arXiv subjects

Prasanna Chaporkar

Publications and source records attributed to Prasanna Chaporkar.

At least 37 records · Page 2Linked to original sources

arXiv2019

Verification and Design of Resilient Closed-Loop Structured System

This paper addresses the resilience of large-scale closed-loop structured systems in the sense of arbitrary pole placement when subject to failure of feedback links. Given a structured system with input, output, and feedback matrices, we first aim to verify whether the closed-loop structured system is resilient to simultaneous failure of any subset of feedback links of a specified cardinality. Subsequently, we address the associated design problem in which given a structured system with input and output matrices, we need to design a sparsest feedback matrix that ensures the resilience of the resulting closed-loop structured system to simultaneous failure of any subset of feedback links of a specified cardinality. We first prove that the verification problem is NP-complete even for irreducible systems and the design problem is NP-hard even for so-called structurally cyclic systems. We also show that the design problem is inapproximable to factor (1-o(1))log n, where n denotes the system dimension. Then we propose algorithms to solve both the problems: a pseudo-polynomial algorithm to address the verification problem of irreducible systems and a polynomial-time O(log n)-optimal approximation algorithm to solve the design problem for a special feedback structure, so-called back-edge feedback structure.

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arXiv2019

Resource Allocation for Loss Tolerant Video Streaming in eMBMS

Bandwidth hungry video content has become the dominant contributor to the data traffic world over. Cellular networks are constantly evolving to meet the growing traffic demands. Over the past few years, wireless multicast has been garnering a lot of attention as a means of efficient resource utilization. Multicast transmission lets spectral resources to be shared between users streaming the same content. Even though multicast transmission allows to serve multiple users on the same resources, in order to serve all these users successfully, the base station cannot transmit the content at a rate greater than that decodable by the user with the worst channel conditions. In this paper, we propose a way to overcome this bottleneck. Video streaming services can sustain a certain amount of packet loss without any significant degradation in the quality experienced by the users. We leverage this loss tolerant nature of video streaming applications to improve the performance of multicast video services in LTE and 5G. We convert the problem of resource allocation for loss tolerant multicasting into the problem of stabilizing a queueing system. We then propose two throughput optimal Maximum Weight (MW) policies that successfully stabilize the constructed queueing system. However, brute force implementation of MW policies is mostly NP-hard. To overcome this, we propose a maximum weight bipartite matching approach that results in a polynomial time implementation of the proposed policies. We also evaluate the performance of our policies via extensive simulations.

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arXiv2019

KLUCB Approach to Copeland Bandits

Multi-armed bandit(MAB) problem is a reinforcement learning framework where an agent tries to maximise her profit by proper selection of actions through absolute feedback for each action. The dueling bandits problem is a variation of MAB problem in which an agent chooses a pair of actions and receives relative feedback for the chosen action pair. The dueling bandits problem is well suited for modelling a setting in which it is not possible to provide quantitative feedback for each action, but qualitative feedback for each action is preferred as in the case of human feedback. The dueling bandits have been successfully applied in applications such as online rank elicitation, information retrieval, search engine improvement and clinical online recommendation. We propose a new method called Sup-KLUCB for K-armed dueling bandit problem specifically Copeland bandit problem by converting it into a standard MAB problem. Instead of using MAB algorithm independently for each action in a pair as in Sparring and in Self-Sparring algorithms, we combine a pair of action and use it as one action. Previous UCB algorithms such as Relative Upper Confidence Bound(RUCB) can be applied only in case of Condorcet dueling bandits, whereas this algorithm applies to general Copeland dueling bandits, including Condorcet dueling bandits as a special case. Our empirical results outperform state of the art Double Thompson Sampling(DTS) in case of Copeland dueling bandits.

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arXiv2019

Minimizing Inputs for Strong Structural Controllability

The notion of strong structural controllability (s-controllability) allows for determining controllability properties of large linear time-invariant systems even when numerical values of the system parameters are not known a priori. The s-controllability guarantees controllability for all numerical realizations of the system parameters. We address the optimization problem of minimal cardinality input selection for s-controllability. Previous work shows that not only the optimization problem is NP-hard, but finding an approximate solution is also hard. We propose a randomized algorithm using the notion of zero forcing sets to obtain an optimal solution with high probability. We compare the performance of the proposed algorithm with a known heuristic [1] for synthetic random systems and five real-world networks, viz. IEEE 39-bus system, re-tweet network, protein-protein interaction network, US airport network, and a network of physicians. It is found that our algorithm performs much better than the heuristic in each of these cases.

