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.

An intelligent Real-Time Sensing (RTS) system must continuously acquire, update, integrate, and apply knowledge to adapt to real-world dynamics. Managing distributed intelligence in this context requires Federated Continual Learning (FCL). However, effectively capturing the diverse characteristics of RTS data in FCL systems poses significant challenges, including severely impacting computational and communication resources, escalating energy costs, and ultimately degrading overall system performance. To overcome these challenges, we investigate how the data distribution shift from ideal to practical RTS scenarios affects Artificial Intelligence (AI) model performance by leveraging thegeneralization gapconcept. In this way, we can analyze how sampling time in RTS correlates with the decline in AI performance, computation cost, and communication efficiency. Based on this observation, we develop a novel Sample-driven Control for Federated Continual Learning (SCFL) technique, specifically designed for mobile edge networks with RTS capabilities. In particular, SCFL is an optimization problem that harnesses the sampling process to concurrently minimize the generalization gap and improve overall accuracy while upholding the energy efficiency of the FCL framework. To solve the highly complex and time-varying optimization problem, we introduce a new soft actor-critic algorithm with explicit and implicit constraints (A2C-EI). Our empirical experiments reveal that we can achieve higher efficiency compared to other DRL baselines. Notably, SCFL can significantly reduce energy consumption up to 85% while maintaining FL convergence and timely data transmission.

This paper introduces a novel method to minimize the peak-to-average power ratio (PAPR) and at the same time enhance the data rates of orthogonal frequency-division multiplexing (OFDM) systems by combining tone reservation (TR) and index modulation (IM). Unlike conventional TR methods, in which a number of tones (or subcarriers) with fixed positions are reserved for canceling the peaks in OFDM signals, the TR-IM method treats the positions of the reserved tones (TR tones) as random and embeds extra information in their positions using IM. In the proposed system, the amplitudes of the TR tones are quantized with a novel quantization method, which not only helps the receiver distinguish between data tones and TR tones, but also enables the TR tones to carry data on their amplitudes. Based on that, we propose a novel forward error correction (FEC) structure to increase the reliability of detection without requiring extra overhead. The proposed FEC design encodes the IM activation pattern rather than the index data bits, and carries the resulting parity bits with a novel mechanism that exploits the extra bits carried on the amplitudes of the TR tones. Simulation results show that, not only does our proposed system have significantly higher data rates, but it can also achieve remarkable PAPR reduction performance as well as a lower bit error rate than the original OFDM systems under the influence of the non-linear distortion caused by power amplifiers.

This paper provides a comprehensive performance analysis of the reconfigurable intelligent surfaces (RIS)-assisted receive generalized space-shift keying (RGSSK) and the RIS-assisted receive generalized spatial modulation (RIS-RGSM) schemes. Specifically, we derive closed-form expressions for the pairwise error probabilities (PEPs) of the RIS-RGSSK and RIS-RGSM schemes when two antennas are activated at the receiver. Our analytical derivations eliminate the need for numerical approximations or complex integration methods. Finally, we verify our analytical results via simulations, demonstrating the accuracy of our expressions.

This paper investigates the detection of spectrally-efficient modulation on conjugate reciprocal zeros (SE-MOCZ) as a promising non-coherent modulation for short packet communications (SPCs). SE-MOCZ combines the recently proposed MOCZ and faster-than-Nyquist (FTN) signaling, by accelerating the pulses carrying the polynomial coefficients of MOCZ beyond the Nyquist limit, which introduces inter-polynomial-coefficient-interference (IPCI) and increases the number of the received complex zeros in the z-domain. We propose to use zero forcing (ZF) to assist the root-finding-minimum-distance (RFMD) detector of SE-MOCZ without the need to optimize the radius or to use the partial-complex-zeros-removal filter. In particular, given that the complex zeros due to the IPCI are fully known at the receiver, ZF can cancel such complex zeros, and hence, assist the RFMD detection. Simulation results show that the proposed ZF-RFMD detector outperforms the RFMD detector that adopts optimal radius and partial-complex-zeros-removal filter (up to 4 dB saving in Eb/N0at the same error rate). Additionally, the proposed ZF-RFMD detector helps SE-MOCZ to approach the performance of MOCZ for high values of the FTN signaling acceleration parameter (up to 8.9% SE gain with no additional increase in Eb/N0).

