In this paper, we propose a spectral-efficient LoRa (SE-LoRa) modulation scheme with a low complexity successive interference cancellation (SIC)-based detector. The proposed communication scheme significantly improves the spectral efficiency of LoRa modulation, while achieving an acceptable error performance compared to conventional LoRa modulation, especially in higher spreading factor (SF) settings. We derive the joint maximum likelihood (ML) detection rule for the SE-LoRa transmission scheme that turns out to be of high computational complexity. To overcome this issue, and by exploiting the frequency-domain characteristics of the dechirped SE-LoRa signal, we propose a low complexity SIC-based detector with a computation complexity at the order of conventional LoRa detection. By computer simulations, we show that the proposed SE-LoRa with low complexity SIC-based detector can improve the spectral efficiency of LoRa modulation up to 445.45%, 1011.11%, and 1071.88% for SF values of 7, 9, and 11, respectively, while maintaining the error performance within less than 3 dB of conventional LoRa at symbol error rate (SER) of 10−3in Rician channel conditions.

In this article, we propose a multiple-input-multiple-output (MIMO) configuration for chirp spread spectrum (CSS) modulation integrated with the permutation matrix modulation (PMM) scheme, namely, MIMO-CSS-PMM. The proposed MIMO-CSS-PMM simultaneously improves the spectral efficiency (SE) and error performance of the CSS-based transmission scheme. For the detection, we formulate the optimum maximum-likelihood (ML) detector that turns out to be of high computational complexity. We propose two low complexity semi-coherent detection schemes, i.e., scheme I and scheme II, in which we average over the fast Fourier transform (FFT) output of the dechirped signal of each receiver antenna. Then, in scheme I, we reduce the ML search set by selecting the number of largest averaged signal values corresponding to the number of transmitter antennas. To further reduce the complexity of scheme I, in scheme II, we define and derive a probability of detection using concepts from order statistics and find a threshold value maximizing this probability of detection. We select the number of the largest averaged signal greater than or equal to the obtained threshold to eliminate the unnecessary cases from the search set of scheme I. With the help of computer simulations, we evaluate the proposed detectors in terms of bit error rates (BERs).

In this paper, we investigate the coexistence between two of the most common long-range wide area network (LoRaWAN) transmission schemes, i.e., LoRa chirp and long-range frequency hopping spread spectrum (LR-FHSS). We consider a system model in which LoRa chirp and LR-FHSS modulations are deployed in two overlapping terrestrial and direct-to-satellite internet of things (DtS-IoT) networks, respectively. We derive the maximum likelihood (ML) detection rule for the joint detection of the LoRa chirp and the interfering portion of LR-FHSS fragments at the terrestrial LoRa gateway (GW). Due to the high complexity of the joint ML detector, and since our focus is primarily on the detection of the LoRa chirp at the LoRa GW, we propose a filtering-based detector for the LoRa chirp which benefits from the narrowband frequency characteristics of the LR-FHSS signal as the interference. Simulation results show that the error performance of the proposed filtering-based detection for the LoRa chirp degrades by 2 dB, 1 dB, and less than 0.5 dB for SF = 7, SF = 9, and SF = 11, respectively, at bit error rate (BER) of 10−4in additive white Gaussian noise (AWGN), compared to the case when the LR-FHSS interference does not exist. Moreover, for Rayleigh fading channel conditions, the proposed filtering-based detection scheme can achieve an acceptable performance in high values of signal-to-interference ratio (SIR).

Long-range frequency-hopping spread spectrum (LR-FHSS) is a new transmission protocol introduced under the long-range wide area network (LoRaWAN) specifications to tackle the issue of extremely long-range and large-scale internet of things (IoT) deployment scenarios. Unlike the other LoRaWanscheme, i.e., the one based on the chirp spread spectrum (CSS) modulation, the physical layer of LR-FHSS exploits a 488 Hz Gaussian minimum shift keying (GMSK) modulation. In this paper, we investigate and model the FHSS-GMSK modulation and evaluate its bit error rate (BER) performance in the LR-FHSS system using simulations. We also propose a low-complexity GMSK signal detection scheme that can be used at the gateway (GW) of an IoT network with a massive number of IoT end devices (EDs). Using computer simulations, we show that our proposed detector can offer a tradeoff between the complexity of the receiver and the bit error rate (BER) performance.

