arXiv subjects

Sudip Biswas

Publications and source records attributed to Sudip Biswas.

9 recordsLinked to original sources

arXiv2026

Joint Antenna Placement and Power Allocation for RSMA-Enabled Pinching Antenna Systems

This letter investigates a rate-splitting multiple access (RSMA)-enabled multi-user pinching antenna system (PASS). A fairness-aware sum-rate maximization problem is formulated to jointly optimize pinching antenna locations and common/private stream power allocation. The resulting mixed discrete-continuous non-convex problem is addressed using an alternating optimization framework that combines greedy antenna placement with successive convex approximation (SCA)-based power allocation. Numerical results demonstrate that the proposed RSMA-enabled PASS significantly improves achievable sum-rate, user fairness, and bit error rate (BER) performance compared with conventional non-RSMA PASS schemes.

eess.SP↗

arXiv2026

A Survey on Security with Quantum Computing

Quantum computing has emerged as a transformative computing paradigm capable of solving problems that remain computationally infeasible for classical systems; however, its rapid advancement also introduces significant security, privacy, and reliability concerns. In this context, this survey presents a comprehensive review of security challenges and mitigation strategies associated with quantum computing, focusing on security issues in quantum computers, security threats caused by quantum computers, and security mechanisms developed for quantum systems. The paper examines vulnerabilities in quantum hardware and software, the impact of quantum computing on existing cryptographic infrastructures and cybersecurity mechanisms, and the development of quantum-resilient solutions such as post-quantum cryptography, quantum-safe communication protocols, quantum intrusion detection systems, and quantum-aware software engineering techniques. In addition, the survey discusses emerging applications of quantum technologies in cybersecurity domains, including malware detection, network intrusion detection, Internet of Things (IoT) security, and secure communication systems. Furthermore, the paper analyzes existing quantum error mitigation and fault-tolerance approaches designed to improve the robustness and trustworthiness of quantum computation under realistic noisy conditions. By consolidating recent advances, open research challenges, and future directions, this survey provides a structured overview of the evolving intersection between quantum computing and cybersecurity, while serving as a reference for researchers and practitioners working toward secure, resilient, and quantum-ready computing infrastructures.

cs.CR↗

arXiv2021

An Analysis on Rate-Splitting Multiple Access for IRS Aided 6G Communication

Integrating intelligent reflecting surface (IRS) and Rate-Splitting Multiple Access (RSMA) is an effective solution to improve the spectral/energy efficiency in next-generation (beyond 5G (B5G) and 6G) wireless networks. In this paper, we investigate a rate-splitting (RS)-based transmission technique for an IRS-aided communication network involving both near and cell-edge users. In particular, we derive a new architecture called IRS-RS that leverages the interplay between RS and IRS, with an aim to maximize the weighted sum-rate (WSR) of users by selecting the reflecting coefficients at the IRS and designing beamformers at the BS under the constraints of power at the base station (BS), quality of service (QoS) at each user and finite resolution at the IRS. To solve the non-convex WSR maximization problem, we propose an alternating algorithm and compare its performance with baseline non-orthogonal multiple access (NOMA) based transmission for an IRS-aided communication network for both perfect and imperfect CSIT cases. Through numerical results, it is shown that the proposed IRS-RS architecture yields better QoS with respect to the cell-edge users when compared to IRS-NOMA transmission scheme.

eess.SP↗

arXiv2021

Block Deep Neural Network-Based Signal Detector for Generalized Spatial Modulation

Generalized Spatial Modulation (GSM) is being considered for high capacity and energy-efficient networks of the future. However, signal detection due to inter-channel interference among the active antennas is a challenge in GSM systems and is the focus of this letter. Specifically, we explore the feasibility of using deep neural networks (DNN) for signal detection in GSM. In particular, we propose a block DNN (B-DNN) based architecture, where the active antennas and their transmitted constellation symbols are detected by smaller sub-DNNs. After N-ordinary DNN detection, the Euclidean distance-based soft constellation algorithm is implemented. The proposed B-DNN detector achieves a BER performance that is superior to traditional block zero-forcing (B-ZF) and block minimum mean-squared error (B-MMSE) detection schemes and similar to that of classical maximum likelihood (ML) detector. Further, the proposed method requires less computation time and is more accurate than alternative conventional numerical methods.

eess.SP↗

arXiv2021

Multiple Antenna Selection and Successive Signal Detection for SM-based IRS-aided Communication

Intelligent reflecting surface (IRS) is being considered as a prospective candidate for next-generation wireless communication due to its ability to significantly improve coverage and spectral efficiency by controlling the propagation environment. One of the ways IRS increases spectral efficiency is by adjusting phase shifts to perform passive beamforming. In this letter, we integrate the concept of IRS-aided communication to the domain of multi-direction beamforming, whereby multiple receive antennas are selected to convey more information bits than existing spatial modulation (SM) techniques at any specific time. To complement this system, we also propose a successive signal detection (SSD) technique at the receiver. Numerical results show that the proposed design is able to improve the average successful bits transmitted (ASBT) by the system, which outperforms other state-of-the-art methods proposed in the literature.

