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Edge computing is increasingly proposed as a solution for reducing resource consumption of mobile devices running simultaneous localization and mapping (SLAM) algorithms, with most edge-assisted SLAM systems assuming the communication resources between the mobile device and the edge server to be unlimited, or relying on heuristics to choose the information to be transmitted to the edge. This paper presents AdaptSLAM, an edge-assisted visual (V) and visual-inertial (VI) SLAM system that adapts to the available communication and computation resources, based on a theoretically grounded method we developed to select the subset of keyframes (the representative frames) for constructing the best local and global maps in the mobile device and the edge server under resource constraints. We implemented AdaptSLAM to work with the state-of-the-art open-source V- and VI-SLAM ORB-SLAM3 framework, and demonstrated that, under constrained network bandwidth, AdaptSLAM reduces the tracking error by 62% compared to the best baseline method., Comment: Proceedings of the 2023 IEEE Conference on Computer Communications (INFOCOM), May 2023

The importance of the dynamics of the viewport pose, i.e., the location and the orientation of users' points of view, for virtual reality (VR) experiences calls for the development of VR viewport pose models. In this paper, informed by our experimental measurements of viewport trajectories across 3 different types of VR interfaces, we first develop a statistical model of viewport poses in VR environments. Based on the developed model, we examine the correlations between pixels in VR frames that correspond to different viewport poses, and obtain an analytical expression for the visibility similarity (ViS) of the pixels across different VR frames. We then propose a lightweight ViS-based ALG-ViS algorithm that adaptively splits VR frames into the background and the foreground, reusing the background across different frames. Our implementation of ALG-ViS in two Oculus Quest 2 rendering systems demonstrates ALG-ViS running in real time, supporting the full VR frame rate, and outperforming baselines on measures of frame quality and bandwidth consumption., Comment: Proceedings of the 2022 IEEE Conference on Computer Communications (INFOCOM), May 2022

The logical separation of control signaling from data transmission in a mobile cellular network has been shown to have significant energy saving potential compared with the legacy systems. As a result, there has been a lot of focus in recent years on development and realization of separation architectures. Our study, however, shows that the energy savings of separation architecture remain under 16-17% when compared with legacy systems and this gain falls to a mere 7% when both architectures are realized under a CloudRAN (CRAN) setting. Moreover, when we strategically place some small base-stations (SBSs) to cover the area in a densely deployed scenario and allow all other base-stations (BSs) to be used only on-demand, the system consumes much less energy than the separation architecture. While we expected that most equipment would be shut down during nights, our study shows that around 70% of the small cells are required to be active to serve randomly distributed minimum data load, i.e., active mobile equipment. Contemporary mobile traffic is predominantly data which does not go to extremely low levels during nights. We discuss, in detail, the assumptions, their implications, and the effects of system parameter values on our conclusions.

The reconfigurable intelligent surface (RIS) is a promising technology that is anticipated to enable high spectrum and energy efficiencies in future wireless communication networks. This paper investigates optimum location-based RIS selection policies in RIS-aided wireless networks to maximize the end-to-end signal-to-noise ratio for product-scaling and sum-scaling path-loss models where the received power scales with the product and sum of the transmitter-to-RIS and RIS-to-receiver distances, respectively. These scaling laws cover the important cases of end-to-end path-loss models in RIS-aided wireless systems. The random locations of all available RISs are modeled as a Poisson point process. To quantify the network performance, the outage probabilities and average rates attained by the proposed RIS selection policies are evaluated by deriving the distance distribution of the chosen RIS node as per the selection policies for both product-scaling and sum-scaling path-loss models. We also propose a limited-feedback RIS selection framework to achieve distributed network operation. The outage probabilities and average rates obtained by the limited-feedback RIS selection policies are derived for both path-loss models as well. The numerical results show notable performance gains obtained by the proposed RIS selection policies., Comment: 34 pages; submitted for possible IEEE publications

This paper studies the performance and key structural properties of the optimum location-based relay selection policy for wireless networks consisting of homogeneous Poisson distributed relays. The distribution of the channel quality indicator at the optimum relay location is obtained. A threshold-based distributed selective feedback policy is proposed for the discovery of the optimum relay location with finite average feedback load. It is established that the total number of relays feeding back obeys to a Poisson distribution and an analytical expression for the average feedback load is derived. The analytical expressions for the average rate and outage probability with and without selective feedback are also obtained for general path-loss models. It is shown that the optimum location-based relay selection policy outperforms other common relay selection strategies notably. It is also shown that utilizing location information from a small number of relays is enough to achieve almost the same performance with the infinite feedback load case. As generalizations, full-duplex relays, isotropic Poisson point processes, and heterogeneous source-to-relay and relay-to-destination links are also studied.

