Kala Praveen Bagadi

Bio: Kala Praveen Bagadi is an academic researcher from VIT University. The author has contributed to research in topics: Multiuser detection & Orthogonal frequency-division multiplexing. The author has an hindex of 10, co-authored 34 publications receiving 273 citations. Previous affiliations of Kala Praveen Bagadi include National Institute of Technology, Rourkela.

Detection of Signals in MC–CDMA Using a Novel Iterative Block Decision Feedback Equalizer

Abstract: This paper presents a technique to mitigate multiple access interference (MAI) in multicarrier code division multiple access (MC-CDMA) wireless communications systems. Although under normal circumstances the MC-CDMA system can achieve high spectral efficiency and resistance towards inter symbol interference (ISI) however when exposed to substantial nonlinear distortion the issue of MAI manifests. Such distortion results when the power amplifiers are driven into saturation or when the transmit signal experiences extreme adverse channel conditions. The proposed technique uses a modified iterative block decision feedback equalizer (IB-DFE) that uses a minimal mean square error (MMSE) receiver in the feed-forward path to nullify the residual interference from the IB-DFE receiver. The received signal is re-filtered in an iterative process to significantly improve the MC-CDMA system’s performance. The effectiveness of the proposed modified IB-DFE technique in MC-CDMA systems has been analysed under various harsh nonlinear conditions, and the results of this analysis presented here confirm the effectiveness of the proposed technique to outperform conventional methodologies in terms of the bit error rate (BER) and lesser computational complexity.

Lattice Reduction Assisted Likelihood Ascent Search Algorithm for Multiuser Detection in Massive MIMO System

Abstract: Massive multiple input multiple output (MIMO) system achieves high spectral and energy efficiency by incorporating a large number of antennas at the transmitter and/or receivers. Multiuser detection is an important task that needs to be done at the receiver of the Massive MIMO system to mitigate multiuser interference. The classical Zero Forcing (ZF) detector suffers from high residual interference. By making channel matrix orthogonal, the Lattice Reduction (LR) techniques can be assisted for the ZF detector to minimize interference. On the other hand, the Likelihood Ascent Search (LAS) is a neighborhood search based low complexity detection algorithm that is used for massive MIMO systems. It takes the Zero Forcing (ZF) solution as initial vector and searches for a near-optimal solution by examining cost values of its neighborhood vectors. The performance of the LAS algorithm is mainly relying on an initial vector. So, this paper investigates the design of LAS algorithm with LR assisted ZF solution as an initial vector to improve performance over the classical ZFLAS detector. The proposed algorithm attains a more achievable trade-off between Bit Error Rate (BER) performance and exponential time detection complexity for large extended systems.

Adaptive channel equalization using recurrent neural network under SUI channel model

Abstract: This paper investigates Neural Networks (NNs) based adaptive channel equalization for standard Stanford University Interim (SUI) channels. The NN models like Multilayer Perceptron Algorithm (MLP) and Recurrent Neural Network (RNN) are used to design adaptive equalizers. The Back Propagation (BP) and Real Time recurrent Learning (RTRL) are used for training MLP and RNN models respectively. As NNs are known for highly non-linear structure, these models are better suitable for equalization of system with high non-linearity. The performance of RNN is compared with MLP in terms of Bit Error Rate (BER). In simulation analysis, BPSK signal are transmitted through various SUI channels, which are modeled for fixed wireless applications. The simulation results illustrates that the RNN equalizer consistently outperform the MLP equalizer by giving better BER.

Likelihood Ascent Search Detection for Coded Massive MU-MIMO Systems to Mitigate IAI and MUI

Abstract: The main aim of massive multiuser multiple-input multiple-output (MU-MIMO) system is to improve the throughput and spectral efficiency in 5G wireless networks. The performance of MU-MIMO system is severely influenced by inter-antenna interference (IAI) and multiuser interference (MUI). The IAI occurs due to space limitations at each user terminal (UT) and the MUI is added when one UT is in the vicinity of another UT in the same cellular network. IAI can be mitigated through a precoding scheme such as singular value decomposition (SVD), and MUI is suppressed by an efficient multiuser detection (MUD) schemes. The maximum likelihood (ML) detector has optimal performance; however, it has a highly complex structure and involves the need of a large number of computations especially in massive structures. Thus, the neighborhood search-based algorithm such as likelihood ascent search (LAS) has been found to be a better alternative for mitigation of MUI as it results in near optimal performance with low complexity. Most of the recent papers are aimed at eliminating either MUI or IAI, whereas the proposed work presents joint SVD precoding and LAS MUD to mitigate both IAI and MUI. The proposed scheme can achieve a near-optimal performance with smaller number of matrix computations.