In this article, we present a device-to-device (D2D) transmission scheme for aiding long-range frequency-hopping spread spectrum (LR-FHSS) LoRaWAN protocol with application in direct-to-satellite Internet of Things (IoT) networks. We consider a practical ground-to-satellite fading model, i.e., shadowed-Rice channel, and derive the outage performance of the LR-FHSS network. With the help of network coding, a D2D-aided LR-FHSS transmission scheme is proposed to improve the network capacity for which a closed-form outage probability expression is also derived. The obtained analytical expressions for both LR-FHSS and D2D-aided LR-FHSS outage probabilities are validated by computer simulations for different parts of the analysis capturing the effects of noise, fading, unslotted ALOHA-based time scheduling, the receiver’s capture effect, IoT device distributions, and distance from node to satellite. The total outage probability for the D2D-aided LR-FHSS shows a considerable increase of 249.9% and 150.1% in network capacity at a typical outage of 10−2 for the data rates of DR6 (325 bps) and DR5 (162 bps), respectively, when compared to LR-FHSS. This is obtained at the cost of a minimum of one and a maximum of two additional transmissions per IoT end device imposed by the D2D scheme in each time slot.

This paper proposes a novel design of multi-symbol unitary constellation for non-coherent single-input multiple-output (SIMO) communications over block Rayleigh fading channels. To facilitate the design and the detection of large unitary constellations at reduced complexity, the proposed constellations are constructed as the Cartesian product of independent amplitude and phase-shift-keying (PSK) vectors, and hence, can be iteratively detected. The amplitude vector is detected by exhaustive search, whose complexity is sufficiently low in short packet transmission scenarios. To detect the PSK vector, we use the posterior probability as a reliability criterion in the sorted decision-feedback differential detection (sort-DFDD), which results in near-optimal error performance for PSK symbols with equal modulation orders. This detector is called posteriori-based-reliability-sort-DFDD (PR-sort-DFDD) and has polynomial complexity. We also propose an improved detector called improved-PR-sort-DFDD to detect a more generalized PSK structure, i.e., PSK symbols with unequal modulation orders. This detector also approaches the optimal error performance with polynomial complexity. Simulation results show the merits of our proposed multi-symbol unitary constellation when compared to competing low-complexity unitary constellations.

This paper proposes a non-coherent communication scheme for short packet communications (SPCs), called spectrally-efficient modulation on conjugate reciprocal zeros (SE-MOCZ). The proposed SE-MOCZ scheme is realized by combining the recently-proposed MOCZ and faster-than-Nyquist (FTN) signaling. Specifically, the pulses carrying the polynomial coefficients of MOCZ are accelerated beyond the Nyquist limit to enhance the spectral efficiency. The spectral efficiency enhancement, however, comes at the expense of inter-polynomial-coefficient interference (IPCI), which increases the number of received complex zeros in the$z$-domain of the polynomial representing the received signal. We design a partial-complex-zeros-removal filter at the receiver to partially remove the pre-defined zeros due to the FTN signaling. Furthermore, we also optimize the radius of the transmit complex zeros in SE-MOCZ to improve the bit error rate (BER) performance. We propose a maximum likelihood (ML) detector for the proposed SE-MOCZ scheme. To strike a balance between the BER performance and computational complexity, we adopt a root-finding minimum distance (RFMD)-based detector. Simulation results clearly show the performance advantage of the proposed SE-MOCZ when compared to MOCZ for a wide range of operating parameters.

In this paper, we propose a novel multi-symbol unitary constellation structure for non-coherent single-input multiple-output (SIMO) communications over block Rayleigh fading channels. To facilitate the design of large unitary constellations at reduced complexity, the proposed constellations are constructed as the Cartesian product of independent amplitude and phase-shift-keying (PSK) vectors. We exploit this structure to formulate an optimization problem to maximize the minimum distance, which has low complexity compared to optimal unitary constellations. Simulation results show that our proposed multi-symbol unitary constellation has higher minimum distance and better error performance than other low-complexity unitary constellations when using maximum likelihood (ML) detector.

