Vijay Kumar Chakka

This article proposes a novel Multiple Access (MA) scheme called Ramanujan Periodic-subspace Division Multiple Access (RPDMA) for subcarrier sizes $N = 2^{m},\ m\in \mathbb {N}$, to address the high Peak-to-Average Power Ratio (PAPR) in Orthogonal Frequency Division Multiple Access (OFDMA). Building on the properties of Ramanujan subspaces, we design transmitter and receiver models that allocate users on a subspace-wise basis, ensuring zero inter-user interference, providing inherent frequency diversity. We analyze the computational complexity of OFDMA, SC-FDMA, and RPDMA, and find that RPDMA has substantially lower per-user transmitter complexity than both OFDMA and SC-FDMA, while its receiver complexity is comparable to SC-FDMA and higher than that of OFDMA. We further introduce a generalized framework, termed Nested Periodic-subspace Division Multiple Access (NPDMA), which unifies both RPDMA and OFDMA under a common family of multi-carrier MA schemes. We derive the theoretical PAPR of RPDMA and demonstrate its superiority over OFDMA. The analysis is validated through numerical simulations under two multi-user scenarios with diverse Quality of Service (QoS) requirements. The results demonstrate that RPDMA achieves lower PAPR than both OFDMA and SC-FDMA, with users assigned larger subspaces benefiting from even greater PAPR reduction. We prove that both the sum and per-user Spectral Efficiency (SE) of RPDMA are identical to those of OFDMA and SC-FDMA. In terms of Bit Error Rate (BER), SC-FDMA achieves the best performance, while RPDMA still outperforms OFDMA as the SNR increases.

Intelligent reflecting surfaces (IRS) are emerging as a promising technology for forthcoming 6G networks, offering a novel paradigm for regulating wireless channels. The IRS can change the wireless propagation environment, considerably enhancing wireless communication performance. However, the non-orthogonal multiple access (NOMA) technique offers significant spectral efficiency and massive connectivity. The power domain variation NOMA employs superposition-coded symbols with varying powers for multiple-user symbols. This paper investigates an IRS-assisted downlink NOMA system that combines the IRS’s and NOMA’s inherent benefits to increase system performance. To use the inherent advantages of green communication, the base station in such a system uses passive reflectors to connect with multiple users. Furthermore, to gain analytical insights, exact and asymptotic closed-form expressions of the average bit error probability (ABEP) are derived, accounting for error propagation due to successive interference cancellation errors. The ABEP results are simulated for different modulation pairs of NOMA end users over multiple IRS elements. The close agreement between the Monte Carlo simulations with the exact and asymptotic closed-form expressions verifies the accuracy of the analysis. Meanwhile, increasing the reflecting elements and modulation pair improves the system’s performance, making it more suitable for various low-power Internet-of-Things devices.

Classification of mental tasks from electroencephalogram (EEG) signals play a crucial role in designing various brain-computer interface (BCI) applications. Most of the current techniques consider each channel as independent, neglecting the functional connectivity of the brain during mental activity and are primarily subject specific. This paper proposes a graph signal representation to classify a pair of mental tasks using multichannel EEG signals (MTMC-EEG) with cross subject classification within the database. Here, each channel of EEG signal corresponds to nodes of the task based graph whose EEG time series resides on the respective nodes. Functional connectivity of the brain between these nodes is obtained using smoothness constraint based Graph Signal Processing (GSP) technique. Graph spectral features namely, two-norm total variation of eigen vector (TNTV) corresponding to weighted adjacency matrix, graph Laplacian energy (GLE) using eigenvalues of Laplacian matrix and convex sum of TNTV and GLE in the form of joint total variation energy (JTVE) are proposed in this paper. The performance of the proposed methodology is evaluated on publicly available two different databases of MTMC EEG signals using benchmark classifiers and compared with the state of the art. Further, the superiority of the proposed metric obtained from the smoothened graph of GSP technique is validated by comparing it with Pearson correlation and Gaussian radial basis function (RBF) based functional connectivity in terms of accuracy, F-Score, and information transfer rate (ITR). The robustness of the proposed method is validated by adding white Gaussian noise (AWGN) to the EEG signals using different SNRs.

Classification of mental tasks from electroencephalogram (EEG) signals play a crucial role in designing various brain-computer interface (BCI) applications. Most of the current techniques consider each channel as independent, neglecting the functional connectivity of the brain during mental activity and are primarily subject specific. This paper proposes a graph signal representation to classify a pair of mental tasks using multi-channel EEG signals (MTMC-EEG) with cross subject classification within the database. Here, each channel of EEG signal corresponds to nodes of the task based graph whose EEG time series resides on the respective nodes. Functional connectivity of the brain between these nodes is obtained using smoothness constraint based Graph Signal Processing (GSP) technique. Graph spectral features namely, two-norm total variation of eigen vector (TNTV) corresponding to weighted adjacency matrix, graph Laplacian energy (GLE) using eigenvalues of Laplacian matrix and convex sum of TNTV and GLE in the form of joint total variation energy (JTVE) are proposed in this paper. The performance of the proposed methodology is evaluated on publicly available two different databases of MTMC EEG signals using benchmark classifiers and compared with the state of the art. Further, the superiority of the proposed metric obtained from the smoothened graph of GSP technique is validated by comparing it with Pearson correlation and Gaussian radial basis function (RBF) based functional connectivity in terms of accuracy, F-Score, and information transfer rate (ITR). The robustness of the proposed method is validated by adding white Gaussian noise (AWGN) to the EEG signals using different SNRs.

Graph learning from the brain signals deals with capturing the changes in functional relationship between the brain regions during mental active and relaxed states. This paper investigates different graph learning techniques, namely geometry, signal similarity, and Graphical LASSO based methods for the classification of mental task from electroencephalogram (EEG) signals. Graph spectral energy based metric using Graph Signal Processing (GSP) technique is presented to classify mental active state from relaxed state. A binary KNN classifier is used to analyse each graph learning technique on publicly available Keirn and Aunon mental task EEG database. Performance of different graphs is then analysed and compared using classification Accuracy and F-Score.

Epilepsy is a chronic brain disorder that is characterized by intermittent epileptic seizures that can be identified in an Electroencephalogram (EEG) signal. This paper proposes a low computational complex method to classify epileptic EEG signals by using a suitable Ramanujan Periodic Subspace (RPS). Initially, this method divides the given single channel EEG signal into multiple non-overlapping EEG blocks, which are projected onto a particular RPS. The energy of the projection is used as a feature to classify each block into epileptic or non-epileptic, using an SVM binary classifier. Here, in order to choose that particular RPS, a few sample blocks from the epileptic (ictal), interictal, and healthy EEG signal are projected onto the divisor RPSs of that block. The difference between the average subspace energy of healthy vs ictal or interictal vs ictal blocks is used as a measure to choose the suitable RPS. Finally, the class of each block of the EEG signal is combined using the majority voting scheme to classify the epileptic EEG signal. A publicly available benchmark EEG database from Bonn University, Germany, is used to evaluate the performance of the proposed method. Further, the EEG signals are added with White Gaussian Noise (WGN) and ocular artifact for testing the robustness of the method against noise. Evaluation results in terms of accuracy, sensitivity, specificity, and F-score demonstrate that the proposed method is comparable with the state-of-the-art techniques and also robust against artifacts and noise.