High-mobility communications, which are crucial for next-generation wireless systems, cause the orthogonal frequency division multiplexing (OFDM) waveform to suffer from strong intercarrier interference (ICI) due to the Doppler effect. In this work, we propose a novel receiver architecture for OFDM that leverages the angular domain to separate multipaths. A block-type pilot is sent to estimate direction-of-arrivals (DoAs), propagation delays, and channel gains of the multipaths. Subsequently, a decision-directed (DD) approach is employed to estimate and iteratively refine the Dopplers. Two different approaches are investigated to provide initial Doppler estimates: an error vector magnitude (EVM)-based method and a deep learning (DL)-based method. Simulation results reveal that the DL-based approach allows for constant bit error rate (BER) performance up to the maximum 6G speed of 1000 km/h.

LoRaWAN technology’s reliability is challenged by weather parameters, which can influence the communication channel design, especially when dealing with outdoor devices. We propose to analyze this effect by evaluating the relationship between the received signal strength indicator (RSSI) and different weather parameters, as well as its temporal changes. A rigorous statistical analysis of the RSSI sequences is conducted to assess if they could be represented by a specific statistical model. For this purpose, several models are investigated. The Artificial Intelligence (AI) algorithms cover machine learning (ML) and deep learning methods are appealing when dealing with time series forecasting. Nevertheless, the classical autoregressive integrated moving average (ARIMA) model can be an attractive alternative due to its simplicity. Therefore, this work proposes a comparative study of ARIMA, AI, and hybrid approaches to forecast the RSSI using weather parameters as regressors. The considered AI algorithms are the artificial neural network (ANN), support vector machine (SVM), random forest (RF), and Long Short-Term Memory (LSTM). Also, hybrid models are constructed, coupling the ARIMA with them. The models are evaluated in time series of RSSI, measured by eight different LoRaWAN transmitter nodes and considering the temperature, pressure, relative humidity, and rain as weather parameters. Our analysis reveals that temperature is the dominant factor among weather parameters, and negatively affects RSSI. The ARIMA model that uses only the temperature as a regressor provides consistently better fits than the ARIMA without regressors. Moreover, coupling the ARIMA with the temperature as a regressor and the ANN (ARIMA-ANN) is the best option among the pure AI and hybrid approaches. However, it provided accuracy measures very close to those obtained from the ARIMA model fitted in the first stage, with similar performance. Therefore, the ARIMA model considering the temperature is the most competitive alternative when analyzing RSSI measurements, with the advantage of being the most straightforward method. These results suggest that the RSSI from the analyzed LoRaWAN receiver nodes may not present nonlinear patterns and, considering several weather parameters, they are affected mainly by the outdoor temperature.

Intra-body communication will facilitate next-generation personalized medicine by enabling interconnection among implanted devices. To this purpose, energy-efficient communication technologies are required such as galvanic coupling (GC). Although some GC testbeds have been developed to implement the entire communication chain, synchronization problems have not yet been tackled exhaustively. While some papers simply assumea-prioriperfect synchronization between GC transmitter and receiver, other studies developed solutions that often are time-consuming. In this paper, an energy-efficient and fast maximum-log-likelihood (ML) synchronization method is proposed, that can operate in real time and follow channel variations. Experiments reveal that the proposed ML synchronization scheme is very effective for short-range GC communication up to 4 cm, with performance similar to the current State-of-the-Art. It shows slightly lower performance levels for higher distances, still it offers the benefit of lower computational requirements than the reference method.

