The next wave of wireless technologies is proliferating in connecting things among themselves as well as to humans. In the era of the Internet of things (IoT), billions of sensors, machines, vehicles, drones, and robots will be connected, making the world around us smarter. The IoT will encompass devices that must wirelessly communicate a diverse set of data gathered from the environment for myriad new applications. The ultimate goal is to extract insights from this data and develop solutions that improve quality of life and generate new revenue. Providing large-scale, long-lasting, reliable, and near real-time connectivity is the major challenge in enabling a smart connected world. This paper provides a comprehensive survey on existing and emerging communication solutions for serving IoT applications in the context of cellular, wide-area, as well as non-terrestrial networks. Specifically, wireless technology enhancements for providing IoT access in fifth-generation (5G) and beyond cellular networks, and communication networks over the unlicensed spectrum are presented. Aligned with the main key performance indicators of 5G and beyond 5G networks, we investigate solutions and standards that enable energy efficiency, reliability, low latency, and scalability (connection density) of current and future IoT networks. The solutions include grant-free access and channel coding for short-packet communications, non-orthogonal multiple access, and on-device intelligence. Further, a vision of new paradigm shifts in communication networks in the 2030s is provided, and the integration of the associated new technologies like artificial intelligence, non-terrestrial networks, and new spectra is elaborated. Finally, future research directions toward beyond 5G IoT networks are pointed out.

Cellular, Wide-Area, and Non-Terrestrial IoT: A Survey on 5G Advances and the Road Towards 6G.

Deep unfolding is a method of growing popularity that fuses iterative optimization algorithms with tools from neural networks to efficiently solve a range of tasks in machine learning, signal and image processing, and communication systems. This survey summarizes the principle of deep unfolding and discusses its recent use for communication systems with focus on detection and precoding in multi-antenna (MIMO) wireless systems and belief propagation decoding of error-correcting codes. To showcase the efficacy and generality of deep unfolding, we describe a range of other tasks relevant to communication systems that can be solved using this emerging paradigm. We conclude the survey by outlining a list of open research problems and future research directions.

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Deep Unfolding for Communications Systems: A Survey and Some New Directions

Low-density parity-check (LDPC) codes have attracted much attention over the past two decades since they can asymptotically approach the Shannon capacity in a variety of data transmission and storage scenarios. As a type of promising structured LDPC codes, the protograph LDPC codes not only inherit the advantage of conventional LDPC codes, i.e., excellent error performance, but also possess simple representations to realize fast encoding and efficient decoding. This paper provides a comprehensive survey on the state-of-the-art in protograph LDPC code design and analysis for different channel conditions, including the additive white Gaussian noise (AWGN) channels, fading channels, partial response (PR) channels, and Poisson pulse-position modulation (PPM) channels. Moreover, the applications of protograph LDPC codes to joint source-and-channel coding (JSCC) and joint channel-and-physical-layer-network coding (JCPNC) are reviewed and studied. In particular, we focus our attention on the encoding design and assume the decoder is implemented by the belief propagation (BP) algorithm. Hopefully, this survey may facilitate research in this area.

/pdf/a-survey-on-protograph-ldpc-codes-and-their-applications-4ydhjcgkir.pdf

A Survey on Protograph LDPC Codes and Their Applications

A novel class of bit-flipping (BF) algorithm for decoding low-density parity-check (LDPC) codes is presented. The proposed algorithms, which are referred to as gradient descent bit flipping (GDBF) algorithms, can be regarded as simplified gradient descent algorithms. The proposed algorithms exhibit better decoding performance than known BF algorithms, such as the weighted BF algorithm or the modified weighted BF algorithm for several LDPC codes.