Maintaining freshness of data collection in Internet-of-Things (IoT) networks has attracted increasing attention. By taking into account age-of-information (AoI), we investigate the trajectory planning problem of an unmanned aerial vehicle (UAV) that is used to aid a cluster-based IoT network. An optimization problem is formulated to minimize the total AoI of the collected data by the UAV from the ground IoT network. Since the total AoI of the IoT network depends on the flight time of the UAV and the data collection time at hovering points, we jointly optimize the selection of hovering points and the visiting order to these points. We exploit the state-of-the-art transformer and the weighted A*, which is a path search algorithm, to design a machine learning algorithm to solve the formulated problem. The whole UAV-IoT system is fed into the encoder network of the proposed algorithm, and the algorithm’s decoder network outputs the visiting order to ground clusters. Then, the weighted A* is used to find the hovering point for each cluster in the ground IoT network. Simulation results show that the trained model by the proposed algorithm has a good generalization ability to generate solutions for IoT networks with different numbers of ground clusters, without the need to retrain the model. Furthermore, results show that our proposed algorithm can find better UAV trajectories with the minimum total AoI when compared to other algorithms.

Employing unmanned aerial vehicles (UAVs) has attracted growing interests and emerged as the state-of-the-art technology for data collection in Internet of Things (IoT) networks. In this article, with the objective of minimizing the total energy consumption of the UAV-IoT system, we formulate the problem of jointly designing the UAV’s trajectory and selecting cluster heads in the IoT network as a constrained combinatorial optimization problem, which is classified as NP-hard, and challenging to solve. We propose a novel deep reinforcement learning (DRL) with a sequential model strategy that can effectively learn the policy represented by a sequence-to-sequence neural network for the UAV’s trajectory design in an unsupervised manner. Through extensive simulations, the obtained results show that the proposed DRL method can find the UAV’s trajectory that requires much less energy consumption when compared to other baseline algorithms and achieves close-to-optimal performance. In addition, simulation results show that the trained model by our proposed DRL algorithm has an excellent generalization ability to larger problem sizes without the need to retrain the model.

Energy load balancing is an essential issue in designing wireless sensor networks (WSNs). Clustering techniques are utilized as energy-efficient methods to balance the network energy and prolong its lifetime. In this article, we propose an improved soft-k-means (IS-k-means) clustering algorithm to balance the energy consumption of nodes in WSNs. First, we use the idea of clustering by fast search and find of density peaks (CFSFDPs) and kernel density estimation (KDE) to improve the selection of the initial cluster centers of the soft k-means clustering algorithm. Then, we utilize the flexibility of the soft-k-means and reassign member nodes considering their membership probabilities at the boundary of clusters to balance the number of nodes per cluster. Furthermore, the concept of multicluster heads is employed to balance the energy consumption within clusters. Extensive simulation results under different network scenarios demonstrate that for small-scale WSNs with single-hop transmission, the proposed algorithm can postpone the first node death, the half of nodes death, and the last node death on average when compared to various clustering algorithms from the literature.

LoRa is an abbreviation for low power and long range and it refers to a communication technology developed for low-power wide-area networks (LPWANs). Based on the principle of chirp spread spectrum (CSS), LoRa technology is very attractive to provide low bit-rate wireless connections over an extended communication range and under very low power consumption. While the medium access control (MAC) layer of LoRa specifications is open for developers, the physical layer is not. In particular, LoRa modulation and demodulation techniques are patented by Semtech and have not been mathematically described in detail. This paper presents novel approaches to modulate and demodulate LoRa signals with very high implementation efficiency, great flexibility, and excellent performance. In particular, compared to the commercially available receiver made by Semtech, the proposed design is shown to yield a saving of transmitted power from 0.9 to 2.5 dB over the spreading factor (SF) range of 6-12. Moreover, this paper suggests a method to exploit the phase information of CSS signals to encode extra information bits, leading to throughput improvement over the conventional CSS system, for example, by 33%, 25%, 20%, and 17% for SFs of 6, 8, 10, and 12, respectively.