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arXiv2018

Multi-Player Multi-Armed Bandit Based Resource Allocation for D2D Communications

Device-to-device (D2D) communications is expected to play a significant role in increasing the system capacity of the fifth generation (5G) wireless networks. To accomplish this, efficient power and resource allocation algorithms need to be devised for the D2D users. Since the D2D users are treated as secondary users, their interference to the cellular users (CUs) should not hamper the CU communications. Most of the prior works on D2D resource allocation assume full channel state information (CSI) at the base station (BS). However, the required channel gains for the D2D pairs may not be known. To acquire these in a fast fading channel requires extra power and control overhead. In this paper, we assume partial CSI and formulate the D2D power and resource allocation problem as a multi-armed bandit problem. We propose a power allocation scheme for the D2D users in which the BS allocates power to the D2D users if a certain signal-to-interference-plus-noise ratio (SINR) is maintained for the CUs. In a single player environment a D2D user selects a CU in every time slot by employing UCB1 algorithm. Since this resource allocation problem can also be considered as an adversarial bandit problem we have applied the exponential-weight algorithm for exploration and exploitation (Exp3) to solve it. In a multiple player environment, we extend UCB1 and Exp3 to multiple D2D users. We also propose two algorithms that are based on distributed learning algorithm with fairness (DLF) and kth-UCB1 algorithms in which the D2D users are ranked. Our simulation results show that our proposed algorithms are fair and achieve good performance.

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arXiv2018

GOSPAL: An Efficient Strategy-Proof mechanism for Constrained Resource Allocation

We consider allocation of a resource to multiple interested users with a constraint that if the resource is allocated to user $i$ then it can not be allocated simultaneously to a predefined set of users $\cS_i$ . This scenario arises in many practical systems that include wireless networks and constrained queuing systems. It is known that socially optimal strategy-proof mechanism is not only NP-hard, but it is also hard to approximate. This renders optimal policy hard to use in practice. Here, we propose a computationally efficient mechanism and prove it to be strategy proof. Using Monte Carlo simulations, we show that the social utility of the proposed scheme is close to that of the optimal. Further, we demonstrate how the proposed mechanism can be used for fair and efficient short-term spectrum allocation in resource constrained large wireless networks.

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arXiv2018

A Structure-aware Online Learning Algorithm for Markov Decision Processes

To overcome the curse of dimensionality and curse of modeling in Dynamic Programming (DP) methods for solving classical Markov Decision Process (MDP) problems, Reinforcement Learning (RL) algorithms are popular. In this paper, we consider an infinite-horizon average reward MDP problem and prove the optimality of the threshold policy under certain conditions. Traditional RL techniques do not exploit the threshold nature of optimal policy while learning. In this paper, we propose a new RL algorithm which utilizes the known threshold structure of the optimal policy while learning by reducing the feasible policy space. We establish that the proposed algorithm converges to the optimal policy. It provides a significant improvement in convergence speed and computational and storage complexity over traditional RL algorithms. The proposed technique can be applied to a wide variety of optimization problems that include energy efficient data transmission and management of queues. We exhibit the improvement in convergence speed of the proposed algorithm over other RL algorithms through simulations.

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arXiv2018

Stability Analysis of Device-to-Device Relay-Assisted Cellular Networks

Motivated by increasing popularity of delay sensitive applications, we investigate the queue stability in device-to-device (D2D) relay-assisted cellular networks. In contrast to prior works on D2D relay-assisted cellular networks, we incorporate practical properties of these networks such as bursty packet arrivals, user mobility and relays generating their own traffic. Assuming network topology evolving in IID fashion, we first evaluate the system stability region to quantify its delay performance. Subsequently, we formulate a policy for joint resource allocation and power control under a more realistic mobility scenario of generalized reflected random walk. Also, the throughput optimality of this policy is investigated. Simulation results are provided to give better understanding of queue stability in the network.

