Down-Link NOMA Networks in the Presence of IQI and Imperfect SIC: Receiver Design and Performance Analysis

Nonorthogonal multiple access (NOMA) and reconfigurable intelligent surface (RIS) methods are explored to enhance spectral and energy efficiency of a wireless network. In this article, we have proposed a downlink RIS-assisted index modulation NOMA for next-generation wireless systems, and it is referred to as R-INOMA. In the proposed R-INOMA system, a base station transmits information through RIS to three users, the first user is assigned in the spatial domain and the remaining two users are multiplexed using power domain NOMA. We provide an analytical analysis of bit error probability (BEP) for the proposed R-INOMA system. Impact of number of surfaces, transmit antennas, modulation order, diversity order, and imperfect channel estimation is analyzed for the R-INOMA. Simulation results show that the proposed R-INOMA achieved better BEP and sum-rate over existing NOMA techniques. In the R-INOMA, doubling of the reflecting surfaces provides a 4 dB gain for a fixed error probability, and the sum-rate improves as the number of transmitting antennas and reflecting surfaces increases. INTRODUCTIONReconfigurable intelligent surface (RIS) has emerged as a promising research topic for sixth-generation (6G) wireless communications. [1][2][3][4] RIS changes the behavior of the wireless propagation environment and helps to obtain high-quality signals at the receiver. An RIS is fabricated by meta-surfaces and it consists of a planar structure to have the ability to reflect with low-cost passive elements. 2,5,6 RIS smartly tunes the phase of the incident signals according to the channel environment, and the reflected signals can be constructively added to enhance signal-to-noise (SNR) toward receivers. 1,7 RIS creates a virtual line-of-sight (LOS) propagation path between transmitter and receivers. Therefore, RIS improves the network coverage, spectral, and energy efficiency. [8][9][10] On the other hand, index modulation (IM) and nonorthogonal multiple access (NOMA) are a promising technology for fifth-generation (5G) and beyond wireless networks to enhance spectrum efficiency and support massive connectivity. 11-16 IM-based spatial modulation (SM) transmits information using antenna indices along with modulation symbols of M-ary phase shift keying or M-ary quadrature amplitude modulation (QAM), therefore it increases the data transmission rate. Moreover, SM transmits information in a single activated antenna in multiple-inputs and multiple-output (MIMO) system by using a single radio frequency (RF) chain. 17,18 Further, a combination of IM and NOMA (IM-NOMA) can serve simultaneously more users at the same time/frequency without the effect of inter-user interference (IUI) [19][20][21] and it consists of superposition coding (SC) at the base station (BS) and successive interference cancelation (SIC) at the receivers. 22

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“…

\varsigma

denotes error in FU decoding. It is assumed Gaussian distributed as

ς \sim 𝒞 𝒩 false(0, N_{ς} false)

38 . Therefore, NU signal

{\mathbf{y}}_{2,\mathrm{ISIC}}

is expressed as,

$$ {\mathbf{y}}_{2,\mathrm{ISIC}}={\mathbf{G}}_2^H\boldsymbol{\Phi} \mathbf{H}\varpi +\varsigma +{\mathbf{n}}_2\in {\mathbb{C}}^{N_{\mathrm{r}}\times 1}.

…”

Section: Proposed R‐inoma System Modelmentioning

confidence: 99%

“…It is assumed Gaussian distributed as 𝜍 ∼  (0, N 𝜍 ). 38 Therefore, NU signal y 2,ISIC is expressed as,…”

Section: 2mentioning

confidence: 99%

Reconfigurable intelligent surface assisted downlink INOMA system

Kumar

Sharma

Thottappan

et al. 2022

Trans Emerging Tel Tech

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“…To identify the trend in the data, NNs take data, train data themselves, and then forecast the outcomes for a new collection of related data. [30][31][32][33] The layers of neurons make up these networks. These neurons are the network's central processing units.…”

Section: Basicsmentioning

confidence: 99%

Investigation of the fifth generation non-orthogonal multiple access technique for defense applications using deep learning

Malladi¹,

Beuria

Ravi³

et al. 2021

Journal of Defense Modeling & Simulation

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In modern wireless communication scenarios, non-orthogonal multiple access (NOMA) provides high throughput and spectral efficiency for fifth generation (5G) and beyond 5G systems. Traditional NOMA detectors are based on successive interference cancellation (SIC) techniques at both uplink and downlink NOMA transmissions. However, due to imperfect SIC, these detectors are not suitable for defense applications. In this paper, we investigate the 5G multiple-input multiple-output NOMA deep learning technique for defense applications and proposed a learning approach that investigates the communication system’s channel state information automatically and identifies the initial transmission sequences. With the use of the proposed deep neural network, the optimal solution is provided, and performance is much better than the traditional SIC-based NOMA detectors. Through simulations, the analytical outcomes are verified.

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“…The precondition of determining I intra u k (t) is to find out the optimal decoding order to guarantee the successful SIC, and then SIC is capable to remove some of the intra-cluster interference at the receiver side [34]. In this case, a dynamic decoding order has to be considered owing to the fact that the channel gain and inter-cluster interference of each user is always changing by the movement.…”

Section: Signal Modelmentioning

confidence: 99%

Multi-Agent Reinforcement Learning in NOMA-aided UAV Networks for Cellular Offloading

Zhong

Liu

et al. 2020

Preprint

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A novel framework is proposed for cellular offloading with the aid of multiple unmanned aerial vehicles (UAVs), while non-orthogonal multiple access (NOMA) technique is employed at each UAV to further improve the spectrum efficiency of the wireless network. The optimization problem of joint three-dimensional (3D) trajectory design and power allocation is formulated for maximizing the throughput. Since ground mobile users are considered as roaming continuously, the UAVs need to be re-deployed timely based on the movement of users. In an effort to solve this pertinent dynamic problem, a K-means based clustering algorithm is first adopted for periodically partitioning users. Afterward, a mutual deep Q-network (MDQN) algorithm is proposed to jointly determine the optimal 3D trajectory and power allocation of UAVs. In contrast to the conventional DQN algorithm, the MDQN algorithm enables the experience of multiagent to be input into a shared neural network to shorten the training time with the assistance of state abstraction. Numerical results demonstrate that: 1) the proposed MDQN algorithm is capable of converging under minor constraints and has a faster convergence rate than the conventional DQN algorithm in the multi-agent case; 2) The achievable sum rate of the NOMA enhanced UAV network is 23% superior to the case of orthogonal multiple access (OMA); 3) By designing the optimal 3D trajectory of UAVs with the aid of the MDON algorithm, the sum rate of the network enjoys 142% and 56% gains than that of invoking the circular trajectory and the 2D trajectory, respectively.

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Down-Link NOMA Networks in the Presence of IQI and Imperfect SIC: Receiver Design and Performance Analysis

Cited by 23 publications

References 15 publications

Reconfigurable intelligent surface assisted downlink INOMA system

Reconfigurable intelligent surface assisted downlink INOMA system

Investigation of the fifth generation non-orthogonal multiple access technique for defense applications using deep learning

Multi-Agent Reinforcement Learning in NOMA-aided UAV Networks for Cellular Offloading

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