Facilitated Local Context Sharing in V2X Environment with NOMA for Small Packet

The papers in this special section focus on signal processing advances in non-orthogonal multiple access (NOMA) and network generation wireless networks. Recent years have witnessed the explosive growth ofmobile data traffic, which has led to ever-increasing demand for high system throughput, massive user access, heterogeneous data traffic, high bandwidth efficiency, and ultra-low latency. Non-orthogonal multiple access (NOMA) has received considerable attention in both industry and academia as an efficient multiple access scheme to meet this demand. The concept of NOMA is to encourage spectrum sharing and to accommodate multiple users in the same orthogonal resource block, such as a time slot, a frequency band, and a spatial direction. By doing so, high bandwidth efficiency and massive connectivity can be attained. Because of its superior performance, NOMA has already been included in the 3rd generation partnership project long-term evolution advanced (3GPP-LTE-A) standard and the next generation digital TV standard (ATSC 3.0), while new proposals for including NOMA in the 5G New Radio (NR) have been put forward. While the principle of NOMA has been wellaccepted by both academia and industry, there are still many open issues related to the required signal processing. The aim of this special issue is to present solutions for signal processing and practical implementation problems related to NOMA, in order to close the gap between theory and practice. In fact, this special issue presents innovative solutions to advanced signal processing designs for of NOMA systems.

In the fifth generation (5G) wireless communication system, the non‐orthogonal multiple access (NOMA) scheme has recently been proposed as a possible candidate for improving spectral efficiency. We look at power‐domain NOMA in this study, in which, multiple simultaneous transmissions are allowed at the same channel with different transmission powers. To achieve reliable transmissions in NOMA, the hybrid automatic repeat request (HARQ) scheme can be integrated into NOMA. The performance of a downlink NOMA system using the Type I HARQ method is investigated in this research. This type of HARQ does not entail expensive device which is considered one of the requirements for a massive 5G‐based Internet of Things network. Unfortunately, in the literature, it has been shown that deriving closed‐form expressions for the outage probabilities of HARQ‐aided NOMA systems is intractable due to the incorporation of multiple fractional random variables. Consequently, in this article, we follow a simple yet efficient methodology to study our considered HARQ‐aided NOMA system. The HARQ‐assisted NOMA system on the downlink is modeled as an absorbing discrete‐time Markov chain (DTMC). Thereafter, under perfect and imperfect channel estimation, we use this model to examine system performance in terms of outage probability and estimated number of retransmissions. Interestingly, we demonstrate that our DTMC‐based analysis is straightforward, repeatable, and accurate. Moreover, in this article, we propose a cooperative HARQ (CHARQ) scheme for the downlink NOMA system. According to CHARQ, the users cooperate with the base station in the retransmission process that will result in improving the performance of NOMA. Monte Carlo simulations have been conducted to not only verify the accuracy of our DTMC‐based analysis, but also manifest the superiority of CHARQ over HARQ.

Non-orthogonal multiple access (NOMA) is becoming one of the promising technologies for the fifth generation (5G) and beyond 5G (B5G) communications due to its ability of connecting massive wireless devices. Meanwhile, backscatter communication (BSCom) is emerging as a key solution to the Internet of Things (IoTs) due to its energy efficiency. In this chapter, we first provide a brief introduction of NOMA technology, discuss its fundamental concepts, and outline its applications. Then, we discuss the basic concepts of BSCom systems in brief, describe its different configurations, highlight the challenges of NOMA-enabled BSCom systems, and discuss the recent solutions. Moreover, we provide the basics of physical layer security (PLS) in wireless communication systems. Using the aforementioned backgrounds, we formulate an optimization problem for secrecy rate maximization in NOMA-enabled BSCom in the presence of multiple eavesdroppers. The problem is subjected to backscatter device (BSD) reflection coefficient and base station (BS) power according to NOMA protocol. To efficiently solve the optimization problem, we exploit the duality theory. For the purpose of comparison, we also present a conventional orthogonal multiple access (OMA)-enabled BSCom system as a benchmark. Finally, we present the simulation results and conclude this chapter with future research directions.

