Understanding the behavior of phase errors between radio frequency (RF) chains in software-defined radios (SDRs) is crucial to the success of implementing many phase-sensitive applications, such as beamforming. Even if SDRs are provided the same clocking signal, initial local oscillator (LO) phase offsets across devices will inevitably be different. Despite its known effect on many wireless applications, there are only few works that experimentally discuss random phase errors in SDRs. To address this issue, we perform experiments and analyze the results of tens of experiments in an attempt to understand the nature of this phase offset. In particular, we target the USRP (Universal Standard Radio Peripheral) N310 platform as it provides up to 4 simultaneous transmit/receive chains that could be attractive for beamforming applications. We first model the system used in this study and demonstrate how phase errors can affect distributed beamforming gains. Then, we introduce our experimental setup, procedures and analysis of the results of the measured phase error. We do so first between two chains of the same/different transceiver boards within the same USRP, and then between chains of distributed USRPs that are geographically separated. We calculate the mean and standard deviation of this phase error, investigate its behavior over time, and demonstrate how the distribution of this error can vary based on whether it is measured in a centralized or a distributed fashion.

In this paper, we propose a multi-band medium access control (MAC) protocol for an infrastructure-based network with an access point (AP) that supports In-Band full-duplex (IBFD) and multiuser transmission to multi-band-enabled stations. The Multi-Band Full Duplex MAC (MB-FDMAC) protocol mainly uses the sub-6 GHz band for control-frame exchange, transmitted at the lowest rate per IEEE 802.11 standards, and uses the 60 GHz band, which has significantly higher instantaneous bandwidth, exclusively for data-frame exchange. We also propose a selection method that ensures fairness among uplink and downlink stations. Our result shows that MB-FDMAC effectively improves the spectral efficiency in the mmWave band by 324%, 234%, and 189% compared with state-of-the-art MAC protocols. In addition, MB-FDMAC significantly outperforms the combined throughput of sub-6 GHz and 60 GHz IBFD multiuser MIMO networks that operate independently by more than 85%. In addition, we study multiple network variables such as the number of stations in the network, the percentage of mmWave band stations, the size of the contention stage, and the selection method on MB-FDMAC by evaluating the change in the throughput, packet delay, and fairness among stations. Finally, we propose a method to improve the utilization of the high bandwidth of the mmWave band by incorporating time duplexing into MB-FDMAC, which we show can enhance the fairness by 12.5 % and significantly reduces packet delay by 80%.

Distributed transmit beamforming (DTBF) can allow a swarm of unmanned aerial vehicles (UAVs) to send a common message to a distant target. DTBF among N nodes can provide N2times the received power compared to a single node and can reduce interference by confining the signal in a certain direction. However, DTBF requires time, frequency, and phase synchronization. Here, we focus on the issue of phase incoherence at the distributed transmit nodes from two sources—different local oscillators (LOs) and hovering position movement—and how to counteract their impact at the receiver via local decisions, namely, rotation. To investigate how the UAV body and its rotation can affect phase coherency, we conduct controlled in-field experiments where we control the phase offset at two distributed antennas and measure the received signal level at four antenna positions on a drone for various rotation angles. We show that significant improvements can be achieved at the receiver through rotation. We also show that there exists an optimal combination of UAV rotation angle and antenna position on the drone to mitigate the effects of phase incoherence among the distributed transmitters. Finally, we demonstrate an interesting trade-off where, due to the heterogeneous nature of the UAV body, rotation angles that yield maximum beamforming gains might not result in the best average (or minimum) beamformed signal level across all possible phase errors at the distributed transmitters.

In this paper, we investigate the user selection techniques for an in-band full-duplex access point that simultaneously transmits and receives data from multiple users for the next-generation wireless local area network. Then, we propose a throughput-fairness tradeoff selection algorithm to enable the AP to maximize the throughput with a maintainable fairness level. In addition, we propose a throughput-fairness medium access control (TFMAC) based on the 802.11 standards to accommodate the requirements of the proposed selection algorithm and support legacy nodes. Our simulation results show that TFMAC improves the throughput compared to multiple state-of-the-art benchmarks while maintaining the desired fairness levels. Also, we study the interplay between the throughput, the uplink fairness, and the downlink fairness for the operation of TFMAC. Finally, we discuss the complexity of the proposed scheme.

Night time vehicle detection and tracking using traditional visible light cameras is a challenging task due to the limited visibility. The current state-of-the-art systems treat the vehicles at night time as paired vehicle headlights or taillights, with no ability to determine the contour of the vehicle or its spatial occupancy. Therefore, this paper proposes the first night time framework that combines the vehicle headlights and taillights to localize the vehicle contours. This new framework includes a novel multi-camera vehicle representation that groups and reconstructs vehicle headlights and taillights following mutual geometric distances between different vehicle components. This novel vehicle contour representation successfully removes duplicated vehicle lights and also compensates for the missing vehicle lights in the detection process. Eventually, vehicle headlight alignment and contour adjustment are used to further refine the vehicle contours. The proposed multi-camera system considers typical four-wheel vehicles, e.g., cars and SUVs, in the monitoring and might not be able to handle large trucks, e.g., 18-wheelers. The experiments are conducted on night time traffic videos under various scenarios and the proposed system attains an average of 0.896 in Multiple Object Tracking Accuracy (MOTA) and an average of 0.904 in Jaccard Coefficient (JC), which indicates 19.2% and 15.9% increases over the baseline system. The night time traffic datasets used in this paper are available athttps://github.com/JustMeZXX/Intelligent-Night-Time-Traffic-Surveillance

In this paper, we develop a unified framework to design a video surveillance network which also ensures that the selected cameras form a fully connected wireless network. The importance of including the wireless connectivity constraints in the design process is established. The results also show how the total number of cameras in the network can be traded-off with the number of terminals at which these cameras are installed. The advantages of including multiple camera types in the design is shown in reducing the minimum number of cameras that are needed. Essentially, our results allow these multiple camera types to be optimally selected to better tile and cover the desired area.

In this paper, we investigate a mobile edge computing (MEC) system in which a set of users with intensive computation tasks, and a set of users with high downlink rate requirement, can cooperate to achieve a mutually-beneficial situation where the task completion time is reduced and the downlink users receive more information from the base station (BS). Specifically, by leveraging uplink and downlink non-orthogonal multiple access (NOMA), the user with an intensive computation task can offload its task bits to the edge cloud and the downlink user. Simultaneously this user relays information to the downlink user from the BS. We consider the joint optimization of computational resource allocation at the edge cloud, communication resource allocation, assignment among the two sets of users, the share of computation, and relay bits to minimize the overall completion time of the tasks while guaranteeing downlink users' incentive requirement. A low complexity iterative algorithm is proposed to find efficient locally optimal solutions by utilizing convex optimization, a graph theory matching algorithm, and block coordinate descent technique. Numerical results show that the proposed technique leads to a significant reduction in users' task completion time and increase in the downlink users rate.

Unmanned Aerial Vehicles (UAVs) often lack the size, weight, and power to support large antenna arrays or a large number of radio chains. Despite such limitations, emerging applications that require the use of swarms, where UAVs form a pattern and coordinate towards a common goal, must have the capability to transmit in any direction in three-dimensional (3D) space from moment to moment. In this work, we design a measurement study to evaluate the role of antenna polarization diversity on UAV systems communicating in arbitrary 3D space. To do so, we construct flight patterns where one transmitting UAV is hovering at a high altitude (80 m) and a receiving UAV hovers at 114 different positions that span 3D space at a radial distance of approximately 20 m along equally-spaced elevation and azimuth angles. To understand the role of diverse antenna polarizations, both UAVs have a horizontally-mounted antenna and a vertically-mounted antenna-each attached to a dedicated radio chain-creating four wireless channels. With this measurement campaign, we seek to understand how to optimally select an antenna orientation and quantify the gains in such selections.

