Future 5th generation networks are expected to enable three key services—enhanced mobile broadband, massive machine type communications and ultra-reliable and low latency communications (URLLC). As per the 3rd generation partnership project URLLC requirements, it is expected that the reliability of one transmission of a 32 byte packet will be at least 99.999% and the latency will be at most 1 ms. This unprecedented level of reliability and latency will yield various new applications, such as smart grids, industrial automation and intelligent transport systems. In this survey we present potential future URLLC applications, and summarize the corresponding reliability and latency requirements. We provide a comprehensive discussion on physical (PHY) and medium access control (MAC) layer techniques that enable URLLC, addressing both licensed and unlicensed bands. This paper evaluates the relevant PHY and MAC techniques for their ability to improve the reliability and reduce the latency. We identify that enabling long-term evolution to coexist in the unlicensed spectrum is also a potential enabler of URLLC in the unlicensed band, and provide numerical evaluations. Lastly, this paper discusses the potential future research directions and challenges in achieving the URLLC requirements.

/pdf/enabling-technologies-for-ultra-reliable-and-low-latency-44un8h2u5m.pdf

Enabling Technologies for Ultra-Reliable and Low Latency Communications: From PHY and MAC Layer Perspectives

Orthogonal frequency division multiplexing (OFDM) is a superior technology for the high-speed data rate of wire-line and wireless communication systems. The OFDM has many advantages over other techniques such as its high capacity and immunity against multipath fading channels. However, one of the main drawbacks of the OFDM system is the high-peak-to-average power ratio (PAPR) that leads the system to produce in-band distortion and out-of-band radiation because of the non-linearity of the high-power amplifiers. Therefore, numerous techniques have been proposed to overcome the PAPR problem such as selective mapping, partial transmit sequence (PTS), clipping, and nonlinear companding. In this paper, the PTS technique was analytically reviewed as one of the important methods to reduce the high PAPR problem. The PAPR performance and the computational complexity level are discussed in terms of modifying the PTS technique in the frequency domain, time domain and modulation stage (inverse fast Fourier transform block). Moreover, the numerical statistic comparison of the current modified-PTS methods is introduced, and the criteria for selecting the suitable modified-PTS method in the OFDM system are also given. The simulation and the numerical calculations results show that the rows exchange-interleaving PTS scheme is the best method for reducing the PAPR value with low complexly in the frequency domain, and the cooperative PTS method is the best among the modulation stage methods, while the cyclic shift sequence PTS method achieves the superior performance in PAPR reduction and computational complexity for the time domain methods.

/pdf/a-review-of-partial-transmit-sequence-for-papr-reduction-in-59x9kyvr2j.pdf

A Review of Partial Transmit Sequence for PAPR Reduction in the OFDM Systems

Tactile Internet of Things (IoT) requires ultraresponsive and ultrareliable connections for massive IoT devices. As a promising enabler of tactile IoT, grant-free nonorthogonal multiple access (NOMA) exploits the joint benefit of grant-free access and nonorthogonal transmissions to achieve low latency massive access. However, it suffers from the reduced reliability caused by random interference. Hence, we formulate a variational optimization problem to improve the reliability of grant-free NOMA. Due to the intractability of this problem, we resort to deep learning by parameterizing the intractable variational function with a specially designed deep neural network, which incorporates random user activation and symbol spreading. The network is trained according to a novel multiloss function where a confidence penalty based on the user activation probability is considered. The spreading signatures are automatically generated while training, which matches the highly automatic applications in tactile IoT. The significant reliability gain of our scheme is validated by simulations.

Deep Learning Aided Grant-Free NOMA Toward Reliable Low-Latency Access in Tactile Internet of Things

Grant-free transmission is an important feature to be supported by future wireless networks since it reduces the signaling overhead caused by conventional grant-based schemes. However, for grant-free transmission, the number of users admitted to the same channel is not capped, which can lead to a failure of multi-user detection. This paper proposes non-orthogonal multiple-access (NOMA) assisted semi-grant-free (SGF) transmission, which is a compromise between grant-free and grant-based schemes. In particular, instead of reserving channels either for grant-based users or grant-free users, the focus here is on an SGF communication scenario, where users are admitted to the same channel via a combination of grant-based and grant-free protocols. As a result, a channel reserved by a grant-based user can be shared by grant-free users, which improves both connectivity and spectral efficiency. Two NOMA assisted SGF contention control mechanisms are developed to ensure that, with a small amount of signaling overhead, the number of admitted grant-free users is carefully controlled and the interference from the grant-free users to the grant-based users is effectively suppressed. Analytical results are provided to demonstrate that the two proposed SGF mechanisms employing different successive interference cancelation decoding orders are applicable to different practical network scenarios.

https://doi.org/10.1109/tcomm.2019.2903443

Simple Semi-Grant-Free Transmission Strategies Assisted by Non-Orthogonal Multiple Access

The tactile internet (TI) is believed to be the prospective advancement of the internet of things (IoT), comprising human-to-machine and machine-to-machine communication. TI focuses on enabling real-time interactive techniques with a portfolio of engineering, social, and commercial use cases. For this purpose, the prospective $5^{th}$ generation (5G) technology focuses on achieving ultra-reliable low latency communication (URLLC) services. TI applications require an extraordinary degree of reliability and latency. The $3^{rd}$ generation partnership project (3GPP) defines that URLLC is expected to provide 99.99% reliability of a single transmission of 32 bytes packet with a latency of less than one millisecond. 3GPP proposes to include an adjustable orthogonal frequency division multiplexing (OFDM) technique, called 5G new radio (5G NR), as a new radio access technology (RAT). Whereas, with the emergence of a novel physical layer RAT, the need for the design for prospective next-generation technologies arises, especially with the focus of network intelligence. In such situations, machine learning (ML) techniques are expected to be essential to assist in designing intelligent network resource allocation protocols for 5G NR URLLC requirements. Therefore, in this survey, we present a possibility to use the federated reinforcement learning (FRL) technique, which is one of the ML techniques, for 5G NR URLLC requirements and summarizes the corresponding achievements for URLLC. We provide a comprehensive discussion of MAC layer channel access mechanisms that enable URLLC in 5G NR for TI. Besides, we identify seven very critical future use cases of FRL as potential enablers for URLLC in 5G NR.