This paper investigates a three-layer IoT-fog-cloud computing system to determine the optimum workload and power allocation at each layer. The objective is to minimize maximum per-layer latency (including both data processing and transmission delays) with individual power constraints. The resulting optimum resource allocation problem is a mixed-integer optimization problem with exponential complexity. Hence, the problem is first relaxed under appropriate modeling assumptions, and then an efficient iterative method is proposed to solve the relaxed but still non-convex problem. The proposed algorithm is based on an alternating optimization approach, which yields close-to-optimum results with significantly reduced complexity. Numerical results are provided to illustrate the performance of the proposed algorithm compared to the exhaustive search method. The latency gain of three-layer distributed IoT-fog-cloud computing is quantified with respect to fog-only and cloud-only computing systems.

Wireless sensor networks (WSNs) are the foundation of the Internet of Things (IoT), and in the era of the fifth generation of wireless communication networks, they are envisioned to be truly ubiquitous, reliable, scalable, and energy efficient. To this end, topology control is an important mechanism to realize self-organized WSNs that are capable of adapting to the dynamics of the environment. Topology optimization is combinatorial in nature, and generally is NP-hard to solve. Most existing algorithms leverage heuristic rules to reduce the number of search candidates so as to obtain a suboptimal solution in a certain sense. In this paper, we propose a deep reinforcement learning-based topology optimization algorithm, a unified search framework, for self-organized energy-efficient WSNs. Specifically, the proposed algorithm uses a deep neural network to guide a Monte Carlo tree search to roll out simulations, and the results from the tree search reinforce the learning of the neural network. In addition, the proposed algorithm is an anytime algorithm that keeps improving the solution with an increasing amount of computing resources. Various simulations show that the proposed algorithm achieves better performance as compared to heuristic solutions, and is capable of adapting to environment and network changes without restarting the algorithm from scratch.

This paper considers a low-resolution wireless communication system in which transmitted signals are corrupted by fading and additive noise. First, a universal lower bound on the average symbol error probability (SEP), correct for all M-ary modulation schemes, is obtained when the number of quantization bits is not enough to resolve M signal points. Second, in the special case of M-ary phase shift keying (M-PSK), the optimum maximum likelihood detector for equiprobable signal points is derived. Third, utilizing the structure of the derived optimum receiver, a general average SEP expression for the M-PSK modulation with n-bit quantization is obtained when the wireless channel is subject to fading with a circularly-symmetric distribution. Finally, an extensive simulation study of the derived analytical results is presented for general Nakagami-m fading channels. It is observed that a transceiver architecture with n-bit quantization is asymptotically optimum in terms of communication reliability if n is greater than or equal to log_2(M +1). That is, the decay exponent for the average SEP is the same and equal to m with infinite-bit and n-bit quantizers for n greater than or equal to log_2(M+1). On the other hand, it is only equal to half and 0 for n = log_2(M) and n < log_2(M), respectively. Hence, for fading environments with a large value of m, using an extra quantization bit improves communication reliability significantly., Comment: arXiv admin note: substantial text overlap with arXiv:1902.10896

This paper considers a wireless communication system with low-resolution quantizers, in which transmitted signals are corrupted by fading and additive noise. For such wireless systems, a universal lower bound on the average symbol error probability (SEP), correct for all M-ary modulation schemes, is obtained when the number of quantization bits is not enough to resolve M signal points. In the special case of M-ary phase shift keying (M-PSK), the optimum maximum likelihood detector for equi-probable signal points is derived. Utilizing the structure of the derived optimum receiver, a general average SEP expression for M-PSK modulation with n-bit quantization is obtained when the wireless channel is subject to fading with a circularly-symmetric distribution. Adopting this result for Nakagami-m fading channels, easy-to-evaluate expressions for the average SEP for M-PSK modulation are further derived. It is shown that a transceiver architecture with n-bit quantization is asymptotically optimum in terms of communication reliability if n is greater than or equal to log_2(M +1). That is, the decay exponent for the average SEP is the same and equal to m with infinite-bit and n-bit quantizers for n is greater than or equal to log_2(M+1). On the other hand, it is only equal to half and 0 for n = log_2(M) and n < log_2(M), respectively. An extensive simulation study is performed to illustrate the derived results and energy efficiency gains obtained by means of low-resolution quantizers.