Nonlinear channel codes are employed to protect two information fields that are related to the daylight saving time (DST) and leap second notifications within the enhanced WWVB broadcast format. The general codebook design criteria and the construction of these codes using integer linear program (ILP) is formulated. Specifically, two different variations are presented: i) minimizing the union bound of probability of word error (PWE), which is a tighter bound but has high computational complexity and ii) minimizing an analytical closed form approximation to the PWE, which has much lower complexity but is a looser bound on the exact PWE. By leveraging the highly non-uniform a-priori probabilities of the different messages being transmitted in these two fields, the proposed nonlinear codes provide coding gains of about 2-3 dB in the presence of additive white Gaussian noise (AWGN). When compared to checksum codes with the same rate, the nonlinear codes offer performance advantages of about 1-2 dB for the DST notification field. In the presence of impulsive noise, simulations and analysis show that the probability of word error when employing the proposed nonlinear code is lower by many orders of magnitude compared with that of the uncoded system.

A new medium access control (MAC) protocol for full-duplex multiuser multi input multi output (FD-MUMAC) is proposed. FD-MUMAC is an 802.11 infrastructure-based MAC protocol where the communication in the network is moderated by a full-duplex access point (AP) that can handle multiple simultaneous uplink and downlink streams from half-duplex users. FD-MUMAC is the first MAC protocol that jointly determines the selection of uplink and downlink users and controls the transmission rate by considering transmit and receive beamforming, channel states, and multiuser-interferences. To demonstrate the usefulness of the proposed protocol, we introduce a joint fairness selection algorithm that ensures that each user obtains a fair allocation of time to utilize the channel and balances its traffic between the uplink and downlink directions. In one specific instantiation, FD-MUMAC achieves a throughput gain of 59%, 177% and 94% compared with a single antenna FD MAC protocol and two half-duplex MU-MIMO protocols, respectively.

The next wave of drone applications is moving from repeatable, single-drone activities such as evaluating propagation environments to team-based, multi-drone objectives such as drone-based emergency services. In parallel, testbeds have sought to evaluate emerging concepts such as highly-directional and distributed wireless communications. However, there is a lack of intersection between the two works to characterize the impact of the drone body, antenna placement, swarm topologies, and multi-dimensional connectivity needs that require in-flight experimentation with a surrounding testbed infrastructure. In this work, we design a Multi-Dimensional Drone Communications Infrastructure (MuDDI) to capture complex spatial wireless channel relationships that drone links experience as applications scale from single-drone to swarm-level networks within a shared three-dimensional space. Driven by the challenges of outdoor experimentation, we identify the need for a highly-controlled indoor environment where external factors can be mitigated. To do so, we first build an open-source drone platform to provide programmable control with visibility into the internal flight control system and sensors enabling specialized coordination and accurate repeatable positioning within the isolated environment. We then design a wireless data acquisition system and integrate distributed software defined radios (SDRs) in order to inspect multi-dimensional wireless behavior from the surrounding area. We achieve and demonstrate the value of measurement perspectives from diverse altitudes and spatial locations with the same notion of time. Finally, we demonstrate how multi-dimensional models from experimental measurements can be implemented to simulate multi-drone networks on a practical scale.

Night time vehicle detection and tracking has been a challenging task in recent years. This paper presents a novel context-aware traffic surveillance system that integrates sensor information from autonomous vehicles to improve performance of night time vehicle detection and tracking. The key elements of the proposed method include a novel vehicle pairing framework that represents vehicles based on the fused sensor contexts and vehicle taillights. These detected vehicles are then tracked in real-time night time traffic videos. Experiments are conducted on real traffic videos and the proposed system attains 0.6319 in multiple object tracking accuracy (MOTA), which represents a 26.1% increase compared with the baseline performance.

Computer-vision methods have been extensively used in intelligent transportation systems for vehicle detection. However, the detection of severely occluded or partially observed vehicles due to the limited camera fields of view remains a challenge. This paper presents a multi-camera vehicle detection system that significantly improves the detection performance under occlusion conditions. The key elements of the proposed method include a novel multi-view region proposal network that localizes the candidate vehicles on the ground plane. We also infer the vehicle position on the ground plane by leveraging multi-view cross-camera context. Experiments are conducted on dataset captured from a roadway in Richardson, TX, USA, and the system attains 0.7849 Average Precision and 0.7089 Multi Object Detection Precision. The proposed system results in an approximately 31.2% increase in AP and 8.6% in MODP than the single-camera methods.

In the next wave of swarm-based applications, unmanned aerial vehicles (UAVs) need to communicate with peer drones in any direction of a three-dimensional (3D) space. On a given drone and across drones, various antenna positions and orientations are possible.We know that, in free space, high levels of signal loss are expected if the transmitting and receiving antennas are cross polarized. However, increasing the reflective and scattering objects in the channel between a transmitter and receiver can cause the received polarization to become completely independent from the transmitted polarization, making the cross-polarization of antennas insignificant. Usually, these effects are studied in the context of cellular and terrestrial networks and have not been analyzed when those objects are the actual bodies of the communicating drones that can take different relative directions or move at various elevations. In this work, we show that the body of the drone can affect the received power across various antenna orientations and positions and act as a local scatterer that increases channel depolarization, reducing the cross-polarization discrimination (XPD). To investigate these effects, we perform experimentation that is staged in terms of complexity from a controlled environment of an anechoic chamber with and without drone bodies to in-field environments where drone-mounted antennas are in-flight with various orientations and relative positions with the following outcomes: (i.) drone relative direction can significantly impact the XPD values, (ii.) elevation angle is a critical factor in 3D link performance, (iii.) antenna spacing requirements are altered for co-located cross-polarized antennas, and (iv.) cross-polarized antenna setups more than double spectral efficiency. Our results can serve as a guide for accurately simulating and modeling UAV networks and drone swarms.

Key Performance Indicators (KPIs) are important measures of the quality of service in cellular networks. There are multiple efforts by cellular carriers and 5G standardization to leverage the KPIs to minimize drive tests (MDT) and self-organize the network for optimal performance via user feedback. Such an approach accounts for user devices in the field of their operation according to their normal usage and circumvents a number of costs (e.g., manpower, equipment) traditionally covered by the carrier, either directly or through a third party. In this paper, we build a Regional Analysis to Infer KPIs (RAIK) framework to establish a relationship between geographical data and user data using crowdsourced measurements. To do so, we use a neural network and crowdsourced data obtained by user equipment (UE) to predict the KPIs in terms of the reference signal's received power (RSRP) and path loss estimation. Since these KPIs are a function of terrain type, we provide a two-layer coverage map by overlaying a performance layer on a 3-dimensional geographical map. As a result, we can efficiently use crowdsourced data (to not overextend user bandwidth and battery) and infer KPIs in areas where measurements have not or can not be performed. For example, we show that RAIK can use only geographical information to predict the KPIs in areas that lack signal quality data with a negligible mean squared error, a seven-fold reduction in error from state-of-the-art solutions.

