in understanding the fundamental limits governing this problem. Specifically, we need to better understand the lower bound on the number of measurements under which accurate channel estimates can be obtained. This work bridges that knowledge gap by deriving a tight asymptotic lower bound on the number of measurements. This not only helps develop a better understanding for the sparse MIMO channel estimation problem, but it also provides a benchmark for evaluating current and future solutions.

The broadcast nature of the vehicular networks makes them vulnerable to Sybil attacks, where an attacker illegitimately claims multiple identities and undermines the networks. We propose a non-cryptographic attack detection approach that is based on signal-level wireless measurements. Our approach exploits the spatial signal variation of wireless channels to detect Sybil attacks. The performance of our approach is verified via extensive simulations and DSRC-based experiments in a real vehicular network. The results show that we achieve the detection rates of 95% in simulations and 99% in real-world experiments. The proposed approach can be deployed on the existing systems without a need for additional hardware or infrastructure.

The speed at which millimeter-Wave (mmWave) channel estimation can be carried out is critical for the adoption of mmWave technologies. This is particularly crucial because mmWave transceivers are equipped with large antenna arrays to combat severe path losses, which consequently creates large channel matrices, whose estimation may incur significant overhead. This paper focuses on the mmWave channel estimation problem. Our objective is to reduce the number of measurements required to reliably estimate the channel. Specifically, channel estimation is posed as a “source compression” problem in which measurements mimic an encoded (compressed) version of the channel. Decoding the observed measurements, a task which is traditionally computationally intensive, is performed using a deep-learning-based approach, facilitating a high-performance channel discovery. Our solution not only outperforms state-of-the-art compressed sensing methods, but it also determines the lower bound on the number of measurements required for reliable channel discovery.

Traffic monitoring is still a major challenge for traffic management systems. Traffic monitoring services are nowadays performed with traditionally wired systems that have high installation costs. Therefore, they are not adequate for dense deployment. In this paper, we propose a traffic monitoring approach that is passive and exploits wireless signals sent in a vehicular ad hoc network. We utilize machine learning techniques to make inferences on traffic conditions, directly based on raw samples observed at the receiver. To verify the feasibility of this approach, we first created simulation data, using a ray-tracing simulator and a traffic simulator. The data contain wireless channel realizations under realistic traffic conditions created by the traffic simulator. Next, we collected data in the form of dedicated short-range communications (DSRC) signals transmitted from a roadside unit (RSU) deployed at an intersection and replicated the same experiments with real data. The results show that our approach successfully distinguishes different traffic intensities with an accuracy of 91.6%, 86.4% on simulation and real-world data, respectively. It also tracks the number of vehicles on the road with a mean absolute error (MAE) of 1.3 on both datasets. The proposed approach can be employed alongside the current monitoring systems to increase the situational awareness in a vehicular network without requiring additional investment in infrastructure.

In this paper, multiple-input-single-output (MISO) point-to-point communication system is considered, in which a multiple-input-multiple-output (MIMO) energy recycling (ER) transmitter is designed such that, each antenna can transmit information (multi-input) or recycle energy (multi-output) at any given point in time. The rate by such an ER-MISO communication system under an average transmission power constraint is shown to be achievable. Moreover, the optimal power allocation and the dynamic antenna selection policies that achieve the maximum communication rate are derived. The optimal dynamic antenna selection policy carefully switches the mode of the antennas between active-antennas (transmitting) and ER-antennas, where most of the harvested energy occurs from the neighboring antennas' transmissions, i.e., recycling. In addition, it is shown that, with ER, the achievable rate exceeds the capacity of the classical non-recycling counterpart. Since the complexity of the optimal dynamic antenna selection is exponential with the number of antennas, a linearithmic algorithm that has a minimal degradation in the achievable rate is proposed. Numerical results show that notable gain can be obtained by enabling ER. To address the major questions on the capability of ER and the impact of antenna coupling, hardware setup and experimental results for a four-antenna ER-transmitter are developed, based on a uniform linear array (ULA). As a result, hardware measurements indicate that the loss in the rate due to antenna coupling can be eliminated with sufficient antenna spacing and therefore, ER can achieve a significant gain.

The future Internet of Things (IoT) networks are expected to be composed of a large population of low-cost devices communicating dynamically with access points or neighboring devices to communicate small bundles of delay-sensitive data. To support the high-intensity and short-lived demands of these emerging networks, we propose an efficient MAC paradigm for IoT (EMIT). Our paradigm bypasses the high overhead and coordination costs of existing MAC solutions by employing an interference-averaging strategy that allows users to share their resources simultaneously. In contrast to the predominant interference-suppressing approaches, EMIT exploits the dense and dynamic nature of IoT networks to reduce the spatio-temporal variability of interference to achieve low-delay and high-reliability in service. This paper introduces foundational ideas of EMIT by characterizing the global interference statistics in terms of single-device operation and develops power-rate allocation strategies to guarantee low-delay high-reliability performance. A significant portion of our work is aimed at validating these theoretical principles in experimental test beds and simulations, where we compare the performance of EMIT with a CSMA-based MAC protocol. Our comparisons confirm the beneficial characteristics of EMIT and reveal significant gains over CSMA strategies in the case of IoT traffic.

The surge in mobile broadband data demands is expected to surpass the available spectrum capacity below 6 GHz. This expectation has prompted the exploration of millimeter wave (mm-wave) frequency bands as a candidate technology for next generation wireless networks. However, numerous challenges to deploying mm-wave communication systems, including channel estimation, need to be met before practical deployments are possible. This paper addresses the mm-wave channel estimation problem and treats it as a beam discovery problem in which locating beams with strong path reflectors is analogous to locating errors in linear block codes. We show that a significantly small number of measurements (compared to the original dimensions of the channel matrix) is sufficient to reliably estimate the channel. We also show that this can be achieved using a simple and energy-efficient transceiver architecture.

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

The surge in mobile broadband data demands is expected to surpass the available spectrum capacity below 6 GHz. This expectation has prompted the exploration of millimeter wave (mm-wave) frequency bands as a candidate technology for next generation wireless networks. However, numerous challenges to deploying mm-wave communication systems, including channel estimation, need to be met before practical deployments are possible. This work addresses the mm-wave channel estimation problem and treats it as a beam discovery problem in which locating beams with strong path reflectors is analogous to locating errors in linear block codes. We show that a significantly small number of measurements (compared to the original dimensions of the channel matrix) is sufficient to reliably estimate the channel. We also show that this can be achieved using a simple and energy-efficient transceiver architecture.