This paper investigates the location-based relay selection problem, where the source node chooses its relay from a set of spatially deployed decode-and-forward relays. The advantages of location-based relay selection are the elimination of excessive relay switching rate and the feedback reduction avoiding the requirement of having full channel state information at the source node. For a homogeneous Poisson point process of candidate relays, we first derive the distribution for the distance of the relay (relative to the source and destination nodes) selected by the optimum location-based relay selection policy. This result is independent of the functional form of the path-loss function as long as it is a non-increasing function of the transmitter-receiver separation. By utilizing the derived optimum relay distance distribution, we then obtain analytical expressions for the average rate and outage probability by considering the power-law decaying path-loss function for the no-fading and Rayleigh fading communication scenarios. It is observed that the optimum relay selection policy outperforms the other common selection strategies notably, including the ones choosing the relay closest to the source, the relay closest to the destination and the relay closest to the mid-point between source and destination.

Fog computing, which distributes computing resources to multiple locations between the Internet of Things (IoT) devices and the cloud, is attracting considerable attention from academia and industry. Yet, despite the excitement about the potential of fog computing, few comprehensive quantitative characteristics of the properties of fog computing architectures have been conducted. In this paper we examine the properties of task completion latencies in fog computing. First, we present the results of our empirical benchmarking-based study of task completion latencies. The study covered a range of settings, and uniquely considered both traditional and serverless fog computing execution points. It demonstrated the range of execution point characteristics in different locations and the relative stability of latency characteristics for a given location. It also highlighted properties of serverless execution that are not incorporated in existing fog computing algorithms. Second, we present a framework we developed for co-optimizing task completion quality and latency, which was inspired by the insights of our empirical study. We describe fog computing task assignment problems we formulated under this framework, and present the algorithms we developed for solving them.

This paper introduces an analytical framework to investigate optimal design choices for the placement of virtual controllers along the cloud-to-things continuum. The main application scenarios include low-latency cyber-physical systems in which real-time control actions are required in response to the changes in states of an IoT node. In such cases, deploying controller software on a cloud server is often not tolerable due to delay from the network edge to the cloud. Hence, it is desirable to trade reliability with latency by moving controller logic closer to the network edge. Modeling the IoT node as a dynamical system that evolves linearly in time with quadratic penalty for state deviations, recursive expressions for the optimum control policy and the resulting minimum cost value are obtained by taking virtual fog controller reliability and response time latency into account. Our results indicate that latency is more critical than reliability in provisoning virtualized control services over fog endpoints, as it determines the swiftness of the fog control system as well as the timeliness of state measurements. Based on a realistic drone trajectory tracking model, an extensive simulation study is also performed to illustrate the influence of reliability and latency on the control of autonomous vehicles over fog.

Streaming video is becoming the predominant type of traffic over the Internet with reports forecasting the video content to account for 80% of all traffic by 2019. With significant investment on Internet backbone, the main bottleneck remains at the edge servers (e.g., WiFi access points, small cells, etc.). In this work, we propose and prove the optimality of a multiuser resource allocation mechanism operating at the edge server that minimizes the probability of stalling of video streams due to buffer under-flows. Our proposed policy utilizes Media Presentation Description (MPD) files of clients that are sent in compliant to Dynamic Adaptive Streaming over HTTP (DASH) protocol to be cognizant of the deadlines of each of the media file to be displayed by the clients. Then, the policy schedules the users in the order of their deadlines. After establishing the optimality of this policy to minimize the stalling probability for a network with links associated with fixed loss rates, the utility of the algorithm is verified under realistic network conditions with detailed NS-3 simulations.

