Successive-cancellation (SC) decoding algorithm is the most commonly used method for decoding the polar codes. It can be viewed as a depth-first binary tree search, traversing from root to all the leaf nodes in the direction of left to right, where each leaf node corresponds to a codeword bit to be decoded. It decodes the bits sequentially one after the other, increasing the decoding latency with the increase in codeword length. In this paper, we propose an adaptive fast SC decoder that uses a link abstraction metric called received bit information rate (RBIR), in identifying the depth level, at which all the intermediate nodes and the attached information bits can be decoded directly without traversing till the leaf node by using the recursive structure of the SC decoder. We show that for a target block error rate (BLER) of 10%, the proposed RBIR-based adaptive fast SC (RBIR-AFSC) decoder has around 48% ~ 54% reduction in decoding latency compared to that of conventional SC decoder, without any loss in BLER performance, for 3GPP defined 5G NR TDL-A channel model.

With sustainability as one of the key requirements and the design principles of the sixth generation (6 G) communications, implementation based power saving solutions that benefit both base station (BS) and user equipment (UE) sides have gained significant interest in the cellular industry research. Channel decoding is one of the receiver modules that can help reduce the power consumption of the receiver significantly, and is applicable to both BS and UE. In this paper, we propose a universal list size and iteration number predictor (ULIP), for reducing the power consumption, applicable for 4G, 5G and beyond-5G channel decoders. We propose to use a link abstraction abstraction metric called received bit information rate (RBIR), that captures the time-varying channel conditions to identify and choose the iteration and list sizes for these decoders, to reduce the power consumption. We evaluate the proposed ULIP for regulating the iteration number in turbo and low-density parity-check (LDPC) decoders, used in 4 G and 5 G data channels, respectively, and the list size in polar decoder, used in 5 G control channel. We also verify the proposed ULIP for regulating the list size in the recently introduced polarization-adjusted convolutional (PAC) decoder, a prospective scheme for 6 G. We show that the proposed solution has upto 54.7 % and 67.4 % reduction of iteration numbers in LDPC and turbo decoders, respectively, for a target block error rate (BLER) of 10 %, and upto$\sim 92 \%$reduction of list sizes in both polar and PAC decoders, for a target BLER of 0.1 %, compared to conventional decoding methods.

Decoding polar codes through the depth-first binary tree-search based successive-cancellation (SC) algorithm takes a lot of time. Multiple fast simplified successive-cancellation (FSSC) decoding algorithms have been proposed in the literature to reduce the decoding latency and thereby the power consumption, by finding special nodes in the decoding tree, at which instantaneous decoding can be done. We observed that the latency can also be reduced by applying instantaneous decoding at all the nodes of an identified depth level in the decoding tree, as early as the root level at higher signal to noise ratio (SNR) conditions, without the need of traversing till the leaf node. Received bit information rate (RBIR) is a link abstraction metric that can be used to capture the SNR and identify the depth-level for instantaneous decoding. Existing FSSC algorithms rely only on the special nodes, and doesn't opportunistically utilize the time-varying and frequency-selective channel conditions for early depth level decoding. Whereas, relying on only RBIR-based opportunistic early depth level SC decoding (RBIR-OSC) will miss out on the special nodes of FSSC, that can provide significant power saving even in lower SNR conditions. In this paper, we propose an RBIR-based opportunistic FSSC decoding (RBIR-OFSSC) for polar codes, which combines the instantaneous decoding at special nodes of FSSC with the RBIR-OSC. We show that the proposed RBIR-OFSSC decoding reduces latency by 98% compared to conventional SC algorithm, 81% compared to FSSC, and 79% compared to RBIR-OSC, without any performance loss, for the 3GPP defined 5G NR uplink control channel.

Cyclic redundancy check (CRC)-aided successive cancellation list (SCL) decoder, also termed as CA-SCL decoder, is used for decoding the polar codes in practical 5G new radio (NR) systems. The list size used in CA-SCL decoder is directly proportional to the power consumption and the complexity of the decoding process. In practice, a fixed list size (L) is used for CA-SCL decoding. However, we have observed that the required list size varies with signal to noise ratio (SNR) for achieving a target block error rate (BLER) performance. In this paper, we propose an adaptive CA-SCL (CA-ASCL) decoding algorithm, that reduces the average list size and hence the complexity of the CA-SCL decoder by identifying the right list size to be used for each decoding process, using a link abstraction metric called received bit information rate (RBIR), that maps to received SNR. We show that for a target BLER of 10%, the proposed CA-ASCL decoder that chooses the list size adaptively among the set {2, 4, 8, 16, 32} has upto 61.51% reduction in list size compared to that of CA-SCL decoder with L = 32, without any loss in BLER performance, and upto 72.23% reduction with performance loss limited to 0.05 dB in SNR, respectively, for 3GPP defined 5G NR TDL-A channel model, with delay spread of 30 ns and maximum Doppler shift of 100 Hz.

