In this work, chaotic phase transformed sequences are explored, where only the phases of modulated symbols are modified according to specific chaotic maps. Alongside conventional chaotic maps like Chebyshev and Bernoulli maps, Poincaré maps derived from the dynamics of a Duffing oscillator are proposed to generate chaotic phase transformed sequences. A detailed evaluation of chaotic properties and a mathematical analysis of the distribution of the magnitude of cross-correlation between sequences are also presented. Since the receiver determines the transmitted chaotic phase transformed sequence using hypothesis testing, it is essential to select a subset of sequences where the maximum cross-correlation between any pair of sequences is minimized. To selectKsuch sequences from a larger pool ofNsequences, Row-Column Elimination, Genetic and Greedy-Search algorithms are proposed, with Poincaré map based sequences providing the best results for largerK.Considering the narrowband Internet of things (NB-IoT) standard, it is shown that data decoding is successful only when the initial conditions of Poincaré map are estimated accurately to the 15thdecimal place. Alternatively, it is computationally infeasible for a malicious node/user to decode data even if the initial condition is estimated accurately to the 14thdecimal place, thereby making it highly secure.

Non-terrestrial networks (NTNs) for Internet of things (IoT) are one of the prominent scenarios envisioned in the fifth generation (5G) of wireless communication and beyond. The third generation partnership project (3GPP) has identified the narrowband IoT (NB-IoT) standard as a candidate technology for NTN-based IoT. In this work, an unmanned aerial vehicle (UAV)-based NTN using NB-IoT for communication is considered, where multiple UAVs act as relays between user equipment (UEs) and the terrestrial base station (BS). The communication procedure for the proposed system is designed by building upon the uplink and downlink communication procedures of a single UAV-aided system. To facilitate efficient data transmission between UAVs and the BS, scheduling mechanisms using orthogonal multiple access (OMA) and non-orthogonal multiple access (NOMA) are explored. For the NOMA scheduling mechanism, two grouping strategies are proposed for UAVs - static grouping and dynamic grouping, where the latter leverages the longest transmission time first (LTTF) algorithm for optimal grouping. Closed-form expressions for average transmission time are derived under both strategies, accounting for UE distribution modeled by discrete uniform, Poisson, and Beta distributions. Finally, we perform end-to-end physical layer simulations using 3GPP-compliant channel models and evaluate the latency and energy consumption of NB-IoT UEs. It is observed from simulations that NOMA, along with dynamic grouping, offers lower latency and reduced energy consumption when compared to OMA and static grouping, validating its suitability for multi-UAV-based NTN applications.

In this work, a novel deep learning (DL) based receiver is developed for narrowband random access channel (NPRACH) detection and uplink synchronization in narrowband Internet of things (NB - IoT) systems. Particularly, a configurable DL-based design is proposed in which the functionality and signal flow of the blocks constituting the network stay unchanged, and only their input/output dimensions are altered in accordance with the preamble format and the coverage area. It is shown that the proposed architecture provides a preamble detection probability 99% at the targeted signal-to-noise (SNR) regimes for both preamble format 0 (PRM-0) and preamble format 1 (PRM-1), along with a time-of-arrival estimation error 3.646-Ls, as required by the third generation partnership project (3GPP). It is also shown that the complexity of the proposed architecture is much lower than that of the conventional NPRACH receiver adopting the two dimensional fast Fourier transform (2D-FFT) algorithm, thereby making it feasible for practical implementation

This work presents an approach for using chaos sequences to assist uplink data transmission in narrowband Internet of things (NB-IoT). Along with exploring the widely known Bernoulli and Chebyshev maps, Poincaré map based chaos sequences are introduced, which are obtained by solving the Duffing oscillator equation. The novelty of this approach is in utilizing chaos sequences to solely modify the phase of the modulated symbols; without altering their amplitudes. Such a transformation preserves the circularly symmetric property of the noise components, which is essential for maintaining the statistical characteristics pertinent to data detection. To support the implementation of the proposed technique, a greedy algorithm for choosing an ideal set of$K$chaos sequences from a bigger pool of$N$sequences based on minimizing the maximum cross-correlation coefficient between any two selected chaos sequences is presented. It is found that Poincare map based sequences are the most efficient for$K\geq 128_{.}$Simulation results show that data decoding performance obtained by applying the proposed chaotic phase transformation technique is nearly the same as that of the conventional system. Since chaos sequences and/or their initial conditions can be programmed during installation, the proposed technique can potentially enhance the security of uplink data transmission.