Conducting in-field performance analysis for wireless carrier coverage and capacity evaluation is extremely costly, in terms of equipment, manpower, and time. Hence, there is a growing number of opportunities that exist for crowdsourcing via smart applications, firmware, and cellular standards. These facilities offer carriers feedback about user-perceived wireless channel quality. Crowdsourcing provides the ability to rapidly collect feedback with dense levels of penetration using client smartphones. However, mobile phones often fail to capture the fidelity and high sampling rate of more advanced equipment (e.g., a channel scanner) used when drive testing for analysis of propagation characteristics. In this work, we study the impact of various effects induced by user equipment (UE), when sampling signal quality. These shortcomings include averaging over multiple samples, imprecise quantization, and non-uniform and/or less frequent channel sampling. We specifically, investigate the accuracy of characterizing large-scale fading using crowdsourced data in presence of the aforementioned phone measurement shortcomings. To do so, we conduct extensive in-field experiments across heterogeneous devices and environments to empirically quantify the perceived channel characteristics by phone measurements. Analyzing the quality of the smartphone measurements in LTE indicates that the inferred radio propagation models, is comparable with models obtained by advanced equipment.

Ensuring cellular coverage is an important and costly concern for carriers due to the expense of in-field experimentation (i.e., drive testing). With the ubiquity of smartphones, apps, and social media, there has been an explosion of crowdsourcing to understand a vast array of trends and topics at a minimal cost to the organization. While cellular carriers might seek to replace the expensive act of drive testing with the nearly cost-free crowdsourcing, questions remain as to: (i) ~the accuracy of crowdsourcing, considering the lack of user control, (ii) ~the detection of when drive testing might still be required, and (iii) ~the quantification of how many additional in-field measurements to perform for a certain accuracy level. In this work, we use geographical features of a region to reduce in-field propagation experimentation by predicting the number of measurements required to accurately characterize its path loss. In particular, we study the path loss prediction accuracy of drive testing and crowdsourcing by taking millions of measurements in a suburban and downtown region. We then use statistical learning to build a relationship between these geographical features and the measurements required. In doing so, we find that the number of measurements collected to achieve a certain path loss accuracy over the entire region can be reduced by up to $58%$ in a high density drive testing scenario.

Cloud platforms are attractive for executing large concurrent applications that require access to a pool of resources for concurrently executing the partitions of their workloads. Historically, application designers have tuned concurrent applications for specific hardware and platforms. But such approaches are not viable in cloud platforms as applications can be deployed on a variety of platforms and the operating environments can vary in each deployment. In this work, we argue and demonstrate that concurrent applications in cloud platforms must be self-tuning. First, we show that applications must incorporate a model of the overheads of operation. Second, we show that applications must determine their resource requirements and tune their operation to the operating conditions using estimations from the model. We build two self-tuning applications, E-Sort and E-MAKER, and demonstrate their ability to achieve high cost-efficiency by determining the right scale of partitions and resources to use for operation and adapting their behavior according to the characteristics of the deployed environment.

In this paper, we develop a new capacity-approaching code, namely, parallel-concatenated (PC)-Low Density Parity Check (LDPC) convolutional code that is based on the parallel concatenation of trellis-based quasi-cyclic LDPC (TQC-LDPC) convolutional codes. The proposed PC-LDPC convolutional code can be derived from any QC-LDPC block code by introducing the trellis-based convolutional dependency to the code. The capacity-approaching PC-LDPC convolutional codes are encoded through parallel concatenated trellis-based QC recursive systematic convolutional (RSC) encoder (namely, QC-RSC encoder) that is also proposed in this paper. The proposed PC-LDPC convolutional code and the associated encoder retain a fine input granularity on the order of the lifting factor of the underlying block code. We also describe the corresponding trellis-based QC maximum a posteriori probability (namely, QC-MAP) decoder that efficiently decodes the PC-LDPC convolutional code. Performance and hardware implementation results show that the PC-LDPC convolutional codes with the QC-MAP decoder have two times lower complexity for a given bit-error-rate (BER), signal-to-noise ratio, and data rate, than conventional QC-LDPC block codes and LDPC convolutional codes. Moreover, the PC-LDPC convolutional code with the QC-MAP decoder outperforms the conventional QC-LDPC block codes by more than 0.5 dB for a given BER, complexity, and data rate and approaches Shannon capacity limit with a gap smaller than 1.25 dB. This low decoding complexity and the fine granularity make it feasible to efficiently implement the proposed capacity-approaching PC-LDPC convolutional code and the associated trellis-based QC-MAP decoder in next generation ultra-high data rate mobile systems.

While there were high hopes for multihop wireless networks (mesh) to provide ubiquitous Wi-Fi in many cities, in-field trials revealed the node spacing required for Wi-Fi propagation induced a prohibitive cost model for network carriers to deploy. However, the digitization of TV channels and new FCC regulations have reapportioned spectrum for data networks with far greater range than WiFi due to lower carrier frequencies. In this paper, we analyze our in-field measurements in the Dallas-Fort Worth metroplex of channel occupancy in both WiFi and white space frequencies to deploy a wireless multihop backhaul tier. We design a measurement-driven heuristic algorithm, Band-based Path Selection (BPS), to approach optimal channel assignment of both white space and WiFi spectrum with reduced computational complexity. Numerical results show that BPS nearly doubles the served traffic of existing multi-channel, multi-radio algorithms, which are agnostic to diverse propagation characteristics across bands. Most importantly, this paper lays a foundation for the optimal use of white space and WiFi bands in the backhaul tiers of mesh networks across diverse population densities.

Environmental factors that lead to the movement and type of obstacles in and around wireless links are well-known to directly affect channel characteristics. However, while mobile users typically have repeatable daily or weekly patterns with common locations being frequently visited, many protocols along the network stack do not attempt to identify when physical locations are revisited. If wireless channels could be recognized as previously visited, the observance of good and bad decisions in that particular context could dramatically improve some network protocols. In this paper, we present a channel recognition framework which uses the geometrical shape of the link-level performance in a particular context across transmission modes and channel qualities. When attempting to recognize a channel condition, the performance of data transmissions is observed and compared against known channel types to detect similar behavior. The matching channel type can be used as an input to the link adaptation training and resulting decision structure. We perform extensive experimentation on controlled repeatable channels as well as in-field channels to show the validity of the classification algorithm.

Super resolution (SR) for real-life video sequences is a challenging problem due to complex nature of the motion fields. In this paper, a novel blind SR method is proposed to improve the spatial resolution of video sequences, while the overall point spread function of the imaging system, motion fields, and noise statistics are unknown. To estimate the blur(s), first, a nonuniform interpolation SR method is utilized to upsample the frames, and then, the blur(s) is(are) estimated through a multi-scale process. The blur estimation process is initially performed on a few emphasized edges and gradually on more edges as the iterations continue. Also for faster convergence, the blur is estimated in the filter domain rather than the pixel domain. The high-resolution frames are estimated using a cost function that has the fidelity and regularization terms of type Huber-Markov random field to preserve edges and fine details. The fidelity term is adaptively weighted at each iteration using a masking operation to suppress artifacts due to inaccurate motions. Very promising results are obtained for real-life videos containing detailed structures, complex motions, fast-moving objects, deformable regions, or severe brightness changes. The proposed method outperforms the state of the art in all performed experiments through both subjective and objective evaluations. The results are available online at http://lyle.smu.edu/~rajand/Video_SR/.

The runtime configuration of parallel and distributed applications remains a mysterious art. To tune an application on a particular system, the end-user must choose the number of machines, the number of cores per task, the data partitioning strategy, and so on, all of which result in a combinatorial explosion of choices. While one might try to exhaustively evaluate all choices in search of the optimal, the end user's goal is simply to run the application once with reasonable performance by avoiding terrible configurations. To address this problem, we present a hybrid technique based on regression models for tuning data intensive bioinformatics applications: the sequential computational kernel is characterized empirically and then incorporated into an ab initio model of the distributed system. We demonstrate this technique on the commonly-used applications BWA, Bowtie2, and BLASR and validate the accuracy of our proposed models on clouds and clusters.

