Successive Interference Cancellation Receiver Research Articles

Performance of massive MIMO‐NOMA systems with low complexity group SIC receivers and low‐resolution ADCs

AbstractMassive multiple‐input multiple‐output and non‐orthogonal multiple access (MIMO‐NOMA) with low‐resolution analog‐to‐digital converters (ADCs) have been widely considered for the next‐generation wireless communication systems. However, the performance of the system including power‐scaling law has not been well investigated for the practical low complexity receivers. Employing the additive quantization noise model, we derive asymptotic approximate expressions of the spectrum efficiency for the system with group successive interference cancellation (GSIC) receivers over Rician fading channels. Based on these approximations, we conduct a unified asymptotic analysis for the system with linear, SIC, and GSIC receivers. The analysis reveals the transmission power can be scaled by the number of antennas for the system with GSIC receivers and shows the effects of crucial parameters including the number of groups, resolution bits, and antennas on the performance. Given a quality of service, the minimum data transmission power is also calculated for each user and the corresponding approximate power allocation is derived. The asymptotic analysis and the accuracy of the power allocation approximation are then verified by simulation results. Numerical results also demonstrate that high spectrum efficiency and energy efficiency can be achieved by the system with medium‐resolution ADCs and low complexity maximum ratio combining‐GSIC receivers with a small number of groups.

Joint altitude and beamwidth optimization for LEO satellite‐based IoT constellation

SummaryLow Earth Orbit (LEO) satellite‐based Internet of Things (IoT) has become a hot topic in IoT networks due to the ability of global coverage, especially in remote areas. How to design a commercial LEO satellite‐based constellation to meet the IoT traffic requirement remains an open problem. In this paper, we propose a throughput optimization algorithm for LEO satellite‐based IoT networks meanwhile reducing the number of LEO satellite. Based on stochastic geometry theory, a closed‐form expression is derived for the throughput of a dynamic LEO satellite‐based IoT networks when LEO satellite equips with capture effect (CE) receiver and successive interference cancelation (SIC) receiver, respectively. Furthermore, a joint altitude and beamwidth optimization problem is formulated under the constraint of Walker constellation to optimize the throughput and the number of LEO satellites. To solve this multi‐objective optimization problem, we design an iterative non‐dominated sorting genetic algorithm II (NSGA‐II) for the rapid development of IoT traffic. Simulation results show that our proposed algorithm can effectively improve the throughput performance of random access (RA) protocol in LEO satellite‐based IoT networks compared to benchmark problems.

Deep Learning-Based Detection Algorithm for the Multi-User MIMO-NOMA System

Recently, non-orthogonal multiple access (NOMA) has become prevalent in 5G communication. However, the traditional successive interference cancellation (SIC) receivers for NOMA still encounter challenges. The near-far effect between the users and the base stations (BS) results in a higher bit error rate (BER) for the SIC receiver. Additionally, the linear detection algorithm used in each SIC stage fails to eliminate the interference and is susceptible to error propagation. Consequently, designing a high-performance NOMA system receiver is a crucial challenge in NOMA research and particularly in signal detection. Focusing on the signal detection of the receiver in the NOMA system, the main work is as follows. (1) This thesis leverages the strengths of deep neural networks (DNNs) for nonlinear detection and incorporates the low computational complexity of the successive interference cancellation (SIC) structure. The proposed solution introduces a feedback deep neural network (FDNN) receiver to replace the SIC in signal detection. By employing a deep neural network for nonlinear detection at each stage, the receiver mitigates error propagation, lowers the BER in NOMA systems, and enhances resistance against inter-user interference (IUI). (2) We describe its algorithm flow and provide simulation results comparing FDNN and SIC receivers under MIMO-NOMA scenarios. The simulations clearly demonstrate that FDNN receivers outperform SIC receivers in terms of BER for MIMO-NOMA systems.

Implementation of SIC Algorithms in the 5G DL

In this paper, we have implemented successive interference cancellation algorithms in the 5G downlink. We have calculated the maximum throughput in Frequency Division Duplex (FDD) mode in the downlink, where we have obtained a value equal to 836932 b/ms. The transmitter is of type Multiple Input Multiple Output (MIMO) with eight transmitting and receiving antennas. Each antenna among eight transmits simultaneously a data rate of 104616 b/ms that contains the binary messages of the three users. In this case, the cyclic redundancy check CRC is negligible, the Block error rate BLER is null, the MIMO category is the spatial diversity. The technology used for this is called Non-Orthogonal Multiple Access (NOMA) with a Quadrature Phase Shift Keying (QPSK) modulation. The transmission is done in a Rayleigh fading channel with the presence of obstacles. The MIMO Successive Interference Cancellation (SIC) receiver with two transmitting and receiving antennas recovers its binary message without errors for certain values of transmission power such as 50 dBm, with 0.054485% errors when the transmitted power is 20 dBm and with 0.00286763% errors for a transmitted power of 32 dBm(in the case of user 1) as well as with 0.0114705% errors when the transmitted power is 20 dBm also with 0.00286763% errors for a power of 24 dBm(in the case of user2) by applying the steps involved in SIC.