This paper investigates the downlink performance of K-tier heteregeneous cellular networks (HCNs) under general settings. First, Gaussian approximation bounds for the standardized aggregate wireless interference (AWI) in dense K-tier HCNs are obtained for when base stations (BSs) in each tier are distributed over the plane according to a spatial and general Poisson point process. The Kolmogorov-Smirnov (KS) distance is used to measure deviations of the distribution of the standardized AWI from the standard normal distribution. An explicit and analytical expression bounding the KS distance between these two distributions is obtained as a function of a broad range of network parameters such as per-tier transmission power levels, per-tier BS intensity, BS locations, general fading statistics, and general bounded path-loss models. Bounds achieve a good statistical match between the standardized AWI distribution and its normal approximation even for moderately dense HCNs. Second, various spatial performance metrics of interest such as outage capacity, ergodic capacity and area spectral efficiency in the downlink of K-tier HCNs for general signal propogation models are investigated by making use of the derived distribution approximation results. Considering two specific BS association policies, it is shown that the derived performance bounds track the actual performance metrics reasonably well for a wide range of BS intensities, with the gap among them becoming negligibly small for denser HCN deployments., Comment: arXiv admin note: substantial text overlap with arXiv:1601.06023, arXiv:1601.07315

Device-to-Device (D2D) communication appears as a key communication paradigm to realizing the vision of Internet of Things (IoT) into reality by supporting heterogeneous objects interconnection in a large scale network. These devices may be many types of objects with embedded intelligence and communication capabilities, e.g., smart phones, cars, or home appliances. The issue in in this type of communication is the interference to cellular communication caused by D2D communication. Thus, proper power control and resource allocation should be coordinated in D2D network to prevent excessive interference and drastic decrease in the throughput of the cellular system. In this paper, we consider the problem of cross-layer resource allocation in time-varying cellular wireless networks with D2D communication and incorporate average interference to cellular system as a quality-of-service constraint. Specifically, each D2D pair in the network injects packets to its queue, at rates chosen in order to maximize a global utility function, subject to network stability and interference constraints. The interference constraint enforces an arbitrarily low interference to the cellular system caused by D2D communication. We first obtain the stability region for the multiuser systems assuming that the nodes have full channel state information (CSI) of their neighbors. Then, we provide a joint flow control and scheduling scheme, which is proven to achieve a utility arbitrarily close to the maximum achievable utility. Finally, we address the consequences of practical implementation issue such as distributed scheduling by a designing algorithm, which is capable of taking advantage of diversity gain introduced by fading channels. We demonstrate the efficacy of our policies by numerical studies under various network conditions., Comment: arXiv admin note: text overlap with arXiv:1310.0720 by other authors

This paper derives tight performance upper and lower bounds on the downlink outage efficiency of K-tier heterogeneous cellular networks (HCNs) for general signal propagation models with Poisson distributed base stations in each tier. In particular, the proposed approach to analyze the outage metrics in a K-tier HCN allows for the use of general bounded path-loss functions and random fading processes of general distributions. Considering two specific base station (BS) association policies, it is shown that the derived performance bounds track the actual outage metrics reasonably well for a wide range of BS densities, with the gap among them becoming negligibly small for denser HCN deployments. A simulation study is also performed for 2-tier and 3-tier HCN scenarios to illustrate the closeness of the derived bounds to the actual outage performance with various selections of the HCN parameters.

This paper derives Gaussian approximation bounds for the standardized aggregate wireless interference (AWI) in the downlink of K-tier heterogeneous cellular networks when base stations in each tier are distributed over the plane according to a (possibly non-homogeneous) Poisson process. The proposed methodology is general enough to account for general bounded path-loss models and fading statistics. The deviations of the distribution of the standardized AWI from the standard normal distribution are measured in terms of the Kolmogorov-Smirnov distance. An explicit expression bounding the Kolmogorov-Smirnov distance between these two distributions is obtained as a function of a broad range of network parameters such as per-tier transmission power levels, base station locations, fading statistics and the path-loss model. A simulation study is performed to corroborate the analytical results. In particular, a good statistical match between the standardized AWI distribution and its normal approximation occurs even for moderately dense heterogeneous cellular networks. These results are expected to have important ramifications on the characterization of performance upper and lower bounds for emerging 5G network architectures.

