Performance Analysis of Sparse Code Multiple Access MIMO Systems

Elkawafi, Salma; Younis, Abdelhamid; Mesleh, Raed

doi:10.1109/pimrc.2019.8904427

Cited by 6 publications

(3 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…, which guarantees the equality in (10). □ Theorem 1 implies that to make the dynamic range of BM (s J :J ), BM (s J −1:J ), … , BM (s 1:J ) in the descending order is equivalent to make 𝜌 J , 𝜌 J −1 , … , 𝜌 1 where…”

Section: Sd Based On Block-wise Sorted Qr Decompositionmentioning

confidence: 96%

“…On the other hand, multiple-input multiple-output (MIMO) has also been viewed as the key technique that is able to improve spectral efficiency [3][4][5][6][7] and has been applied in several wireless communication systems for a long time [8]. To date, researchers have investigated the combination of MIMO and SCMA, namely MIMO-SCMA, to further improve the spectral efficiency and support massive connectivity [9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Low‐complexity sphere decoding for MIMO‐SCMA systems

et al. 2021

View full text Add to dashboard Cite

Multiple‐input multiple‐output‐sparse code multiple access, a non‐trivial integration of sparse code multiple access and multiple‐input multiple‐output techniques, is able to achieve high spectrum efficiency and massive user connections. However, this integration also increases the complexity of signal detection. Here, the signal detection problem of multiple‐input multiple‐output sparse code multiple access is transformed into a tree search problem and use sphere decoding to detect the signal. By setting the initial radius to positive infinity, sphere decoding can achieve optimal maximum likelihood performance while the complexity is high. In order to further reduce the complexity of sphere decoding, a block‐wise sorted QR decomposition algorithm is proposed. Based on block‐wise sorted QR decomposition, the improved sphere decoding, namely block‐wise sorted QR decomposition‐sphere decoding, is able to make the tree search more efficient. Since only the detection order of each user's signal has been changed, block‐wise sorted QR decomposition‐sphere decoding can maintain the optimal maximum likelihood performance. Simulation results and complexity analysis show that block‐wise sorted QR decomposition‐sphere decoding can achieve optimal performance and both hard‐output and soft‐output block‐wise sorted QR decomposition‐sphere decoding have much lower complexity than joint message passing algorithm. Furthermore, given the same signal‐to‐noise ratio, the complexity of block‐wise sorted QR decomposition‐sphere decoding decreases with the increase of receiving antennas, while the complexity of joint message passing algorithm increases linearly.

show abstract

Section: Sd Based On Block-wise Sorted Qr Decompositionmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

Low‐complexity sphere decoding for MIMO‐SCMA systems

et al. 2021

View full text Add to dashboard Cite

show abstract

“…He found that SCMA can increase link adaptation as a result of reduced colored interference and enhanced SE of wireless networks. In [92], the authors derived and implemented SCMA-MIMO by combining MIMO and SCMA technologies to accommodate the ever-increasing number of users, reliability, and SE improvement for the next-generation 5G networks. Jienan et al [93] proposed an efficient and affordable decoding algorithm based on a Bayesian program learning scheme and Monte Carlo Markov Chain (MCMC) which can reduce 60% computation complexity and only 0.5-dB performance loss related to a similar decoding algorithm.…”

Section: Sparse Code Multiple Access (Scma)mentioning

confidence: 99%

From 5G to beyond 5G: A Comprehensive Survey of Wireless Network Evolution, Challenges, and Promising Technologies

et al. 2023

View full text Add to dashboard Cite

The histrionic growth of mobile subscribers, disruptive ecosystems such as IoT-based applications, and astounding channel capacity requirements to connect trillions of devices are massive challenges of the earlier mobile generations, 5G turned up the key solution. The prime objective of the 5G network is not only to maintain a 1000-fold capacity gain and 10 Giga Bits per second delivered to a single user, but it also assured quality-of-service, higher spectral efficiency, the ultra-reliable and improved battery lifetime of devices and massive machine-type communication (mMTC). The huge traffic load and high amount of resource consumption in 5G applications, augmented reality and virtual reality for magnificent virtual experience, and wireless body area networks will seriously affect the channel capacity of cellular cells and interrupt the admission and service of other users which makes compulsory new means of channel capacity and spectral efficiency enhancement techniques. In this research, we review several key emerging wireless technologies to increase channel capacity and spectral efficiency that will not only lead to improve network performance but also meets the ever-increasing user demands. We investigate various benefits and current research challenges of using these technologies. We analyze massive multi-input multi-output technology (mMIMO) an efficient technique and promising solution for the 5G and Beyond 5G (B5G) networks with several benefits and features. Moreover, this paper will be of vast help to the researchers who will involve advance investigation and also to the wireless network operator industry that is in the search for smooth development of state-of-the-art 5G and B5G networks.

show abstract