Intra-body communication (IBC) will foster personalized medicine by enabling interconnection of implanted devices. Communication takes place through energy-efficient technologies such as capacitive coupling (CC) and galvanic coupling (GC); however, their modeling is still incomplete. This paper tackles characterization of the human body channel using impulse response, including a first-ever comparison of CC and GC in both wearable and implantable configurations. Experimental data are leveraged to evaluate the measured impulse response in ex-vivo chicken tissue and in-vivo human tissue in a frequency range up to 100 kHz. Pseudorandom noise (PN) sequences are transmitted in baseband and a correlative channel sounding system is implemented. Experimental results demonstrate that the channel is relatively flat in the frequency range of interest, thus offering the opportunity to simplify the design of an IBC transceiver. The relationship between the channel responses and the transmitter-to-receiver distance is also examined using linear correlation, and two regression models are developed. The results show that CC channels are not affected by distance within the range of investigation, while a negative relationship is found for GC channels. Finally, experiments reveal that implantable CC with isolated ground -not deeply investigated yet- is a very promising solution for IBC.

In space applications, hardware (HW) implementation is made more expensive not only by the levels of performance required, but also by complex and rigorous HW qualification tests. Reducing qualification cost and time is thus a key design requirement. In this paper, a new versatile transmitter is proposed for space telemetry, capable of soft-switching across different linear and continuous phase modulation schemes while maintaining the same hardware structure. This permits a single HW qualification to “cover” diverse uses of the same hardware, and thus avoid re-qualification in case of configuration changes. The envisaged solution foresees the use of a single filter, suitable not only for linear modulations such as M-QAM, but also for continuous phase modulation methods. At this stage, we focus on pulse code modulation/frequency modulation (PCM/FM), for which we propose a minimum mean square error (MMSE) algorithm. The proposed algorithm, which adds to the system flexibility and effectiveness, may use a single first filter based on Laurent decomposition for initialization, if needed. Performances are assessed using the mean square error (MSE) measure between the proposed MMSE-modulated signal and the completely modulated signal. Simulation results confirm that the proposed algorithm leads to MSE values that are lower than the case of Laurent decomposition using the first component only.

The Internet of Medical Things (IoMT) paradigm will enable next generation healthcare by enhancing human abilities, supporting continuous body monitoring and restoring lost physiological functions due to serious impairments. This paper presents intra-body communication solutions that interconnect implantable devices for application to the nervous system, challenging the specific features of the complex intra-body scenario. The presented approaches include both speculative and implementative methods, ranging from neural signal transmission to testbeds, to be applied to specific neural diseases therapies. Also future directions in this research area are considered to overcome the existing technical challenges mainly associated with miniaturization, power supply, and multi-scale communications.

Implants are poised to revolutionize personalized healthcare by monitoring and actuating physiological functions. Such implants operate under challenging constraints of limited battery energy, heterogeneous tissue-dependent channel conditions and human-safety regulations. To address these issues, we propose a new cross-layer protocol for galvanic coupled implants wherein weak electrical currents are used in place of classical radio frequency (RF) links. As the first step, we devise a method that allows multiple implants to communicate individual sensed data to each other through CDMA code assignments, but delegates the computational burden of decoding only to the on-body surface relays. Then, we devise a distributed beamforming approach that allows coordinated transmissions from the implants to the relays by considering the specific tissue path chosen and tissue heating-related safety constraints. Our contributions are two fold: First, we devise a collision-free protocol that prevents undue interference at neighboring implants, especially for multiple deployments. Second, this is the first application of near-field distributed beamforming in human tissue. Results reveal significant improvement in the network lifetime for implants of up to 79% compared to the galvanic coupled links without beamforming.

This paper explores protograph-based and ACE-based methods for constructing q-ary low-density parity-check (LDPC) matrices. The ACE approach maximizes approximate cycle extrinsic message degree, explicitly avoiding small q-ary stopping sets and implicitly avoiding small absorbing sets. In addition to ACE, this paper applies linear-dependent-set maximization (LDSM) to the binary image of the q-ary LDPC matrix. Performance is studied for binary and q-ary instances of erasure channels and additive white Gaussian noise channels. The combination of the ACE approach and LDSM provides dramatic error floor improvement for the binary erasure channel and both binary and q-ary AWGN channels.