Since the emergence of mobile communication systems as networks specifically for telephone service, they have experienced rapid progress. The evolution of mobile generations has seen a diversity of services, which includes data as the first service, then video, and other services. The fifth-generation network (5G) is the future of information networks. It is not really a further advancement of the fourth generation (4G) but another kind of mobile system model that supports high frequencies, extreme node densities, large-scale antennas, high bandwidth huge, etc. In this context, 5G networks can call on a wide range of new technologies. This allows for a leap in performance that eclipses its predecessors. These innovations will affect the transmission and design of the physical layer in addition to introducing disruption to the upper layers of the network. In fact, 5G New Radio (NR) will use many key technologies to achieve new levels of performance and efficiency. Some of the potential innovations in the 5G physical layer include Non-orthogonal Multiple Access (NOMA). The users are superimposed in the power domain, and the decoding mode is based on the Successive Interference Cancellation (SIC). To aim with the challenges in NOMA method, the deep learning (DL) models are a recommended solution. In this paper, we treat the technique NOMA for 5G and how the use of deep learning techniques can improve the performance of network.Keywords5GNRMultiple accessNOMASICCSIOFDMADL

Multi-User Multiple-Input Multiple-Output (MU-MIMO) and Non-Orthogonal Multiple Access (NOMA) belong to the key physical layer technologies under the context of 5th Generation (5G) New Radio (NR) communication. Both MU- MIMO and NOMA exhibit capability to significantly enhance the spectral efficiency from different aspects. In this paper, we investigate and compare MU-MIMO and NOMA in link level with respect to some practical issues, e.g. imperfect channel estimation, imperfect sectorization. Numerical results reveal that the coexistence of MU-MIMO and NOMA provide the degrees of freedom to allow the system to selectively focus on spectral efficiency per user or the number of supported users, and effectively be associated with scheduled traffic and contention-based traffic, respectively.

The PHY layer presented by 3GPP in Rel-15 as 5G New Radio (NR) has considerably increased the spectrum efficiency of broadband systems, especially, due to the inclusion of Low-Density Parity-Check (LDPC) codes. Non-Orthogonal Multiple Access (NOMA) techniques can take efficiency a step further and they are currently considered a promising complementary technology for future releases. This paper addresses this technology and presents a performance evaluation of the use of multiple NOMA layers in 5G networks on a network simulator, which provides a set of new evaluation metrics. The design of the required architecture for introducing multiple layer communications in 5G is presented as the main contribution of the paper. In addition, two 5G use cases have been designed. In one of the cases, an innovative priority-based access communication architecture is proposed. Finally, the use cases are tested in a discrete-event network level simulator and their performance is evaluated in terms of reliability, capacity and latency.

Non-orthogonal multiple access (NOMA) on shared resources has been identified as a promising technology in 5G to improve resource efficiency and support massive access in all kinds of transmission modes. Power domain and code domain NOMA have been extensively studied and evaluated in both literatures and 3GPP standardization, especially for the uplink where large number of users would like to send their messages to the base station. Though different in the transmitter side design, power domain NOMA and code domain NOMA share the same need of the advanced multi-user detection (MUD) design at the receiver side. Various multi-user detection algorithms have been proposed, balancing performance and complexity in different ways, which is important for the implementation of NOMA in practical networks. In this paper, we introduce a unified variational inference (VI) perspective on various universal NOMA MUD algorithms such as belief propagation (BP), expectation propagation (EP), vector EP (VEP), approximate message passing (AMP) and vector AMP (VAMP), demonstrating how they could be derived from and adapted to each other within the VI framework. Moreover, we unveil and prove that conventional elementary signal estimator (ESE) and linear minimum mean square error (LMMSE) receivers are special cases of EP and VEP, respectively, thus bridging the gap between classic linear receivers and message passing based nonlinear receivers. Such a unified perspective would not only help the design and adaptation of NOMA receivers, but also open a door for the systematic design of joint active user detection and multi-user decoding for sporadic grant-free transmission.

In the fifth generation (5G) and beyond 5G (B5G) mobile communication, non-orthogonal multiple access (NOMA) has attracted great attention due to higher spectral efficiency and massive connectivity. We investigate the impacts of the channel estimation errors on the bit-error rate (BER) of NOMA, especially with the single-user decoding (SUD) receiver, which does not perform successive interference cancellation (SIC), in contrast to the conventional SIC NOMA scheme. First, an analytical expression of the BER for SUD NOMA with channel estimation errors is derived. Then, it is demonstrated that the BER performance degrades severely up to the power allocation less than about 20%. Additionally, we show that for the fixed power allocation of 10% in such power allocation range, the signal-to-noise (SNR) loss owing to channel estimation errors is about 5 dB. As a consequence, the channel estimation error should be considered for the design of the SUD NOMA scheme

A low-complexity ADMM-based secure beamforming for IRS-assisted uplink cognitive radio networks with NOMA

