User capacity for uplink SCMA system

Gamal, Doaa; Mehana, Ahmed Hesham; Elsayed, Khaled M.

doi:10.1016/j.phycom.2019.100979

Cited by 3 publications

(5 citation statements)

References 17 publications

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“…22,23 The number of multiplexed users (codebooks) in SCMA is larger than the number of resources which results in an overloading gain. 24 In the conventional SCMA encoder, the bit streams of each user are mapped to multi-dimensional codewords using a predefined codebook, where each user is assigned a dedicated codebook. All SCMA users' codewords are then multiplexed over the shared orthogonal resources.…”

Section: Deep Learning For Single User Physical Layer Communicationmentioning

confidence: 99%

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Deep SCMA receiver: A low‐complexity joint decoder and channel estimator for SCMA over time‐varying channels using RNNs

Gamal

Elsayed

Mehana

2023

Trans Emerging Tel Tech

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Sparse code multiple access (SCMA) is a promising non‐orthogonal multiple access scheme for cellular Internet of things (IoT) due to its ability to support massive connectivity, grant‐free transmission and scalability. Inspired by the recent developments of deep learning for physical layer communications, we present a design of an uplink SCMA receiver using deep learning. We propose the use of recurrent neural networks (RNNs) for joint channel estimation and multiuser data detection of uplink SCMA under time‐varying Rayleigh channel using a single deep learning structure. The use of RNNs enables the receiver to learn the time correlation between the received samples with a very low pilot density and with low complexity. Compared with the conventional SCMA receiver, the simulation results show that the proposed deep learning‐based receiver can achieve BER performance similar to that of the conventional SCMA receivers (such as sparse pilot channel estimator and message‐passing algorithm detector) with very low pilot density and with much lower complexity. Moreover, the proposed SCMA receiver shows good resilience to small changes in the receiver speed (second‐order channel statistics) which enables the proposed deep learning receiver to work over a reasonable range of receiver speeds without parameter tuning. Fine tuning the network parameters to capture the variations of the channel, during online transmission using small data sets and small training period, is also checked and provides additional BER benefits.

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Section: Deep Learning For Single User Physical Layer Communicationmentioning

confidence: 99%

“…SCMA is a promising candidate for massive machine type communications (mMTC) as it can support massive connectivity, grant‐free transmission and scalability 22,23 . The number of multiplexed users (codebooks) in SCMA is larger than the number of resources which results in an overloading gain 24 …”

Section: Introductionmentioning

confidence: 99%

Deep SCMA receiver: A low‐complexity joint decoder and channel estimator for SCMA over time‐varying channels using RNNs

Gamal

Elsayed

Mehana

2023

Trans Emerging Tel Tech

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“…The need for improving SE through nonorthogonal multiple access (NOMA) techniques was addressed by Ding et al, 1 and emphasis was placed on the application of NOMA in current long term evolution (LTE) advanced and fifth‐generation (5G) networks. The lower bound on the maximum number of users that can utilize a single radio resource in the uplink of an SCMA‐based network was derived by Gamal et al 10 and solved using the difference of convex functions and swap matching.…”

Section: Related Workmentioning

confidence: 99%

Particle swarm optimization of the spectral and energy efficiency of an SCMA‐based heterogeneous cellular network

Noma‐Osaghae

Misra

Ahuja

et al. 2022

Trans Emerging Tel Tech

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Background: The effect of stochastic small base station (SBS) deployment on the energy efficiency (EE) and spectral efficiency (SE) of sparse code multiple access (SCMA)-based heterogeneous cellular networks (HCNs) is still mostly unknown.Aim: This research study seeks to provide insight into the interaction between SE and EE in SBS sleep-mode enabled SCMA-based HCNs.Methodology: A model that characterizes the energy-spectral-efficiency (ESE) of a two-tier SBS sleep-mode enabled SCMA-based HCN was derived. A multiobjective optimization problem was formulated to maximize the SE and EE of the SCMA-based HCN simultaneously. The multiobjective optimization problem was solved using a proposed weighted sum modified particle swarm optimization algorithm (PSO). A comparison was made between the performance of the proposed weighted sum modified PSO algorithm and the genetic algorithm (GA) and the case where the SCMA-based HCN is unoptimized. Results: The Pareto-optimal front generated showed a simultaneous maximization of the SE and EE of the SCMA-based HCN at high traffic levels and a convex front that allows network operators to select the SE-EE tradeoff at low traffic levels flexibly. The proposed PSO algorithm offers a higher SBS density, and a higher SBS transmit power at high traffic levels than at low traffic levels. The unoptimized SCMA-based HCN achieves an 80% lower SE and a 51% lower EE than the proposed PSO optimized SCMA-based HCN. The optimum SE and EE achieved by the SCMA-based HCN using the proposed PSO algorithm or the GA are comparable, but the proposed PSO uses a 51.85% lower SBS density and a 35.96% lower SBS transmit power to achieve the optimal SE and EE at moderate traffic levels. Conclusion:In sleep-mode enabled SCMA-based HCNs, network engineers have to decide the balance of SBS density and SBS transmit power that helps achieve the desired SE and EE.

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“…We consider the downlink transmission scenario between a single transmitter and I users denoted by u i , where i ∈ I = {1, • • • , I}. The channel fading experienced by user 1 It is not straightforward to analyse theoretically SCMA with MPA because the closed-form expression of the signal-to-interference ratio after MPA detection is not easy to obtain [36]. Thus we approximate MPA with SIC as it is also widely adopted in the literature of SCMA [37], [38], [39].…”

Section: A Noma System Modelmentioning

confidence: 99%

“…where s th0 =ŝ 0 (i, j; k), s th1 , s th2 , s th3 are given in (34), (35), (36) and A, B are given below.…”

Section: A First Round: M =mentioning

confidence: 99%

On the Performance of HARQ Protocols With Blanking in NOMA Systems

Mheich

Xiao

et al. 2020

IEEE Trans. Wireless Commun.

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In this paper, we investigate the throughput performance of single-packet and multi-packet hybrid-automatic repeat request (HARQ) with blanking for downlink non-orthogonal multiple access (NOMA) systems. While conventional singlepacket HARQ achieves high throughput at the expense of high latency, multi-packet HARQ, where several data packets are sent in the same channel block, can achieve high throughput with low latency. Previous works have shown that multi-packet HARQ outperforms single-packet HARQ in orthogonal multiple access (OMA) systems, especially in the moderate to high signal-tonoise ratio regime. This work amalgamates multi-packet HARQ with NOMA to achieve higher throughput than the conventional single-packet HARQ and OMA, which has been adopted in the legacy mobile networks. We conduct theoretical analysis for the throughput per user and also investigate the optimization of the power and rate allocations of the packets, in order to maximize the weighted-sum throughput. It is demonstrated that the gain of multi-packet HARQ over the single-packet HARQ in NOMA systems is reduced compared to that obtained in OMA systems due to inter-user interference. It is also shown that NOMA-HARQ cannot achieve any throughput gain with respect to OMA-HARQ when the error propagation rate of the NOMA detector is above a certain threshold.

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User capacity for uplink SCMA system

Cited by 3 publications

References 17 publications

Deep SCMA receiver: A low‐complexity joint decoder and channel estimator for SCMA over time‐varying channels using RNNs

Deep SCMA receiver: A low‐complexity joint decoder and channel estimator for SCMA over time‐varying channels using RNNs

Particle swarm optimization of the spectral and energy efficiency of an SCMA‐based heterogeneous cellular network

On the Performance of HARQ Protocols With Blanking in NOMA Systems

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