We consider the problem of detecting the active wireless stations among a very large population. This problem is highly relevant in applications involving passive and active RFID tags and dense IoT settings. The state of the art mainly utilizes interference avoiding (e.g., CSMA-based) approaches with the objective of identifying one station at a time. We first derive basic limits of the achievable delay with interference avoiding paradigm. Then, we consider the setting in which each station is assigned a signature sequence, picked at random from a specific alphabet and active stations transmit their signatures simultaneously upon activation. The challenge at the detector is to detect all active stations from the combined signature signal with low probability of misdetection and false positives. We show that, such an interference embracing approach can substantially reduce the detection delay, at an arbitrarily low probability of both types of detection errors, as the number of stations scale. We show that, under a randomized activation model the collision embracing detection scheme achieves Θ(log2(n)/log(log(n))) delay while the expected delay of existing CSMA schemes are Ω(log2(n)) for a population of n stations. Finally, we discuss large-scale implementation issues such as the design of low-complexity detection schemes and present numerical investigations.

Communications in the millimeter wave (mmWave) band faces significant challenges due to variable channels, intermittent connectivity, and high energy usage. Moreover, speeds for electronic processing of data is of the same order as typical rates for mmWave interfaces, making the use of complex algorithms for tracking channel variations and adjusting resources impractical. In order to mitigate some of these challenges, we propose an architecture that integrates the sub-6 GHz and mmWave technologies. Our system exploits the spatial correlations between the sub-6 GHz and mmWave interfaces for beamforming and data transfer. Based on extensive experimentation in indoor and outdoor settings, we demonstrate that analog beamforming can be used in mmWave without incurring large overhead, thanks to the spatial correlations with sub-6 GHz. In addition, we incorporate the sub-6 GHz interface as a fallback (secondary) data transfer mechanism such that: 1) the negative effects of highly intermittent mmWave connectivity are mitigated and 2) the abundant mmWave capacity is fully exploited. To achieve these goals, we consider the problem of scheduling the arrival traffic over the mmWave or sub-6 GHz in order to maximize the mmWave throughput while delay (due to mmWave outages) is guaranteed to be bounded. We prove using subadditivity analysis that the optimal scheduling policy is based on a single threshold that can be easily adopted despite high link variations. Numerical results demonstrate that our scheduler provides a bounded mmWave delay performance, while it achieves a similar throughput performance as the throughput-optimal policies (e.g., MaxWeight).

We consider a single-cell downlink time-division duplex-based massive MIMO communication in the presence of an adversary capable of jamming and eavesdropping simultaneously. We show that the massive MIMO communication is naturally resilient to no training-phase jamming attack in which the adversary jams only the data communication and eavesdrops both the data communication and the training. Specifically, we show that the secure degrees of freedom (SDoF) attained in the presence of such an attack are identical to the maximum DoF attainable under no attack. Furthermore, we evaluate the number of base station (BS) antennas necessary in order to establish information theoretic security without even a need for Wyner encoding for a given rate of information leakage to the attacker. Next, we show that things are completely different once the adversary starts jamming the training phase. Specifically, we consider the pilot contamination attack, called training-phase jamming in which the adversary jams and eavesdrops both the training and the data communication. We show that under such an attack, the maximum achieved SDoF is identical to zero. Furthermore, the maximum achievable secure rates of users also vanish, even in the asymptotic regime in the number of the BS antennas. We finally address this attack and show that, under training-phase jamming, if the number of pilot signals is scaled in a certain way and the pilot signal assignments can be hidden from the adversary, the users achieve an SDoF identical to the maximum achievable DoF under no attack.

Compound MIMO wiretap channel with double sided uncertainty is considered under channel mean information model. In mean information model, channel variations are centered around its mean value which is fed back to the transmitter. We show that the worst case main channel is anti-parallel to the channel mean information resulting in an overall unit rank channel. Further, the worst eavesdropper channel is shown to be isotropic around its mean information. Accordingly, we provide the capacity achieving beamforming direction. We show that the saddle point property holds under mean information model and, thus, compound secrecy capacity equals to the worst case capacity over the class of uncertainty. Moreover, capacity achieving beamforming direction is found to require matrix inversion, thus, we derive null steering (NS) beamforming as an alternative sub-optimal solution that precludes the necessity of matrix inversion. NS beamformer is the beamforming direction orthogonal to the eavesdropper mean channel that maintains the maximum possible gain in the direction mean main channel. Extensive computer simulation reveals that NS beamforming performs very close to the optimal solution. It also verifies that, NS beamforming outperforms both maximum ratio transmission (MRT) and zero forcing (ZF) beamforming approaches over the entire SNR range. Finally, an equivalence relation with MIMO wiretap channel in Rician fading environment is established.

We propose a hybrid architecture to integrate RF (i.e., sub-6 GHz) and millimeter wave (mmWave) interfaces for 5G cellular systems. To alleviate the challenges associated with mmWave communications, our proposed architecture integrates the RF and mmWave interfaces for beamforming and data transfer, and exploits the spatio-temporal correlations between the interfaces. Based on extensive experimentation in indoor and outdoor settings, we demonstrate that an integrated RF/mmWave signaling and channel estimation scheme can remedy the problem of high training overhead associated with mmWave beamforming. In addition, cooperation between two interfaces at the higher layers effectively addresses the high delays caused by highly intermittent connectivity in mmWave channels. Subsequently, we formulate an optimal scheduling problem over the RF and mmWave interfaces where the goal is to maximize the delay-constrained throughput of the mmWave interface. We prove using subadditivity analysis that the optimal scheduling policy is based on a single threshold that can be easily adopted despite high link variations. We design an optimal scheduler that opportunistically schedules the packets over the mmWave interface, while the RF link acts as a fallback mechanism to prevent high delay.

In this paper, we study how to optimally manage the freshness of information updates sent from a source node to a destination via a channel. A proper metric for data freshness at the destination is the age-of-information, or simply age, which is defined as how old the freshest received update is, since the moment that this update was generated at the source node (e.g., a sensor). A reasonable update policy is the zero-wait policy, i.e., the source node submits a fresh update once the previous update is delivered, which achieves the maximum throughput and the minimum delay. Surprisingly, this zero-wait policy does not always minimize the age. This counter-intuitive phenomenon motivates us to study how to optimally control information updates to keep the data fresh and to understand when the zero-wait policy is optimal. We introduce a general age penalty function to characterize the level of dissatisfaction on data staleness and formulate the average age penalty minimization problem as a constrained semi-Markov decision problem with an uncountable state space. We develop efficient algorithms to find the optimal update policy among all causal policies and establish sufficient and necessary conditions for the optimality of the zero-wait policy. Our investigation shows that the zero-wait policy is far from the optimum if: 1) the age penalty function grows quickly with respect to the age; 2) the packet transmission times over the channel are positively correlated over time; or 3) the packet transmission times are highly random (e.g., following a heavy-tail distribution).

In this paper, energy-efficient transmission schemes achieving maximal throughput over a finite time interval are studied in a problem setting, including energy harvests, data arrivals, and channel variation. The goal is to express the offline optimal policy in a way that facilitates a good online solution. We express any throughput maximizing energy-efficient offline schedule (EE-TM-OFF) explicitly in terms of water levels. This allows per-slot real-time evaluation of transmit power and rate decisions, using estimates of the associated offline water levels. To compute the online power level, we construct a stochastic dynamic program that incorporates the offline optimal solution as a stochastic process. We introduce the immediate fill measure, which provides a lower bound on the efficiency of any online policy with respect to the corresponding optimal offline solution. The online algorithms obtained this way exhibit performance close to the offline optimal, not only in the long run but also in short problem horizons, deeming them suitable for practical implementations.