This paper proposes a convolutional neural network (CNN)-based detector for faster-than-Nyquist (FTN) signaling that employs structured fixed kernel layers with domain-informed masking to mitigate intersymbol interference (ISI). Unlike standard CNNs with sliding kernels, the proposed method utilizes fixed-position kernels to directly capture ISI effects at varying distances from the central symbol. A hierarchical filter allocation strategy is also introduced, assigning more filters to earlier layers for strong ISI patterns and fewer to later layers for weaker ones. This design improves detection accuracy while reducing redundant operations. Simulation results show that the detector achieves near-optimal bit error rate (BER) performance for τ ≥ 0.7, closely matching the BCJR algorithm, and offers computational gains of up to 46% and 84% over M-BCJR for BPSK and QPSK, respectively. Comparative analysis with other methods further highlights the efficiency and effectiveness of the proposed approach. To the best of our knowledge, this is the first application of a fixed-kernel CNN architecture tailored for FTN detection in the literature.

This paper introduces a successive channel estimation method for multiuser massive multiple-input-multiple-output (MIMO) long-range (LoRa) networks with asynchronous (on packet or symbol levels) transmission. The proposed channel estimation method exploits LoRa packets’ preambles as pilot sequences, which enables semi-coherent detection with maximum ratio combining (MRC). The channel state information (CSI) acquisition and data detection are performed in a successive manner in time-of-arrival (ToA) order, where the channel estimation of an end device (ED) is enabled by the detected data of previous (in the ToA order) EDs in the network. To achieve the best channel estimation quality for each ED, we formulate and solve an optimization problem to maximize the preamble power to noise power ratio (PNR). Simulation results show that with the proposed preamble-based CSI acquisition method, massive MIMO LoRa networks can utilize MRC to detect overlapped and asynchronous packets from multiple EDs, at the cost of a reasonable deterioration in error performance as compared to the single ED case.

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).

This paper presents a novel convolutional neural network (CNN)-based detector for faster-than-Nyquist (FTN) signaling, introducing structured fixed kernel layers with domain-informed masking to effectively mitigate intersymbol interference (ISI). Unlike standard CNN architectures that rely on moving kernels, the proposed approach employs fixed convolutional kernels at predefined positions to explicitly learn ISI patterns at varying distances from the central symbol. To enhance feature extraction, a hierarchical filter allocation strategy is employed, assigning more filters to earlier layers for stronger ISI components and fewer to later layers for weaker components. This structured design improves feature representation, eliminates redundant computations, and enhances detection accuracy while maintaining computational efficiency. Simulation results demonstrate that the proposed detector achieves near-optimal bit error rate (BER) performance, comparable to the BCJR algorithm for the compression factor τ ≥ 0.7, while offering up to 46% and 84% computational cost reduction over M-BCJR for BPSK and QPSK, respectively. Additional evaluations confirm the method’s adaptability to high-order modulations (up to 64-QAM), resilience in quasi-static multipath Rayleigh fading channels, and effectiveness under LDPC-coded FTN transmission, highlighting its robustness and practicality.