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arXiv2018

Optimal Network Topology Design in Composite Systems with Constrained Neighbors for Structural Controllability

Composite systems are large complex systems con- sisting of interconnected agents (subsystems). Agents in a com- posite system interact with each other towards performing an in- tended goal. Controllability is essential to achieve desired system performance in linear time-invariant composite systems. Agents in a composite system are often uncontrollable individually, further, only a few agents receive input. In such a case, the agents share/communicate their private state information with pre-specified neighboring agents so as to achieve controllability. Our objective in this paper is to identify an optimal network topology, optimal in the sense of minimum cardinality information transfer between agents to guarantee the controllability of the composite system when the possible neighbor set of each agent is pre-specified. We focus on graph-theoretic analysis referred to as structural controllability as numerical entries of system matrices in complex systems are mostly unknown. We first prove that given a set of agents and the possible set of neighbors, finding a minimum cardinality set of information (interconnections) that must be shared to accomplish structural controllability of the composite system is NP-hard. Subsequently, we present a polynomial-time algorithm that finds a 2-optimal solution to this NP-hard problem. Our algorithm combines a minimum weight bipartite matching algorithm and a minimum spanning tree algorithm and gives a subset of interconnections which when established guarantees structural controllability, such that the worst-case performance is 2-optimal. Finally, we show that our approach directly extends to weighted constrained optimal net- work topology design problem and constrained optimal network topology design problem in switched linear systems.

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arXiv2018

Towards Optimal Grouping and Resource Allocation for Multicast Streaming in LTE

Multimedia traffic is predicted to account for 82% of the total data traffic by the year 2020. With the increasing popularity of video streaming applications like YouTube, Netflix, Amazon Prime Video, popular video content is often required to be delivered to a large number of users simultaneously. Multicast transmission can be used for catering to such applications efficiently. The common content can be transmitted to the users on the same resources resulting in considerable resource conservation. This paper proposes various schemes for efficient grouping and resource allocation for multicast transmission in LTE. The optimal grouping and resource allocation problems are shown to be NP-hard and so, we propose heuristic algorithms for both these problems. We also formulate a Simulated Annealing based algorithm to approximate the optimal resource allocation for our problem. The LP-relaxation based resource allocation proposed by us results in allocations very close to the estimated optimal.

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arXiv2018

A Learnable Distortion Correction Module for Modulation Recognition

Modulation recognition is a challenging task while performing spectrum sensing in a cognitive radio setup. Recently, the use of deep convolutional neural networks (CNNs) has shown to achieve state-of-the-art accuracy for modulation recognition \cite{survey}. However, a wireless channel distorts the signal and CNNs are not explicitly designed to undo these artifacts. To improve the performance of CNN-based recognition schemes we propose a signal distortion correction module (CM) and show that this CM+CNN scheme achieves accuracy better than the existing schemes. The proposed CM is also based on a neural network that estimates the random carrier frequency and phase offset introduced by the channel and feeds it to a part that undoes this distortion right before CNN-based modulation recognition. Its output is differentiable with respect to its weights, which allows it to be trained end-to-end with the modulation recognition CNN based on the received signal. For supervision, only the modulation scheme label is used and the knowledge of true frequency or phase offset is not required.

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arXiv2018

Approximating Constrained Minimum Cost Input-Output Selection for Generic Arbitrary Pole Placement in Structured Systems

This paper is about minimum cost constrained selection of inputs and outputs for generic arbitrary pole placement. The input-output set is constrained in the sense that the set of states that each input can influence and the set of states that each output can sense is pre-specified. Our goal is to optimally select an input-output set that the system has no structurally fixed modes. Polynomial algorithms do not exist for solving this problem unless P=NP. To this end, we propose an approximation algorithm by splitting the problem in to three sub-problems: a) minimum cost accessibility problem, b) minimum cost sensability problem and c) minimum cost disjoint cycle problem. We prove that problems a) and b) are equivalent to a suitably defined weighted set cover problems. We also show that problem c) is equivalent to a minimum cost perfect matching problem. Using these we give an approximation algorithm which solves the minimum cost generic arbitrary pole placement problem. The proposed algorithm incorporates an approximation algorithm to solve the weighted set cover problem for solving a) and b) and a minimum cost perfect matching algorithm to solve c). Further, we show that the algorithm is polynomial time an gives an order optimal solution to the minimum cost input-output selection for generic arbitrary pole placement problem.

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arXiv2017

Optimal Selection of Interconnections in Composite Systems for Structural Controllability

In this paper, we study structural controllability of a linear time invariant (LTI) composite system consisting of several subsystems. We assume that the neighbourhood of each subsystem is unconstrained, i.e., any subsystem can interact with any other subsystem. The interaction links between subsystems are referred as interconnections. We assume the composite system to be structurally controllable if all possible interconnections are present, and our objective is to identify the minimum set of interconnections required to keep the system structurally controllable. We consider structurally identical subsystems, i.e., the zero/non-zero pattern of the state matrices of the subsystems are the same, but dynamics can be different. We present a polynomial time optimal algorithm to identify the minimum cardinality set of interconnections that subsystems must establish to make the composite system structurally controllable.