Coordinated Multipoint (CoMP) techniques have promised substantial throughput improvement by exploiting the cooperation across base stations in cellular networks. In addition to the high computation and implementation complexity, existing CoMP proposals also require different base stations to exchange co-channel condition information through backhaul links. Such cooperation incurs additional costs in pilots and backhaul bandwidth, which in turn reduces the resource allocated to data transmission. Therefore, the promised throughput gain is greatly degraded in practical applications. Aiming to overcome this limitation, we develop a novel coordinated power control scheme for uplink cellular networks, named Checks and Balances (C&B), which can realize the potential benefits of CoMP with minimum complexity and cost. C&B checks the signal strength of one user and its generated interference to neighboring base stations, and tries to balance the two. We evaluate the throughput performance of C&B on an LTE system-level simulation platform, which is carefully calibrated with Huawei. Our simulation results suggest that C&B achieves up to 52% increase in average throughput, and up to 156% increase in edge-user throughput, compared to existing power control schemes.

The future Internet of Things (IoT) networks are expected to be composed of a large population of low-cost devices communicating dynamically with access points or neighboring devices to communicate small bundles of delay-sensitive data. To support the high-intensity and short-lived demands of these emerging networks, we propose an Efficient MAC paradigm for IoT (EMIT). Our paradigm bypasses the high overhead and coordination costs of existing MAC solutions by employing an interference-averaging strategy that allow users to share their resources simultaneously. In contrast to the predominant interference-suppressing approaches, EMIT exploits the dense and dynamic nature of IoT networks to reduce the spatio-temporal variability of interference to achieve low-delay and high-reliability in service. This paper introduces foundational ideas of EMIT by characterizing the global interference statistics in terms of single-device operation and develops power-rate allocation strategies to guarantee low-delay high-reliability performance. A significant portion of our work is aimed at validating these theoretical principles in experimental testbeds, where we compare the performance of EMIT to a CSMA-based MAC protocol. Our comparisons confirm the beneficial characteristics of EMIT, and reveal significant gains over CSMA strategies in the case of IoT traffic.

In this work we study how to manage the freshness of status updates sent from a source to a remote monitor via a network server. A proper metric of data freshness at the monitor is the age-of-information, which is defined as how old the freshest update is since the moment this update was generated at the source. A logical policy is the zero-wait policy, i.e., the source submits a fresh update once the server is free, which achieves the maximum throughput and the minimum average delay. Surprisingly, this zero-wait policy does not always minimize the average age. This motivates us to study how to optimally control the status updates to keep data fresh and to understand when the zero-wait policy is optimal. We introduce a penalty function to characterize the level of “dissatisfaction” on data staleness, and formulate the average age penalty minimization problem as a constrained semi-Markov decision process (SMDP) with an uncountable state space. Despite of the difficulty of this problem, we develop efficient algorithms to find the optimal status update policy. We show that, in many scenarios, the optimal policy is to wait for a certain amount of time before submitting a new update. In particular, the zero-wait policy can be far from the optimum if (i) the penalty function grows quickly with respect to the age, and (ii) the update service times are highly random and positive correlated. To the best of our knowledge, this is the first optimal control policy which is proven to minimize the age-of-information in status update systems.

RF fingerprinting exploits the variations in the RF chain of radios to uniquely identify transmitters, and distinguish adversarial transmissions from the transmissions of legitimate nodes. We provide a systematic approach rooted from the information theory to evaluate the basic performance limits of RF fingerprinting. We develop a novel channel model for RF fingerprinting, where the imperfections in the RF chain are modeled as a fingerprint channel, cascaded to the actual physical channel. We address the authentication problem in the presence of an adversary, where both the legitimate transmitter and the adversary are equipped with unique fingerprint channels, in addition to a possible secret key available at the legitimate nodes. We provide bounds for the error exponents for reliable communication of the legitimate nodes, and the success exponent for impersonation and substitution attacks of the adversary, as a function of certain parameters based on their RF-fingerprints, and the shared key rate. We illustrate that keyless authentication is possible via RF fingerprints when the legitimate channel is not simulatable. We also show that the probability of these attacks can be reduced significantly by employing additional dedicated authenticated nodes.

The problem of online packet scheduling with hard deadlines has been studied extensively in the single-hop setting, whereas it is notoriously difficult in the multihop setting. This difficulty stems from the fact that packet scheduling decisions at each hop influence and are influenced by decisions on other hops, and only a few provably efficient online scheduling algorithms exist in the multihop setting. We consider a multihop wired network (interference-free and full duplex transmissions) in which packets with various deadlines and weights arrive at and are destined to different nodes through given routes. We study the problem of joint admission control and packet scheduling in order to maximize the cumulative weights of the packets that reach their destinations within their deadlines. We first focus on uplink transmissions in the tree topology and show that the well-known Earliest Deadline First algorithm achieves the same performance as the optimal offline algorithm for any feasible arrival pattern. We then address the general topology with multiple source-destination pairs, develop a simple online algorithm, and show that it is O(PMlog PM)-competitive, where PMis the maximum route length among all packets. Our algorithm only requires information along the route of each packet, and our result is valid for general arrival samples. Moreover, we show that O(PMlog PM)-competitive is the best any online algorithm can do. Via numerical results, we also show that our algorithm achieves performance that is comparable to the noncausal optimal offline algorithm. To the best of our knowledge, this is the first algorithm with a provable (based on a sample-path construction) competitive ratio, subject to hard deadline constraints for general network topologies.

We consider the problem of resource allocation and control of multihop networks in which multiple source-destination pairs communicate confidential messages, to be kept confidential from the intermediate nodes. We pose the problem as that of network utility maximization, into which confidentiality is incorporated as an additional quality of service constraint. We develop a simple, and yet provably optimal dynamic control algorithm that combines flow control, routing and end-to-end secrecy-encoding. In order to achieve confidentiality, our scheme exploits multipath diversity and temporal diversity due to channel variability. Our end-to-end dynamic encoding scheme encodes confidential messages across multiple packets, to be combined at the ultimate destination for recovery. We first develop an optimal dynamic policy for the case in which the number of blocks across which secrecy encoding is performed is asymptotically large. Next, we consider encoding across a finite number of packets, which eliminates the possibility of achieving perfect secrecy. For this case, we develop a dynamic policy to choose the encoding rates for each message, based on the instantaneous channel state information, queue states and secrecy outage requirements. By numerical analysis, we observe that the proposed scheme approaches the optimal rates asymptotically with increasing block size. Finally, we address the consequences of practical implementation issues such as infrequent queue updates and de-centralized scheduling. We demonstrate the efficacy of our policies by numerical studies under various network conditions.