Spectrum scarcity necessitates innovative, spectral-efficient strategies to meet the ever-growing demand for high data rates. Faster-than-Nyquist (FTN) signaling emerges as a compelling spectral-efficient transmission method that pushes transmit data symbols beyond the Nyquist limit, offering en-hanced spectral efficiency (SE). While FTN signaling maintains SE with the same energy and bandwidth as the Nyquist signaling, it introduces increased complexity, particularly at higher modulation levels. This complexity predominantly arises from the detection process, which seeks to mitigate the intentional intersymbol interference generated by FTN signaling. Another challenge involves the generation of reliable log-likelihood ratios (LLRs) vital for soft channel decoders. In this study, we introduce a lookup table (LUT) aided soft output semidefinite relaxation (soSDR) based sub-optimal FTN detector, which can be extended to higher modulation levels. This detector possesses polyno-mial computational complexity, given the negligible complexity associated with soft value generation. Our study assesses the performance of this soft output detector against that of the optimal FTN detector, Bahl, Cocke, Jelinek and Raviv (BCJR) algorithm as the benchmark. The likelihood values produced by our LUT aided semidefinite relaxation (SDR) based FTN signaling detector show promising viability in coded scenario.

Faster-than-Nyquist (FTN) signaling is a promising approach to increase the spectral efficiency (SE) of next-generation wireless communication systems. In this paper, we investigate the detection of FTN signaling in the presence of in-phase and quadrature (IQ) imbalance in frequency-selective fading channels. We show that IQ imbalance at the transmitter and the receiver of FTN signaling results in an image of the transmit and the received signal, respectively, and this image represents an additional interference. We use concepts from widely linear processing to exploit the correlation between the received signal and its complex conjugate. In particular, we propose a widely-linear minimum mean square error (WL-MMSE) algorithm to estimate the transmit FTN signaling in the presence of IQ imbalance and frequency-selective channels. We additionally prove that the mean square error (MSE) of the proposed WL-MMSE is small than its counterpart of the linear-MMSE (L-MMSE). Simulation results verify our findings in terms of bit error rate (BER) and MSE performance.

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.

This article investigates a new model to improve the scalability of low-power long-range (LoRa) networks by allowing a group of multiple end devices (EDs) to communicate with multiple multi-antenna gateways simultaneously (i.e., in the same time slot) on the same frequency band and using the same spreading factor. The maximum-likelihood (ML) decision rule is first derived for noncoherent detection of information bits transmitted by multiple devices in a group. To overcome the high complexity of the ML detection, we propose a suboptimal two-stage detection algorithm to balance the computational complexity and error performance. In the first stage, we identify transmitted chirps (without knowing which EDs transmit them). In the second stage, we determine the EDs that transmit the specific chirps identified from the first stage. To improve the detection performance in the second stage, we also optimize the transmit powers of EDs to minimize the similarity, measured by the Jaccard coefficient, between the received powers of any pair of EDs in the same group. As the power control optimization problem is nonconvex, we use concepts from successive convex approximation to transform it to an approximate convex optimization problem that can be solved iteratively and guaranteed to reach a suboptimal solution. Simulation results demonstrate and justify the tradeoff between transmit power penalties and network scalability of the proposed LoRa network model. In particular, by grouping two or three EDs in each group for concurrent transmission, the uplink capacity of the proposed network can be doubled or tripled over that of a conventional LoRa network, albeit at the expense of additional 3.0 or 4.7 dB transmit power.

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.

Faster-than-Nyquist (FTN) signaling is a candidate non-orthonormal transmission technique to improve the spectral efficiency (SE) of future communication systems. However, such improvements of the SE are at the cost of additional computational complexity to remove the intentionally introduced intersymbol interference. In this paper, we investigate the use of deep learning (DL) to reduce the detection complexity of FTN signaling. To eliminate the need of having a noise whitening filter at the receiver, we first present an equivalent FTN signaling model based on using a set of orthonormal basis functions and identify its operation region. Second, we propose a DL-based list sphere decoding (DL-LSD) algorithm that selects and updates the initial radius of the original LSD to guarantee a pre-defined number NLof lattice points inside the hypersphere. This is achieved by training a neural network to output an approximate initial radius that includes NLlattice points. At the testing phase, if the hypersphere has more than NLlattice points, we keep the NLclosest points to the point corresponding to the received FTN signal; however, if the hypersphere has less than NLpoints, we increase the approximate initial radius by a value that depends on the standard deviation of the distribution of the output radii from the training phase. Then, the approximate value of the log-likelihood ratio (LLR) is calculated based on the obtained NLpoints. Simulation results show that the computational complexity of the proposed DL-LSD is lower than its counterpart of the original LSD by orders of magnitude.

