Papers by Naga Raju Challa
Cardiovascular Disease Prediction Using Machine Learning Algorithms
Implementation of an Efficient IoT Enabled Automated Paralysis Healthcare System
Enhancing Performance of Massive MU-MIMO System with LR-RTS: A Low-Complexity Detection Algorithm
IEEE access, 2024

Lattice Reduction Assisted Likelihood Ascent Search Algorithm for Multiuser Detection in Massive MIMO System
2018 15th IEEE India Council International Conference (INDICON), 2018
Massive multiple input multiple output (MIMO) system achieves high spectral and energy efficiency... more 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.

Lenstra Lenstra Lovász (LLL) Assisted Likelihood Ascent Search (LAS) Algorithm for Signal Detection in Massive MIMO
2019 International Conference on Vision Towards Emerging Trends in Communication and Networking (ViTECoN), 2019
In this paper, we propose a new algorithm called Lenstra Lenstra Lovász (LLL) assisted Likelihood... more In this paper, we propose a new algorithm called Lenstra Lenstra Lovász (LLL) assisted Likelihood Ascent Search (LAS) algorithm for signal detection in Massive MIMO which attains a near to optimum performance. This algorithm is developed by collaborating two existing algorithms to satisfy the tradeoff between performance and complexity in Massive Multiple Input Multiple Output (MIMO) systems. The Linear Detection and Lattice Reduction (LR) are some of the prominent suboptimal algorithms whose performance is far from optimal. In the proposed LLL-LAS Algorithm, the LLL algorithm is an LR based detection which serves as the initial solution to the LAS algorithm. The Simulation results substantiate the decrement in the Bit Error Rate which makes it better than the other classical detection techniques

Journal of Communications, 2020
Massive Multi-user Multiple Input Multiple Output (MU‒MIMO) system is aimed to improve throughput... more Massive Multi-user Multiple Input Multiple Output (MU‒MIMO) system is aimed to improve throughput and spectral efficiency in 5G communication networks. Inter-antenna Interference (IAI) and Multi-user Interference (MUI) are two major factors that influence the performance of MU–MIMO system. IAI arises due to closely spaced multiple antennas at each User Terminal (UT), whereas MUI is generated when one UT comes in the vicinity of another UT of the same cellular network. IAI can be mitigated by the use of a pre-coding scheme such as Singular Value Decomposition (SVD) and MUI can be cancelled through efficient Multi-user Detection (MUD) schemes. The highly complex and optimal Maximum Likelihood (ML) detector involves a large number of computations, especially when in massive structures. Therefore, the local 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 using lesser ...

Radioelectronics and Communications Systems, 2020
The main aim of massive multiuser multiple-input multiple-output (MU-MIMO) system is to improve t... more 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.

Design of Large Scale MU-MIMO System with Joint Precoding and Detection Schemes for Beyond 5G Wireless Networks
Wireless Personal Communications, 2021
The large scale multiuser multiple input multiple output (MU-MIMO) is one of the promising commun... more The large scale multiuser multiple input multiple output (MU-MIMO) is one of the promising communication technology for 5G wireless networks as it offers reliability, high spectral efficiency and high throughput. The lattice reduction (LR) precoding based user level local likelihood ascent search (ULAS) detection scheme is proposed in this paper for efficient signal detection in large scale MU-MIMO system. The initial solution of ULAS algorithm is obtained from the LR precoding assisted zero forcing detector. The LR precoding transforms the non-orthogonal channel matrix into nearly orthogonal channel, which helps to mitigate inter antenna interference (IAI) exists at each user. The remaining multiuser interference (MUI) imposed to each user from undesired users is cancelled by the proposed ULAS multiuser detection scheme. Thus, the proposed LR precoding assisted ULAS mitigates both IAI and MUI unlike the classical detector, those try to moderate either IAI or MUI. By contrast, the proposed ULAS detector provides performance close to optimal maximum likelihood detector with just a fraction of its complexity.

Детектор с использованием вероятностного восходящего поиска для кодированной большой многопользовательской MIMO-системы, обеспечивающий подавление межантенной помехи и внутрисистемных помех
Известия высших учебных заведений. Радиоэлектроника, 2020
Основная цель большой системы с многими пользователями и групповым входом и групповым выходом MU-... more Основная цель большой системы с многими пользователями и групповым входом и групповым выходом MU-MIMO (MultiUser Multiple-Input Multiple-Output) состоит в улучшении пропускной способности и спектральной эффективности в сетях беспроводной связи пятого поколения 5G. Рабочая эффективность системы MU-MIMO зависит от межантенной помехи IAI (Inter-Antenna Interference) и внутрисистемных помех MUI (MultiUser Interference). Помеха IAI возникает из-за ограничений пространства на каждом пользовательском терминале UT (User Terminal), а помеха MUI добавляется тогда, когда один UT оказывается вблизи другого UT в одной и той же сотовой сети связи. Помеха IAI может быть минимизирована с помощью схемы предварительного кодирования, например, схемы сингулярного разложения SVD (Singular Value Decomposition), а помеха MUI подавляется с помощью эффективных схем многопользовательского детектирования MUD (MultiUser Detection). Детектор максимального правдоподобия ML (Maximum Likelihood) является оптимальным, однако он имеет очень сложную структуру и требует большого количества вычислений, особенно в случае больших структур. Установлено, что алгоритм на базе поиска окрестности, например, алгоритм вероятностного восходящего поиска LAS (Likelihood Ascent Search), является лучшей альтернативой для подавления MUI, поскольку обеспечивает почти оптимальную характеристику эффективности при невысокой сложности. Большинство последних работ ориентировано на устранение или MUI или IAI, тогда как предлагаемая работа представляет совместное выполнение предварительного кодирования SVD и алгоритма LAS MUD для подавления обеих помех IAI и MUI. Предлагаемая схема обеспечивает почти оптимальную рабочую эффективность при меньшем количестве матричных вычислений.
Design of near-optimal local likelihood search-based detection algorithm for coded large-scale MU-MIMO system
International Journal of Communication Systems
Proceedings of the International Conference on Information Systems Design and Intelligent Applications 2012 (India 2012) Held in Visakhapatnam, India, …
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Papers by Naga Raju Challa