We consider a system in which two nodes take correlated measurements of a random source with time-varying and unknown statistics. The observations of the source at the first node are to be losslessly replicated with a given probability of outage at the second node, which receives data from the first node over a constant-rate channel. We develop a system and associated strategies for joint distributed source coding (encoding and decoding) and transmission control in order to achieve low end-to-end delay. Slepian-Wolf coding in its traditional form cannot be applied in our scenario, since the encoder requires the joint statistics of the observations and the associated decoding delay is very high. We analytically evaluate the performance of our strategies and show that the delay achieved by them are order optimal, as the conditional entropy of the source approaches to the channel rate. We also evaluate the performance of our algorithms based on real-world experiments using two cameras recording videos of a scene at different angles. Having realized our schemes, we demonstrated that, even with a very low-complexity quantizer, a compression ratio of approximately 50% is achievable for lossless replication at the decoder, at an average delay of a few seconds.

One key requirement for storage clouds is to be able to retrieve data quickly. Recent system measurements have shown that the data retrieving delay in storage clouds is highly variable, which may result in a long latency tail. One crucial idea to improve the delay performance is to retrieve multiple data copies by using parallel downloading threads. However, how to optimally schedule these downloading threads to minimize the data retrieving delay remains to be an important open problem. In this paper, we develop low-complexity thread scheduling policies for several important classes of data downloading time distributions, and prove that these policies are either delay-optimal or within a constant gap from the optimum delay performance. These theoretical results hold for an arbitrary arrival process of read requests that may contain finite or infinite read requests, and for heterogeneous MDS storage codes that can support diverse storage redundancy and reliability requirements for different data files. Our numerical results show that the delay performance of the proposed policies is significantly better than that of First-Come-First-Served (FCFS) policies considered in prior work.

There has been numerous studies on the security of vehicular networks, focusing mainly at higher layers of the network stack. Vulnerabilities of vehicular communications at the physical layer have not been explored thoroughly. To that end, we study internal and external vehicular communication under a variety of attack strategies involving jamming at the physical layer. We consider a couple of attack strategies, differing in how the attacker jams the resource blocks within a targeted spectrum. In particular, we focus on flat and random jamming, which are oblivious to the existing assignments and smart jamming, which senses the assignments and allocates power in frequency accordingly. For intra- vehicular setting, we consider an attacker attached to various locations within the vehicle. We used software-defined radios in an actual vehicle to emulate potential realistic attack setups and tested the effectiveness of various attack strategies. We show that, the smart jamming attack can lead to significant degradations in communication performance, to the extent of a complete blockage even with a low jamming power, in most setups. For external communication setting, we consider a busy intersection and study uplink communication in a multicellular LTE network. Using a system-level LTE simulator we developed, we show that a flat jamming attack by a static attacker can degrade the global performance significantly, while the smart jamming attack by a stalking attacker can almost block the communication initiated from a targeted vehicle. In short, we demonstrate via real-world experiments and system-level simulations that, with an appropriate strategy, the impact of a physical layer jamming attacker can be highly detrimental to both internal and external vehicular communication, seriously threatening the security of vehicular networks.

We consider a block fading wiretap channel, where a transmitter attempts to send messages securely to a receiver in the presence of a hybrid half-duplex adversary, which arbitrarily decides to either jam or eavesdrop the transmitter-to-receiver channel. We provide bounds to the secrecy capacity for various possibilities on receiver feedback and show special cases where the bounds are tight. We show that, without any feedback from the receiver, the secrecy capacity is zero if the transmitter-to-adversary channel stochastically dominates the effective transmitter-to-receiver channel. However, the secrecy capacity is nonzero even when the receiver is allowed to feed back only one bit at the end of each block. Our novel achievable strategy improves the rates proposed in the literature for the nonhybrid adversarial model. We also analyze the effect of multiple adversaries and delay constraints on the secrecy capacity. We show that our novel time sharing approach leads to positive secrecy rates even under strict delay constraints.

We consider the problem of cross-layer resource allocation with information-theoretic secrecy for uplink transmissions in time-varying cellular wireless networks. Particularly, each node in an uplink cellular network injects two types of traffic, confidential and open at rates chosen in order to maximize a global utility function while keeping the data queues stable and meeting a constraint on the secrecy outage probability. The transmitting node only knows the distribution of channel gains. Our scheme is based on Hybrid Automatic Repeat Request (HARQ) transmission with incremental redundancy. We prove that our scheme achieves a utility, arbitrarily close to the maximum achievable. Numerical experiments are performed to verify the analytical results and to show the efficacy of the dynamic control algorithm.

Many opportunistic scheduling techniques are impractical because they require accurate channel state information (CSI) at the transmitter. In this paper, we investigate the scheduling of unicast and multicast services in a downlink network with a very limited amount of feedback information. Specifically, unicast users send imperfect (or no) CSI and infrequent acknowledgements (ACKs) to a base station, and multicast users only report infrequent ACKs to avoid feedback implosion. We consider the use of physical-layer rateless codes, which not only combats channel uncertainty, but also reduces the overhead of ACK feedback. A joint scheduling and power allocation scheme is developed to realize multiuser diversity gain for unicast service and multicast gain for multicast service. We prove that our scheme achieves a near-optimal throughput region. Our simulation results show that our scheme significantly improves the network throughput over schemes employing fixed-rate codes or using only unicast communications.

Secret key generation among wireless devices using physical layer information of radio channel has been an attractive alternative for ensuring security in mobile environments. Received signal strength (RSS) based secret key extraction gains much attention due to its easy accessibility in wireless infrastructure. However, the problem of using RSS to generate keys among multiple devices to ensure secure group communication in practice remains open. In this work, we propose a framework for collaborative key generation among multiple wireless devices leveraging RSS. To deal with mobile devices not within each other’s communication range, we employ relay nodes to achieve reliable key extraction. To enable secure group communication, two protocols are developed to perform collaborative group key generation via star and chain topologies respectively. We further provide the theoretic analysis on the achievable secrecy rate for both star and chain topologies in the presence of an eavesdropper. Our prototype development using MICAz motes and extensive experiments using fading trend based key extraction demonstrate the feasibility of using RSS for group key generation in both indoor and outdoor environments, and concurrently achieving a lower bit mismatch rate compared to existing studies.

The problem of online job or packet scheduling with hard deadlines has been studied extensively in the single hop setting, whereas it is notoriously difficult in the multihop setting. This difficulty stems from the fact that packet scheduling decisions at each hop influences and are influenced by decisions on other hops and only a few provably efficient online scheduling algorithms exist in the multihop setting. We consider a general multihop network topology in which packets with various deadlines and weights arrive at and are destined to different nodes through given routes. We study the problem of joint admission control and packet scheduling in order to maximize the cumulative weights of the packets that reach their destinations within their deadlines. We first focus on uplink transmissions in the tree topology and show that the well known earliest deadline first algorithm achieves the same performance as the optimal off-line algorithm for any feasible arrival pattern. We then address the general topology with multiple source-destination pairs, develop a simple online algorithm and show that it is O(PM log PM)-competitive where PM is the maximum route length among all packets. Our algorithm only requires information along the route of each packet and our result is valid for general arrival samples. Via numerical results, we show that our algorithm achieves performance that is comparable to the non-causal optimal off-line algorithm. To the best of our knowledge, this is the first algorithm with a provable (based on a sample-path construction) competitive ratio, subject to hard deadline constraints for general network topologies.

