Faculty Publications
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Item Redesigned Spatial Modulation for Spatially Correlated Fading Channels(Springer New York LLC barbara.b.bertram@gsk.com, 2017) G.D., G.S.; Koila, K.; Neha, N.; Raghavendra, R.; Sripati, U.In this paper, a new variant of Spatial Modulation (SM) Multiple-Input Multiple-Output (MIMO) transmission technique, designated as Redesigned Spatial Modulation (ReSM) has been proposed. In ReSM scheme, a dynamic mapping for antenna selection is adopted. This scheme employs both single antenna as well as double antenna combinations depending upon channel conditions to combat the effect of spatial correlation. When evaluated over spatially correlated channel conditions, for a fixed spectral efficiency and number of transmit antennas, ReSM exhibits performance improvement of at least 3 dB over all the conventional SM schemes including Trellis Coded Spatial Modulation (TCSM) scheme. Furthermore, a closed form expression for the upper bound on Pairwise Error Probability (PEP) for ReSM has been derived. This has been used to calculate the upper bound for the Average Bit Error Probability (ABEP) for spatially correlated channels. The results of Monte Carlo simulations are in good agreement with the predictions made by analytical results. The relative gains of all the comparison plots in the paper are specified at an ABER of 10?4. © 2017, Springer Science+Business Media, LLC.Item A comprehensive framework for Double Spatial Modulation under imperfect channel state information(Elsevier B.V., 2017) G.D., G.S.; Koila, K.; Raghavendra, R.; Shripathi Acharya, U.The essential requirement for a 5G wireless communication system is the realization of energy efficient as well as spectrally efficient modulation schemes. Double Spatial Modulation (DSM) is a recently proposed high rate Index Modulation (IM) scheme, designed for use in Multiple Input Multiple Output (MIMO) wireless systems. The aim of this scheme is to increase the spectral efficiency of conventional Spatial Modulation (SM) systems while keeping the energy efficiency intact. In this paper, the impact of imperfect channel knowledge on the performance of DSM system under Rayleigh, Rician and Nakagami-m fading channels has been quantified. Later, a modified low complexity decoder for the DSM scheme has been designed using ordered block minimum mean square error (OB-MMSE) criterion. Its performance under varied fading environments have been quantified via Monte Carlo simulations. Finally, a closed form expression for the pairwise error probability (PEP) for a DSM scheme under conditions of perfect and imperfect channel state information has been derived. This is employed to calculate the upper bound on the average bit error probability (ABEP) over aforementioned fading channels. It is observed that, under perfect and imperfect channel conditions DSM outperforms all the other variants of SM by at least 2dB at an average bit error ratio (ABER) of 10?5. Tightness of the derived upper bound is illustrated by Monte Carlo simulation results. © 2017 Elsevier B.V.Item Enhancing Deep Compression of CNNs: A Novel Regularization Loss and the Impact of Distance Metrics(Institute of Electrical and Electronics Engineers Inc., 2024) Pragnesh, P.; Mohan, B.R.Transfer learning models tackle two critical problems in deep learning. First, for small datasets, it reduces the problem of overfitting. Second, for large datasets, it reduces the computational cost as fewer iterations are required to train the model. Standard transfer learning models such as VGGNet, ResNet, and GoogLeNet require significant memory and computational power, limiting their use on devices with limited resources. The research paper contributes to overcoming this problem by compressing the transfer learning model using channel pruning. In current times, computational cost is more significant compared to memory cost. The convolution layer with fewer parameters contributes more to computational cost. Thus, we focus on pruning the convolution layer to reduce computational cost. Total loss is a combination of prediction loss and regularization loss. Regularization loss is the sum of the magnitudes of parameter values. The training process aims to reduce total loss. In order to reduce total loss, the regularization loss also needs to be reduced. Therefore, training not only minimizes prediction error but also manages the magnitude of the model's weights. Important weights are maintained at higher values to keep the prediction loss low, while unimportant weight values can be reduced to decrease regularization loss. Thus regularization adjusts the magnitudes of parameters at varying rates, depending on their importance. Quantitative pruning methods select parameters based on their magnitude, which improves the effectiveness of the pruning process. Standard L1 and L2 