Deep-Neural-Network-Based Receiver Design for Downlink Non-Orthogonal Multiple-Access Underwater Acoustic Communication

The excavation of the ocean has led to the submersion of numerous autonomous vehicles and sensors. Hence, there is a growing need for multi-user underwater acoustic communication. On the other hand, due to the limited bandwidth of the underwater acoustic channel, downlink non-orthogonal multiple access (NOMA) is one of the fundamental pieces of technology for solving the problem of limited bandwidth, and it is expected to be beneficial for many modern wireless underwater acoustic applications. NOMA downlink underwater acoustic communication (UWA) is accomplished by broadcasting data symbols from a source station to several users, which uses superimposed coding with variable power levels to enable detection through successive interference cancellation (SIC) receivers. Nevertheless, comprehensive information of the channel condition and channel state information (CSI) are both essential for SIC receivers, but they can be difficult to obtain, particularly in an underwater environment. To address this critical issue, this research proposes downlink underwater acoustic communication using a deep neural network utilizing a 1D convolution neural network (CNN). Two cases are considered for the proposed system in the first case: in the first case, two users with different power levels and distances from the transmitter employ BPSK and QPSK modulations to support multi-user communication, while, in the second case, three users employ BPSK modulation. Users far from the base station receive the most power. The base station uses superimposed coding. The BELLHOP ray-tracing algorithm is utilized to generate the training dataset with user depth and range modifications. For training the model, a composite signal passes through the samples of the UWA channel and is fed to the model along with labels. The DNN receiver learns the characteristic of the UWA channel and does not depend on CSI. The testing CIR is used to evaluate the trained model. The results are compared to the traditional SIC receiver. The DNN-based DL NOMA underwater acoustic receiver outperformed the SIC receiver in terms of BER in simulation results for all the modulation orders.

Nonlinear symbolic regression for bit error rate prediction of NOMA systems in 5G cellular communications

Machine learning (ML) has been suggested as a promising tool in the design of telecommunication systems to overcome the challenges of traditional methods and meet the requirements regarding future cellular networks. As an enabler of 5G communications, Non-orthogonal multiple access (NOMA) has emerged to adaptively manage the existing resources and received huge attention in terms of performance analysis to confirm its efficiency in resource management. This work addresses the applications of nonlinear symbolic regression (NLSR) techniques to evolve mapping models for bit error rate (BER) prediction in the downlink NOMA systems with perfect and imperfect successive interference cancellation (SIC) receivers. In this regard, a multi-stage structure is proposed to evaluate the BER using the related inputs as well as the output of the previous stages for imperfect SIC signal decoding. Each step consists of an NLSR block with different inputs and an output, which inputs include the effective parameters and the output of the previous stages. Experimental results confirm that the NLSR techniques are able to establish modeling expressions with good prediction accuracy compared to other benchmark methods. In particular, these ML-based techniques overcome the limitation of the classical approaches in modeling a closed-form BER formula, especially for the NOMA systems with more than two users. In addition, this BER prediction tool can be utilized by the NOMA base station as new side information for more optimal resource management.

Model-Based Deep Learning Receiver Design for Rate-Splitting Multiple Access

Effective and adaptive interference management is required in next generation wireless communication systems. To address this challenge, Rate-Splitting Multiple Access (RSMA), relying on multi-antenna rate-splitting (RS) at the transmitter and successive interference cancellation (SIC) at the receivers, has been intensively studied in recent years, albeit mostly under the assumption of perfect Channel State Information at the Receiver (CSIR) and ideal capacity-achieving modulation and coding schemes. To assess its practical performance, benefits, and limits under more realistic conditions, this work proposes a novel design for a practical RSMA receiver based on model-based deep learning (MBDL) methods, which aims to unite the simple structure of the conventional SIC receiver and the robustness and model agnosticism of deep learning techniques. The MBDL receiver is evaluated in terms of uncoded Symbol Error Rate (SER), throughput performance through Link-Level Simulations (LLS), and average training overhead. Also, a comparison with the SIC receiver, with perfect and imperfect CSIR, is given. Results reveal that the MBDL receiver outperforms by a significant margin the SIC receiver with imperfect CSIR, due to its ability to generate on demand non-linear symbol detection boundaries in a pure data-driven manner.