Even though the 5G new radio (NR) system at mmWave frequencies provides large bandwidth and user data rates, it suffers severely from blockages and absorption losses, making its coverage limited to few meters. A wide range of new technologies and schemes are being investigated to overcome this coverage issue at high frequencies. Reconfigurable intelligent surfaces (RISs) is one among them. An RIS consists of a panel that reflects the incident signal towards the receiver, adding an extra signal path, thereby, aiding in extending the cell coverage. However, the placement and deployment of an RIS in the cellular system plays an important role in order to take the advantage of reflection. In this paper, we propose and analyse a practical method of RIS placement in an urban cellular network, that benefits all the users whose direct link from base station (BS) is blocked. We further propose a method in identifying the range of distance at which an RIS can be placed from a blocked region. We show that the RIS placement with the proposed method has around 8.2 dB gain in signal to interference and noise ratio (SINR), compared to randomly identified positioning methods, near to the network elements. We further show that the user coverage percentage improves significantly with the proposed method. We also discuss about a method through which blockage regions can be extracted from a real time system so that the suitable locations for RIS deployment can be chosen in 5G NR system.

With the exponential growth of cellular traffic in recent times, larger bandwidth and higher spectral efficiency (SE) have become indispensable for higher data rate applications. Discrete Fourier transform spreading orthogonal frequency division multiplexing (DFT-s-OFDM) waveform is being considered as one of the potential candidates for the next generation communications, because of its low peak to average power ratio (PAPR), which is more suitable for large bandwidth systems that are limited by inefficient power amplifiers. However, DFT-s-OFDM suffers from low SE, thus affecting the system throughput. Recently, faster than Nyquist (FTN) signaling combined with DFT-s-OFDM is being studied for its improved spectral efficiency with less PAPR. In this paper, we analyze the performance benefits of applying FTN signaling on DFT-s-OFDM waveform with 5G new radio uplink system. We show that the FTN combined with DFT-s-OFDM has 2% ∼ 75% more throughput than that of of DFT-s-OFDM for the same signal to noise ratio (SNR), using fading channel model. We further show through link adaptation method that the SE of DFT-s-OFDM waveform improves significantly when combined with FTN signaling. We also discuss about the implementation aspects and the performance trade-off with FTN integration into beyond 5G systems.

Terahertz (THz) band (0.1 - 10 THz) communications is being considered as a promising technology for the sixth generation (6G), as it can provide the peak data rates in the order of Tbps, with large bandwidths available at these bands. However, due to high path loss and molecular absorption noise experienced in this band, the cell coverage will be limited to shorter distances. In this paper, we propose hierarchical transmit beam sequencing method for synchronization signals, that enables beam combining at the receiver for an efficient cell search in THz network, and show its benefits for improved cell coverage. We show that the proposed algorithm has upto 4.8 dB gain in signal to noise ratio (SNR) for 90% cell detection probability, and reduces latency by 35.54%, compared to conventional algorithm with no beam combining at the receiver. Further, it is shown that the combining gain in cell detection performance improves the THz cell coverage by 237.76%.

Turbo and low-density parity-check (LDPC) codes have been chosen in wireless communications because of their near channel capacity performance. However, they consume huge power and also induce delay because of the iterative nature of the decoders. Various types of early termination (ET) techniques have been introduced within these decoders to decrease the power consumption and delay. Recently, machine learning (ML) algorithms are being explored to replace or improve the complex receiver algorithms in wireless communications, as a part of 6G research. In this paper, we propose a novel algorithm to use ML within the turbo and LDPC decoders, to identify the iteration for ET. We show that the proposed algorithm outperforms the improved hard decision aided ET method by 25% ~ 57%, in reducing the average number of iterations (ANI) of turbo decoder at 10% block error rate (BLER), for multiple modulation schemes. We also show that the proposed ET method outperforms parity check equation ET method of LDPC decoder by 30% ~ 36%, in reducing the ANI at 10% BLER, for multiple modulation schemes. The proposed method has negligible loss in BLER performance compared to typical implementation of fixed iteration decoders.