Robust Neural Network based Multiuser Detector in MC-CDMA for Multiple Access Interference Mitigation

Abstract: The aim of this paper is to design a multiuser detector technique using artificial Neural Network under multiple access interference mitigation. To improve the performance of Multi Carrier Code Division Multiple Access (MC-CDMA) under multiple access interference, we have used a multilayered perceptron model of Neural Network that has three layers namely, input layer, hidden layer and output layer. The Neural Network is further trained by Levenberg-Marquardt algorithm. This algorithm uses the error function as key factor based on which the weights are adjusted to get the desired output. The Bit Error Rate (BER) performance of the system has been evaluated under Rayleigh and Stanford University Interim (SUI) channels for Binary Phase Shift Keying (BPSK) and Quadrature Amplitude Modulation (QAM) techniques. The proposed Neural Network based receiver is compared with Equal Gain Combining (EGC) and Maximal Ratio Combining (MRC) with varying number of users and Signal to Noise Ratio (SNR). Under SUI channel conditions and for a BER of 10 -3 , the Neural Network based receiver shows an improvement of 1 dB and 8 dB than EGC and MRC receivers, respectively.

A Comprehensive Review of Path Planning Algorithms for Autonomous Underwater Vehicles

Abstract: The underwater path planning problem deals with finding an optimal or sub-optimal route between an origin point and a termination point in marine environments. The underwater environment is still considered as a great challenge for the path planning of autonomous underwater vehicles (AUVs) because of its hostile and dynamic nature. The major constraints for path planning are limited data transmission capability, power and sensing technology available for underwater operations. The sea environment is subjected to a large set of challenging factors classified as atmospheric, coastal and gravitational. Based on whether the impact of these factors can be approximated or not, the underwater environment can be characterized as predictable and unpredictable respectively. The classical path planning algorithms based on artificial intelligence assume that environmental conditions are known apriori to the path planner. But the current path planning algorithms involve continual interaction with the environment considering the environment as dynamic and its effect cannot be predicted. Path planning is necessary for many applications involving AUVs. These are based upon planning safety routes with minimum energy cost and computation overheads. This review is intended to summarize various path planning strategies for AUVs on the basis of characterization of underwater environments as predictable and unpredictable. The algorithms employed in path planning of single AUV and multiple AUVs are reviewed in the light of predictable and unpredictable environments.

OFDM for Optical Communications

Abstract: Orthogonal frequency division multiplexing (OFDM) is a modulation technique which is now used in most new and emerging broadband wired and wireless communication systems because it is an effective solution to intersymbol interference caused by a dispersive channel. Very recently a number of researchers have shown that OFDM is also a promising technology for optical communications. This paper gives a tutorial overview of OFDM highlighting the aspects that are likely to be important in optical applications. To achieve good performance in optical systems OFDM must be adapted in various ways. The constraints imposed by single mode optical fiber, multimode optical fiber and optical wireless are discussed and the new forms of optical OFDM which have been developed are outlined. The main drawbacks of OFDM are its high peak to average power ratio and its sensitivity to phase noise and frequency offset. The impairments that these cause are described and their implications for optical systems discussed.

A Survey on LoRaWAN Architecture, Protocol and Technologies

Abstract: Internet of Things (IoT) expansion led the market to find alternative communication technologies since existing protocols are insufficient in terms of coverage, energy consumption to fit IoT needs. Low Power Wide Area Networks (LPWAN) emerged as an alternative cost-effective communication technology for the IoT market. LoRaWAN is an open LPWAN standard developed by LoRa Alliance and has key features i.e., low energy consumption, long-range communication, builtin security, GPS-free positioning. In this paper, we will introduce LoRaWAN technology, the state of art studies in the literature and provide open opportunities.