Optimized Low Density Superposition Modulation for 5G Mobile Multimedia Wireless Networks

Lu, Kun; Jiang, Chengxin

doi:10.1109/access.2019.2957368

Cited by 7 publications

(4 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In Reference 93, an algorithm based on PSO is reported, which circumvents the cooperative coded caching placement. The bare‐bone PSO (BBPSO) algorithm, another version of PSO, is presented in Reference 94. This method is used to optimize the degree distribution of low density superposition modulation (LDSM) matrix.…”

Section: Optimization Methods In Communicationsmentioning

confidence: 99%

Overview of evolutionary algorithms and neural networks for modern mobile communication

Kouhalvandi

Shayea

Özoğuz

et al. 2022

Trans Emerging Tel Tech

View full text Add to dashboard Cite

The sixth generation (6G) of mobile networks must support the huge growth of mobile connections and provides various intelligent services in future mobile networks. For that, designing high-performance network architecture and communication systems (CSs) as well as finding coherent solutions for the determined problems is critical demand that needs to be achieved in 6G networks.Optimal solutions are mainly sought by using modifiable, smart, and perceptive algorithms aimed at optimizing more specific tasks. Therefore, advanced optimization methods are highly required to accommodate the requirements of CSs efficiently. That will be in various parts of the future networks such as advanced mobility management, multi communication links, efficient power consumption, ultra-lower latency, ultra-security, high speed, and reliable connectivity.Accordingly, highly accurate and smart functions must be modeled to optimize the required communication parameters in the network. This study provides a comprehensive overview of optimization methods that may need further investigations and developments to be applied in 6G networks at various parts of networks. A detailed theoretical description for each method is presented and discussed to elucidate future research directions for optimizing specific characteristics with multi-objective optimizations. Moreover, this article illustrates the conceptual and structural viewpoints of reported optimization methods. Also, the capability of various optimization methods that can offer industrial solutions in 6G is discussed. The potential applications of each method are also analyzed. Finally, this article presented the research issues and future directions of optimization technology and research gaps that need to be addressed before the standardization of 6G networks. INTRODUCTIONRecently, innovative solutions of communication systems (CSs) have been developed impressively, as data traffic is increasing exponentially by more than 50% per year. 1 Several factors will contribute for increasing data demand massively, for example, as predicted by CISCO, the video will make up 82% of the all IP traffic by 2022. 2,3 This massive increase in data traffic will cause several new issues that will facing the implementation of future mobile networks. That will include a dramatic increase for wider spectrum need, 4 the necessity for big system capacity, 5 further mobility issues, 6,7 and higher energy consumption, 8 in which cannot be handled efficiently by the current cellular networks.

show abstract

Section: Optimization Methods In Communicationsmentioning

confidence: 99%

Overview of evolutionary algorithms and neural networks for modern mobile communication

Kouhalvandi

Shayea

Özoğuz

et al. 2022

Trans Emerging Tel Tech

View full text Add to dashboard Cite

show abstract

“…A number of studies have been undertaken to design spreading code sets for sparse spreading based NOMA [5]- [7]. In [5] the authors proposed an LDS structure based on LDPC codes, where the user's symbols are arranged in such a way that the interference seen by each user on each chip is different, while in [8], the authors designed the spreading sequences based on an LDPC indicator matrix.…”

Section: Introductionmentioning

confidence: 99%

“…Notably, they design signature matrices that have factor graphs exhibiting very few short cycles and large superposed signal constellation distances. In [7], the authors optimize the degree distribution of the LDS signature matrix.…”

Section: Introductionmentioning

confidence: 99%

Low-Density Spreading Design Based on an Algebraic Scheme for NOMA Systems

Millar¹,

Kulhandjian²,

Alaca³

et al. 2022

Preprint

View full text Add to dashboard Cite

In this paper, a code-domain nonorthogonal multiple access (NOMA) technique based on an algebraic design is studied. We propose an improved low-density spreading (LDS) sequence design based on projective geometry. In terms of its bit error rate (BER) performance, our proposed improved LDS code set outperforms the existing LDS designs over the frequencynonselective Rayleigh fading and additive white Gaussian noise (AWGN) channels. We demonstrated that achieving the best BER depends on the minimum distance.

show abstract

“…The large dimension LDSM's user data symbols are more dispersed in time-frequency resources, so it has higher diversity gain. In [13], a method to optimize the degree distribution of the LDSM sparse signature matrix is proposed, which structures a larger girth for LDSM. Most of the research on NOMA technology is carried out in the perfect channel state information (CSI) scenario.…”

Section: Introductionmentioning

confidence: 99%

Sparse Signature Matrix Optimization for NOMA under Imperfect CSI

Yang

2021

Preprint

Self Cite

View full text Add to dashboard Cite

Non-orthogonal multiple access (NOMA) can support the rapid development of the Internet of Things (IoT) with its potential to support high spectral efficiency and massive connectivity. The low-density superposition modulation (LDSM) scheme is one of the NOMA schemes and uses the sparse signature matrix to reduce multiple access interferences (MAI). In order to improve the NOMA system performance in practice, this paper focuses on designing the sparse signature matrix with a large girth for LDSM under imperfect channel state information (CSI). Based on the orthogonal pilot and linear minimum mean square error (LMMSE) estimation, the LDSM optimized by bare-bone particle swarm optimization (BBPSO) algorithm has a larger girth and can gather more accurate information in the process of iterative decoding convergence. An extrinsic information transfer (EXIT) chart analysis is designed for the LDSM-OFDM system as a theoretical analysis tool. The simulation results show that the optimized LDSM outperforms the reference LDSM system, bringing about a 0.5 dB performance gain.

show abstract

Optimized Low Density Superposition Modulation for 5G Mobile Multimedia Wireless Networks

Cited by 7 publications

References 27 publications

Overview of evolutionary algorithms and neural networks for modern mobile communication

Overview of evolutionary algorithms and neural networks for modern mobile communication

Low-Density Spreading Design Based on an Algebraic Scheme for NOMA Systems

Sparse Signature Matrix Optimization for NOMA under Imperfect CSI

Contact Info

Product

Resources

About