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.

Proactive caching shows great potential to minimize peak download rates by caching popular data, in advance, at the edge. Fast-changing file features, such as fast-changing file popularities and fast-changing file contents (data freshness), represent a challenge for proactive caching if cache content update is much slower, which decreases the efficiency and usability of caching. We present a dynamic caching scheme that updates local user caches and optimizes the use of caching resources. The developed scheme index-code the updates with the delivery messages. The developed scheme is presented for a network with one cache-enabled server, that has a pool of files, communicating with$K$cache-enabled receivers with requests limited to the server’s file pool. The developed scheme assumes partial knowledge of features variation. Asynchronous file delivery is assumed as a result of non-flexible receivers’ request timing. We show that the file delivery messages can be used to proactively and constantly update the receivers’ finite caches by index-coding the update messages with delivery messages at no additional rate-cost. We also show that this mechanism reduces the downloaded traffic and can be used to reduce other QoS metrics.

The paper studies the downlink of non-orthogonal multiple access (NOMA) under imperfect successive interference cancellation (SIC). In an effort to overcome the SIC imperfection problem, we propose to adopt improper Gaussian signaling (IGS) for the signal transmission of the NOMA users. In particular, we jointly optimize the IGS circularity coefficient and transmit power to maximize the spectral efficiency subject to minimum Quality-of-Service (QoS) per each user and total power budget. Simulations results show the effectiveness of IGS to compensate for the SIC imperfections compared with proper Gaussian signaling (PGS)-based NOMA systems.

Ultra-reliable low-latency communication (URLLC) requires short packets of data transmission. It is known that when the packet length becomes short, the achievable rate is subject to a penalty when compared to the channel capacity. In this paper, we propose to use faster-than-Nyquist (FTN) signaling to compensate for the achievable rate loss of short packet communications. We investigate the performance of a combination of a low complexity detector of FTN signaling used with nonbinary low-density parity-check (NB-LDPC) codes that is suitable for low-latency and short block length requirements of URLLC systems. Our investigation shows that such combination of low-complexity FTN signaling detection and NB-LDPC codes outperforms the use of close-to-optimal FTN signaling detectors with LDPC codes in terms of error rate performance and also has a considerably lower computational complexity.

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.

Reduced complexity faster-than-Nyquist (FTN) signaling systems are gaining increased attention as they provide improved bandwidth utilization for an acceptable level of detection complexity. In order to have a better understanding of the tradeoff between performance and complexity of the reduced complexity FTN detection techniques, it is necessary to study these techniques in the presence of channel coding. In this paper, we investigate the performance a polar coded FTN system which uses a reduced complexity FTN detection, namely, the recently proposed “successive symbol-by-symbol with go-back- K sequence estimation (SSSgbKSE)” technique. Simulations are performed for various intersymbol-interference (ISI) levels and for various go-back-K values. Bit error rate (BER) performance of Bahl-Cocke-Jelinek-Raviv (BCJR) detection and SSSgbKSE detection techniques are studied for both uncoded and polar coded systems. Simulation results reveal that polar codes can compensate some of the performance loss incurred in the reduced complexity SSSgbKSE technique and assist in closing the performance gap between BCJR and SSSgbKSE detection algorithms.

Due to the battery-limited nature of mobile devices, improving energy efficiency (EE) of individual users and ensuring EE fairness among those users are one of the key design issues in uplink transmission of cellular networks. In this paper, we consider the joint optimization of discrete power and resource blocks allocations to maximize the minimum EE among users subject to individual power budget constraints. The optimization problem is combinatorial. Thus, we propose an efficient algorithm, based on semidefinite relaxation with Gaussian randomization, to solve the resultant non-convex problem in polynomial time complexity. The numerical results show how well the proposed algorithm performs against the optimal one and indicate the impact of discrete power levels on the fairness-oriented EE optimization.