Data security has always been a major concern and a huge challenge for governments and individuals throughout the world since early times. Recent advances in technology, such as the introduction of cloud computing, make it even a bigger challenge to keep data secure. In parallel, high throughput mobile devices such as smartphones and tablets are designed to support these new technologies. The high throughput requires power-efficient designs to maintain the battery-life. In this paper, we propose a novel Joint Security and Advanced Low Density Parity Check (LDPC) Coding (JSALC) method. The JSALC is composed of two parts: the Joint Security and Advanced LDPC-based Encryption (JSALE) and the dual-step Secure LDPC code for Channel Coding (SLCC). The JSALE is obtained by interlacing Advanced Encryption System (AES)-like rounds and Quasi-Cyclic (QC)-LDPC rows into a single primitive. Both the JSALE code and the SLCC code share the same base quasi-cyclic parity check matrix (PCM) which retains the power efficiency compared to conventional systems. We show that the overall JSALC Frame-Error-Rate (FER) performance outperforms other cryptcoding methods by over 1.5 dB while maintaining the AES-128 security level. Moreover, the JSALC enables error resilience and has higher diffusion than AES-128.

Existing rate adaptation protocols have advocated training to establish the relationship between channel conditions and the optimal modulation and coding scheme. However, wireless devices for outdoor and vehicular communications frequently enter environments they have not yet encountered and therefore, have insufficient training for rate adaptation decisions. In addition, protocols are often optimally tuned for indoor environments but, when taken outdoors, perform poorly. In both cases, the decision structure formed offline lacks the ability to acclimate to a new situation on the fly. The diverse and ever-changing environments of increasingly mobile wireless devices call for a rate adaption scheme that can quickly adjust accordingly to form a unique environment set established by the user. In this paper, we propose an on-the-fly, in-situ training (FIT) mechanism which addresses the challenges of making rate decisions with unpredictable fluctuation and lack of repeatability of real wireless channels. We design and conduct extensive experiments on emulated and in-field wireless channels to evaluate the in-situ training process, showing that the rate decision structure can be updated as channel conditions change using existing traffic flows.

In this paper, we propose a hypothesis testing based sequential frame synchronization technique that is robust against fluctuations and imperfect estimation of channel gain at the receiver. The proposed detector uses division in the decision rule to cancel the effect of channel gain, and therefore is called division based hypothesis testing (DHT). We show that the receiver operating characteristic (ROC) of DHT is close to the optimal likelihood ratio test (LRT), while the optimal LRT requires perfect knowledge of noise and channel gain at the receiver. Further, we use three performance metrics, namely, the probability of false synchronization Pfs, average reception duration lrx, and probability of missed synchronization Pms, to compare the performance of hypothesis testing against that of point estimation. We analytically show that hypothesis testing can have better reliability and lower energy consumption than point estimation if the probability of false alarm of the hypothesis testing is lower than the minimum of two thresholds. The cost of hypothesis testing is an arbitrarily small Pmswhile point estimation can have zero Pms. A modified implementation of hypothesis testing is also introduced to further reduce the energy consumption.

In this paper, we propose a new type of code called Trellis-based Quasi-Cyclic (TQC)-LDPC convolutional code, which is a special case of protograph-based LDPC convolutional codes. The proposed TQC-LDPC convolutional code can be derived from any QC-LDPC block code by introducing trellis-based convolutional dependency to the code. The main advantage of the proposed TQC-LDPC convolutional code is that it allows reduced decoder complexity and input granularity (which is defined as the minimum number of input information bits the code requires to generate a codeword) while maintaining the same bit error-rate as the underlying QC-LDPC block code ensemble. We also propose two related power-efficient encoding methods to increase the code rate of the derived TQC-LDPC convolutional code. The newly derived short constraint length TQC-LDPC convolutional codes enable low complexity trellis-based decoders and one such decoder is proposed and described in this paper (namely, QC-Viterbi). The TQC-LDPC convolutional codes and the QC-Viterbi decoder are compared to conventional LDPC codes and Belief Propagation (BP) iterative decoders with respect to bit-error-rate (BER), signal-to-noise ratio (SNR), and decoder complexity. We show both numerically and through hardware implementation results that the proposed QC-Viterbi decoder outperforms the BP iterative decoders by at least 1 dB for same complexity and BER. Alternatively, the proposed QC-Viterbi decoder has 3 times lower complexity than the BP iterative decoder for the same SNR and BER. This low decoding complexity, low BER, and fine granularity makes it feasible for the proposed TQC-LDPC convolutional codes and associated trellis-based decoders to be efficiently implemented in high data rate, next generation mobile systems.

The architecture and algorithms for the first all-digital radio-controlled clock receiver for the new WWVB broadcast format are proposed. To address the potentially low signal-to-noise ratio conditions and the relatively large frequency offsets experienced in the receiver, two alternative timing synchronization approaches are investigated, i.e., one based on a maximum-likelihood (ML) criterion and the other based on correlation. We show that the correlation-based synchronization technique reduces the implementation complexity by over 50%, while its performance is only less than 1 dB inferior to that of the ML-based technique. Decision and detection algorithms are proposed for two operating regimes in the receiver: tracking and acquisition. In tracking, the proposed decision strategies reduce the timing mean squared error by as much as 63% compared with what the synchronizer produces without any additional processing. In acquisition, the proposed joint synchronization and decoding technique significantly improves the robustness by exploiting the channel code in the data. Compared with receivers based on the legacy broadcast, over 15 dB performance gains are achieved by the modulation and algorithms proposed for the tracking and acquisition operations. In addition to reception performance analyses, energy consumption tradeoffs are also presented.

We study a cognitive radio network in which a secondary user must sense the medium and share this information with a fusion center prior to data transmission. The objective of this work is to derive bounds on the overhead required to convey such information. We formulate a rate-distortion framework to capture this effect and compute upper and lower bounds on the rate-distortion function. We also derive in closed form the optimal value of the sampling period at the secondary user as a function of the primary user traffic. Numerical results demonstrate the accuracy of this optimal value and also captures the tradeoff between the rate and the resulting distortion.

In this paper, we develop transmission schemes that maximize the spatial multiplexing rate in multiple-input multiple-output (MIMO) systems with a constraint on the total transmit and processing power. The proposed transmission strategy uses a combination of antenna selection with a bursty transmission strategy to achieve increased rate when complete channel state information is available at the transmitter. We also develop an algorithm, based on dynamic programming, to determine the optimal combinations of antenna configurations and their fractional transmission durations. For a MIMO system with partial channel state information at transmitter (CSIT), we design a feedback scheme, where the information of both the antenna configuration and the precoding matrix is sent to the transmitter. We also provide a heuristic method to design the codebook that sends back antenna configuration information to the transmitter. Numerical results indicate that significant improvements in rate are obtained with the proposed strategy over a traditional precoder feedback strategy.

Repeatability, isolation and accuracy are the most desired factors while testing wireless devices. However, they cannot be guaranteed by traditional drive tests. Channel emulators play a major role in filling these gaps in testing. In this paper we present an efficient channel emulator which is better than existing commercial products in terms of cost, remote access, support for complex network topologies and scalability. We present the hardware and software architecture of our channel emulator and describe the experiments we conducted to evaluate its performance against a commercial channel emulator.

We study a cognitive radio network in which a group of secondary users sense the presence of a primary user and convey this information to a fusion center. The trade-off between the bit rate required and the accuracy of the sensed information is captured using a rate-distortion framework, which provides a lower bound on the overhead required for any spectrum sensing protocol. The effect of the number of sensors and the measurement noise at the sensors on the trade-off is quantified.