Downlink Non-Orthogonal multiple access underwater acoustic communication receiver design Deep neural network

Downlink non-orthogonal multiple access (NOMA) is necessary to handle the underwater acoustic channel's limitations on bandwidth. NOMA downlink underwater acoustic (UWA) communication transmits data symbols from a source station to many users. Superimposed coding with variable power levels allows successive interference cancelation (SIC) receivers to decode data. However, SIC receivers require knowledge of channel conditions and channel state information (CSI). This is difficult to acquire, particularly in UWA communication. To address this problem, this paper proposes downlink underwater acoustic using 1D Convolution neural network (CNN). Two users with different power levels and distances from the transmitter employ BPSK modulation to support multiuser communication. Users far from the base station receive the most power. The base station uses superimposed coding. BELLHOP algorithm generates the training dataset with user depth modifications. For training the model, a composite signal passes through the samples of the UWA channel and is fed to the model along with labels. DNN receiver learns the characteristic of the UWA channel and does not depend on CSI. The testing CIR is used to evaluate the trained model. The results are compared to the traditional SIC receiver. The DNN-based DL NOMA underwater acoustic receiver outperformed the SIC receiver in simulations.

Stochastic Resonance Assisted Successive Interference Cancellation Receiver for Signal Detection Performance Improvement

Stochastic Resonance Assisted Successive Interference Cancellation Receiver for Signal Detection Performance Improvement

Low complexity successive interference cancellation receiver for DCT-NOMA-OFDM system with CFO

The integration of orthogonal frequency division multiplexing (OFDM) with non-orthogonal multiple access (NOMA) for multiplexing of multiple users has been previously studied for the 5G and beyond networks. It has been used to fulfill the demand for high spectral and energy efficiency, massive connectivity, and ultra-low latency with user fairness. However, with higher mobility, the residual interference power remaining after the Successive interference cancellation (SIC) and equalization process significantly deteriorates the system’s symbol error rate (SER) performance. This paper considers a downlink discrete cosine transform (DCT) based NOMA-OFDM system with equalization and interference-compensation jointly in the fractional Fourier domain (FFD). The traditional full matrix compensation strategy attracts a lot of complexity. So, the proposed system architecture considers a low complexity banded joint equalization and interference-compensation (JEIC) method for each user equipment (UE). With the help of the proposed architecture, an SNR gain of 3 dB in the case of UE-2 and 8 dB in the case of UE-1 has been achieved over the conventional system in the presence of normalized carrier frequency offset (ϵ) = 0.05 at BER =10−3. Further, in the presence of ϵ= 0.1, the UE-2 with the proposed architecture gains 5 dB over the conventional system at 1×10−3 SNR range. The proposed architecture brings the performance of the system similar to the interference-free system with reduced complexity in O(N) compared to the full matrix compensation scheme having O(N3) complexity.

Massive MIMO With Group SIC Receivers and Low-Resolution ADCs Over Rician Fading Channels

This paper investigates the performance of uplink massive multiple-input multiple-output (MIMO) systems with low-resolution analog-to-digital converters (ADCs) operating over Rician fading channels. We study zero-forcing (ZF) group successive interference cancellation (GSIC) receivers, which cover the ZF and ZF-SIC receivers as two special cases. The spectrum efficiency (SE) is analyzed for general power allocation under both perfect and imperfect channel state information (CSI). We further derive the optimal power allocation for a given quality-of-service (QoS) for the GSIC receivers. Based on random matrix theory, the corresponding asymptotic approximations of the signal-to-interference-plus-noise-ratio (SINR) for the system and the power allocation are derived for both perfect and imperfect CSI. The derived approximations are verified and shown highly accurate, providing fast characterization of the performance and near-optimal resource allocation for different system parameters. The numerical and approximation results both indicate that power allocation improves the overall energy efficiency (EE), while the GSIC receiver can yield a high EE with a very small number of groups. Besides, the EE of the system can be improved with intermediate-resolution ADCs and ordered SIC (OSIC).