Data over cable service interface specification (DOCSIS) introduced the possibility of a variable bit-loading over the subcarriers within a channel in its release DOCSIS 3.1. This variable bit-loading will improve the data rates. However, to limit the encoding processing overhead, the concept of profiles was introduced. Each profile defines the modulation per subcarrier for a given channel while the number of allowed profiles is limited. Thus, an efficient profile assignment scheme, which determines the best set of profiles based on the users’ channel conditions, is needed. Although various profile assignment algorithms have been proposed in the literature, realistic evaluation of these schemes has been difficult, as channel quality measurements of real DOCSIS 3.1 systems has not previously been available. In this paper, we exploit DOCSIS 3.1 measurement data to evaluate performance of the proposed algorithms. We propose to employ principal component analysis to derive low-dimensional clustering variables in order to ensure efficient profile optimization. We show how this technique can be employed with different clustering algorithms to improve the spectrum efficiency of the profiles by extracting the most important information of the channels in low-dimensional vectors. This not only reduces the complexity of the clustering, but also ensures better throughput. Moreover, we adapt the clustering algorithms to tailor them to the profile optimization problem. Finally, we present an exhaustive simulation-based performance analysis to compare the different algorithms for various scenarios using extrapolation of the measurements data.

In this paper, we investigate the detection problem of binary faster-than-Nyquist (FTN) signaling and propose a novel sequence estimation technique that exploits its special structure. In particular, the proposed sequence estimation technique is based on sphere decoding (SD) and exploits the following two characteristics about the FTN detection problem: 1) the correlation between the noise samples after the receiver matched filter, and 2) the structure of the intersymbol interference (ISI) matrix. Simulation results show that the proposed SD-based sequence estimation (SDSE) achieves the optimal performance of the maximum likelihood sequence estimation (MLSE) at reduced computational complexity. This paper demonstrates that FTN signaling has the great potential of increasing the data rate and spectral efficiency substantially, when compared to Nyquist signaling, for the same bit-error-rate (BER) and signal-to-noise ratio (SNR).

Proactive caching is a promising technique used to minimize peak traffic rates by storing popular data, in advance, at different nodes in the network. We study a cellular network with one base station (BS) communicating with multiple mobile units (MUs). The BS has a number of cached files to be delivered to the MUs upon demand, and the popularities of these files are changing over time. We show that proactively and constantly updating the MU finite caches and jointly encoding the delivery of different demanded files to the MUs over different time slots minimize the delivery sum rate. We propose two different schemes for a two different scenarios, where the file popularities over time can be either arbitrary increasing or decreasing for the first scheme and decreases with demand for the second scheme. Numerical results show the benefits of the proposed schemes, over conventional caching schemes, in terms of reducing the delivery sum rate.

Internet of Things (IoT) holds a great promise in providing autonomous and ubiquitous connectivity between devices in future communication systems. Due to the spectrum scarcity, very high frequency (VHF) and ultra high frequency (UHF) bands are viewed as valuable resources for IoT communications, especially to connect to distant locations that are hard to reach using higher frequencies. Existing propagation models in the VHF/UHF frequency bands are mainly for broadcasting and cellular systems with high transmit antenna heights, and hence, they are not suitable for IoT communications characterized by low antenna heights at both the transmitter and receiver. In this paper, we present new statistical path loss and delay spread models for IoT communications based on quasi-simultaneous wideband channel measurements conducted in the VHF/UHF frequency bands (from 37.8 to 370 MHz) at the city of Halifax, Canada. In particular, we present two log-distance path loss models (frequency-independent path loss exponent and frequency- dependent path loss exponent), as well as, a new statistical distribution of the delay spread.