The pressing need to support connectivity to an ever-increasing number of devices has motivated the development of new channel access schemes, which achieve throughput gains with low channel access latency. In this paper, we characterize the performance achieved by an uplink grant-free channel access scheme adopting a spreading-based NOMA scheme. Modeling the network as a stochastic geometry problem and assuming flat fading, we characterize the distribution of the received power from each interferer, the aggregate interference, and the signal-to-interference-plus-noise ratio (SINR) for a given transmitter when Zadoff-Chu (ZC) sequences are adopted to spread the data in orthogonal frequency division multiplexing (OFDM) symbols. The probabilities of false-alarm and detection are derived and compared with simulation results, validating the accuracy of the proposed model. Finally, we provide results of the distribution of the number of successfully decoded transmissions depending on the number of competing nodes, the detection threshold, the networks’ density, and ZC sequence lengths, showing essential insights that can be taken into consideration to design new medium access control (MAC) schemes capable of regulating the uplink channel access of multiple nodes adopting spreading-based NOMA schemes.

In the IEEE 802.11ax standard, a mode of operation called target wake time (TWT) is introduced towards enabling deterministic scheduling in WLAN networks. In the TWT mode, a group of stations (STAs) can negotiate with the access point (AP) a periodically repeating time window, referred to as TWT Service Period (TWT-SP), over which they are awake and outside which they sleep for saving power. The offset from a common starting time to the first TWT-SP is referred to as the TWT Offset (TWT-O) and the periodicity of TWT-SP is referred to as the TWT Wake Interval (TWT-WI). In this work, we consider communication between multiple STAs with heterogeneous traffic flows and an AP of an IEEE 802.11ax network operating in the TWT mode. Our objective is to maximize a long-term weighted average timely throughput across the STAs, where the instantaneous timely throughput is defined as the number of packets delivered successfully before their deadlines at a decision instant. To achieve this, we obtain algorithms, composed of (i) an inner resource allocation (RA) routine that allocates resource units (RUs) and transmit powers to STAs, and (ii) an outer grouping routine that assigns STAs to (TWT-SP, TWT-O,TWT-WI) triplets. For inner RA, we propose a near-optimal low-complexity algorithm using the drift-plus-penalty (DPP) framework and we adopt a greedy algorithm as outer grouping routine. Via numerical simulations, we observe that the proposed algorithm, composed of a DPP based RA and a greedy grouping routine, performs better than other competitive algorithms.

Grant-free (GF) access is considered in the uplink for efficiently supporting massive machine type communication (mMTC) scenarios in the Internet of things (IoT). Being a contention-based procedure, GF transmissions consisting of preamble followed by data are susceptible to collisions. This results in an increased number of failed users who retransmit and add to the load on the network. Given that the network is already dense, it is essential to reduce collisions and hence minimize the number of failed users. In this work, a system model for multi-user GF uplink transmission with repetitions is proposed, where the time-frequency resources are divided into transmission opportunity (TO) groups whose size is equal to the number of repetitions adopted by the users. Considering preamble transmissions at the link layer, the key performance indicators (KPIs) corresponding to the per-user and all-user success probabilities of the users in such a system are derived. Further, a method to determine the number of preambles based on the sum of the all-user success probability and the probability of a single user failing in the system is proposed, and closedform expressions for the same are derived. It is demonstrated that our proposed method to find the number of preambles ensures that the collisions (and hence the number of failed users) are reduced considering two scenarios based on the number of users arriving - a) fixed and b) random (following a Poisson distribution). The correctness of our analysis is verified through Monte-Carlo simulations in both the scenarios.