Wireless network scheduling and control techniques (e.g., opportunistic scheduling) rely heavily on access to Channel State Information (CSI). However, obtaining this information is costly in terms of bandwidth, time, and power, and could result in large overhead. Therefore, a critical question is how to optimally manage network resources in the absence of such information. To that end, we develop a cross-layer solution for downlink cellular systems with imperfect (and possibly no) CSI at the transmitter. We use rateless codes to resolve channel uncertainty. To keep the decoding complexity low, we explicitly incorporate time-average block-size constraints, and aim to maximize the system utility. The block-size of a rateless code is determined by both the network control decisions and the unknown CSI of many time slots. Therefore, unlike standard utility maximization problems, this problem can be viewed as a constrained partial observed Markov decision problem (CPOMDP), which is known to be hard due to the “curse of dimensionality.” However, by using a modified Lyapunov drift method, we develop a dynamic network control scheme, which yields a total network utility within O(1/Lav) of utility-optimal point achieved by infinite block-size channel codes, where Lavis the enforced value of the time-average block-size of rateless codes. This opens the door of being able to trade complexity/delay for performance gains in the absence of accurate CSI. Our simulation results show that the proposed scheme improves the network throughput by up to 68% over schemes that use fixed-rate codes.

We consider information theoretic secrecy over flat fading channels under relaxed delay constraints. More specifically, we extend the definition of outage secrecy capacity for single-input single-output single-eavesdropper case (SISOSE) to account for relaxed delay constraints, and study the fundamental limits under two different assumptions on the transmitter CSI (channel state information). First, we provide bounds on secrecy outage capacity with k+1 block delay constraint. We show that the bounds are tight for several special cases. We also provide a weaker lower bound that is easier to compute, and show that under low SNR, delay constraint has significant impact on secrecy outage capacity. The analysis serves as an important step towards complete characterization of information theoretic security with delay and outage constraints.

We consider the wiretap channel model under the presence of a hybrid, half duplex adversary that is capable of either jamming or eavesdropping at a given time. We analyzed the achievable rates under a variety of scenarios involving different methods for obtaining transmitter CSI. Each method provides a different grade of information, not only to the transmitter on the main channel, but also to the adversary on all channels. Our analysis shows that main CSI is more valuable for the adversary than the jamming CSI in delay-limited scenarios. Similarly, in certain cases under the ergodic scenario, interestingly, no CSI may lead to higher achievable secrecy rates than with CSI.

This paper considers reliable communications over a multiple-input multiple-output (MIMO) Gaussian channel, where the channel matrix is within a bounded channel uncertainty region around a nominal channel matrix, i.e., an instance of the compound MIMO Gaussian channel. We study the optimal transmit covariance design to achieve the capacity of compound MIMO Gaussian channels, where the channel uncertainty region is characterized by the spectral norm. This design problem is a challenging non-convex optimization problem. However, in this paper, we reveal that this design problem has a hidden convexity property, and hence it can be simplified as a convex optimization problem. Towards this goal, we first prove that the optimal transmit design is to diagonalize the nominal channel, and then show that the duality gap between the capacity of the compound MIMO Gaussian channel and the minimal channel capacity is zero, which proves the conjecture of Loyka and Charalambous (IEEE Trans. Inf. Theory, vol. 58, no. 4, pp. 2048-2063, 2012). The key tools for showing these results are a novel matrix determinant inequality and some unitarily invariant properties.

In this paper, we consider the downlink of large OFDMA-based networks and study their performance bounds as a function of the number of - transmitters B, users K, and resource-blocks N. Here, a resource block is a collection of subcarriers such that all such collections, that are disjoint have associated independently fading channels. In particular, we analyze the expected achievable sum-rate as a function of above variables and derive novel upper and lower bounds for a general spatial geometry of transmitters, a truncated path-gain model, and a variety of fading models. We establish the associated scaling laws for dense and extended networks, and propose design guidelines for the regulators to guarantee various QoS constraints and, at the same time, maximize revenue for the service providers. Thereafter, we develop a distributed resource allocation scheme that achieves the same sum-rate scaling as that of the proposed upper bound for a wide range of K, B, N. Based on it, we compare low-powered peer-to-peer networks to high-powered single-transmitter networks and give an additional design principle. Finally, we also show how our results can be extended to the scenario where each of the B transmitters have M (>;1) co-located antennas.

We consider a single-user, single-hop wireless communication system, in which data packets arrive at a data queue to be transmitted to a receiver over a block fading channel, privately from an eavesdropper. We assume that the eavesdropper listens to the transmitter over another independently fading channel and that the transmitter only has knowledge of the distribution of the eavesdropper's channel. We propose a joint secrecy rate, transmission, and admission controller based on a simple index policy that only relies on the distribution of the eavesdropper's channel rate. Given any arrival sample path, we show that our controller achieves the maximum possible data admission rate, while keeping the data queue stable as well as meeting an upper bound on the rate of secrecy outage, i.e., the fraction of data packets that are in part or fully decodable by the eavesdropper. While the solution is not unique, i.e., there are other schemes that can achieve the aforementioned performance, we show that our scheme also achieves a low queuing delay for the data packets enqueued at the data queue by striking the correct balance between direct secrecy encoding for data bits and secret key generation and utilization. To obtain this result, our transmission controller makes use of the secret key queue to smooth out the variations in the achievable secrecy rate of the associated fading wiretap channel.

This paper considers point-to-point secure communication over flat fading channels under an outage constraint. More specifically, we extend the definition of outage capacity to account for the secrecy constraint and obtain sharp characterizations of the corresponding fundamental limits under two different assumptions on the transmitter channel state information (CSI). First, we find the outage secrecy capacity assuming that the transmitter has perfect knowledge of the legitimate and eavesdropper channel gains. In this scenario, the capacity achieving scheme relies on opportunistically exchanging private keys between the legitimate nodes. These keys are stored in a key buffer and later used to secure delay sensitive data using the Vernam's one time pad technique. We then extend our results to the more practical scenario where the transmitter is assumed to know only the legitimate channel gain. Here, our achievability arguments rely on privacy amplification techniques to generate secret key bits. In the two cases, we also characterize the optimal power control policies which, interestingly, turn out to be a judicious combination of channel inversion and the optimal ergodic strategy. Finally, we analyze the effect of key buffer overflow on the overall outage probability.