Next generation wireless communication systems are considering orthogonal time frequency space (OTFS) waveform as an alternative to the existing orthogonal frequency division multiplexing (OFDM) waveform. It is more robust to high Doppler and high carrier frequencies. Multiple recent papers have evaluated OTFS at higher Doppler with long term evolution (LTE) system, to show its performance benefits over OFDM. In this paper, we provide the performance evaluation of OTFS waveform at mmWave frequencies using the third generation partnership project (3GPP) 5G new radio (NR) transmit-receive chain for different quadrature amplitude modulation (QAM) schemes. We compare the OTFS performance with that of OFDM, for 3GPP defined tapped delay line (TDL) and cluster delay line (CDL) channel models at multiple mobility conditions. We analyze the performance difference between OTFS and OFDM using both minimum mean-squared error (MMSE) and decision feedback equalizers (DFE) at the receiver, and show the need for DFE at higher modulation order, to compensate the inter symbol interference. We show that the OTFS has 10 dB gain in signal to noise ratio (SNR) over OFDM, for TDL-C channel with delay spread of 300 ns, at high speed train mobility of 500 kmph for 64-QAM, when evaluated with DFE. We further present the computational complexity of OTFS system, in comparison to OFDM.

Ahstract-5G millimeter wave(mmWave) systems are prone to blockage from various objects in the typical propagation environment. Among these, human blockage and vehicular blockage are more prominent scenarios that can lead to link failure. In this paper, we analyze the effect of vehicular blockage on 5G mmWave systems using stochastic geometric models. Using this analysis, we derive the blockage probability of the link and provide a relation to latency in communication with vehicular blockage in various scenarios. Further, we show and quantify the fact that with dynamic point selection (DPS) scheme where user maintains simultaneous links with geographically separated transmit and receive points (TRP), the coverage probability can be improved significantly and the latency due to blockage can be reduced. We analyze the blockage probability and latency for DPS system in terms of blocker density, length and typical speed of the vehicle and number of simultaneous links maintained by the user. The proposed analytical model is validated using system level simulations. We show the trade off between the number of simultaneous links and latency using these results which helps in 5G system design for latency critical applications.

Ultra-reliable and low-latency communications (URLLC) is an emerging service supported by the 5G new radio (NR). The reliability requirement for one transmission of a packet is 99.999% for 32 bytes with a user plane latency of 1 ms for the applications supported by URLLC. To support these requirements, third generation partnership project (3GPP) has introduced enhanced grant free transmission scheme in the uplink (UL) with multiple active configured grants (CGs) for URLLC user equipments (UEs). With multiple active CGs for UL, UE can choose any of these grants as soon as the data arrives. In this work, we propose an algorithm to optimally select one of the grants configured by the network that meets both the latency and reliability requirements, using received bit information rate (RBIR) based link prediction. We show that the proposed algorithm performs significantly better compared to the traditional grant selection methods in terms of reliability and latency when considered as a joint optimization problem. At 0.001% block error rate (BLER), the proposed algorithm shows at least 74.7% reduction in latency compared to the other algorithms which provide almost same or lesser reliability. Similarly, the proposed algorithm performs 1 dB better in reliability in terms of signal to noise ratio (SNR) compared to the latency optimizing algorithm.

Narrow Band Internet of Things (NB-IoT) is the specification defined by 3GPP for cellular IoT devices spanning a variety of IoT applications. Typical NB-IoT applications demand very long battery life. Improving battery life without impacting the application's communication needs is of great importance to extend the battery life in many applications. Coverage enhancement feature of NB-IoT requires eNB to choose a repetition from the finite set of values ranging from 1 to 2048. When the channel is favourable, user equipment (UE) may not need all the repetitions for decoding in downlink (DL). In this paper, we propose, an opportunistic early decode algorithm for downlink, using received bit information rate (RBIR) based link abstraction. This helps in reducing the total active time and hence the power consumption of device aiding the longer battery life. Performance gain of this algorithm is evaluated in various channel conditions of a fading environment and further validated the gains with a simplified analytical model of the considered system. Typical power savings observed are in the range of ∼45%.

Machine Type Communication (MTC) device for cellular communication is being defined by 3GPP based on LTE in Rel-12 and Rel-13. Low cost has been the major objective of the definition for Rel-12 based specification. In Rel-13, coverage enhancement of MTC devices has been the major focus along with reduced power consumption, complexity and narrow bandwidth operation. Coverage enhancement (CE) of ∼15dB is expected to be achieved by using multiple power aggregation techniques, e.g. boosting Power Spectral Density (PSD), repetitions and relaxed requirements in few other cases. In CE scenarios, many of the receiver algorithms designed for normal coverage (NC) do not perform well. Requirement to operate at very low SINR changes the problem setup for few receiver algorithms. In this paper we introduce multiple UE receiver algorithms spanning various receiver functions, for coverage enhancement with reduced complexity. Specifically, practical cell search algorithm with complete procedure with joint time and frequency uncertainty hypothesis, novel timing estimation algorithm using PBCH, improved Channel Quality Index (CQI) estimation and novel method for opportunistic early decoding criterion based on mutual information metric. Algorithms proposed in this paper are first of its kind addressing coverage enhancement requirement for 3GPP LTE Rel-13 MTC devices. We show the performance of different algorithms proposed at very low SNRs as required for CE case.