This paper studies the outage capacity of a network consisting of a multitude of heterogenous mobile users, and operating according to the classical opportunistic beamforming framework. The base station is located at the center of the cell, which is modeled as a disk of finite radius. The random user locations are modeled using a homogenous spatial Poisson point process. The received signals are impaired by both fading and location dependent path loss. For this system, we first derive an expression for the beam outage probability. This expression holds for all path loss models that satisfy some mild conditions. Then, we focus on two specific path loss models (i.e., an unbounded model and a more realistic bounded one) to illustrate the applications of our results. In the large system limit where the cell radius tends to infinity, the beam outage capacity and its scaling behavior are derived for the selected specific path loss models. It is shown that the beam outage capacity scales logarithmically for the unbounded model. On the other hand, this scaling behavior becomes double logarithmic for the bounded model. Intuitive explanations are provided as to why we observe different scaling behavior for different path loss models. Numerical evaluations are performed to give further insights, and to illustrate the applicability of the outage capacity results even to a cell having a small finite radius., Comment: To appear in Globecom 2013, Atlanta, USA

This paper studies optimal distributed power allocation and scheduling policies (DPASPs) for distributed total power and interference limited (DTPIL) cognitive multiple access networks in which secondary users (SU) independently perform power allocation and scheduling tasks using their local knowledge of secondary transmitter secondary base-station (STSB) and secondary transmitter primary base-station (STPB) channel gains. In such networks, transmission powers of SUs are limited by an average total transmission power constraint and by a constraint on the average interference power that SUs cause to the primary base-station. We first establish the joint optimality of water-filling power allocation and threshold-based scheduling policies for DTPIL networks. We then show that the secondary network throughput under the optimal DPASP scales according to $\frac{1}{\e{}n_h}\log\logp{N}$, where $n_h$ is a parameter obtained from the distribution of STSB channel power gains and $N$ is the total number of SUs. From a practical point of view, our results signify the fact that distributed cognitive multiple access networks are capable of harvesting multiuser diversity gains without employing centralized schedulers and feedback links as well as without disrupting primary's quality-of-service (QoS), Comment: arXiv admin note: substantial text overlap with arXiv:1209.1426

In cognitive multiple access networks, feedback is an important mechanism to convey secondary transmitter primary base station (STPB) channel gains from the primary base station (PBS) to the secondary base station (SBS). This paper investigates the optimal sum-rate capacity scaling laws for cognitive multiple access networks in feedback limited communication scenarios. First, an efficient feedback protocol called $K$-smallest channel gains ($K$-SCGs) feedback protocol is proposed in which the PBS feeds back the $\K$ smallest out of $N$ STPB channel gains to the SBS. Second, the sum-rate performance of the $K$-SCG feedback protocol is studied for three network types when transmission powers of secondary users (SUs) are optimally allocated. The network types considered are total-power-and-interference-limited (TPIL), interference-limited (IL) and individual-power-and-interference-limited (IPIL) networks. For each network type studied, we provide a sufficient condition on $\K$ such that the $K$-SCG feedback protocol is {\em asymptotically} optimal in the sense that the secondary network sum-rate scaling behavior under the $K$-SCG feedback protocol is the same with that under the full-feedback protocol. We allow distributions of secondary-transmitter-secondary-base-station (STSB), and STPB channel power gains to belong to a fairly general class of distributions called class $\mathcal{C}$-distributions that includes commonly used fading models.

This paper studies optimum power control and sum-rate scaling laws for the distributed cognitive uplink. It is first shown that the optimum distributed power control policy is in the form of a threshold based water-filling power control. Each secondary user executes the derived power control policy in a distributed fashion by using local knowledge of its direct and interference channel gains such that the resulting aggregate (average) interference does not disrupt primary's communication. Then, the tight sum-rate scaling laws are derived as a function of the number of secondary users $N$ under the optimum distributed power control policy. The fading models considered to derive sum-rate scaling laws are general enough to include Rayleigh, Rician and Nakagami fading models as special cases. When transmissions of secondary users are limited by both transmission and interference power constraints, it is shown that the secondary network sum-rate scales according to $\frac{1}{\e{}n_h}\log\logp{N}$, where $n_h$ is a parameter obtained from the distribution of direct channel power gains. For the case of transmissions limited only by interference constraints, on the other hand, the secondary network sum-rate scales according to $\frac{1}{\e{}\gamma_g}\logp{N}$, where $\gamma_g$ is a parameter obtained from the distribution of interference channel power gains. These results indicate that the distributed cognitive uplink is able to achieve throughput scaling behavior similar to that of the centralized cognitive uplink up to a pre-log multiplier $\frac{1}{\e{}}$, whilst primary's quality-of-service requirements are met. The factor $\frac{1}{\e{}}$ can be interpreted as the cost of distributed implementation of the cognitive uplink.