This paper considers reliable communications over a multiple-input multiple-output (MIMO) Gaussian channel, where the channel matrix is within a bounded channel uncertainty region around a nominal channel matrix, i.e., an instance of the compound MIMO Gaussian channel. We study the optimal transmit covariance matrix design to achieve the capacity of compound MIMO Gaussian channels, where the channel uncertainty region is characterized by the spectral norm. This design problem is a challenging nonconvex optimization problem. However, in this paper, we reveal that this problem has a hidden convexity property, which can be exploited to map the problem into a convex optimization problem. We first prove that the optimal transmit design is to diagonalize the nominal channel, and then show that the duality gap between the capacity of the compound MIMO Gaussian channel and the min-max channel capacity is zero, which proves and generalizes a conjecture of Loyka and Charalambous. The key tools for showing these results are a new matrix determinant inequality and some unitarily invariant properties.

We consider the problem of cross-layer resource allocation in time-varying cellular wireless networks and incorporate information theoretic secrecy as a quality-of-service constraint. Specifically, each node in the network injects two types of traffic, private and open, at rates chosen in order to maximize a global utility function, subject to network stability and secrecy constraints. The secrecy constraint enforces an arbitrarily low mutual information leakage from the source to every node in the network, except for the sink node. We first obtain the achievable rate region for the problem for single- and multiuser systems assuming that the nodes have full channel state information (CSI) of their neighbors. Then, we provide a joint flow control, scheduling, and private encoding scheme, which does not rely on the knowledge of the prior distribution of the gain of any channel. We prove that our scheme achieves a utility arbitrarily close to the maximum achievable utility. Numerical experiments are performed to verify the analytical results and to show the efficacy of the dynamic control algorithm.

As many sensor network applications require deployment in remote and hard-to-reach areas, it is critical to ensure that such networks are capable of operating unattended for long durations. Consequently, the concept of using nodes with energy replenishment capabilities has been gaining popularity. However, new techniques and protocols must be developed to maximize the performance of sensor networks with energy replenishment. Here, we analyze limits of the performance of sensor nodes with limited energy, being replenished at a variable rate. We provide a simple localized energy management scheme that achieves a performance close to that with an unlimited energy source and at the same time keeps the probability of complete battery discharge low. Based on the insights developed, we address the problem of energy management for energy-replenishing nodes with finite battery and finite data buffer capacities. To this end, we give an energy management scheme that achieves the optimal utility asymptotically while keeping both the battery discharge and data loss probabilities low.

In this paper, we consider the downlink of large-scale multi-cellular OFDMA-based networks and study performance bounds of the system as a function of the number of users K, the number of base-stations B, and the number of resource-blocks N. Here, a resource block is a collection of subcarriers such that all such collections, that are disjoint have associated independently fading channels. We derive novel upper and lower bounds on the sum-utility for a general spatial geometry of base stations, a truncated path loss model, and a variety of fading models (Rayleigh, Nakagami-m, Weibull, and LogNormal). We also establish the associated scaling laws and show that, in the special case of fixed number of resource blocks, a grid-based network of base stations, and Rayleigh-fading channels, the sum information capacity of the system scales as Θ(B log log K/B) for extended networks, and as O(B log log K) and Ω(log log K) for dense networks. Interpreting these results, we develop some design principles for the service providers along with some guidelines for the regulators in order to achieve provisioning of various QoS guarantees for the end users and, at the same time, maximize revenue for the service providers.

We consider the problem of resource allocation in a wireless cellular network, in which nodes have both open and private information to be transmitted to the base station over block fading uplink channels. We develop a cross-layer solution, based on hybrid ARQ transmission with incremental redundancy. We provide a scheme that combines power control, flow control, and scheduling in order to maximize a global utility function, subject to the stability of the data queues, an average power constraint, and a constraint on the privacy outage probability. Our scheme is based on the assumption that each node has an estimate of its uplink channel gain at each block, while only the distribution of the cross channel gains is available. We prove that our scheme achieves a utility, arbitrarily close to the maximum achievable utility given the available channel state information.

We consider the problem of joint link scheduling and power control for wireless networks with average transmission power constraints. Due to the high computational complexity of the optimal policies, we extend the class of greedy link scheduling policies to handle average power constraints. We develop a greedy link scheduling and power control scheme GECS, with provable performance guarantees. We show that the performance of our greedy scheduler can be characterized using the Local Pooling Factor (LPF) of a network graph, which has been previously used to characterize the stability of the Greedy Maximal Scheduling (GMS) policy for wireless networks. We also simulate the performance of GECS on wireless network, and compare its performance to another candidate greedy link scheduling and power control policy.

This paper studies the achievable secure rate per source-destination pair in wireless networks. First, a path loss model is considered, where the legitimate and eavesdropper nodes are assumed to be placed according to Poisson point processes with intensities λ and λe, respectively. It is shown that, as long as λe/λ =o((logn)-2), almost all of the nodes achieve a perfectly secure rate of Ω(1/√n) for the extended and dense network models. Therefore, under these assumptions, securing the network does not entail a loss in the per-node throughput. The achievability argument is based on a novel multihop forwarding scheme where randomization is added in every hop to ensure maximal ambiguity at the eavesdropper(s). Second, an ergodic fading model withnsource-destination pairs and neeavesdroppers is considered. Employing the ergodic interference alignment scheme with an appropriate secrecy precoding, each user is shown to achieve a constant positive secret rate for sufficiently largen. Remarkably, the scheme does not require eavesdropper CSI (only the statistical knowledge is assumed) and the secure throughput per node increases as we add more legitimate users to the network in this setting. Finally, the effect of eavesdropper collusion on the performance of the proposed schemes is characterized.

Information-theoretic broadcast channels (BCs) and multiple-access channels (MACs) enable a single node to transmit data simultaneously to multiple nodes, and multiple nodes to transmit data simultaneously to a single node, respectively. In this paper, we address the problem of link scheduling in multihop wireless networks containing nodes with BC and MAC capabilities. We first propose an interference model that extends protocol interference models, originally designed for point-to-point channels, to include the possibility of BCs and MACs. Due to the high complexity of optimal link schedulers, we introduce the Multiuser Greedy Maximum Weight algorithm for link scheduling in multihop wireless networks containing BCs and MACs. Given a network graph, we develop new local pooling conditions and show that the performance of our algorithm can be fully characterized using the associated parameter, the multiuser local pooling factor. We provide examples of some network graphs, on which we apply local pooling conditions and derive the multiuser local pooling factor. We prove optimality of our algorithm in tree networks and show that the exploitation of BCs and MACs improve the throughput performance considerably in multihop wireless networks.

A coding problem, over a slotted system, is introduced where the sender has to transmit one out of several packets to the receiver, but learns the request only at the beginning of each slot with prior statistical information about which packet is needed at the receiver. There is an associated cost of sending bits at each slot, and the goal is to minimize the expected cost of the communication. A proactive coding scheme is proposed, where the source proactively communicates with the receiver before the receiver requests the message. This way, by designing a cost optimal side information at the receiver, the scheme is able to minimize the expected cost of the communication. Numerical results are provided demonstrating the gains obtained by proactive coding over the conventional coding technique.

