K. Deergha Rao

Bio: K. Deergha Rao is an academic researcher from Osmania University. The author has contributed to research in topics: Turbo code & Block code. The author has an hindex of 2, co-authored 4 publications receiving 81 citations.

Channel Coding Techniques for Wireless Communications

Abstract: The book discusses modern channel coding techniques for wireless communications such as turbo codes, low parity check codes (LDPC), space-time coding, Reed Solomon (RS) codes and convolutional codes Many illustrative examples are included in each chapter for easy understanding of the coding techniques The text is integrated with MATLAB-based programs to enhance the understanding of the subjects underlying theories It includes current topics of increasing importance such as turbo codes, LDPC codes, LT codes, Raptor codes and space-time coding in detail, in addition to the traditional codes such as cyclic codes, BCH and RS codes and convolutional codes MIMO communications is a multiple antenna technology, which is an effective method for high-speed or high-reliability wireless communications PC-based MATLAB m-files for the illustrative examples are included and also provided on the accompanying CD, which will help students and researchers involved in advanced and current concepts in coding theory Channel coding, the core of digital communication and data storage, has undergone a major revolution as a result of the rapid growth of mobile and wireless communicationsThe book is divided into 11 chapters Assuming no prior knowledge in the field of channel coding, the opening chapters (1 - 2) begin with basic theory and discuss how to improve the performance of wireless communication channels usingchannel coding Chapters 3 and 4 introduce Galois fields and present detailed coverage of BCH codes and Reed-Solomon codes Chapters 57 introduce the family of convolutional codes, hard and soft-decision Viterbi algorithms, turbo codes, BCJR algorithm for turbo decoding and studies trellis coded modulation (TCM), turbo trellis coded modulation (TTCM), bit-interleaved coded modulation (BICM) as well as iterative BICM (BICM-ID) and compares them under various channel conditions Chapters 8 and 9 focus on low-density parity-check (LDPC) codes, LT codes and Raptor codes Chapters 10 and 11 discuss MIMO systems and space-time (ST) coding

Performance of Digital Communication Over Fading Channels

Abstract: In this chapter, bit error rate (BER) performance of some of digital modulation schemes and different wireless communication techniques is evaluated in additive white Gaussian noise (AWGN) and fading channels. Further, the BER performance of different diversity techniques such as selective diversity, EGC, and MRC is also evaluated in Rayleigh fading channel.

LT and Raptor Codes

Abstract: To partially compensate the inefficiency of random codes, we can use Reed–Solomon codes, these codes can be decoded from a block with the maximum possible number of erasures in time quadratic in the dimension. But in practice, these algorithms are often too complicated and quadratic running times are still too large for many applications. Hence, a new class of codes is needed to construct robust and reliable transmission schemes and such a class of codes is known as fountain codes.

Bandwidth Efficient Coded Modulation

Abstract: The block codes, convolutional, and turbo codes discussed in the previous chapters achieve performance improvement expanding the bandwidth of the transmitted signal. However, when coding is t p being applied to bandwidth limited channels, coding gain is to be achieved without signal bandwidth expansion. The coding gain for bandwidth limited channels can be achieved by a scheme called trellis coded modulation (TCM). The TCM is a combined coding and modulation technique that increases the number of signals over the corresponding uncoded system to compensate for the redundancy introduced by the code for digital transmission over band-limited channels.

Near Maximum-Likelihood Detector and Channel Estimator for Uplink Multiuser Massive MIMO Systems With One-Bit ADCs

Abstract: In massive multiple-input multiple-output (MIMO) systems, it may not be power efficient to have a pair of high-resolution analog-to-digital converters (ADCs) for each antenna element. In this paper, a near maximum likelihood (nML) detector for uplink multiuser massive MIMO systems is proposed where each antenna is connected to a pair of one-bit ADCs, i.e., one for each real and imaginary component of the baseband signal. The exhaustive search over all the possible transmitted vectors required in the original maximum likelihood (ML) detection problem is relaxed to formulate an ML estimation problem. Then, the ML estimation problem is converted into a convex optimization problem which can be efficiently solved. Using the solution, the base station can perform simple symbol-by-symbol detection for the transmitted signals from multiple users. To further improve detection performance, we also develop a two-stage nML detector that exploits the structures of both the original ML and the proposed (one-stage) nML detectors. Numerical results show that the proposed nML detectors are efficient enough to simultaneously support multiple uplink users adopting higher-order constellations, e.g., 16 quadrature amplitude modulation. Since our detectors exploit the channel state information as part of the detection, an ML channel estimation technique with one-bit ADCs that shares the same structure with our proposed nML detector is also developed. The proposed detectors and channel estimator provide a complete low power solution for the uplink of a massive MIMO system.