While many metropolitan areas sought to deploy city-wide WiFi networks, the densest urban areas were not able to broadly leverage the technology for large-scale Internet access. Ultimately, the small spatial separation required for effective 802.11 links in these areas resulted in prohibitively large up-front costs. The FCC has reapportioned spectrum from TV white spaces for the purposes of large-scale Internet connectivity via wireless topologies of all kinds. The far greater range of these lower carrier frequencies are especially critical in rural areas, where high levels of aggregation could dramatically lower the cost of deployment and is in direct contrast to dense urban areas in which networks are built to maximize spatial reuse. Thus, leveraging a broad range of spectrum across diverse population densities becomes a critical issue for the deployment of data networks with WiFi and white space bands. In this paper, we measure the spectrum utility in the Dallas-Fort Worth metropolitan and surrounding areas and propose a measurement-driven band selection framework, Multiband Access Point Estimation (MAPE). In particular, we study the white space and WiFi bands with in-field spectrum utility measurements, revealing the number of access points required for an area with channels in multiple bands. In doing so, we find that networks with white space bands reduce the number of access points by up to 1650% in sparse rural areas over similar WiFi-only solutions. In more populated rural areas and sparse urban areas, we find an access point reduction of 660% and 412%, respectively. However, due to the heavy use of white space bands in dense urban areas, the cost reductions invert (an increase in required access points of 6%). Finally, we numerically analyze band combinations in typical rural and urban areas and show the critical factor that leads to cost reduction: considering the same total number of channels, as more channels are available in the white space bands, less access points are required for a given area.

Clusters, clouds, and grids offer access to large scale computational resources at low cost. This is especially appealing to scientific applications that require a very large scale to compete in the research space. However, the resources available across these platforms differ significantly in their availability, hardware, environment, performance, cost of use, and more. This requires the use of elastic applications that can adapt to the resources available at run-time, transparently handling heterogeneity and failures. In this paper, we present case studies of several elastic applications built using the Work Queue programming framework. From this experience, we offer six general guidelines for the design and implementation of elastic applications that run on thousands of processors.

Researchers with large-scale data-intensive applications often wish to scale up applications to run on multiple clusters, employing a middleware layer for resource management across clusters. However, at the very largest scales, such middleware is often “unfriendly” to individual clusters, which are usually designed to support communication within the cluster, not outside of it. To address this problem we have modified the Work Queue master-worker application framework to support a hierarchical configuration that more closely matches the physical architecture of existing clusters. Using a synthetic application we explore the properties of the system and evaluate its performance under multiple configurations, with varying worker reliability, network capabilities, and data requirements. We show that by matching the software and hardware architectures more closely we can gain both a modest improvement in runtime and a dramatic reduction in network footprint at the master. We then run a scalable molecular dynamics application (AWE) to examine the impact of hierarchy on performance, cost and efficiency for real scientific applications and see a 96% reduction in network footprint, making it much more palatable to system operators and opening the possibility of increasing the application scale by another order of magnitude or more.

In this paper, we evaluate the expected rate with quantized feedback over a fading channel with both transmit and computational power constraints. We derive the optimal quantization scheme which can achieve the maximum expected rate in two different scenarios. First, for systems with a short-term power constraint, we derive an algorithm to find the optimum quantization schemes with bursty transmission. Second, for systems with a long-term power constraint, we prove that no explicit bursty transmission is required. We also propose a two-step power allocation method, based on the traditional waterfilling followed by a water adjustment step, which achieves the maximum expected rate. We quantify the gain that the proposed algorithms can achieve for different quantization levels.

Channel emulators are valuable tools for controllable and repeatable wireless experimentation. Often, however, the high cost of such emulators preclude their widespread usage, especially in large-scale wireless networks. Moreover, existing channel emulators offer either very realistic channels for simplistic topologies or complex topologies with highly-abstracted, low-fidelity channels. To bridge the gap in offering a low-cost channel emulation solution which can scale to a large network size, in this paper, we study the tradeoff in channel emulation fidelity versus the hardware resources consumed using both analytical modeling and FPGA-based implementation. To reduce the memory footprint of our design, we optimize our channel emulation using an iterative structure to generate the Rayleigh fading channel. In addition, the channel update rate and word length selection are also evaluated in the paper which greatly improve the efficiency of implementation. We then extend our analysis of a single channel to understand how the implementation scales for the emulation of a large-scale wireless network, showing that up to 24 vehicular channels can be emulated in real-time on a single Virtex-4 FPGA.

A number of rate adaptation protocols have proposed using instantaneous channel quality to select the physical layer data rate. However, due to fast channel variations, even aggressive probing of the channel before each data packet is often unable to offer an up-to-date notion of channel quality. In this paper, we propose a coherence-aware channel indication and prediction algorithm for rate adaptation (CIPRA) and evaluate it analytically and experimentally, considering both the measurement errors and the staleness of the channel quality indicator. CIPRA uses Minimum-Mean-Square-Error (MMSE), first-order prediction and jointly considers the time interval over which the prediction will occur and the coherence time of the channel to determine the optimal window size for previous channel quality indicator measurements. We also implement a Doppler shift estimation method in hardware to assist the proposed channel prediction algorithm. We show that CIPRA outperforms existing methods in terms of prediction fidelity and throughput via experimental results from an FPGA-based platform on emulated and in-field wireless channels. In our experiments in the field, CIPRA achieves up to 1.66 times the throughput achieved by the indication and prediction method currently used by off-the-shelf cards. CIPRA could be easily applied to other channel indicators, although we only evaluated RSSI-based rate adaptation in our experiments to isolate our gains.

In hardware, packet loss may happen due to overflow from a finite-depth transmit buffer. To prevent such losses and further improve rate selection, we exploit statistical knowledge of transmit buffer occupancy and source packet distribution in IEEE 802.11-based systems, which have variable frame slots. We consider a traditional method of rate adaptation based on channel quality information and evaluate the throughput gain in hardware when the buffer occupancy and source packet distribution information are known. Our optimization objective is to maximize the throughput with constant transmit power since most IEEE 802.11 APs and nodes operate in this manner. We also derive an upper bound of the improvement introduced by exploiting the offered load distribution and buffer status information. By evaluating the effect of diverse buffer sizes with different packet arrival distributions, both our theoretical analysis and our experimental results show that the throughput can be greatly improved in many cases when the source packet distribution and buffer status information are exploited.

The high mobility of vehicular networks makes channel estimation and phase tracking challenging and an important problem for OFDM receiver design. In IEEE 802.11p vehicular networks, the channel estimation based on a long preamble in the PHY frame performs poorly in fast-fading channels. Moreover, the pilot-based phase tracking usually suffers from large residual phase errors. In this paper, we propose and implement a novel phase tracking algorithm, leveraging the decoded data. We also provide a detailed hardware design for a decoder-based channel estimation algorithm with a pipeline structure on an FPGA-based platform. Through diverse experiments via an advanced channel emulator, our results show that the proposed algorithm could significantly improve the system performance in terms of packet error rate, while adding less than 3% of additional hardware resources. We also jointly evaluate the packet error rate versus the bitwidth of the data in the FPGA, which is important to achieve a good balance between the hardware cost and the system performance.

In this paper, we study the effects of finite size resources in point-to-point energy harvesting wireless networks. We consider a transmitter and a receiver with finite size buffers and batteries, both powered by energy harvesters. We use a discrete-time analysis technique to study total packet loss at the transmitter and receiver when both are constrained by finite resources. We show how resources at the transmitter can be traded off with resources at the receiver to achieve best performance. We characterize the benefits of feedback and show that feedback is beneficial at smaller battery and buffer sizes, and when the energy harvested is highly varying.