Nonlinear Transceiver Design for Full-Duplex MIMO Relaying with Direct Link

This paper investigates the joint transceiver design for full-duplex (FD) multiple-input multiple-output relay systems where the direct link (DL) and nonlinear successive-interference-cancellation (SIC) detection are considered. The objective is to jointly optimize the source precoder, relay precoder, and receiver such that the resultant symbol-vector error rate (SVER) is minimized. The joint design is challenging since the coexistence of DL and FD relaying will introduce the inter symbol-vector interference. To address this problem, we first propose a novel QR-SIC receiver structure by performing the dual-filtering (DuF) and channel stacking before SIC. Then, the source and relay precoders are optimized for the proposed DuF-based QR-SIC receiver. Compared with the source precoder, optimizing the relay precoder is much more involved due to the complicated objective function. Hence, we transform the original optimization problem into a trace-minimization one, enabling a closed-form solution for the relay precoder. Furthermore, we theoretically show that the proposed design framework can be extended to the system with the DuF-based minimum mean-squared-error SIC receiver. Simulations show that our transceivers are indeed able to significantly improve the overall SVER performance.

Deep Learning-Based Signal Detection for Rate-Splitting Multiple Access Under Generalized Gaussian Noise

In this paper, we propose a long short-term memory-based deep learning (DL) architecture for signal detection in uplink and downlink rate-splitting multiple access systems with multi-carrier modulation, over Nakagami-m fading and generalized Gaussian noise (GGN). The proposed DL detector completely eliminates the need for the use of successive interference cancellation (SIC), which suffers from disadvantages such as error propagation. In an orthogonal frequency division multiplexing setting, we show that the proposed DL detector outperforms the standard SIC receivers such as the least squares detector and the minimum mean-squared error receiver, and attains the performance of the optimal maximum likelihood detector, in terms of the symbol error rate (SER). Furthermore, we study the effects of the shaping parameter of GGN, hyperparameters of the DL network such as batch size and learning rate on the SER performance.

BER performance analysis of asynchronous NOMA with arbitrary phase offset

An asynchronous transmission scenario for non-orthogonal multiple access (NOMA) user signals with arbitrary phase offset is investigated in this paper. To improve the system performance in the user power-balanced conditions, we adopt a synthetic detection method at the receiver, i.e., the jointly optimal maximal likelihood detection aided triangular successive interference cancellation (JO ML-TSIC) method. Analytical bit error rate (BER) solutions are obtained for a two-user case with the optimal, intentional one-half symbol period time delay implemented between the user signals. Furthermore, closed-form BER solutions for the case using the triangular successive interference cancellation (TSIC) detection method are also derived for comparisons. Numerical results show that the JO ML-TSIC receiver for the asynchronous system outperforms the TSIC receiver as well as the synchronous successive interference cancellation (SIC) receiver in all the conditions concerned. The results also show that the superiority of the JO ML-TSIC receiver is strengthened when the signals experience flat Rayleigh fading channels compared to the TSIC and the synchronous SIC receivers.

Non-orthogonal multiple access system based on time diversity for 5G applications

Novel coding and modulation methods are required for fulfilling the demands of 5G and beyond cellular communication systems. In this paper, an innovative time diversity non-orthogonal multiple access (TD-NOMA) approach is proposed for 5G applications. The suggested system uses time diversity (TD) in conjunction with NOMA to improve the system bit error rate (BER) performance. The NOMA technique basically employes data transmission based on non-orthogonal basis functions. The data pass through the channel, and the desired signal can be obtained by a successive interference cancelation (SIC) receiver. Using different subcarriers and channel models, different modulation schemes such as QPSK and BPSK are investigated. When compared to conventional methodologies, the simulation results reveal that the proposed system outperforms them; it reduces the effect of fading for the multipath channel and enhances the BER.

Constellation Shared Multiple Access - A Noma Scheme for Increased User Capacity in 5G MMTC

While the legacy cyclic prefix orthogonal frequency division multiple access is retained as the preferred multiple access scheme for 5G enhanced mobile broadband the research is now focussed on the multiple access schemes for massive machine type communication (mMTC) and ultra-reliable low latency communication .Though orthogonal multiple access schemes provide simple reception, they limit number of simultaneous user equipment as against the primary requirement of mMTC. On the other hand, the various non-orthogonal multiple access schemes which have been proposed so far as the likely solution, need complex successive interference cancellation receivers. So a simplified scheme named constellation shared multiple access is proposed here which substantially increases the number of simultaneous users to be served within a single resource block (RB) in LTE or 5G New Radio, thus aiding the massive connectivity requirement of mMTC. This is achieved by differentiating among the users in constellation domain. Moreover, the simple architecture compatible with 5G eMBB makes it a strong contender multiple access contender for 5G mMTC.