Gradient descent bit flipping algorithms for decoding LDPC codes

We consider a trainable point-to-point communication system, where both transmitter and receiver are implemented as neural networks (NNs), and demonstrate that training on the bit-wise mutual information (BMI) allows seamless integration with practical bit-metric decoding (BMD) receivers, as well as joint optimization of constellation shaping and labeling Moreover, we present a fully differentiable neural iterative demapping and decoding (IDD) structure which achieves significant gains on additive white Gaussian noise (AWGN) channels using a standard 80211n low-density parity-check (LDPC) code The strength of this approach is that it can be applied to arbitrary channels without any modifications Going one step further, we show that careful code design can lead to further performance improvements Lastly, we show the viability of the proposed system through implementation on software-defined radios (SDRs) and training of the end-to-end system on the actual wireless channel Experimental results reveal that the proposed method enables significant gains compared to conventional techniques

Trainable Communication Systems: Concepts and Prototype

An adaptive-normalized/offset min-sum (AN-/AO-MS) algorithm for decoding low-density parity-check (LDPC) codes is proposed. Unlike the normalized/offset min-sum (NMS/OMS) algorithm, the normalization/offset factor is adaptively adjusted according to the state of check nodes in each iteration. Simulation results show that the proposed AN-/AO-MS algorithm can perform better than the NMS/OMS algorithm while still preserving the low complexity of the min-sum algorithm.

Adaptive-Normalized/Offset Min-Sum Algorithm

In this paper, we propose a generalized minimum-sum decoding algorithm using a linear approximation (LAMS) for protograph-based low-density parity-check (PB-LDPC) codes with quasi-cyclic (QC) structures The linear approximation introduces some factors in each decoding iteration, which linearly adjust the check node updating and channel output These factors are optimized iteratively using machine learning, where the optimization can be efficiently solved by a small and shallow neural network with training data produced by the LAMS decoder The neural network is built according to the parity check matrix of a PB-LDPC code with a QC structure which can greatly reduce the size of the neural network Since, we optimize the factors once per decoding iteration, the optimization is not limited by the number of the iterations Then, we give the optimized results of the factors in the LAMS decoder and perform decoding simulations for PB-LDPC codes in fifth generation mobile networks (5G) In the simulations, the LAMS algorithm shows noticeable improvement over the normalized and the offset minimum-sum algorithms and even better performance than the belief propagation algorithm in some high signal-to-noise ratio regions

Decoding Optimization for 5G LDPC Codes by Machine Learning

An improvement is presented to the offset min-sum decoding algorithm for low-density parity check codes. The proposed algorithm introduces a more efficient adjustment for check-node update computation in view of different minimum values. According to the optimal correction factor of normalized min-sum algorithm, we can determine the adaptive offset item. The improved algorithm achieves a noticeable performance gain with only a modest increase in computation complexity

Adaptive offset min-sum algorithm for low-density parity check codes

In this letter, we propose a class of rate-compatible raptor-like protographs, which is aimed at the design of low-rate low-density parity-check (LDPC) codes with short length and fast convergence for ultra-reliable and low-latency communications in 5G. In the proposed protographs, the connection degrees can be progressively optimized by the protograph-based extrinsic information transfer analysis with a limited number of iterations. Following the protograph optimization, we construct the rate-compatible LDPC codes by a simplified error minimization progressive edge-growth algorithm and carry out simulations for performance comparisons. The simulation results show that our proposed LDPC codes have noticeable advantages over the existing low-rate LDPC codes with short length and limited numbers of decoding iterations.

Low-Rate PBRL-LDPC Codes for URLLC in 5G

A natural relationship between weighted bit-flipping (WBF) decoding and belief-propagation-like (BP-like) decoding is explored. This understanding can help us develop WBF algorithms from BP-like algorithms. For min-sum decoding, one can find that its WBF algorithm is the algorithm proposed by Jiang et al. For BP decoding, we propose a new WBF algorithm and show its performance advantage. The proposed WBF algorithms are parallelized to achieve rapid convergence. Two efficient simulation-based procedures are proposed for the optimization of the associated thresholds.

New insights into weighted bit-flipping decoding

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