arXiv subjects

V. Udaya Sankar

Publications and source records attributed to V. Udaya Sankar.

3 recordsLinked to original sources

arXiv2026

Data driven approach for Outdoor Channel Prediction in 5G and Beyond

An evolution of Wireless Communications towards 5G and beyond provides improved user experience in terms of quality of services. Understanding and estimating Channel information plays crucial role in providing better user experience. Traditional methods of channel estimation involves periodically sending pilots (known signals), estimating channel and send back estimated channel information to the BS which increases computational complexity and communication complexity. Hence, we focus on data driven approach for channel estimation. In this work, we explore a channel estimation mechanism at 7GHz frequency band for a given user location. This work involves data generation using Ray tracing mechanism and Machine learning model training that contains feature variables such as transmitter location, user location and target variable as channel coefficient . We explored Support Vector Regression, K-nearest neighbor (KNN), Random Forest, XGBoost and MLP. We found via simulations that XG Boost and proposed MLP performs better than Support Vector Regression, KNN and Random forest regression.

eess.SP↗

arXiv2026

Deep Learning Meets Mechanism Design: Key Results and Some Novel Applications

Mechanism design is essentially reverse engineering of games and involves inducing a game among strategic agents in a way that the induced game satisfies a set of desired properties in an equilibrium of the game. Desirable properties for a mechanism include incentive compatibility, individual rationality, welfare maximisation, revenue maximisation (or cost minimisation), fairness of allocation, etc. It is known from mechanism design theory that only certain strict subsets of these properties can be simultaneously satisfied exactly by any given mechanism. Often, the mechanisms required by real-world applications may need a subset of these properties that are theoretically impossible to be simultaneously satisfied. In such cases, a prominent recent approach is to use a deep learning based approach to learn a mechanism that approximately satisfies the required properties by minimizing a suitably defined loss function. In this paper, we present, from relevant literature, technical details of using a deep learning approach for mechanism design and provide an overview of key results in this topic. We demonstrate the power of this approach for three illustrative case studies: (a) efficient energy management in a vehicular network (b) resource allocation in a mobile network (c) designing a volume discount procurement auction for agricultural inputs. Section 6 concludes the paper.

cs.GT↗

arXiv2017

Algorithms for Nash and Pareto Equilibria for Resource Allocation in Multiple Femtocells

We consider a cellular system with multiple Femtocells operating in a Macrocell. They are sharing a set of communication channels. Each Femtocell has multiple users requiring certain minimum rate guarantees. Each channel has a peak power constraint to limit interference to the Macro Base Station (BS). We formulate the problem of channel allocation and power control at the Femtocells as a noncooperative Game. We develop decentralized algorithms to obtain a Coarse Correlated equilibrium that satisfies the QoS of each user. If the QoS of all the users cannot be satisfied, then we obtain a fair equilibrium. Finally we also provide a decentralized algorithm to reach a Pareto and a Nash Bargaining solution which has a much lower complexity than the algorithm to compute the NE.

cs.GT↗