In this paper, we adopt a multiobjective optimization for the bit and power allocation problem in order to meet the requirements of emerging wireless systems, i.e., achieving higher throughput without considerably increasing the transmit power. More specifically, we propose to simultaneously maximize the throughput and minimize the transmit power of an OFDM system subject to average bit error rate (BER), power budget, and maximum allocated number of bits per subcarrier constraints. The formulated optimization problem is not convex and we use an evolutionary algorithm, i.e., genetic algorithm, in order to obtain the solution. We study the structure of the problem and the obtained solution and notice that the constraint on the average BER can be replaced by a BER per subcarrier constraint. As such, we propose an approximate non-convex optimization problem. We further exploit the structure of the approximate optimization problem and notice that the BER constraint per subcarrier (i.e., the source of the non-convexity) must be satisfied with an equality sign and can be substituted; this leads to an equivalent convex optimization problem where the global optimality of the Pareto solutions is guaranteed. Closed-form expressions are obtained for the bit and power allocations with reduced complexity. Simulation results show that the proposed multiobjective optimization approach provides significant performance improvements over single objective optimization techniques presented in the literature, without incurring additional complexity.

In this paper, we consider a low-complexity uniform power loading scheme for orthogonal frequency division multiplexing (OFDM) systems with two reduced feedback mechanisms and analyze its performance over Rayleigh fading channels. In the first feedback mechanism, the receiver feeds back to the transmitter the channel gains and the indices of the best$M$subchannels; while for the second feedback mechanism, the receiver feeds back only the indices of the best$M$subchannels. The available power budget is equally distributed over the best$M$subchannels for both feedback mechanisms. We derive closed-form expressions for the achievable capacity and an upper bound on the outage capacity of the first and second mechanisms, respectively. A simple elimination algorithm is provided to find the optimal number of best subchannels$M$that maximizes the achievable capacity. Numerical results show the dependence of the optimal number of the best subchannels$M$on the system parameters. Additionally, the presented results interestingly show that the low-complexity uniform power loading scheme can achieve up to 98.72% of the channel capacity, obtained using the well-known waterfilling solution, when the optimal value of$M$is used. Moreover, the uniform power loading scheme can achieve up to 88.86% of the energy efficiency at reduced complexity.

In this paper, we analyze the performance of two low-complexity uniform power loading with reduced- overhead orthogonal frequency division multiplexing (OFDM) schemes over Rayleigh fading channels. In the first feedback scheme, the receiver feeds back to the transmitter the channel gains and the indices of the best $M$ subchannels; while for the second feedback scheme, the receiver feeds back only the indices of the best $M$ subchannels. In both schemes, the available power budget is equally distributed over the best $M$ subchannels. We derive closed-form expressions for the capacity and the outage capacity of the first and second schemes, respectively. Numerical results show that there is an optimal number of the best subchannels, $M$, that maximizes the achievable capacity and it depends on the system parameters.

In this paper, we adopt an energy-efficiency (EE) metric, namedworst-EE, that is suitable for EE fairness optimization in the uplink transmission of amplify-and-forward (AF) cooperative orthogonal frequency division multiple access (OFDMA) networks. More specifically, we assign subcarriers and allocate powers for mobile and relay stations in order to maximize the worst-EE, i.e., to maximize the EE of the mobile station (MS) with the lowest EE value, subject to MSs transmit power, relay station (RS) transmit power, and MSs quality-of-service (QoS) constraints. The formulated primal max–min optimization problem is nonconvex fractional mixed integer nonlinear program, i.e., NP-hard to solve. We provide a novel optimization framework that studies the structure of the primal problem and prove that the dual min–max optimization problem attains the same optimal solution of the primal problem. Additionally, we propose a modified Dinkelbach algorithm, named dual Dinkelbach, to achieve the optimal solution of the dual problem in a polynomial time complexity. We further exploit the structure of the obtained optimal solution and develop a low complexity suboptimal heuristic. Numerical results show the effectiveness of the proposed algorithm to improve the network performance in terms of fairness between MSs, worst-EE, and average network transmission rate when compared to traditional schemes that maximize the EE of the whole network. Presented results also show that the suboptimal heuristic balances the achieved performance and the computational complexity.