Unmanned Air Vehicles (UAVs) in wireless communications are mainly used to support existing terrestrial links when they are overloaded. UAV-based Non-Terrestrial Networks (NTNs) are being envisioned to meet the increased network demand incurred by the advent of Internet of Things (IoT). A primary wireless communication technology being considered for UAV-based NTNs is the Narrowband IoT (NB-IoT), which is inherently designed to provide improved coverage for a large number of low-throughput, low-power devices with low cost. In this work, communication procedures are proposed for a UAV-based NTN scenario employing the UAV as a relay between the NB-IoT User Equipments (UEs) and a terrestrial Base Station (BS). It is ensured NB-IoT UEs only interact with the UAV over Line of Sight (LoS) communication links and a simple energy based active user detection mechanism is proposed. Finally, it is demonstrated that the energy efficiency of the UEs in the proposed UAV-based NTN scheme is better than that of the conventional NB-IoT system where the UEs directly access the terrestrial BS.

The third-generation partnership project (3GPP) has proposed the narrowband Internet of Things (NB-IoT) standard for serving Internet of Things (IoT) users. Improvements to the NB-IoT standard are being considered to efficiently support massive machine-type communication (mMTC) IoT applications. Subsequent releases of the NB-IoT standard envision to support mMTC using different access mechanisms. In this work, a grant-free (GF) nonorthogonal multiple access (NOMA) system compatible with the current NB-IoT standard is proposed, where the resources are divided into transmission opportunities (TOs). The TOs are further divided into multiple groups based on the repetitions adopted by the users in the system. Physical (PHY) layer procedures for preamble transmission/detection, channel estimation, data transmission/decoding, and acknowledgment (ACK) transmission/reception are described in detail for the proposed GF-NOMA system. Specifically, the preamble sequences are also used as spreading sequences for data transmission and three types of sequences—Zadoff–Chu, random Gaussian, and Golay sequences are explored. Results are presented for per-user success probability, latency, and energy consumption considering two NB-IoT compatible settings along with repetitions and retransmissions. It is demonstrated that the proposed GF-NOMA scheme meets the mMTC requirement recommended by the 3GPP with more than 99% per-user success probability and with the energy consumption reduced by at least 80% compared to NB-IoT, while being readily compatible with the current NB-IoT standard.

Grant free non-orthogonal multiple access (GF-NOMA) is a promising access method for massive machine type communication (mMTC), which has several advantages when compared to the conventional grant based access method, such as, reduced latency, smaller scheduling and signalling overheads, and improved energy efficiency. Since there is no explicit grant given to each user in GF-NOMA, detecting all the active users present, i.e., active user detection (AUD) at the base station (BS) becomes crucial. Typically, AUD is performed using correlation with all possible preamble sequences transmitted by the GF-NOMA users. Recently, deep learning (DL) based models have emerged as a viable alternatives for AUD. However most of these works assume perfect timing and frequency synchronization of users, which hardly occurs in practice. In this work, a GF-NOMA scheme and a deep neural network (DNN) model for AUD are proposed. The proposed scheme is compatible with the narrowband Internet of things (NB-IoT) and the proposed DNN-based AUD mechanism accounts for the impact of timing and frequency offsets. It is demonstrated that the performance of proposed DNN based AUD scheme is comparable (or slightly better) than the conventional method while providing a significant reduction in the computational complexity.

Narrowband Internet of things (NB-IoT) is a promising cellular IoT standard from the third generation partnership project (3GPP), designed to provide extended coverage and connectivity to a large number of low-cost devices. An NB-IoT user equipment (UE) requiring network access first transmits a preamble on the NB-IoT physical random access channel (NPRACH). The NPRACH receiver at the NB-IoT base station (eNB) detects the preamble, estimates the time-of-arrival (ToA) and calculates the residual carrier frequency offset (RCFO) for each UE. Conventional NPRACH receivers are based on cross-correlation and energy-based detection. In this work, a novel deep learning based NPRACH receiver is developed. It is demonstrated that the proposed deep learning based receiver not only performs nearly as good as the traditional receiver, but is also computationally feasible and scalable.