eess.SP↗

arXiv2019

Coexistence of MIMO Radar and FD MIMO Cellular Systems with QoS Considerations

In this work, the feasibility of spectrum sharing between a multiple-input multiple-output (MIMO) radar system (RS) and a MIMO cellular system (CS), comprising of a full duplex (FD) base station (BS) serving multiple downlink and uplink users at the same time and frequency is investigated. While a joint transceiver design technique at the CS's BS and users is proposed to maximise the probability of detection (PoD) of the MIMO RS, subject to constraints of quality of service (QoS) of users and transmit power at the CS, null-space based waveform projection is used to mitigate the interference from RS towards CS. In particular, the proposed technique optimises the performance of PoD of RS by maximising its lower bound, which is obtained by exploiting the monotonically increasing relationship of PoD and its non-centrality parameter. Numerical results show the utility of the proposed spectrum sharing framework, but with certain trade-offs in performance corresponding to RS's transmit power, RS's PoD, CS's residual self interference power at the FD BS and QoS of users.

eess.SP↗

arXiv2019

Analysis of Cached-Enabled Hybrid Millimter Wave & Sub-6 GHz Massive MIMO Networks

This paper focuses on edge caching in mm/μWave hybrid wireless networks, in which all mmWave SBSs and μWave MBSs are capable of storing contents to alleviate the traffic burden on the backhaul link that connect the BSs and the core network to retrieve the non-cached contents. The main aim of this work is to address the effect of capacity-limited backhaul on the average success probability (ASP) of file delivery and latency. In particular, we consider a more practical mmWave hybrid beamforming in small cells and massive MIMO communication in macro cells. Based on stochastic geometry and a simple retransmission protocol, we derive the association probabilities by which the ASP of file delivery and latency are derived. Taking no caching event as the benchmark, we evaluate these QoS performance metrics under MC and UC placement policies. The theoretical results demonstrate that backhaul capacity indeed has a significant impact on network performance especially under weak backhaul capacity. Besides, we also show the tradeoff among cache size, retransmission attempts, ASP of file delivery, and latency. The interplay shows that cache size and retransmission under different caching placement schemes alleviates the backhaul requirements. Simulation results are present to valid our analysis.

eess.SP↗

arXiv2018

An Analysis on Caching Placement for Millimeter/Micro Wave Hybrid Networks

In this paper, we consider a hybrid millimeter wave (mmWave) and micro wave ($μ$Wave) network from the perspective of \emph{wireless caching} and study the optimal probabilistic content/file caching placement at desirable base stations (BSs) using a stochastic geometric framework. Considering the average success probability (ASP) of file delivery as the performance metric, we derive expressions for the association probability of the typical user to the mmWave and $μ$Wave networks. Accordingly, we provide an upper bound for the ASP of file delivery and formulate the content caching placement scheme as an optimization problem with respect to caching probabilities, that jointly optimizes the ASP of file delivery considering both content placement and delivery phases. In particular, we consider the caching placement strategy under both noise-limited and interference-limited environments. We numerically evaluate the performance of the proposed caching schemes under essential factors, such as blockages in the mmWave network, cluster radius, BS density, and path loss and compare it with uniform caching placement, caching $M$ most popular contents, and random caching placement. Numerical results demonstrate the superiority of the proposed caching scheme over others, albeit certain trade-offs.

eess.SP↗

arXiv2015

Probabilistic Threshold Indexing for Uncertain Strings

Strings form a fundamental data type in computer systems. String searching has been extensively studied since the inception of computer science. Increasingly many applications have to deal with imprecise strings or strings with fuzzy information in them. String matching becomes a probabilistic event when a string contains uncertainty, i.e. each position of the string can have different probable characters with associated probability of occurrence for each character. Such uncertain strings are prevalent in various applications such as biological sequence data, event monitoring and automatic ECG annotations. We explore the problem of indexing uncertain strings to support efficient string searching. In this paper we consider two basic problems of string searching, namely substring searching and string listing. In substring searching, the task is to find the occurrences of a deterministic string in an uncertain string. We formulate the string listing problem for uncertain strings, where the objective is to output all the strings from a collection of strings, that contain probable occurrence of a deterministic query string. Indexing solution for both these problems are significantly more challenging for uncertain strings than for deterministic strings. Given a construction time probability value $τ$, our indexes can be constructed in linear space and supports queries in near optimal time for arbitrary values of probability threshold parameter greater than $τ$. To the best of our knowledge, this is the first indexing solution for searching in uncertain strings that achieves strong theoretical bound and supports arbitrary values of probability threshold parameter. We also propose an approximate substring search index that can answer substring search queries with an additive error in optimal time. We conduct experiments to evaluate the performance of our indexes.

cs.DB↗