In cognitive radio (CR) networks, secondary users (SUs) are allowed to opportunistically access the primary users (PUs) spectrum to improve the spectrum utilization; however, this increases the interference levels at the PUs. In this paper, we consider an orthogonal frequency division multiplexing OFDM-based CR network and investigate the tradeoff between increasing the SU transmission rate (hence improving the spectrum utilization) and reducing the interference levels at the PUs. We formulate a new multiobjective optimization (MOOP) problem that jointly maximizes the SU transmission rate and minimizes its transmit power, while imposing interference thresholds to the PUs. Further, we propose an algorithm to strike a balance between the SU transmission rate and the interference levels to the PUs. The proposed algorithm considers the practical scenario of knowing partial channel state information (CSI) of the links between the SU transmitter and the PUs receivers. Simulation results illustrate the performance of the proposed algorithm and its superiority when compared to the work in the literature.

In this paper, the power loading problem for orthogonal frequency division multiplexing (OFDM) with imperfect channel estimation is investigated considering the trade-off between energy efficiency (EE) and spectral efficiency (SE). Unlike traditional research that uses the EE as an objective function and imposes constraints either on the SE or the achievable rate, we propound a multiobjective optimization approach that can flexibly switch between the EE and the SE functions or change the priority level of each function using a trade-off parameter. Our dynamic approach is more tractable than conventional approaches and more convenient to realistic communication applications and scenarios. The system model considers the path loss and shadowing effect in modeling the EE and SE metrics, in addition to taking the channel estimation error into account. We first solve the marginal problems of maximizing the EE and the SE individuality, and then prove that the multiobjective optimization of the EE and the SE is equivalent to a simple problem that maximizes the capacity and minimizes the total power consumption. Finally, we use numerical results to discuss the choice of the trade-off parameter and study the effect of the estimation error, transmission power budget and channel-to-noise ratio on the multiobjective optimization.

This paper adopts a multiobjective optimization (MOOP) approach to investigate the optimal link adaptation problem of orthogonal frequency division multiplexing (OFDM)-based cognitive radio (CR) systems, where secondary users (SUs) can opportunistically access the spectrum of primary users (PUs). For such a scenario, we solve the problem of jointly maximizing the CR system throughput and minimizing its transmit power, subject to constraints on both SU and PUs. The optimization problem imposes predefined interference thresholds for the PUs, guarantees the SU quality of service in terms of a maximum bit-error-rate (BER), and satisfies a transmit power budget and a maximum number of allocated bits per subcarrier. Unlike most of the work in the literature that considers perfect SU spectrum sensing capabilities, the problem formulation takes into account errors due to imperfect sensing of the PUs bands. Closed-form expressions are obtained for the optimal bit and power allocations per SU subcarrier. Simulation results illustrate the performance of the proposed algorithm and demonstrate the superiority of the MOOP approach when compared to single optimization approaches presented in the literature, without additional complexity. Furthermore, results show that the interference thresholds at the PUs receivers can be severely exceeded due to the perfect spectrum sensing assumption or due to partial channel information on links between the SU and the PUs receivers. Additionally, the results show that the performance of the proposed algorithm approaches that of an exhaustive search for the discrete optimal allocations with a significantly reduced computational effort.

