Research Papers - Department of Electrical and Electronic Engineering
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Publication Embargo Receiver-Centric Waveform Design: A New Frontier in SWIPT(Institute of Electrical and Electronics Engineers Inc., 2026-01-15) Vithanage, G. S; Jayakody, D. N.K; Krikidis, IIn this work a receiver-centric waveform design technique for simultaneous wireless information and power transfer (SWIPT) is proposed, eliminating the traditional trade-off between energy harvesting (EH) efficiency and information transfer (IT) integrity. By injecting pulses into the receiver, the peak-to-average power ratio (PAPR) of the received signal is increased, using diode nonlinearity to enhance EH without affecting IT. Particle swarm optimization (PSO) is used to tune the pulse parameters to obtain the maximum harvest power under practical constraints. The Monte Carlo simulation results demonstrate superior EH performance compared to existing waveform optimization schemes. The method remains robust under common IT optimizations, such as selective mapping (SLM) and partial transmit sequence (PTS), confirming its compatibility and scalability for real-world SWIPT systems.Publication Open Access Multi-User Sparse Vector Coding for eXtreme Ultra-Reliable Low-Latency Communication in Beyond 5G(Institute of Electrical and Electronics Engineers Inc., 2025-03-14) Sabapathy, S; Maruthu, S; Jayakody, D. N.KShort A short packet transmission scheme, such as Sparse Vector Coding (SVC), is a primary candidate for achieving ultra-low latency and high-reliability communication (URLLC). This paper proposes a spectral-efficient multi-user SVC (MU-SVC) scheme for achieving next-generation URLLC or eXtreme URLLC (xURLLC) in beyond 5G (B5G) communications. The key idea is to transmit multiple user information within a single sparse vector where the users are segregated into far users (FU) and near users (NU) depending on the distance from the base station. The classification into FU and NU paves way to optimize resource allocation, user fairness, manage interference, ensure reliable communication and quality of service requirements. Firstly, the FU binary data is converted into a sparse vector and secondly, the NU data is modulated and embedded into the non-zero positions of the sparse vector to form an MU-SVC. On transmission, the FU data is obtained through sparse demapping, while the NU adopts symbol detection techniques like the maximum likelihood detector. A new performance metric, called position error rate (PoER), is introduced to study the performance of the FU since it is based on the correct identification of the non-zero positions. Theoretical analyses of PoER and symbol error rate (SER) were carried out for FU and NU, respectively and the results are also validated through Monte-Carlo simulations. Further, the bit error rate, complexity, spectral and latency analyses are performed for MU-SVC and compared with the SVC and enhanced SVC schemes. The simulation results demonstrate an improved spectral efficiency and low latency with high reliability for the proposed MU-SVC scheme, thus, achieving xURLLC with reduced complexity in the multi-user scenario for B5G.Publication Open Access Channel Estimation of full-duplex relay-assisted RSMA-OFDM based wireless networks(Frontiers Media SA, 2025-10-03) Chaudhary, U; Rajkumar, S; Jayakody, D. N.KThis paper analyzes the channel estimation of rate splitting multiple access (RSMA) wireless network through the full-duplex amplify-and-forward (AF) relay. Basically, full-duplex transmission can improve temporal efficiency, however the loop interference is an unavoidable problem that occurs in the strong user of this proposed network. The orthogonal frequency division multiplexing (OFDM) system is used to provide high data rate communication, assuming the presence of phase noise (PN) in local oscillators. Using the least square (LS) estimate, the channel coefficients of the proposed RSMA relay network are estimated. In addition, convex optimization techniques are applied to estimate the phase noise components of this network. The problem is formulated by optimizing phase noise under transmit power constraints. We analyze the Bit Error Rate (BER) performance of the proposed network under binary phase shift keying (BPSK) modulation and 16-quadrature amplitude modulation (QAM). Simulation results demonstrate that channel estimation achieves better performance after the PN compensationPublication Open Access Optimized Resource Allocation for Delay-Tolerant ALOHA–NOMA for Enhancing the Performance of Underwater Acoustic Sensor Networks(Institute of Electrical and Electronics Engineers, 2025-09-22) Goutham, V; Harigovindan V.P; Mahesh, M; Jayakody, D. N.KIn this work, we introduce a propagation delay-tolerant ALOHA–NOMA-based cross-layer protocol for enhancing the performance of Underwater Acoustic Sensor Networks (UASNs). Various phenomena such as multi-path fading, Doppler spread, frequency as well as distance-dependent path loss, and limited available distance-dependent bandwidth have a significant impact on performance of UASNs. Due to these distinct characteristics, ALOHA is often considered a viable medium access control (MAC) protocol for UASNs, even though ALOHA is inefficient as far as channel utilization is concerned. Recently, non-orthogonal multiple access (NOMA) has been envisioned as a thriving enabling technology to meet the burgeoning demands of energy-constrained and bandwidth-constrained UASNs. As a result, we propose propagation delay-tolerant ALOHA-NOMA, where NOMA is employed in the physical layer with optimal utilization of distance-dependent bandwidth and transmission power, to improve the performance of ALOHA-based UASNs. We derive closed-form expressions for the MAC layer utilization factor, goodput, and energy consumption in UASNs by taking into account UASN channel characteristics. Results show that the proposed ALOHA-NOMA scheme significantly improves the performance of UASNs. Finally, we also derive mathematical expressions for the optimal channel attempt rate to maximize the MAC layer utilization factor. The analytical results are validated through extensive ns-3 simulations.Publication Open Access Facial identity recognition using StyleGAN3 inversion and improved tiny YOLOv7 model(Nature Research, 2025-03-17) Kumar, A; Bhattacharjee, S; Kumar, Ambrish; Jayakody, D. N.KFacial identity recognition is one of the challenging problems in the domain of computer vision. Facial identity comprises the facial attributes of a person’s face ranging from age progression, gender, hairstyle, etc. Manipulating facial attributes such as changing the gender, hairstyle, expressions, and makeup changes the entire facial identity of a person which is often used by law offenders to commit crimes. Leveraging the deep learning-based approaches, this work proposes a one-step solution for facial attribute manipulation and detection leading to facial identity recognition in few-shot and traditional scenarios. As a first step towards performing facial identity recognition, we created the Facial Attribute Manipulation Detection (FAM) Dataset which consists of twenty unique identities with thirty-eight facial attributes generated by the StyleGAN3 inversion. The Facial Attribute Detection (FAM) Dataset has 11,560 images richly annotated in YOLO format. To perform facial attribute and identity detection, we developed the Spatial Transformer Block (STB) and Squeeze-Excite Spatial Pyramid Pooling (SE-SPP)-based Tiny YOLOv7 model and proposed as FIR-Tiny YOLOv7 (Facial Identity Recognition-Tiny YOLOv7) model. The proposed model is an improvised variant of the Tiny YOLOv7 model. For facial identity recognition, the proposed model achieved 10.0% higher mAP in the one-shot scenario, 30.4% higher mAP in the three-shot scenario, 15.3% higher mAP in the five-shot scenario, and 0.1% higher mAP in the traditional 70% − 30% split scenario as compared to the Tiny YOLOv7 model. The results obtained with the proposed model are promising for general facial identity recognition under varying facial attribute manipulation.