Renewable energy sources can be attached to sensor nodes to provide energy replenishment for prolonging the lifetime of sensor networks. However, for networks with replenishment, conservative energy expenditure may lead to missed recharging opportunities due to battery capacity limitations, while aggressive usage of energy may result in reduced coverage or connectivity for certain time periods. Thus, new power allocation schemes need to be designed to balance these seemingly contradictory goals, in order to maximize sensor network performance. In this paper, we study the problem of how to jointly control the data queue and battery buffer to maximize the long-term average sensing rate of a single communication link in rechargeable sensor networks. The coupling between the battery and data buffers does not lend itself amenable to traditional resource optimization techniques. Thus, we develop a new power and rate allocation scheme that explicitly takes this coupling into account. The new scheme is a simple myopic scheme whose performance is shown to be arbitrarily close to optimal analytically and via simulations.

In recent years, the famous wiretap channel has been revisited by many researchers and information theoretic secrecy has become an active area of research in this setting. In this paper, we design a wireless communication system that achieves constant bit rate data transmission over a block fading channel, securely from an eavesdropper that listens to the transmitter over another independent block fading channel. It is well known that, the method of sending secure information using the binning techniques inspired by the wiretap channel fails to secure the information at times when the eavesdropper channel has favorable conditions over the main channel. This phenomenon is called secrecy outage. In our system, however, we exploit the times at which the main channel is favorable over the eavesdropper channel for us to be able to transmit some random secret key bits along with the data bits. These key bits are stored in a separate key queue at the transmitter as well as the receiver, and are utilized to secure data bits, whenever the channel conditions favor the eavesdropper. We show that, our system achieves a high performance at any given desired outage probability by jointly controlling the key queue and the transmit power. We show that the optimal power control involves a time sharing between secure waterfilling and channel inversion strategies and the key queue operates in the heavy traffic regime to achieve the maximum delay limited rate possible, under a small outage constraint. This work can be viewed as a first step in providing a framework that combines both information theory and queueing analysis for the study of information theoretic security.

Energy harvesting sensor platforms have opened up a new dimension to the design of network protocols. In order to sustain the network operation, the energy consumption rate cannot be higher than the energy harvesting rate, otherwise, sensor nodes will eventually deplete their batteries. In contrast to traditional network resource allocation problems where the resources are static, the time-varying recharging rate presents a new challenge. In this paper, We first explore the performance of an efficient dual decomposition and subgradient method based algorithm, called QuickFix, for computing the data sampling rate and routes. However, fluctuations in recharging can happen at a faster time-scale than the convergence time of the traditional approach. This leads to battery outage and overflow scenarios, that are both undesirable due to missed samples and lost energy harvesting opportunities respectively. To address such dynamics, a local algorithm, called SnapIt, is designed to adapt the sampling rate with the objective of maintaining the battery at a target level. Our evaluations using the TOSSIM simulator show that QuickFix and SnapIt working in tandem can track the instantaneous optimum network utility while maintaining the battery at a target level. When compared with IFRC, a backpressure-based approach, our solution improves the total data rate by 42% on the average while significantly improving the network utility.

Information theoretic Broadcast Channels (BC) and Multiple Access Channels (MAC) enable a single node to transmit data simultaneously to multiple nodes, and multiple nodes to transmit data simultaneously to a single node respectively. In this paper, we address the problem of link scheduling in multihop wireless networks containing nodes with BC and MAC capabilities. We first propose an interference model that extends protocol interference models, originally designed for point to point channels, to include the possibility of BC and MAC. Due to the high complexity of optimal link schedulers, we introduce the Multiuser Greedy Maximum Weight algorithm for link scheduling in multihop wireless networks containing BCs and MACs. Given a network graph, we develop new local pooling conditions} and show that the performance of our algorithm can be fully characterized using the associated parameter, the multiuser local pooling factor. We provide examples of some network graphs, on which we apply local pooling conditions and derive the multiuser local pooling factor. We prove optimality of our algorithm in tree networks and show that the exploitation of BCs and MACs improve the throughput performance considerably in multihop wireless networks.

The problem of providing quality-of-service (QoS) guarantees for multicast traffic over crossbar switches has received limited attention despite the popularity of its counterpart for unicast traffic. Providing a 100% throughput to all admissible multicast traffic has been shown to be a very difficult task, and it requires a very high speedup in the switching fabric. In this paper, we introduce the concept of rate quantization and use rate quantization to show an analogy between packet scheduling in crossbar switches and circuit switching in three-stage Clos networks. We exploit the analogy to adopt circuit-switching algorithms in wide-sense and strict-sense nonblocking Clos networks in order to construct nonblocking packet schedulers for unicast and multicast traffic. We illustrate a simple multicast nonblocking packet scheduler, for which a speedup of 6logn/loglogn is sufficient to support 100% throughput for any admissible multicast traffic in an n×n crossbar switch. Moreover, we revisit some problems in unicast switch scheduling. We illustrate that the analogy provides useful perspectives, and we give a simple proof for a well-known result.

One of the main issues in the design of sensor networks is energy efficient communication of time-critical data. Energy wastage can be caused by failed packet transmission attempts at each node due to channel dynamics and interference. Therefore transmission control techniques that are unaware of the channel dynamics can lead to suboptimal channel use patterns. In this paper we propose a transmission controller that utilizes different "grades" of channel side information to schedule packet transmissions in an optimal way, while meeting a deadline constraint for all packets waiting in the transmission queue. The wireless channel is modeled as a finite-state Markov channel (FSMC). We are specifically interested in the case where the transmitter has low-grade channel side information that can be obtained based solely on the ACK/NAK sequence for the previous transmissions. Our scheduler is readily implementable and it is based on the dynamic programming solution to the finite-horizon transmission control problem. We also calculate the information theoretic capacity of the finite state Markov channel with feedback containing different grades of channel side information including that, obtained through the ACK/NAK sequence. We illustrate that our scheduler achieves a given throughput at a power level that is fairly close to the fundamental limit achievable over the channel.

We consider adapting the transmission rate to maximize the goodput, i.e., the amount of data transmitted without error, over a continuous Markov flat-fading wireless channel. In particular, we consider schemes in which transmitter channel state is inferred from degraded causal error-rate feedback, such as packet-level ACK/NAKs in an automatic repeat request (ARQ) system. In such schemes, the choice of transmission rate affects not only the subsequent goodput but also the subsequent feedback, implying that the optimal rate schedule is given by a partially observable Markov decision process (POMDP). Because solution of the POMDP is computationally impractical, we consider simple suboptimal greedy rate assignment and show that the optimal scheme would itself be greedy if the error-rate feedback was non-degraded. Furthermore, we show that greedy rate assignment using non-degraded feedback yields a total goodput that upper bounds that of optimal rate assignment using degraded feedback. We then detail the implementation of the greedy scheme and propose a reduced-complexity greedy scheme that adapts the transmission rate only once per block of packets. We also investigate the performance of the schemes numerically, and show that the proposed greedy scheme achieves steady-state goodputs that are reasonably close to the upper bound on goodput calculated using non-degraded feedback. A similar improvement is obtained in steady-state goodput, drop rate, and average buffer occupancy in the presence of data buffers. We also investigate an upper bound on the performance of optimal rate assignment for a discrete approximation of the channel and show that such quantization leads to a significant loss in achievable goodput.