A novel and efficient method for emulating a Weibull fading channel is presented. The proposed Weibull fading channel generator employs CORDIC technology which can work without the need for a multiplier, a scarce FPGA resource when emulating wireless channels. Using the proposed hardware implementation structure, the resource consumption is effectively reduced. Further, the proposed generator is able to produce the Weibull fading channel with arbitrary values of the shape parameter γ, in contrast to existing state-of-the-art methods, which can only generate Weibull channels with fixed values of γ. In simulation, the PDF of the Weibull fading channel agrees closely with the theoretical values. The simulation results also show the effectiveness of the proposed structure to generate Lognormal and Suzuki fading channels.

An increasing number of adaptive protocols use training data to learn optimal parameter choices for adaptation in wireless communication networks. For instance, several recent papers have studied link adaptation protocols based on context information such as node velocity and SNR. However, a number of embedded sensors providing context information frequently report erroneous values, e.g., GPS errors and accelerometer lag, producing incorrect information about motion. As a result, the relationship between the context information and optimal parameter choices that the adaptive algorithm is attempting to establish is erroneous. In this paper, we propose an outlier detection algorithm, which detects the corrupted information due to system errors. The proposed outlier detection algorithm is based on an alternating minimization approach. To evaluate the performance of the proposed algorithm, we apply it to a link-level context-aware rate adaptation system. Numerical results on emulated channels and in-field testing demonstrate that the proposed algorithm increases the prediction accuracy of the optimal transmission mode by 87% and the throughput by 18%.

This paper presents, for the first time, a unified blind method for multi-image super-resolution (MISR or SR), single-image blur deconvolution (SIBD), and multi-image blur deconvolution (MIBD) of low-resolution (LR) images degraded by linear space-invariant (LSI) blur, aliasing, and additive white Gaussian noise (AWGN). The proposed approach is based on alternating minimization (AM) of a new cost function with respect to the unknown high-resolution (HR) image and blurs. The regularization term for the HR image is based upon the Huber-Markov random field (HMRF) model, which is a type of variational integral that exploits the piecewise smooth nature of the HR image. The blur estimation process is supported by an edge-emphasizing smoothing operation, which improves the quality of blur estimates by enhancing strong soft edges toward step edges, while filtering out weak structures. The parameters are updated gradually so that the number of salient edges used for blur estimation increases at each iteration. For better performance, the blur estimation is done in the filter domain rather than the pixel domain, i.e., using the gradients of the LR and HR images. The regularization term for the blur is Gaussian (L2 norm), which allows for fast noniterative optimization in the frequency domain. We accelerate the processing time of SR reconstruction by separating the upsampling and registration processes from the optimization procedure. Simulation results on both synthetic and real-life images (from a novel computational imager) confirm the robustness and effectiveness of the proposed method.

In this paper, we present a practical application of dirty paper coding (DPC) for the Gaussian cognitive Z-interference channel. A two stage transmission scheme is proposed in which the cognitive transmitter first obtains the interference signal from the primary transmitter and then uses DPC to improve the performance of the cognitive link. Numerical results show that causal knowledge of the interference provides more than 3 dB improvement in performance in certain scenarios over a scheme that does not use interference cancellation. Results are also shown when the cognitive transmitter operates in both half-duplex and full-duplex modes.

Molecular modeling is a field that traditionally has large computational costs. Until recently, most simulation techniques relied on long trajectories, which inherently have poor scalability. A new class of methods is proposed that requires only a large number of short calculations, and for which minimal communication between computer nodes is required. We considered one of the more accurate variants called Accelerated Weighted Ensemble Dynamics (AWE) and for which distributed computing can be made efficient. We implemented AWE using the Work Queue framework for task management and applied it to an all atom protein model (Fip35 WW domain). We can run with excellent scalability by simultaneously utilizing heterogeneous resources from multiple computing platforms such as clouds (Amazon EC2, Microsoft Azure), dedicated clusters, grids, on multiple architectures (CPU/GPU, 32/64bit), and in a dynamic environment in which processes are regularly added or removed from the pool. This has allowed us to achieve an aggregate sampling rate of over 500 ns/hour. As a comparison, a single process typically achieves 0.1 ns/hour.

Context awareness has received increasing attention with the proliferation of various types of sensors on mobile devices. However, while wireless performance is known to be highly correlated with environmental settings, mobile devices have yet to fully exploit the awareness of context to improve wireless performance. In this paper, we leverage available context information to improve link-level adaptation via decision-tree classifiers and extensively evaluate its performance over emulated channels as well as with in-field trials. We first propose a classification method based on decision trees to select the optimal transmission parameters such as modulation, coding rate and packet size. We then quantify the throughput improvement using the proposed scheme and show that in some scenarios the throughput increases by over 100% compared to traditional SNR-based rate adaptation protocols. Second, we analyze the amount of training to assess the classification scheme. Third, we validate classification-based method by implementation on two different test platforms for extensive experimentation. We reveal the importance of the various contextual attributes used and identify channel type as a key parameter that affects classification performance. Finally, we study and quantify the use of context information across multiple different frequency bands and demonstrate the significant throughput gains that can be obtained.

The application of machine learning algorithms in wireless communications has attracted increasing attention due to the promising performance gains recently achieved. Static classification algorithms have been successfully applied to training protocols that adapt transmission parameters according to context information. However, in reality, there are many time-varying reasons for fading channel quality including mobility of sender, receiver, and/or obstacles within the environment. Moreover, time-varying noise further exacerbates the dynamics of the channel. These problems pose new challenges for the application of static classification algorithms in context-aware algorithms and suggest that sequential classifiers which leverage the temporal dynamics and correlation of context information might be more appropriate. In this paper, we apply sequential training to rate adaptation (ASTRA), leveraging the temporal correlation of context information. In particular, linear and non-linear sequential coding schemes are used in the training process for selecting the modulation/coding rate that achieves the highest throughput for the given context. Experimental results on measurements from emulated and in-field channels demonstrate that ASTRA can significantly increase the accuracy of selecting these target rates by up to 175% and increase the resulting throughput by up to 66% over rate adaptation training which uses static classifier-based methods.

In this paper, we propose a novel optimization framework for Roadside Unit (RSU) deployment and configuration in a vehicular network. We formulate the problem of placement of RSUs and selecting their configurations (e.g. power level, types of antenna and wired/wireless back haul network connectivity) as a linear program. The objective function is to minimize the total cost to deploy and maintain the network of RSU's. A user specified constraint on the minimum coverage provided by the RSU is also incorporated into the optimization framework. Further, the framework also supports the option of specifying selected regions of higher importance such as locations of frequently occurring accidents and incorporating constraints requiring stricter coverage in those areas. Simulation results are presented to demonstrate the feasibility of deployment on the campus map of Southern Methodist University (SMU). The efficiency and scalability of the optimization procedure for large scale problems are also studied and results shows that optimization over an area with the size of Cambridge, Massachusetts is completed in under 2 minutes. Finally, the effects of variation in several key parameters on the resulting design are studied.

In this paper, we obtain the capacity region for the AWGN Multiple Access Channel (MAC) with transmitters equipped with energy harvesters. We next obtain the sum-of-rates capacity for the flat fading Multiple Access Channel with energy harvesting transmitters. We then develop low delay, adaptive transmission strategies that surprisingly achieve near optimal performance under finite battery resource constrained settings. We study the effect of asymmetries in the energy harvesting and characterize the achievable performance.