In this paper, we adopt a multiobjective optimization approach to jointly optimize the rate and power in OFDM-based cognitive radio (CR) systems. We propose a novel algorithm that jointly maximizes the OFDM-based CR system throughput and minimizes its transmit power, while guaranteeing a target bit error rate per subcarrier and a total transmit power threshold for the secondary user (SU), and restricting both co-channel and adjacent channel interferences to existing primary users (PUs) in a statistical manner. Since the interference constraints are met statistically, the SU transmitter does not require perfect channel-state-information (CSI) feedback from the PUs receivers. Closed-form expressions are derived for bit and power allocations per subcarrier. Simulation results illustrate the performance of the proposed algorithm and compare it to the case of perfect CSI. Further, the results show that the performance of the proposed algorithm approaches that of an exhaustive search for the discrete global optimal allocations with significantly reduced computational complexity.

In this paper, we investigate the resource allocation problem (joint bit and power loading) of secondary users sharing the radio spectrum with primary users in cognitive radio networks. We consider the co-existence scenario where a secondary user is allowed to access the shared spectrum while guaranteeing tolerable interference to primary users. For such a scenario, we formulate and solve an optimization problem that jointly maximizes the secondary user throughput and minimizes its transmit power while satisfying target bit error rate per subcarrier and certain limits of co-channel and adjacent channel interferences to existing primary users. Simulation results are described that illustrate the performance of the proposed algorithm, and show its closeness to that of an exhaustive search for the equivalent discrete formulation.

This paper studies the joint rate and power allocation problem for OFDM-based cognitive radio systems where secondary users (SUs) can opportunistically access the spectrum of primary users (PUs). We propose a novel algorithm that jointly maximizes the OFDM-based cognitive radio system throughput and minimizes its transmit power, while guaranteeing a target bit error rate per subcarrier and restricting both co-channel interference (CCI) and adjacent channel interference (ACI) to existing primary users. Since estimating the instantaneous channel gains on the links between the SU transmitter and the PUs receivers is impractical, we assume only knowledge of the path loss on these links. Closed-form expressions are derived for the close-to-optimal bit and power distributions. Simulation results are described that illustrate the performance of the proposed scheme and show its closeness to that of an exhaustive search for the discrete optimal allocations. Further, the results quantify the violation ratio of both the CCI and ACI constraints at the PUs receivers due to the partial channel information. The effect of adding a fading margin to reduce the violation ratio is also studied.

This paper proposes a novel low complexity joint bit and power suboptimal allocation algorithm for multicarrier systems operating in fading environments. The algorithm jointly maximizes the throughput and minimizes the transmitted power, while guaranteeing a target bit error rate (BER) per subcarrier and meeting a constraint on the total transmit power. Simulation results are described that illustrate the performance of the proposed scheme and demonstrate its superiority when compared to the algorithm in [4] with similar or reduced computational complexity. Furthermore, the results show that the performance of the proposed suboptimal algorithm approaches that of an optimal exhaustive search with significantly lower computational complexity.

In this paper, a novel joint bit and power loading algorithm is proposed for orthogonal frequency division multiplexing (OFDM) systems operating in fading environments. The algorithm jointly maximizes the throughput and minimizes the transmitted power, while guaranteeing a target average bit error rate (BER) and meeting a constraint on the total transmit power. Simulation results are described that illustrate the performance of the proposed scheme and demonstrate its superiority when compared to the algorithm in [1].

In this paper, a novel low complexity bit and power loading algorithm is formulated for orthogonal frequency division multiplexing (OFDM) systems operating in fading environments and in the presence of unknown interference. The proposed non-iterative algorithm jointly maximizes the throughput and minimizes the transmitted power, while guaranteeing a target bit error rate (BER) per subcarrier. Closed-form expressions are derived for the optimal bit and power distributions per subcarrier. The performance of the proposed algorithm is investigated through extensive simulations. A performance comparison with the algorithm shows the superiority of the proposed algorithm with reduced computational effort.

This paper proposes a novel joint bit and power allocation algorithm for multicarrier systems operating in fading environments. The algorithm jointly maximizes the throughput and minimizes the transmitted power, while guaranteeing a target average bit error rate (BER). Simulation results are described and they illustrate the performance of the proposed scheme and demonstrate its superiority with respect to existing schemes.