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arXiv2017

Rapidly Mixing Markov Chain Monte Carlo Technique for Matching Problems with Global Utility Function

This paper deals with a complete bipartite matching problem with the objective of finding an optimal matching that maximizes a certain generic predefined utility function on the set of all matchings. After proving the NP-hardness of the problem using reduction from the 3-SAT problem, we propose a randomized algorithm based on Markov Chain Monte Carlo (MCMC) technique for solving this. We sample from Gibb's distribution and construct a reversible positive recurrent discrete time Markov chain (DTMC) that has the steady state distribution same as the Gibb's distribution. In one of our key contributions, we show that the constructed chain is `rapid mixing', i.e., the convergence time to reach within a specified distance to the desired distribution is polynomial in the problem size. The rapid mixing property is established by obtaining a lower bound on the conductance of the DTMC graph and this result is of independent interest.

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arXiv2017

Optimal Feedback Selection for Structurally Cyclic Systems with Dedicated Actuators and Sensors

This paper solves the sparsest feedback selection problem for linear time invariant structured systems, a long-standing open problem in structured systems. We consider structurally cyclic systems with dedicated inputs and outputs. We prove that finding a sparsest feedback selection is of linear complexity for the case of structurally cyclic systems with dedicated inputs and outputs. This problem has received attention recently but key errors in the hardness-proofs have resulted in an erroneous conclusion there. This is also elaborated in this brief paper together with a counter-example.

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arXiv2017

A Framework for Wireless Broadband Network for Connecting the Unconnected

A significant barrier in providing affordable rural broadband is to connect the rural and remote places to the optical Point of Presence (PoP) over distances of few kilometers. A lot of work has been done in the area of long distance Wi-Fi networks. However, these networks require tall towers and high gain (directional) antennas. Also, they work in the unlicensed band which has Effective Isotropically Radiated Power (EIRP) limit (e.g. 1 W in India) which restricts the network design. In this work, we propose a Long Term Evolution-Advanced (LTE-A) network operating in TV UHF to connect the remote areas to the optical PoP. In India, around 100 MHz of TV UHF band IV (470-585 MHz) is unused at any location and can be put to an effective use in these areas. We explore the idea of multi-hop topology for the proposed network. We also compare the performance of the multi-hop network with the Point to Multipoint (PMP) topology. The results show that multi-hop network performs much better than the PMP network. We then formulate a Linear Programming (LP) problem of generating optimal topology and compare its performance with the multi-hop network. Overall, the analysis implies that an optimally planned LTE-A network in TV UHF band can be a potential solution for affordable rural broadband.

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arXiv2017

Spectrum Sharing for LTE-A Network in TV White Space

Rural areas in the developing countries are predominantly devoid of Internet access as it is not viable for operators to provide broadband service in these areas. To solve this problem, we propose a middle mile Long erm Evolution Advanced (LTE-A) network operating in TV white space to connect villages to an optical Point of Presence (PoP) located in the vicinity of a rural area. We study the problem of spectrum sharing for the middle mile networks deployed by multiple operators. A graph theory based Fairness Constrained Channel Allocation (FCCA) algorithm is proposed, employing Carrier Aggregation (CA) and Listen Before Talk (LBT) features of LTE-A. We perform extensive system level simulations to demonstrate that FCCA not only increases spectral efficiency but also improves system fairness.

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arXiv2017

Minimum Cost Feedback Selection for Arbitrary Pole Placement in Structured Systems

This paper addresses optimal feedback selection for generic arbitrary pole placement of structured systems when each feedback edge is associated with a cost. Given a structured system and a feedback cost matrix, our aim is to find a feasible feedback matrix of minimum cost that guarantees arbitrary pole placement of the closed-loop structured system. We first give a polynomial time reduction of the weighted set cover problem to an instance of the feedback selection problem and thereby show that the problem is NP-hard. Then we prove the inapproximability of the problem by showing that constant factor approximation for the problem does not exist unless the set cover problem can be approximated within a constant factor. Since the problem is hard, we study a subclass of systems whose directed acyclic graph constructed using the strongly connected components of the state digraph is a line graph and the state bipartite graph has a perfect matching. We propose a polynomial time optimal algorithm based on dynamic programming for solving the problem on this class of systems. Further, over the same class of systems we relax the perfect matching assumption, and provide a polynomial time 2-optimal solution based on dynamic programming and a minimum cost perfect matching algorithm.

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