One-Bit Sphere Decoding for Uplink Massive MIMO Systems With One-Bit ADCs

Abstract: This paper presents a low-complexity near-maximum-likelihood-detection (near-MLD) algorithm called one-bit sphere decoding for an uplink massive multiple-input multiple-output system with one-bit analog-to-digital converters. The idea of the proposed algorithm is to estimate the transmitted symbol vector sent by uplink users (a codeword vector) by searching over a sphere, which contains a collection of codeword vectors close to the received signal vector at the base station in terms of a weighted Hamming distance . To reduce the computational complexity for the construction of the sphere, the proposed algorithm divides the received signal vector into multiple subvectors each with a reduced dimension. Then, it generates multiple spheres in parallel, where each sphere is centered at the subvector and contains a list of subcodeword vectors. The detection performance of the proposed algorithm is also analyzed by characterizing the probability that the proposed algorithm performs worse than the MLD. The analysis shows how the dimension of each sphere and the size of the subcodeword list are related to the performance-complexity tradeoff achieved by the proposed algorithm. Simulation results demonstrate that the proposed algorithm achieves near-MLD performance, while reducing the computational complexity compared to the existing MLD method.

One-Bit Sphere Decoding for Uplink Massive MIMO Systems with One-Bit ADCs

Abstract: This paper presents a low-complexity near-maximum-likelihood-detection (near-MLD) algorithm called one-bit-sphere-decoding for an uplink massive multiple-input multiple-output (MIMO) system with one-bit analog-to-digital converters (ADCs). The idea of the proposed algorithm is to estimate the transmitted symbol vector sent by uplink users (a codeword vector) by searching over a sphere, which contains a collection of codeword vectors close to the received signal vector at the base station in terms of a weighted Hamming distance. To reduce the computational complexity for the construction of the sphere, the proposed algorithm divides the received signal vector into multiple sub-vectors each with reduced dimension. Then, it generates multiple spheres in parallel, where each sphere is centered at the sub-vector and contains a list of sub-codeword vectors. The detection performance of the proposed algorithm is also analyzed by characterizing the probability that the proposed algorithm performs worse than the MLD. The analysis shows how the dimension of each sphere and the size of the sub-codeword list are related to the performance-complexity tradeoff achieved by the proposed algorithm. Simulation results demonstrate that the proposed algorithm achieves near-MLD performance, while reducing the computational complexity compared to the existing MLD method.

One-Bit Successive-Cancellation Soft-Output (OSS) Detector for Uplink MU-MIMO Systems With One-Bit ADCs

Abstract: We study an uplink multiuser multiple-input multiple-output (MU-MIMO) system with one-bit analog-to-digital converters (ADCs) in which one base station (BS) with Nr receive antennas serve K users with a single antenna. For this system, the soft-output (SO) detector was recently proposed where a softmetric (e.g., a log-likelihood ratio (LLR)) is computed from a hard-decision channel output by introducing a novel distance measure in the binary Hamming space. This makes it possible to be naturally incorporated into the state-of-the-art channel codes (e.g., low-density parity-check code or polar code). In this paper, we further improve the performance of the SO detector by exploiting a priori information (e.g., the previously decoded messages), which is called the one-bit successive-cancellation soft-output (OSS) detector. The key idea of the proposed OSS detector is that each user k's message is decoded sequentially via the associated channel decoder k in ascending order and a refined search-space is constructed using the previously decoded messages (i.e., the enhanced LLRs are generated). We then present a multiple OSS detector by taking into account a more practical scenario where the BS is equipped with multiple channel decoders. In addition, we propose an efficient way to determine a good decoding order by introducing a novel set-distance measure. Finally, simulation results demonstrate that the proposed OSS detector can significantly improve the existing SO detector for the coded MU-MIMO systems with one-bit ADCs.

Channel Coding Scheme for 5G Mobile Communication System for Short Length Message Transmission

Abstract: Some channel coding schemes for 5G mobile communication system is facing difficulty in satisfying the user requirements in machine-type communication. This paper evaluates different channel coding schemes (LDPC, turbo, polar, systematic convolutional, and non-systematic convolutional codes) on an AWGN channel with BPSK modulation of code rate 1/2, in order to suggest the optimum channel coding scheme for the 5G mobile communication system for short length message transmission in machine-type communication. The analysis of the different channel coding schemes is based on flexibility, complexity, latency, and reliability according to the user requirements in machine-type communication. The main user requirements of machine-type communication for 5G channel coding scheme are better flexibility, low complexity, low latency, and high reliability in communication. Hence, the evaluation of different channel coding schemes is mainly based on satisfying user requirements in machine-type communication. The evaluation of the results shows that the systematic convolutional code is the optimum channel coding scheme in terms of better flexibility, low encoding computational latency, and higher reliability for the 5G mobile communication system for short length message transmission ( $$k \le 1024$$ bits) in machine-type communication. Whereas, the polar code has the lowest decoding computational complexity.