Over the past decade, high performance applications have embraced parallel programming and computing models. While parallel computing offers advantages such as good utilization of dedicated hardware resources, it also has several drawbacks such as poor fault-tolerance, scalability, and ability to harness available resources during run-time. The advent of cloud computing presents a viable and promising alternative to parallel computing because of its advantages in offering a distributed computing model. In this work, we establish directives that serve as guidelines for the design and implementation or identification of a suitable cloud computing framework to build or convert a high performance application to run in the cloud. We show that following these directives leads to an elastic implementation that has better scalability, run-time resource adaptability, fault tolerance, and portability across cloud computing platforms, while requiring minimal effort and intervention from the user. We illustrate this by converting an MPI implementation of replica exchange, a parallel tempering molecular dynamics application, to an elastic cloud application using the Work Queue framework that adheres to these directive. We observe better scalability and resource adaptability of this elastic application on multiple platforms, including a homogeneous cluster environment (SGE) and heterogeneous cloud computing environments such as Microsoft Azure and Amazon EC2.

An optimization scheme is developed for a cosine modulated perfect- reconstruction (PR) filter bank that is tailored to mitigate the effects of narrowband radio frequency interference (RFI). The conventionally used optimization criterion for bandpass filtering is to maximize the sidelobe attenuation by minimizing the stopband energy and minimizing the maximum stopband ripple. The proposed optimization scheme is designed particularly to combat RFI with completely known or partially known statistics. Simulation results for the scenario of strong narrowband RFI show that the proposed optimization scheme offers about 14% data rate improvement in the system's data throughput when compared to the traditional schemes.

LDPC codes are becoming popular in next generation high throughput wireless standards since they can provide a level of parallelism with sufficient performance to support the high gigabit rate. In this paper, we propose a new method for LDPC decoding called Parallel Processing Layered (PPL). The new method aims to optimize the latency and power efficiency of LDPC decoding to enable significant increase of the processing rate, thereby saving battery power for mobile devices. We provide performance results in different channel models using the newly defined WiGig standards, and compare them to the conventional decoding methods. We show that the new proposed LDPC decoding architecture converges 2x faster than conventional (i.e. Flooding) methods.

In this paper, we show that packet blocking probability at the transmitter can be traded off with average queue delay for fading channels in environmentally powered wireless networks with bursty packet arrivals. Varying the size of the energy storage unit as well as that of the transmit buffer queue aids in the trade off. We cast the problem into a Markov Decision Process (MDP) and use Dynamic Programming to obtain the optimal scheduler. Further, we develop two different low complexity schedulers that achieve near optimal performance in different system settings.

We present experimental results of digital super resolution (DSR) techniques on low resolution data collected using PANOPTES, a multi-aperture miniature folded imaging architecture. The flat form factor of PANOPTES architecture results in an optical system that is heavily blurred with space variant PSF which makes super resolution challenging. We also introduce a new DSR method called SRUM (Super-Resolution with Unsharpenning Mask) which can efficiently highlight edges by embedding an unsharpenning mask to the cost function. This has much better effect than just applying the mask after all iterations as a post-processing step.

In this paper, we compute an achievable rate of a Gaussian Z-interference channel as shown in Fig. 1, when transmit node C has causal, imperfect cognitive knowledge of the signal sent by transmit node A. This achievable rate is derived using a two-phase transmission scheme in which node C uses a combination of a linear minimum mean square error (LMMSE) estimator and dirty paper code and node D employs a combination of LMMSE estimator and partial interference canceler. Numerical results indicate that the achievable rate of the Gaussian Z-interference channel increases significantly with cognition under certain channel conditions. We also derive an upper bound on the capacity of this channel with cognition and quantify the channel conditions under which the proposed achievable scheme equals the upper bound.

In this paper, we study the capacity region of a two user Gaussian interference channel with half duplex node constraints. We develop an achievable region and outer bound for the case when the system allows either transmitter or receiver cooperation. We show that by using our transmitter cooperation scheme, there is significant capacity improvement compared to the previous results [1], [2], especially when the cooperation link is strong. Further, if the cooperation channel gain is infinity, both our transmitter and receiver cooperation rates achieve their respective outer bound. It is also shown that transmitter cooperation provides larger achievable region than receiver cooperation under the same channel and power conditions.

Reservation-based channel access has been shown to be effective in providing Quality of Service (QoS) guarantees (e.g., timeliness) in wireless embedded real-time applications such as mobile media streaming and networked embedded control systems. While the QoS scheduling at the central authority (i.e., base station) has received extensive attention recently, the computation of resource requirements at each individual node has been widely ignored. An inappropriate resource requirement may lead to degraded support for real-time traffic and overprovisioning of scarce network resources. This work addresses this issue by presenting a strategy for nodes to determine minimal resource reservations that guarantee the real-time constraints of their network traffic. In addition, this paper examines the relationship between timeliness constraints of the traffic and resource requirements.

Using intelligent resource allocation based on the information content in the imaging system's field-of-view for the successful design of a flat-profile multiplexed optical imaging system requires the use of adaptive techniques. This paper describes a model-based technique for determining regions of interest in aerial images using the 2D normalized power spectral density within Gilles' saliency map estimator. The proposed technique exploits the 1/falphaspatial spectral shape of such natural imagery in a computationally-simple approach that is robust to additive noise. Application of the method to candidate aerial images shows its ability to identify consistent regions of interest for such data.

We study the impact of random message arrivals in a two user Gaussian interference channel with one-way co-operation, also called as cognitive radio. In this model, one user (labeled cognitive user) has information about the message of the other user (labeled primary user). Specifically, we quantify the reduction in queuing delay for both the primary and cognitive users when the cognitive user has either non-causal or causal information about primary users data. Surprisingly, we find that the delay for the primary user also reduces even though the cognitive user does not use any of its transmission power to relay the information of the primary user.

We derive a lower bound on the diversity-multiplexing tradeoff for multiple antenna multiple access channels using temporal power control (PC). We quantify the substantial improvements in diversity gains with finite rate feedback over schemes that use no feedback, even when users have asymmetric diversity and rate requirements. For small multiplexing gains, all users in the MAC achieve their single user performance as if each receives B distinct bits of feedback. We also show that for a SISO MAC with finite rate feedback, the diversity orders with PC and with opportunistic user selection are the same at all multiplexing gains.

In this paper, we study the relationship between queuing delay for a random packet arrival process and physical layer parameters in a multiple access channel with multiple antennas at the input and output. Our main contribution is the derivation of a simple, analytical approximation for the average delay that clearly indicates the effect of number of transmit and receive antennas, transmission power, the packet arrival rate and the desired reliability. Comparison with numerical analysis indicates that the proposed analytical approximation of the delay is accurate for medium and large SNRs.

This work is based on the observation that existing energy management techniques for mobile devices, such as dynamic voltage scaling (DVS), are non-cooperative in the sense that they reduce the energy consumption of a single device, disregarding potential consequences for other constraints (e.g., end-to- end deadlines) and/or other devices (e.g., energy consumption on neighboring devices). This paper argues that energy management in distributed real-time systems has to be end-to-end in nature, requiring a coordinated approach among communicating devices. A cooperative distributed energy management technique (Co-DVS) is proposed that: i) adapts and maintains end-to-end latencies within specified timeliness requirements (deadlines); and ii) enhances energy savings at the nodes with the highest pay-off factors that represent the relative benefits or significance of conserving energy at a node. The proposed technique employs a feedback-based approach to dynamically distribute end-to-end slack among the devices based on their pay-off factors.

Reducing energy consumption is an important consideration in embedded real-time system development. This work examines systems that contain a DVFS managed CPU executing packet producing tasks and a DPM-controlled network interface. We introduce a novel approach to minimize energy consumed by the network resource on such a system, through careful selection of voltage and frequency levels on the CPU. Contrary to existing claims which state that DVFS should not be employed when the CPU is not a significant consumer of energy, we show that our DVFS technique can reduce system energy by as much as 35%, even when the CPU energy consumption is negligible. Furthermore, we motivate the need to balance the CPU and network energy and present two techniques to do so. One is based on off-line analysis and the other is a conservative on-line approach. We then validate the proposed methods using both simulation and an implementation in the Linux kernel

We consider a multiple access channel (MAC) with multiple antennas, where each user has different diversity and multiplexing gain requirement. For this configuration, we characterize the fundamental tradeoff region for each user. Specifically, we compute the maximum achievable diversity gain for a user in a MAC, given an achievable point in the multiplexing gain region.