Non-orthogonal multiple access (NOMA) will provide massive connectivity for future Internet of Things. However, the intrinsic non-orthogonality in NOMA makes it non-trivial to approach the performance limit with only conventional communication-theoretic tools. In this paper, we resort to deep multi-task learning for end-to-end optimization of NOMA, by regarding the overlapped transmissions as multiple distinctive but correlated learning tasks. First of all, we establish a unified multi-task deep neural network (DNN) framework for NOMA, namely DeepNOMA, which consists of a channel module, a multiple access signature mapping module, namely DeepMAS, and a multi-user detection module, namely DeepMUD. DeepMAS and DeepMUD are automatically trained in a data-driven fashion, and a multi-task balancing technique is then proposed to guarantee fairness among tasks as well as to avoid local optima. To further exploit the benefits of communication-domain expertise, we introduce constellation shape prior and inter-task interference cancellation structure into DeepMAS and DeepMUD, respectively. These sophisticated designs help to reduce the implementation complexity without sacrificing DNN's universal function approximation property, which makes DeepNOMA a universal transceiver optimization approach. Detailed experiments and link-level simulations show that higher transmission accuracy and lower computational complexity can be simultaneously achieved by DeepNOMA under various channel models, compared with state-of-the-art.

Combining transmit antenna selection and full-duplex aerial relay drone for performance enhancement of multiuser NOMA millimeter-wave communications

The fifth-generation (5G) and beyond 5G (B5G) wireless networks introduced massive machine-type communications (mMTC) to cope with the growing demand of massive Internet of Things (IoT) applications. However, the heterogeneous characteristics of massive IoT and diverse quality of service (QoS) requirements may lead to severe interference that could degrade the expected QoS of the cellular ecosystem. Therefore, this article studies the impact of interference caused by mMTC connections. We theoretically model the intercell interference (ICI) minimization problem for the existing orthogonal multiple access (OMA) technique and propose its corresponding solution. Furthermore, we jointly solve the ICI and the cochannel interference minimization problem for the IoT users when the nonorthogonal multiple access (NOMA) technique is used. For the proposed OMA and NOMA schemes, we design a cooperative scheduler to reduce the impact of such interference. The results show that our proposed schemes provide up to 58%, 75%, and 100% more improvements in terms of user’s data rates, energy consumption, and connection density, respectively.

In this paper, the concept of grant-free non-orthogonal multiple access (NOMA) for uplink data transmission is elaborated. NOMA in combination with grant-free can be applied to ultra reliability low latency communication (URLLC), massive machine type communication (mMTC), enhanced mobile broadband (eMBB) small packet and two-step random-access channel (RACH) scenarios. The advantages of grant-free NOMA are low latency and signaling overhead, high access capability and efficient resource utilization. Candidate uplink NOMA schemes are summarized and preliminary comparison among a subset of schemes are presented. Furthermore, design aspects for grant-free NOMA are discussed, with special notes on particular issues such as blind UE identification and transmitter/ receiver (Tx/Rx) impairments in realistic deployment.

Vehicular communication network is a core application scenario in the fifth generation (5G) mobile communication system which requires ultra high data rate and ultra low latency. Most recently, non-orthogonal multiple access (NOMA) has been regarded as a promising technique for future 5G systems due to its capability in significantly improving the spectral efficiency and reducing the data transmission latency. In this paper, we propose to introduce NOMA in D2D-enabled V2X networks, where resource sharing based on spatial reuse for different V2X communications are permitted through centralized resource management. Considering the complicated interference scenario caused by NOMA and spatial reuse-based resource sharing in the investigated NOMA-integrated V2X networks, we construct an interference hypergraph to model the interference relationships among different communication groups. In addition, based on the constructed hypergraph, we further propose an interference hypergraph-based resource allocation (IHG-RA) scheme with cluster coloring algorithm, which can lead to both effective and efficient QoS-guaranteed resource block (RB) assignment with low computational complexity. Simulation results verify the efficiency of our proposed IHG-RA scheme for NOMA-integrated V2X communications in improving the network sum rate.

Blind detection of signals is a crucial technique in the 5G/B5G wireless communication systems, especially for the cognitive spectrum radio network, where the parameters of the transmit signals working on the free spectrum can not be known by the receiver. Following the 5G New Radio (NR) protocols, we propose a joint modulation and coding scheme (M-CS) recognition framework based on the supervised learning architecture and the given candidate set of the LDPC encoder. Specifically, the framework is composed of two cascaded modules. Firstly, the type of digital modulation according to the SG NR protocols is recognized blindly based on the proposed Res-Inception convolutional neural network (RICNN). Then, the low-density parity check (LDPC) coding scheme implemented under various bitrates is identified by exhaustively searching the validation candidate to maximize the corresponding average log-likelihood ratio (ALLR). Numerical results show the effectiveness of our proposed blind recognition framework, especially for the practical 5G NR protocols. Moreover, it is demonstrated that our proposed method can guarantee the robustness of the recognition under various channel fading model scenarios.