In this paper, a novel Cognitive Radio Resource Management (RRM) is proposed to improve the spectrum utilization efficiency. In this system, heterogeneous Primary Users (PUs) with multiple features are considered where these PU features are exploited to improve the adaptability in Cognitive Radio (CR) networks and, thus, to design an efficient Cognitive RRM. An optimization framework is developed by considering heterogeneous PUs and variable CR demands while assuring interference protection towards PUs. A suboptimal solution is proposed after showing that an optimal solution is computationally infeasible. Simulation results are conducted in terms of total achieved data rate and satisfaction of CRs requirements.

The ability of adapting to the environment in the most efficient way is a crucial issue in Cognitive Radio (CR) networks. For this purpose, an accurate estimation of the characteristics and activity of the Primary Users (PUs) is required. A system that takes into account heterogeneous PUs with several features is developed. A new scheme is integrated in the system to exploit these motleys and to improve the adaptability in CR networks. Through the proposed PU signal type recognition, the PU signal is detected and classified. The features of each PU type: the allowed interference levels, the bandwidth and the idle time, are extracted and exploited for CR adaptability effects. For this, a new CR throughput/interference adapter is proposed. The CR throughput is efficiently increased depending on the specific characteristics of PU types. Simulation results show that the proposed PU type recognition detects, distinguishes and classifies PU signals in Additive White Gaussian Noise (AWGN). It is shown that CR throughput varies with PU features for the improvement of CR adaptability.

Recently q-ary Low-Density Parity-Check (LDPC) codes have been used to achieve performance close to the channel capacity in different channel environments, from satellite communications to magnetic data storage systems. Design of receivers employing these codes for transmissions over channels affected by InterSymbol Interference (ISI) is still an open issue. In fact, detection-and-decoding systems have to face the trade-off between error-rate performance and complexity. In this paper we compare some receiver architectures for 16-ary LDPC codes: serial and turbo concatenated schemes and a joint Message-Passing (MP) based receiver as well. Performance of these systems are evaluated over three different Partial Response (PR) channels, using simulations. Finally, ongoing future directions for research are discussed.

Orthogonal frequency division multiplexing (OFDM) is a transmission technique which is robust in multipath channels. It is though sensitive to frequency errors that may be caused by several factors such as frequency offset at the local oscillator, phase noise and mobility of the receiver. These errors may destroy orthogonality among subcarriers and introduce intercarrier interference (ICI) that may be represented in the frequency domain by means of an ICI matrix. Estimation of this matrix is crucial to optimize performance. In this work we propose two iterative methods that use pilot tones in the frequency domain and converge very quickly. The first proposed method works in two steps. Initially, correlation between received signal and estimated transmitted one is used to recover the channel matrix, and then it estimates the actual transmitted data by means of MMSE. This method is shown to be very effective in AWGN channel with frequency offset at the local oscillator or in one time variant path channel. The second method represents an extension to more severe channel conditions such as multipath fading. In this case a different frequency domain OFDM channel representation is needed and a suitable scheme is implemented to recover channel and data. Simulation results show that the proposed algorithms look promising to be used in actual implementations.

This paper presents a novel iterative symbol timing recovery (STR) scheme for short burst transmission formats, a paradigm commonly found in modern wireless systems, like, for instance, time division multiple access (TDMA) schemes and future wireless packet data networks. Both data-aided (DA) and decision-directed (DD) solutions are considered and performance is pursued by means of an iterative burst-by-burst scheme which exploits the Farrow structure for the polynomial interpolation filter. The convergence of the algorithm is discussed according to the expectation maximization (EM) framework. Performance is evaluated by simulating 4-QAM and 16-QAM transceivers and simulations results are compared under different modulation orders and channel conditions, for both the decision-directed and data-aided cases.