This paper investigates the problem of Gaussian approximation for the wireless multi-access interference distribution in large spatial wireless networks. First, a principled methodology is presented to establish rates of convergence of the multi-access interference distribution to a Gaussian distribution for general bounded and power-law decaying path-loss functions. The model is general enough to also include various random wireless channel dynamics such as fading and shadowing arising from multipath propagation and obstacles existing in the communication environment. It is shown that the wireless multi-access interference distribution converges to the Gaussian distribution with the same mean and variance at a rate $\frac{1}{\sqrt{\lambda}}$, where $\lambda>0$ is a parameter controlling the intensity of the planar (possibly non-stationary) Poisson point process generating node locations. An explicit expression for the scaling coefficient is obtained as a function of fading statistics and the path-loss function. Second, an extensive numerical and simulation study is performed to illustrate the accuracy of the derived Gaussian approximation bounds. A good statistical fit between the interference distribution and its Gaussian approximation is observed for moderate to high values of $\lambda$. Finally, applications of these approximation results to upper and lower bound the outage capacity and ergodic sum capacity for spatial wireless networks are illustrated. The derived performance bounds on these capacity metrics track the network performance within one nats per second per hertz.

This paper studies the structure of downlink sum-rate maximizing selective decentralized feedback policies for opportunistic beamforming under finite feedback constraints on the average number of mobile users feeding back. Firstly, it is shown that any sum-rate maximizing selective decentralized feedback policy must be a threshold feedback policy. This result holds for all fading channel models with continuous distribution functions. Secondly, the resulting optimum threshold selection problem is analyzed in detail. This is a non-convex optimization problem over finite dimensional Euclidean spaces. By utilizing the theory of majorization, an underlying Schur-concave structure in the sum-rate function is identified, and the sufficient conditions for the optimality of homogenous threshold feedback policies are obtained. Applications of these results are illustrated for well known fading channel models such as Rayleigh, Nakagami and Rician fading channels, along with various engineering and design insights. Rather surprisingly, it is shown that using the same threshold value at all mobile users is not always a rate-wise optimal feedback strategy, even for a network with identical mobile users experiencing statistically the same channel conditions. For the Rayleigh fading channel model, on the other hand, homogenous threshold feedback policies are proven to be rate-wise optimal if multiple orthonormal data carrying beams are used to communicate with multiple mobile users simultaneously., Comment: Submitted to IEEE Transactions on Information Theory

This paper considers the optimum single cell power-control maximizing the aggregate (uplink) communication rate of the cell when there are peak power constraints at mobile users, and a low-complexity data decoder (without successive decoding) at the base station. It is shown, via the theory of majorization, that the optimum power allocation is binary, which means links are either "on" or "off". By exploiting further structure of the optimum binary power allocation, a simple polynomial-time algorithm for finding the optimum transmission power allocation is proposed, together with a reduced complexity near-optimal heuristic algorithm. Sufficient conditions under which channel-state aware time-division-multiple-access (TDMA) maximizes the aggregate communication rate are established. Finally, a numerical study is performed to compare and contrast the performance achieved by the optimum binary power-control policy with other sub-optimum policies and the throughput capacity achievable via successive decoding. It is observed that two dominant modes of communication arise, wideband or TDMA, and that successive decoding achieves better sum-rates only under near-perfect interference cancellation efficiency., Comment: 24 pages, 11 figures