We show an isomorphism between maximal matching for packet scheduling in crossbar switches and strictly non-blocking circuit switching in three-stage Clos networks. We use the analogy for a crossbar switch of size n times n to construct a simple multicast packet scheduler of complexity O(n log n) based on maximal matching. We show that, with this simple scheduler, a speedup of O(log n/log log n) is necessary to support 100% throughput for any admissible multicast traffic. If fanout splitting of multicast packets is not allowed, we show that an extra speedup of 2 is necessary, even when the arrival rates are within the admissible region for mere unicast traffic. Also we revisit some problems in unicast switch scheduling. We illustrate that the analogy provides useful perspectives and we give a simple proof for a well known result.

In sensor networks, application layer QoS requirements are critical to meet while conserving energy. One of the leading factors for energy wastage is failed transmission attempts due to channel dynamics and interference. Existing techniques are unaware of the channel dynamics and lead to suboptimal channel access patterns. We propose a MAC layer solution called pushback, that appropriately delays packet transmissions to overcome periods of poor channel quality and high interference, while ensuring that the throughput requirement of the node is met. It uses a hidden Markov model (HMM) based channel model that is maintained without any additional signaling overhead. The pushback algorithm is shown to improve the packet success rate by up to 71% and reduce the number of transmissions needed by up to 38% while ensuring the same throughput.

We study the relationship between the degree of blocking and the amount of resource speedup necessary for blocking switches to possess the capabilities of nonblocking switches. We construct an analogy between switch configurations and the codewords of certain error control codes for which we use space covering ideas to derive relations between speedup and number of switch configurations. To derive the necessary speedup for nonblocking, we use two sphere packing bounds: the Hamming bound and the Gilbert-Varshamov bound. To construct nonblocking switches with a given speedup we use maximum distance separable codes. We consider both multicast and point to point scenarios.

While the problem of analyzing network traffic at the granularity of individual connections has seen considerable previous work and tool development, understanding traffic at a higher level - the structure of user-initiated sessions comprised of groups of related connections - remains much less explored. Some types of session structure, such as the coupling between an FTP control connection and the data connections it spawns, have prespecified forms, though the specifications do not guarantee how the forms appear in practice. Other types of sessions, such as a user reading email with a browser, only manifest empirically. Still other sessions might exist without us even knowing of their presence, such as a botnet zombie receiving instructions from its master and proceeding in turn to carry them out. We present algorithms rooted in the statistics of Poisson processes that can mine a large corpus of network connection logs to extract the apparent structure of application sessions embedded in the connections. Our methods are semi-automated in that we aim to present an analyst with high-quality information (expressed as regular expressions) reflecting different possible abstractions of an application's session structure. We develop and test our methods using traces from a large Internet site, finding diversity in the number of applications that manifest, their different session structures, and the presence of abnormal behavior. Our work has applications to traffic characterization and monitoring, source models for synthesizing network traffic, and anomaly detection.

Constructing an n times n non-blocking wavelength router using n times n optical cross-connects may be impractical due to certain constraints such as the cost or space limitations. Moreover, in many cases the traffic requirements can be handled without a non-blocking router. In this paper, we study blocking wavelength routers constructed using x times x, x < n optical cross-connects without wavelength conversion. We find the set of rates that can be supported between the input and output fibers of a certain set of symmetric blocking routers. We propose a method to construct blocking routers to achieve any given supportable rate in this region for any given x

This paper considers the problem of selecting good paths in a wireless mesh network. It is well-known that picking the path with the smallest number of hops between two nodes often leads to poor performance, because such paths tend to use links that could have marginal quality. As a result, quality-aware routing metrics are desired for networks that are built solely from wireless radios. Previous work has developed metrics (such as ETX) that work well when wireless channel conditions are relatively static (DeCouto , 2003), but typical wireless channels experience variations at many time-scales. For example, channels may have low average packet loss ratios, but with high variability, implying that metrics that use the mean loss ratio will perform poorly. In this paper, we describe two new metrics, called modified expected number of transmissions (mETX) and effective number of transmissions (ENT) that work well under a wide variety of channel conditions. In addition to analyzing and evaluating the performance of these metrics, we provide a unified geometric interpretation for wireless quality-aware routing metrics. Empirical observations of a real-world wireless mesh network suggest that mETX and ENT could achieve a 50% reduction in the average packet loss rate compared with ETX

This paper describes the Multi-Radio Diversity (MRD) wireless system, which uses path diversity to improve loss resilience in wireless local area networks WLANs). MRD coordinates wireless receptions among multiple radios to improve loss resilience in the face of path-dependent frame corruption over the radio. MRD incorporates two techniques to recover from bit errors and lower the loss rates observed by higher layers, without consuming much extra bandwidth. The first technique is frame combining in which multiple, possibly erroneous, copies of a given frame are combined together in an attempt to recover the frame without retransmission. The second technique is a low-overhead retransmission scheme called request-for-acknowledgment (RFA), which operates above the link layer and below the network layer to attempt to recover from frame combining failures. We present an analysis that determines how the parameters for these algorithms should be chosen.We have designed and implemented MRD as a fully functional WLAN infrastructure based on 802.11a. In our testbed, we measured throughput gains up to 2.3 - over single radio communication schemes employing 802.11's autorate adaptation scheme.

We study the provision of deterministic rate guarantees over single crossbar switches. Birkhoff decomposition yields a general approach for this problem, but the required complexity can be very high and the quality of service can be unsatisfactory for practical traffic sources. We develop a method called rate quantization which works with any resource speedup greater than 1 to convert the set of desired rates into a certain discrete set in such a way that the complexity and the quality of service guarantees can be greatly improved over a Birkhoff switch. Moreover, quantization enables us to develop a Slepian-Duguid-like algorithm that enables the switch to both adapt to dynamically varying traffic and simplify switch scheduling significantly.

The relation between the degree of blocking and the amount of resource speedup necessary for blocking switches to possess the capabilities of nonblocking switches is studied in this paper and considers both multicast and point to point scenarios. We show that the number of configurations that must be supported by a switching interconnection fabric can be significantly reduced even with small speedup values. We construct an analogy between switch configurations and the codewords of certain error control codes for which we use space covering ideas to derive relations between speedup and number of switch configurations. To derive the necessary speedup for non-blocking, we use two sphere packing bounds: the hamming bound and the Gilbert-Varshamov bound to construct nonblocking switches with a given speedup we use maximum distance separable codes.

No abstract available.