Ketan Rajawat

Cross-layer designs in coded wireless fading networks with multicast

A cross-layer design along with an optimal resource allocation framework is formulated for wireless fading networks, where the nodes are allowed to perform network coding. The aim is to jointly optimize end-to-end transport-layer rates, network code design variables, broadcast link flows, link capacities, average power consumption, and short-term power allocation policies. As in the routing paradigm where nodes simply forward packets, the cross-layer optimization problem with network coding is nonconvex in general. It is proved, however, that with network coding, dual decomposition for multicast is optimal so long as the fading at each wireless link is a continuous random variable. This lends itself to provably convergent subgradient algorithms, which not only admit a layered-architecture interpretation, but also optimally integrate network coding in the protocol stack. The dual algorithm is also paired with a scheme that yields near-optimal network design variables, namely multicast end-to-end rates, network code design quantities, flows over the broadcast links, link capacities, and average power consumption. Finally, an asynchronous subgradient method is developed, whereby the dual updates at the physical layer can be affordably performed with a certain delay with respect to the resource allocation tasks in upper layers. This attractive feature is motivated by the complexity of the physical-layer subproblem and is an adaptation of the subgradient method suitable for network control.

A low complexity symbol timing estimator for MIMO systems using two samples per symbol

A new algorithm for data-aided symbol timing estimation in multiple antenna systems is proposed. By utilizing the information about the transmitted pulse, the proposed method achieves better performance and lower computational complexity than the recently proposed discrete Fourier transform (DFT) based interpolation method. Further, the method needs only two samples per symbol which is half of the oversampling factor required by the DFT based interpolation method.

Resource Allocation and Fairness in Wireless Powered Cooperative Cognitive Radio Networks

We integrate a wireless powered communication network with a cooperative cognitive radio network, where multiple secondary users (SUs) powered wirelessly by a hybrid access point (HAP) help a primary user relay the data. As a reward for the cooperation, the secondary network gains the spectrum access where SUs transmit to HAP using time division multiple access. To maximize the sum throughput of SUs, we present a secondary sum-throughput optimal resource allocation (STORA) scheme. Under the constraint of meeting target primary rate, the STORA scheme chooses the optimal set of relaying SUs and jointly performs the time and energy allocation for SUs. In particular, by exploiting the structure of the optimal solution, we find the order in which SUs are prioritized to relay primary data. Since the STORA scheme focuses on the sum throughput, it becomes inconsiderate toward individual SU throughput, resulting in low fairness. To enhance fairness, we investigate three resource allocation schemes, which are: 1) equal time allocation; 2) minimum throughput maximization; and 3) proportional time allocation. Simulation results reveal the tradeoff between sum throughput and fairness. The minimum throughput maximization scheme is the fairest one as each SU gets the same throughput, but yields the least SU sum throughput.

Dynamic Network Delay Cartography

Path delays in IP networks are important metrics, required by network operators for assessment, planning, and fault diagnosis. Monitoring delays of all source-destination pairs in a large network are, however, challenging and wasteful of resources. This paper advocates a spatio-temporal Kalman filtering approach to construct network-wide delay maps using measurements on only a few paths. The proposed network cartography framework allows efficient tracking and prediction of delays by relying on both topological as well as historical data. Optimal paths for delay measurement are selected in an online fashion by leveraging the notion of submodularity. The resulting predictor is optimal in the class of linear predictors, and outperforms competing alternatives on real-world data sets.

Prediction of Partially Observed Dynamical Processes Over Networks via Dictionary Learning

Prediction of dynamical processes evolving over network graphs is a basic task encountered in various areas of science and engineering. The prediction challenge is exacerbated when only partial network observations are available, that is when only measurements from a subset of nodes are available. To tackle this challenge, the present work introduces a joint topology- and data-driven approach for network-wide prediction able to handle partially observed network data. First, the known network structure and historical data are leveraged to design a dictionary for representing the network process. The novel approach draws from semi-supervised learning to enable learning the dictionary with only partial network observations. Once the dictionary is learned, network-wide prediction becomes possible via a regularized least-squares estimate which exploits the parsimony encapsulated in the design of the dictionary. Second, an online network-wide prediction algorithm is developed to jointly extrapolate the process over the network and update the dictionary accordingly. This algorithm is able to handle large training datasets at a fixed computational cost. More important, the online algorithm takes into account the temporal correlation of the underlying process, and thereby improves prediction accuracy. The performance of the novel algorithms is illustrated for prediction of real Internet traffic. There, the proposed approaches are shown to outperform competitive alternatives.