Grant-free (GF) access is a multiple access mechanism being considered for supporting high user density in massive machine-type communication (mMTC). Since the GF schemes are predominantly contention based, a major challenge would be to handle user packet collisions. Additionally, users experiencing collision will retransmit their packets, leading to an increased load on an already dense network. Thus, the probability of successful transmission of an individual user and that of all users become the key performance indicators (KPIs) to analyze such systems. In this article, a GF uplink system is modeled for cases when the number of users is—1) fixed and 2) random (following a Poisson distribution). Analytical closed-form expressions for all-user and per-user success probabilities are derived in both the cases. Conventionally, GF access schemes have been analyzed using a fixed number of preambles. In this work, it is proposed to choose the number of preambles in the GF system such that the probability of success of at least “all but two” users is greater than or equal to 99%. It is shown that such a choice limits the maximum number of colliding users to be within four, thereby reducing the number of retransmissions. The analysis is extended to the case of transmission using multiple repetitions and numerical simulations are performed to validate the theoretical analysis. Finally, a tradeoff between the number of preambles, repetitions, and retransmissions is presented in terms of the per-user success probability, transmission latency, and energy consumption.

Massive machine-type communication (mMTC) is one of the most rewarding and at the same time challenging components in the fifth-generation (5G) cellular solutions supporting the Internet of Things (IoT). The 5G mMTC is considering the use of a combination of two key mMTC enabling technologies-grant-free (GF) transmission and nonorthogonal multiple-access (NOMA), called GF-NOMA, which can potentially exploit the advantages of both schemes. A primary challenge in GF-NOMA is to reduce the packet drop rate. Owing to the decentralized nature of the GF schemes and the lack of control over user equipment, only hybrid automatic repeat request (HARQ) Type I has been employed for enhancing the reliability of GF-NOMA so far. In this article, uplink GF-NOMA transmission schemes using HARQ Type III are proposed. Two types of packet combining-1) chase combining and 2) incremental redundancy combining are considered. Moreover, we introduce a GF single-transmission (GFST) scheme where all redundancy versions of the packet are transmitted in one shot. We present a comprehensive evaluation of both the GF and the conventional grant-based methods in mMTC scenarios and demonstrate the superiority of our proposed methods over the existing ones.

This paper considers the application of widely linear (WL) receivers in an uplink multi-user system using real-valued modulation schemes, where the cellular base station (BS) with multiple antennas provides connectivity for randomly deployed single-antenna users. The targeted use case is massive machine type communication (mMTC) with grant-free access in the uplink, where the network is required to host a large number of low data rate devices transmitting in an uncoordinated fashion. Four types of WL receivers are investigated, namely the WL zero-forcing (ZF) and the WL minimum mean-squared error (MMSE) receivers, along with their enhanced versions employing successive interference cancellation (SIC) with channel-dependent ordering, i.e., the WL-ZF-SIC and WL-MMSE-SIC receivers. The outage performances of these receivers are analytically characterized in the high signal-to-noise ratio (SNR) regime and compared to those of conventional linear (CL) receivers using complex-valued modulation schemes. For the non-SIC receivers, we show that, when compared to the CL counterparts, the WL receivers yield a higher diversity gain when decoding the same number of users and have the same diversity gain but a decreased coding gain when the number of users is nearly doubled. The outage performance analysis of WL-SIC receivers is facilitated by the marginal distribution of ordered eigenvalues of a real-valued Wishart matrix. It is shown that the SIC operation with channel-dependent ordering brings no additional diversity gain to the WL receivers but instead increases the coding gain. Moreover, the coding gain of WL-SIC receivers grows as the number of users increases and even exceeds that of CL-SIC receivers under suitable conditions. For the mMTC scenario with grant-free transmission, it is demonstrated that the WL receivers outperform their CL counterparts in terms of offering a lower outage (and packet drop) probability and a higher system throughput for a given packet drop probability.