/pdf/urllc-for-5g-and-beyond-requirements-enabling-incumbent-51nw7e7xtk.pdf

URLLC for 5G and Beyond: Requirements, Enabling Incumbent Technologies and Network Intelligence

The next-generation cellular network is to provide a large variety of services for different kinds of terminals, from traditional voice and data services over mobile phones to small packet transmission over massive machine-type terminals. Although orthogonal-subcarrier-based waveforms are widely used nowadays in many practical systems, they can hardly meet the requirements in the coming 5G networks. Therefore, more flexible waveforms have been proposed to address the unprecedented challenges. In this paper, we will provide comprehensive analysis and comparison for typical waveforms. To offer an insightful analysis, we will not only introduce the basic principles of the waveforms, but also reveal the underlying characteristics. Moreover, a comprehensive comparison in terms of different performance metrics will also be presented in thispaper, which provides an overall understanding of the new waveforms.

Waveform Design for 5G Networks: Analysis and Comparison

Future IoT applications provoke the connectivity and reliability requirements of machine-type communications, which yield mMTC and URLLC, respectively. In mMTC, grant-free NOMA is adopted to serve massive machine-type devices with low signaling overhead. The challenge of grantfree NOMA for mMTC is the collision induced by non-orthogonal resource allocation. Various grantfree NOMA techniques have been proposed to address this challenge, aiming at high connectivity. In this article, we introduce a novel grant-free NOMA approach, namely coded tandem spreading multiple access (CTSMA). The basic idea is that the data packet of each user is divided into segments, and redundancy segments are generated by coding through the data segments, and multiple orthogonal spreading codes are utilized to tandemly spread the data symbols of different segments from one user. Thereby, collision only occurs on the segments using identical spreading codes, which can be resolved by the redundancy segments to achieve reliable access. In addition to the transmitter and receiver designs, a closedform expression of the collision resolution capability is derived in this article to reveal the trade-off among the connectivity, the reliability, and the user data rate when using CTSMA. Theoretical analysis shows that CTSMA achieves both high connectivity and ultra-reliability at the expense of user data rate.

Coded Tandem Spreading Multiple Access for Massive Machine-Type Communications

Reliability-Based-Layered Belief Propagation (RBL-BP) decoding algorithm for LDPC codes is proposed in this paper. Being an informed dynamic schedule (IDS), it measures convergence and selects iterations by the absolute value of logarithmic likelihood ratio information obtained from channel (ABS-LLR-CH). Each variable node can select its own iterations by estimating its own convergence, and finish iterative decoding. Simulation results show that the proposed RBL-BP algorithm has better performance than those of flooding schedule, layered belief propagation (LBP) and variable-to-check residual belief propagation (VC-RBP). For the convergence property of IDS, the proposed algorithm has faster speed than those of flooding schedule and LBP with a little increase in complexity while it has almost no speed decrease with much lower computational complexity than VC-RBP.

Reliability-Based-Layered Belief Propagation for Iterative Decoding of LDPC Codes

In this paper, a new Belief Propagation (BP) decoding algorithm named Piggybacking Belief Propagation (PBP) decoding algorithm is proposed for rateless codes based on repeat-accumulate (RA) structure over AWGN channel. The "piggybacking" characteristic of the proposed algorithm is transmitting the Log-Likelihood Ratio (LLR) results calculated from the previous decode attempt to the new decoding process after receiving more information from the channel. Moreover, the proposed algorithm introduces a new stopping criterion to stop the decoding attempt when it is almost impossible to succeed. The stopping criterion is based on the weight change ratio of syndrome. The simulation results show that, compared with the traditional BP decoding algorithm, the proposed algorithm has lower decoding overhead and the a reduced decoding delay.

Piggybacking Belief Propagation Decoding for Rateless Codes Based on RA Structure

Non-orthogonal multiple access (NOMA) is a promising candidate for future 5G multiple access. Particularly NOMA uses power domain for multiple access and successive interference canceler (SIC) algorithm for multiuser detection (MUD). Different from the current downlink transmission scheme for NOMA, a novel joint coding scheme is proposed in this paper and corresponding improved SIC algorithms: zigzag SIC and iterative SIC are designed at the receiver side. Both zigzag SIC and iterative SIC algorithms are based on the novel offset frame structure. The goal is to take advantage of time domain diversity gain and certain long code gain of joint coding and advanced receiver to improve system performance without introduce additional degree of freedom. Practical considerations of NOMA, such as power allocation ratio, SIC, error propagation and channel fading are discussed. Simulation results show the proposed iterative SIC receiver and zigzag SIC receiver can both provide a better performance than conventional SIC receiver, so that the proposed joint coding scheme has obvious advantage over conventional NOMA.

A novel joint coding scheme for downlink non-orthogonal multiple access (NOMA)

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