In this paper an improved decision feedback equalizer (DFE) for mobile orthogonal frequency division modulation (OFDM) systems is presented. The time-varying nature of the channel, due to the Doppler effect, produces inter carrier interference (ICI) that may be counteracted by using a DFE frequency- domain OFDM symbol equalizer. The proposed algorithm is based on a recently proposed block DFE equalizer exploiting an LDLHdecomposition. The main novelty of this work is the idea to modify the backward filter in order to have a complete MMSE linear equalization before the decisions needed for the interference cancellation. Performance may be further improved by re-filtering the signal in an iterative algorithm which makes use of the proposed backward filter at the first iteration. The proposed scheme performance is evaluated by considering a mobile WiMAX IEEE 812.16e standard transceiver.

Despite the tremendous improvements in transmission techniques, bandwidth is still a scarce resource as new services are introduced and user expectations grow. For this reason, optimization algorithms are required to efficiently exploit the diversity gain intrinsic in orthogonal frequency division multiple access (OFDMA) access networks. In a multiuser packet based multiservice environment, other issues that must be addressed relate to maintain some degree of fairness and service quality constraints. In this paper we introduce some algorithms at different complexity levels to adjust channel allocation and power in a 802.16e access network mixing different real-time and non-real-time services.

Cell sectorization is a common approach used in cellular systems to increase system capacity. In this paper a novel approach to downlink spatial filtering based on clustering algorithms is proposed for UMTS-FDD cellular networks. In more details, the well known segmentation algorithm K-means is applied for grouping users in order to define the spatial beams. It is assumed that the direction of arrival of received uplink channels known at the base-station. The ability of the proposed automated scheme is evaluated by comparing the computed beams with the optimum ones obtained heuristically in a static spatial configuration of users. Furthermore, simulation results in presence of DOA measurements errors demonstrates the reliability of the presented solution.

This paper presents a joint symbol timing recovery and equalization scheme for short burst transmission formats, useful in modern wireless systems, like, for instance, time- division multiple access schemes and future wireless packet data networks. Both data aided and decision directed solutions are considered and the joint optimization performance is pursued by means of an iterative scheme which exploits a timing error function sampled at symbol rate, implemented by means of the Farrow structure for the interpolation filter. Equalization is obtained by zero forcing linear filtering, considering a baud spaced implementation. Performance is evaluated by simulating QPSK transceivers and simulations results are compared with the ideal solutions for both symbol timing recovery and zero forcing channel equalization under frequency selective multipath fading.

In this work, an all-digital synchronization recovery circuit is proposed for the uplink scheme of the ETSI BRAN Hiperaccess receiver. In particular a fast, open-loop algorithm, which exploits the TDMA burst preamble made of 16 or 32 cazac symbols, is tested by means of a link-level simulator operating at twice the symbol rate. At the receiver we suppose to use a fixed local oscillator and the symbol timing recovery is performed by means of digital interpolation. The fractional delay parameter for a fourth and sixth order Farrow interpolator is computed by means of a second order polynomial representation of the maximum likelihood function. After the recovery of the symbol timing, the phase offset is adjusted by simply taking the argument of the log-likelihood function. Performances of the proposed architecture are evaluated by measuring the symbol error rate and the synchronization error variances on AWGN channel.

Applications of clustering and neural network techniques to channel equalization have revealed the classification nature of this problem. This paper illustrates an implementation of a global system for mobile communications (GSM) receiver in which channel equalization and demodulation are realized by means of the nearest neighbor (NN) classifier algorithm. The most important advantage in using such techniques is the significant reduction in terms of the computational complexity compared with the maximum likelihood sequence estimation (MLSE) equalizer. The proposed approach involves symbol-by-symbol interpretation and the knowledge of the channel is embedded in the mapping process of the received symbols over the symbols of the training sequence. This means that no explicit channel estimation need be carried out, either with correlative blocks or using neural networks thus speeding up the entire process. The performance of the proposed receiver, evaluated through a channel simulator for mobile radio communications, is compared with the results obtained by means of a 16-state Viterbi algorithm and other suboptimal receivers. It is shown that the presented algorithm increases the bit error rate (BER) compared with the MLSE demodulator, but the performance degradation, despite the simplicity of the receiver, is kept within the limits imposed by the GSM specifications.