Cross-Layer Design of Coded Multicast for Wireless Random Access Networks

Joint optimization of network coding and Aloha-based medium access control (MAC) for multi-hop wireless networks is considered. The multicast throughput with a power consumption-related penalty is maximized subject to flow conservation and MAC achievable rate constraints to obtain the optimal transmission probabilities. The relevant optimization problem is inherently non-convex and hence difficult to solve even in a centralized manner. A successive convex approximation technique is employed to obtain a Karush-Kuhn-Tucker solution. A separable problem structure is obtained and the dual decomposition technique is adopted to develop a distributed solution. The algorithm is thus applicable to large networks, and amenable to online implementation. Numerical tests verify performance and complexity advantages of the proposed approach over existing designs. A network simulation with implementation of random linear network coding shows performance very close to the one theoretically designed.

Network-Compressive Coding for Wireless Sensors with Correlated Data

A network-compressive transmission protocol is developed in which correlated sensor observations belonging to a finite alphabet are linearly combined as they traverse the network on their way to a sink node. Statistical dependencies are modeled using factor graphs. The sum-product algorithm is run under different modeling assumptions to estimate the maximum a posteriori set of observations given the compressed measurements at the sink node. Error exponents are derived for cyclic and acyclic factor graphs using the method of types, showing that observations can be recovered with arbitrarily low probability of error as the network size grows. Simulated tests corroborate the theoretical claims.

Joint Scheduling and Network Coding for Multicast in Delay-Constrained Wireless Networks

This paper deals with network-coded multicast for real-time and streaming-media applications where packets have explicit expiration deadlines. Most of the popular network coding approaches require asymptotically large block-lengths, thereby incurring long decoding delays. The present paper introduces a joint scheduling and network coding design that aims to maximize the average throughput while respecting the packet deadlines. The novel approach relies on a time-unwrapped graph expansion in order to construct the network codes. The resultant algorithm draws from the well-known augmenting-path algorithm, and is both distributed as well as scalable. For networks with primary interference, a lower-bound on the worst-case performance of the algorithm is provided. The associated optimization problem is also analyzed from an integer programming perspective, and a set of valid inequalities is derived to obtain an upper bound.

Asynchronous Optimization Over Heterogeneous Networks Via Consensus ADMM

This paper considers the distributed optimization of a sum of locally observable, nonconvex functions. The optimization is performed over a multiagent networked system, and each local function depends only on a subset of the variables. An asynchronous and distributed alternating directions method of multipliers (ADMM) method that allows the nodes to defer or skip the computation and transmission of updates is proposed in the paper. The proposed algorithm utilizes different approximations in the update step, resulting in proximal and majorized ADMM variants. Both variants are shown to converge to a local minimum under certain regularity conditions. The proposed asynchronous algorithms are also applied to the problem of cooperative localization in wireless ad hoc networks, where it is shown to outperform the other state-of-the-art localization algorithms.

Advances in Spectrum Sensing and Cross-Layer Design for Cognitive Radio Networks

Abstract Spectrum sensing is the key task for cognitive radio (CR) networks with significant challenges that have attracted a flux of research and innovation in recent years. Various signal processing, learning and optimization techniques have been employed to tackle different aspects. In this paper, progresses made in this area are reviewed with emphasis on cross-layer design issues. The recent spectrum cartography techniques that capture the spatio-temporal RF environment in which the CRs operate is described in detail for physical layer sensing. MAC layer issues of scheduling the sensing operation based on the observation history are also outlined. The trade-off between sensing accuracy and the system-wide objective is highlighted in the context of sequential sensing schemes. The cross-layer benefit of rich cognition modalities toward network-wide performance is illustrated, and promising research directions are pointed out.