In this work, we exploit the potential benefits of multi-arm bandit scheme in cooperative multiple-input multiple output (MIMO) wireless networks. In particular, we consider an online-policy for amplify-and-forward MIMO relay selection (RS), where relays are provided with uncertain channel state information (CSI). We design the RS policy as a sequential experience-driven learning algorithm with a contextual bandit (CB) approach, where the algorithm learns to select an optimal relay node using the imperfect CSI provided as a context vector and the past experience of rewards procured with current policy, with the aim of maximizing the cumulative mean reward over time. Further, with extensive simulation result, we demonstrate that proposed CB based RS policy achieves superior performance gains compared to conventional Gram-Schmidt method.

Future wireless cellular communication networks are expected to provide connectivity for massive machine-type communication (mMTC) devices. The main challenge of supporting mMTC traffic for a cellular network lies in the high density of these devices, which individually have relatively little data to transmit. This suggests the use of low overhead, grant-free access scheme for uplink data transmission, which, however, suffers from packet collisions when devices attempt to access the channel. In this article, we suggest the use of real-valued transmission together with widely linear (WL) reception for improving resource access and thus data throughput in the uplink of mMTC traffic scenarios. We show that not surprisingly, the WL scheme can virtually double the number of receive antennas at the base station (BS). We analyze the effect of this on the supported user density and data throughput for grant-free uplink transmission. As a specific example, we consider the narrowband Internet-of-Things (NB-IoT) cellular communication system, which already includes real-valued modulation modes. Our numerical results show that the supported user density and data throughput of grant-free NB-IoT systems can be significantly improved due to the use of WL receivers. For example, considering an NB-IoT system with 1% packet drop probability, we obtain a tenfold (for single-antenna BS) and a sixfold (for dual-antenna BS) increase in the supported user density by using WL receivers instead of their conventional linear counterparts.

Narrowband Internet of Things (NB-IoT) is one of the most prominent low power wide area (LPWA) technologies to support massive connections among low-cost devices with enhanced coverage. Addressing the further increase in the number of supported connections in IoT applications, we investigate the use of real-valued modulated signals together with widely linear detection (WLD) to NB-IoT. In this paper, we show that the use of WLD to resolve collisions of two simultaneously transmitted real- valued signals generated from two user equipments (UEs) enhances the capability of NB-IoT systems to facilitate massive connectivity. More specifically, to demonstrate the performance improvements, we analyze the uplink transmission reliability and the device density supported in NB-IoT systems with WLD in a grant-free scenario. The numerical results show that, for a 1% probability of decoding failure, the supported device density can be increased by one order of magnitude compared to that of conventional complex-valued signalling schemes.

Non-orthogonal multiple access (NOMA) is one of the key technologies being evaluated for the fifth generation (5G) wireless communications. In this paper, a novel NOMA mechanism combining both the code-domain and power-domain techniques is proposed to potentially support a higher number of users. In particular, considering the code-domain NOMA method of sparse code multiple access (SCMA) as the baseline, new low data rate (LDR) users are added on top of it using power-domain NOMA. The optimization problem involving resource and power allocation in such a system is solved such that the overall achievable sum-rate is maximized. Simulation results indicate that the proposed mechanism not only supports higher number of users, but also demonstrates a higher achievable sum-rate than the original SCMA-based system.

The fifth generation (5G) wireless communication technologies are analyzing non-orthogonal multiple access (NOMA) methods to facilitate the Internet of things (IoT) scenarios hosting a large number of users. In this paper, we consider a scenario where the network operator shares the uplink spectrum between the original long term evolution (LTE) users and new users using a code-domain NOMA technique called sparse code multiple access (SCMA). Since obtaining the optimal solution for resource allocation in such systems is exponentially complex, we propose heuristic algorithms for LTE and SCMA, with the objective of maximizing the overall achievable rate in the network. The simulation results indicate that the performance of the proposed algorithms is close to 90% of that of the optimal solution.