In this paper, we propose a framework for designing power efficient schedulers for transmitting bursty traffic sources over Gaussian wireless channels that provides deterministic and statistical guarantees on absolute delays experienced by the source packets. The proposed schedulers compute the transmission rate and power using temporal water-filling techniques without any knowledge of the arrival traffic statistics. The schedulers reduce the average transmission power substantially (55% in some scenarios) for small increases in delay. The framework allows us to design schedulers that artfully tradeoff the performance with the complexity of computing the schedulers. We also introduce an iterative process to compute a lower bound on the transmit power of any scheduler that provides absolute delay guarantees. The utility of having accurate traffic predictors is demonstrated; specifically, we show that a perfect one step predictor achieves near optimal performances for small delay bounds. The proposed schedulers and iterative method of computing the lower bound are also shown to provide statistical guarantees on packet delays.

This paper proposes a channel estimation algorithm using super-imposed pilots for OFDM systems. The pilot symbols are added linearly to the modulated data symbol at a fraction of the total transmit power, making the method spectrally efficient. We present a series of iterative and non-iterative receivers in which complexity is traded for performance. In particular, a high complexity non-iterative receiver that achieves the theoretically lowest BER and a low complexity iterative receiver that show near-optimal performance are presented. The proposed iterative receiver offers a 50 - 98% reduction in complexity over a non- iterative receiver having equivalent performance, depending on the modulation used. We observe that the performance of the receiver is independent of the channel's Doppler frequency and delay spread. Finally, we compute an analytic bound for the bit error rate of the proposed non-iterative receiver.

In this communication, we investigate the effect of spatial dimension in a Gaussian multiple access system and study its impact on the physical layer capacity and the end to end delay from a queueing theory perspective. For a multiple-input, single-output (MISO) multiple access channel, we derive conditions on the transmit power and number of transmit antennas for which additional users increase or decrease the capacity. The capacity as a function of the number of users is used for calculating the end-to-end delay. Numerical analysis on the end-to-end delay for a Rayleigh fading MISO channel with no channel state information at transmitter (CSIT) indicates that increasing the number of transmit antennas can actually increase the delay.

Multiplexed image reconstruction, estimating high resolution images from multiple low resolution images with highly overlapped fields of view, is improved when the magnification of the imagers is diverse. No assumptions of shift invariance or Toeplitz structure are required for computational manageability because localized reconstruction is possible and sensitivity to boundary conditions is reduced. Such multiplexed diverse image sensors have applications in flat sensor systems for surveillance and pervasive personal imaging.

Dynamic voltage scaling (DVS) is a frequently used technique in mobile and embedded systems, aimed at reducing the energy consumption of mobile processors. In systems with a discrete number of frequency levels, existing dual-speed DVS approaches compute an optimal theoretical CPU speed and approximate it by choosing the two neighboring discrete speed levels. By comparing experimentally the energy savings attained with different frequency combinations on a mobile platform, this work shows that choosing the two neighboring frequency levels does not necessarily yield the highest energy savings. As a result of the above observation, this work introduces an online approach to dual-speed DVS that a) formulates a model for speed selection based on the workload characteristics of the current task set, b) computes a frequency pair that yields the best possible energy savings for a given taskset and workload

This paper proposes HRPU, a hybrid routing algorithm for wireless mesh networks. In HRPU, the mesh portal periodically broadcasts a mesh update message, which allows all nodes to have a route towards the mesh portal stored semi-permanently in their routing table. Whenever a node has data to be sent to backbone network, it sends the data without any route establishment delay using the route to the mesh portal. Numerical results show the higher throughput and lower overhead of proposed HRPU. In HRPU the mesh portals and mesh points adapt some critical parameters to further improve performance

We propose a novel method for the optimal co-design of the optical and reconstruction filters in a computational imaging system. Closed form solutions are presented for the design of an optimal observation matrix for a fixed reconstruction matrix. An iterative method for computing global optimal filters is then proposed based on the derived analytical solution and the well known Wiener filter. The performance of the proposed optimal filters represents a universal bound on the performance of any physically realizable computational imaging system.

Reconstruction algorithms to compute a single improved resolution image from multiple lower resolution images have application in the design of cameras with flat form factors. The accuracy of these reconstructions depend on measurement noise, measurement quantization, the structure of the image acquisition system, and the accuracy of the image acquisition model. The paper compares the expected and simulated performance for reconstructions from multiple lower resolution images. The analysis shows that designs using lenses with different imaging characteristics significantly improve the theoretical performance results. In addition, lens diversity allows the reconstruction problem to be naturally partitioned into a set of loosely coupled smaller reconstructions that are computationally more manageable.

In this paper, we investigate the optimal tradeoff between source and channel coding for channels with bit or packet erasure. Upper and lower bounds on the optimal channel coding rate are computed to achieve minimal end-to-end distortion. The bounds are calculated based on a combination of sphere packing, straight line and expurgated error exponents and also high rate vector quantization theory. By modeling a packet erasure channel in terms of an equivalent bit erasure channel, we obtain bounds on the packet size for a specified limit on the distortion

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

In this paper, we propose power efficient schedulers for transmitting bursty traffic sources over Gaussian wireless channels that provide guarantees on absolute delays experienced by the source packets. The proposed schedulers find the transmission rate and power using temporal water-filling techniques without any knowledge of the arrival traffic statistics. We also introduce an iterative process to compute a lower bound on the transmit power of any scheduler that provides absolute delay guarantees. The proposed schedulers and iterative method of computing the lower bound are also shown to provide statistical guarantees on packet delays.

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

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

We propose a new scheme for multiple antenna transmission in the context of spread-spectrum signaling. The new scheme consists of using shifted Gold sequences to modulate independent information on the multiple antennas. We show that this strategy of using multiphase spreading (MPS) on different antennas greatly improves the throughput over currently known spread-spectrum multiple-antenna methods. We also find the optimal power allocation strategy among multiple transmit antennas for a fixed rate of channel state information, which might be provided via a feedback link, at the transmitter. We demonstrate the differences in optimal power distribution for maximizing capacity and minimizing probability of outage. When the transmission from the two antennas uses orthogonal spreading, we find that optimizing the power does not give much gain over the equal power transmission. However, when the transmissions are not orthogonal as in the case of MPS, then allocating power to maximize throughput gives considerable gain over equal power transmission. We also consider the effect of imperfections in the feedback channel on the optimal power allocation and show that our power allocation scheme is robust to feedback errors.

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

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

We propose a new technique for the estimation of chaotic signals in the presence of additive noise. The new method uses statistical measures to restrict the space over which the signals are received. The proposed technique is applicable to a large variety of chaotic signals and has good performance indicated by the low estimation error bias and variance. The complexity of the algorithm is shown to be low.

Transmit diversity offers an advantage in the forward link of the cdma2000 system by balancing the spectrum efficiency in the uplink and downlink. Three schemes for performing transmit diversity are examined in this paper-orthogonal transmit diversity, time switched transmit diversity, and selection transmit diversity for vehicular, indoor-outdoor pedestrian and indoor office environments. Also considered are some issues related to implementation complexity in the mobile handset. Under certain channel conditions, we show that low rate feedback of antenna selection and no forward link power control offers performance advantages over transmit diversity schemes with forward link power control and no antenna selection.