Threshold feedback policies are well known and provably rate-wise optimal selective feedback techniques for communication systems requiring partial channel state information (CSI). However, optimal selection of thresholds at mobile users to maximize information theoretic data rates subject to feedback constraints is an open problem. In this paper, we focus on the optimal threshold selection problem, and provide a solution for this problem for finite feedback systems. Rather surprisingly, we show that using the same threshold values at all mobile users is not always a rate-wise optimal feedback strategy, even for a system with identical users experiencing statistically the same channel conditions. By utilizing the theory of majorization, we identify an underlying Schur-concave structure in the rate function and obtain sufficient conditions for a homogenous threshold feedback policy to be optimal. Our results hold for most fading channel models, and we illustrate an application of our results to familiar Rayleigh fading channels., Comment: Submitted to IEEE International Symposium on Information Theory, St. Petersburg, Russia, Aug 2011

Beamforming techniques utilizing only partial channel state information (CSI) has gained popularity over other communication strategies requiring perfect CSI thanks to their lower feedback requirements. The amount of feedback in beamforming based communication systems can be further reduced through selective feedback techniques in which only the users with channels good enough are allowed to feed back by means of a decentralized feedback policy. In this paper, we prove that thresholding at the receiver is the rate-wise optimal decentralized feedback policy for feedback limited systems with prescribed feedback constraints. This result is highly adaptable due to its distribution independent nature, provides an analytical justification for the use of threshold feedback policies in practical systems, and reinforces previous work analyzing threshold feedback policies as a selective feedback technique without proving its optimality. It is robust to selfish unilateral deviations. Finally, it reduces the search for rate-wise optimal feedback policies subject to feedback constraints from function spaces to a finite dimensional Euclidean space., Comment: Submitted to IEEE International Symposium on Information Theory, St. Petersburg, Russia, Aug 2011

In this article, we develop a model for the evolution of real estate prices. A wide range of inputs, including stochastic interest rates and changing demands for the asset, are considered. Maximizing their expected utility, home owners make optimal sale decisions given these changing market conditions. Using these optimal sale decisions, we simulate the implied evolution of housing prices providing insights into the recent subprime lending crisis., Comment: This paper was presented in part at INFORMS 2007 Annual Meeting, Bachelier Finance Society 2008, and AREUEA 2008. It was among the finalists for the best student research paper award at INFORMS 2007. This version was submitted to Management Science in November 2008

This paper analyzes the behavior of selfish transmitters under imperfect location information. The scenario considered is that of a wireless network consisting of selfish nodes that are randomly distributed over the network domain according to a known probability distribution, and that are interested in communicating with a common sink node using common radio resources. In this scenario, the wireless nodes do not know the exact locations of their competitors but rather have belief distributions about these locations. Firstly, properties of the packet success probability curve as a function of the node-sink separation are obtained for such networks. Secondly, a monotonicity property for the best-response strategies of selfish nodes is identified. That is, for any given strategies of competitors of a node, there exists a critical node-sink separation for this node such that its best-response is to transmit when its distance to the sink node is smaller than this critical threshold, and to back off otherwise. Finally, necessary and sufficient conditions for a given strategy profile to be a Nash equilibrium are provided., Comment: Presented in the Fourth International Wireless Internet Conference

This paper analyzes the asymptotic behavior of a multiple-access network comprising a large number of selfish transmitters competing for access to a common wireless communication channel, and having different utility functions for determining their strategies. A necessary and sufficient condition is given for the total number of packet arrivals from selfish transmitters to converge in distribution. The asymptotic packet arrival distribution at Nash equilibrium is shown to be a mixture of a Poisson distribution and finitely many Bernoulli distributions., Comment: Proceedings of the 2008 IEEE International Symposium on Information Theory, Toronto, ON, Canada, July 6 - 11, 2008

Small-world networks are networks in which the graphical diameter of the network is as small as the diameter of random graphs but whose nodes are highly clustered when compared with the ones in a random graph. Examples of small-world networks abound in sociology, biology, neuroscience and physics as well as in human-made networks. This paper analyzes the average delivery time of messages in dense small-world networks constructed on a plane. Iterative equations for the average message delivery time in these networks are provided for the situation in which nodes employ a simple greedy geographic routing algorithm. It is shown that two network nodes communicate with each other only through their short-range contacts, and that the average message delivery time rises linearly if the separation between them is small. On the other hand, if their separation increases, the average message delivery time rapidly saturates to a constant value and stays almost the same for all large values of their separation., Comment: Proceedings of the 2008 IEEE International Symposium on Information Theory, Toronto, ON, Canada, July 6 - 11, 2008