The fifth-generation (5G) wireless communication systems envision the deployment of massive multiple-input multiple-output (MIMO) technology for enhanced network coverage and capacity. With frequency division duplex (FDD) being the prominent mode of operation, addressing the challenges for massive MIMO systems in FDD is extremely important. One such challenge is the downlink channel estimation, which should be accomplished with minimal pilot overhead. To address this challenge, we propose a compressive sensing (CS) algorithm based on alternating direction method of multipliers (ADMM). We demonstrate how our proposed algorithm fully exploits the block sparsity inherent in the spatial correlations of MIMO channels to provide improved performance than the conventional CS based algorithms used for massive MIMO FDD systems.

In this work, we consider a joint grouping and scheduling algorithm for fair user scheduling in massive multiple-input multiple-output (MIMO) systems that support Internet of Things (IoT) application scenarios. Our proposed method serves the users consecutively in groups, where the group sizes are derived from the optimal number of active users in the zero-forcing downlink channel. To select the best group members we adopt a version of the popular semi-orthogonal user selection (SUS) algorithm. Specifically, we identify the drawbacks of the original SUS algorithm when operating in a massive MIMO system and present a modified SUS strategy (SUS-M) for user selection. Simulation results demonstrate that our proposed method outperforms the conventional SUS strategy and provides improved fairness in terms of the individual user rates.

Machine Type Communications (MTC) is one of the major drivers for the future growth of the Third Generation Partnership Project (3GPP) Long Term Evolution (LTE)/LTE-Advanced (LTE-A) standards. A primary challenge associated with MTC using LTE/LTE-A is to cater to varied requirements of the User Equipment (UE) like coverage enhancement, low complexity, low power consumption, etc. The current LTE/LTE-A standards incorporate the Discontinuous Reception (DRX) mechanism for reduced energy consumption where the UE wakes up periodically to check for a paging message from the base-station. On each wake-up occasion, the UE has to decode the paging information, which requires accurate timing resynchronization. In the case of the MTC UEs, especially when the devices are deployed in low coverage areas, the resynchronization consumes a significant amount of time which increases the ON time of the UE leading to higher energy consumption. In this paper, we introduce the Enhanced Primary Synchronization Signal (ePSS) within the LTE/LTE-A standardization framework and propose a novel DRX mechanism which uses our ePSS for faster resynchronization and reduced energy consumption. We demonstrate that our mechanisms are more energy efficient than the legacy DRX mechanism for the MTC UEs with coverage enhancement.

The Third Generation Partnership Project (3GPP) has recognized machine-type communications (MTCs) as a vital medium to drive the Internet of Things (IoT). One of the key challenges in MTC is to reduce the energy consumption of the MTC user equipment (UE). Currently, the 3GPP Long Term Evolution (LTE)/LTE-advanced (LTE-A) standards incorporate discontinuous reception (DRX) mechanism for this purpose. In this paper, we propose a modified DRX mechanism incorporating the quick sleeping indication (QSI) as a novel, simple, and energy-efficient solution for low-complexity, low-mobility MTC UEs. We demonstrate our QSI transmission mechanisms using the broadcast and synchronization channels of LTE/LTE-A for MTC UEs in normal coverage and using the data channel for MTC UEs operating in “coverage enhancement” (CE) mode. For MTC UEs in normal coverage, our simulation results and analysis show that our DRX with QSI mechanisms result in 45% improvement in the energy efficiency and 66% reduction in the computational complexity at the UE receiver, when compared to the current DRX mechanism. For MTC UEs with CE, the energy and computational efficiency increase to 63% and 68%, respectively.

This paper analyzes a scenario where a cognitive radio (CR) moves through a field of primary users (PU). As the CR moves, it goes through periods when a given channel is available and when the channel is unavailable (i.e. an outage) due to the CR avoiding a primary user. In addition, there are periods when the channel is deemed available but, in fact, CR communication could interfere with the primary user. The paper derives the distribution of these different periods under different detection and interference scenarios. The results provide insights into how useful a channel is to a CR and the impact of the CR on the PU. The model applies to a scenario of unmanned aircraft access to TV whitespace.