regularization focus on individual parameters, aiding in unstructured pruning. However, group regularization is required for structured pruning. To address this, we introduce a novel group regularization loss designed specifically for structured channel pruning. This new regularization loss optimizes the pruning process by focusing on entire groups of parameters belonging to the channel rather than just individual ones. This method ensures that structured pruning is more efficient and targeted. Custom Standard Deviation (CSD) is calculated by summing the absolute differences between each parameter value and the mean value. To evaluate the parameters of a given channel, both the L1 norm and CSD are computed. The novel regularization loss for a channel in the convolutional layer is defined as the ratio of L1 norm to CSD (L1Norm/CSD). This approach groups the regularization loss for all parameters within a channel, making the pruning process more structured and efficient. Custom regularization loss further improves pruning efficiency, enabling a 46.14% reduction in parameters and a 61.91% decrease in FLOPs. This paper also employs the K-Means algorithm for similarity-based pruning and evaluates three distance metrics: Manhattan, Euclidean, and Cosine. Results indicate that pruning by K-Means algorithms using Manhattan distance leads to a 35.15% reduction in parameters and a 49.11% decrease in FLOPs, outperforming Euclidean and Cosine distances using the same algorithm. © 2013 IEEE.Item A Low-Complexity Solution for Optimizing Binary Intelligent Reflecting Surfaces towards Wireless Communication(Multidisciplinary Digital Publishing Institute (MDPI), 2024) Janawade, S.A.; Krishnan, P.; Kandasamy, K.; Holla, S.S.; Rao, K.; Chandrasekar, A.Intelligent Reflecting Surfaces (IRSs) enable us to have a reconfigurable reflecting surface that can efficiently deflect the transmitted signal toward the receiver. The initial step in the IRS usually involves estimating the channel between a fixed transmitter and a stationary receiver. After estimating the channel, the problem of finding the most optimal IRS configuration is non-convex, and involves a huge search in the solution space. In this work, we propose a novel and customized technique which efficiently estimates the channel and configures the IRS with fixed transmit power, restricting the IRS coefficients to (Formula presented.). The results from our approach are numerically compared with existing optimization techniques.The key features of the linear system model under consideration include a Reconfigurable Intelligent Surface (RIS) setup consisting of 4096 RIS elements arranged in a 64 × 64 element array; the distance from RIS to the access point measures 107 m. NLOS users are located around 40 m away from the RIS element and 100 m from the access point. The estimated variance of noise (Formula presented.) is 3.1614 (Formula presented.). The proposed algorithm provides an overall data rate of 126.89 (MBits/s) for Line of Sight and 66.093 (MBits/s) for Non Line of Sight (NLOS) wireless communication. © 2024 by the authors.Item Geometry-based stochastic channel modeling of a semi-urban environment using simultaneously transmitting and reflecting reconfigurable intelligentsurface(Elsevier B.V., 2024) Rashmi, H.; Chaturvedi, A.; D'Souza, J.Simultaneously Transmitting and Reflecting (STAR) Reconfigurable Intelligent Surface (RIS) demonstrates the ability to split incoming electromagnetic beams to transmit and reflect signals in a concurrent manner. Thus, compared to conventional RIS, service area coverage is extended on deploying STAR-RIS. This paper presents a geometry-based stochastic channel model (GBSM) of STAR-RIS-assisted outdoor wireless channel. For the considered semi-urban environment, STAR-RIS operates in energy-splitting mode. Channel between a base station (BS) and users (UR/UT) located on the reflect/transmit (R/T) side of STAR-RIS is characterised using a GBSM. An elliptical model incorporates the inevitable presence of scatterers in the considered semi-urban segment. Statistical properties of the wireless channel under test are analysed using space–time cross-correlation function (ST-CCF) and temporal auto-correlation function (ACF). Further, to gain holistic insight about the wireless channel behaviour, normalised Doppler power spectral density (ND-PSD) is estimated for semi-urban segment having three distinct underlying hypothesis as: (i) Wireless channel is governed by Rayleigh fading model, (ii) Wireless Channel is equipped with conventional RIS and (iii) STAR-RIS is an integral part of the considered wireless channel. Simulation results confirm that STAR-RIS performs at par with RIS, however, facilitating an additional degree of coverage. It is observed that temporal ACF and ST-CCF improves with an increase in the number of elements in STAR-RIS. © 2024 Elsevier B.V.