Fundamental Limits on the Uplink Performance of the Dynamic-Ordered SIC Receiver

Due to the rapid and widespread growth of the Internet-of-Things (IoT) paradigm, present days witness an exponential increase in the number of connected devices. In this regard, the orthogonal transmission techniques featured by conventional 4G and 5G systems can only support a limited number of simultaneously active users, due to their low spectral efficiency and poorly flexible resource allocation. To overcome such limitations, the 6G framework will include novel Next Generation Multiple Access (NGMA) solutions that will efficiently and flexibly connect a significantly larger number of devices over the same portion of spectrum. Under the NGMA umbrella, the Power-Domain Non-Orthogonal Multiple Access (PD-NOMA) technology is able to accommodate multiple users on the same frequencies by carefully assigning different power levels to the active users and employing Successive Interference Cancellation (SIC) receivers. In this work, we put forth a novel analytical approach to evaluate the performance that PD-NOMA achieves on the uplink of a single cell when a dynamic-ordered SIC receiver is considered. With respect to other existing works, the fundamental limits on the system performance are assessed analytically for an arbitrary number <i>n</i> of simultaneously transmitting users, and both the case of Rayleigh and lognormal-shadowed Rayleigh fading are examined. The closed-form expressions presented in this work, whose correctness and excellent accuracy are validated through Monte Carlo simulations, disclose the impact of lognormal shadowing and an increasingly larger number of active users on the PD-NOMA performance.

Exact SER and BER Analysis on Jointly Optimal Maximum Likelihood Detection of Two QPSK Signals With Phase Offset

Analysis on the exact error rate of jointly optimal (JO) maximum likelihood (ML) multiuser detection is limited in the existing literature owing to its high computational complexity. In this paper, the JO ML detection of two quadrature phase-shift keying (QPSK) signals with an arbitrary relative phase offset is investigated. Exact, analytical symbol error rate (SER) and bit error rate (BER) formulas are derived for two users with no less than 3 dB power difference between them. Closed-form union bounds on the user SER and BER with an arbitrary phase offset and an arbitrary user power ratio are also given as a simpler approach to acquire the asymptotic error rate performance. Furthermore, insights on the potentials and properties of the JO ML detector regarding its error rate behaviors for each user are revealed through comparisons with the successive interference cancellation receiver. The accuracies of the obtained formulas are demonstrated through simulation. The results show the error rates for the two-user QPSK case no longer follow the classic knowledge on the error rate behaviors in the single signal detection. In addition, the performance and dB penalty employing different modulation types are also compared and discussed.

Rate-Splitting Multiple Access for Multi-Antenna Joint Radar and Communications

Dual-Functional Radar-Communication (DFRC) system is an essential and promising technique for beyond 5G. In this work, we propose a powerful and unified multi-antenna DFRC transmission framework, where an additional radar sequence is transmitted apart from communication streams to enhance radar beampattern matching capability, and Rate-Splitting Multiple Access (RSMA) is adopted to better manage the interference. RSMA relies on multi-antenna Rate-Splitting (RS) with Successive Interference Cancellation (SIC) receivers, and the split and encoding of messages into common and private streams. We design the message split and the precoders of the radar sequence and communication streams to jointly maximize the Weighted Sum Rate (WSR) and minimize the radar beampattern approximation Mean Square Error (MSE) subject to the per antenna power constraint. An iterative algorithm based on Alternating Direction Method of Multipliers (ADMM) is developed to solve the problem. Numerical results first show that RSMA-assisted DFRC achieves a better tradeoff between WSR and beampattern approximation than Space-Division Multiple Access (SDMA)-assisted DFRC with or without radar sequence, and other simpler radar-communication strategies using orthogonal resources. We also show that the RSMA-assisted DFRC frameworks with and without radar sequence achieve the same tradeoff performance. This is because that the common stream is better exploited in the proposed framework. The common stream of RSMA fulfils the triple function of managing interference among communication users, managing interference between communication and radar, and beampattern approximation. Therefore, by enabling RSMA in DFRC, the system performance is enhanced while the system architecture is simplified since there is no need to use additional radar sequence and SIC. We conclude that RSMA is a more powerful multiple access for DFRC.

BER Analysis for Downlink MIMO-NOMA Systems over Rayleigh Fading Channels

The Multiple-input multiple-output (MIMO) technique combined with non-orthogonal multiple access (NOMA) has been considered to enhance total system performance. This paper studies the bit error rate of two-user power-domain NOMA systems using successive interference cancellation receivers, with zeroforcing equalization over quasi-static Rayleigh fading channels. Successive interference cancellation technique at NOMA receivers has been the popular research topic due to its simple implementation, despite its vulnerability to error propagation. Closed-form expressions are derived for downlink NOMA in single-input single-output and uncorrelated quasi-static MIMO Rayleigh fading channel. Analytical results are consolidated with Monte Carlo simulation.