This article presents a unified detection framework for integrated sensing and communication (ISAC) using fused frequency-modulated continuous-wave (FMCW) and orthogonal frequency-division multiplexing (OFDM) waveforms. A 3D-DFT-based monostatic multiple-input multiple-output (MIMO) radar model is developed, and closed-form expressions for detection and false-alarm probabilities are derived under Neyman–Pearson and generalized likelihood ratio tests. A sub-optimal best linear unbiased estimator (BLUE) fusion combines FMCW and OFDM radar returns, providing up to 3 dB additive signal-to-noise-ratio (SNR) gain while remaining backward compatible with existing OFDM standards. The impact of waveform peak-to-average power ratio (PAPR) and power-amplifier back-off is analyzed, and the computational complexity and energy proxy of the 3D-DFT plus BLUE receiver are evaluated. Simulations confirm the theoretical analysis, demonstrating improved sensing accuracy under realistic mmWave MIMO configurations.

In this paper, we develop a machine learning model to detect active eavesdroppers in a Massive Multiple Input Multiple Output (MIMO) system. Massive MIMO systems are naturally immune to passive eavesdroppers, but this is dramatically degraded by active eavesdroppers. We propose two machine learning-based schemes, i.e. a Support Vector Machine (SVM) based scheme and a Naive-Bayes (NB) based scheme, to classify and detect the presence of an active eavesdropper. Then, we apply a Deep Neural Network (DNN) for detecting the presence of an active eavesdropper. We first build structured datasets based on the Received Signal Strength (RSS) and then apply SVM classifiers, NB classifiers, and DNN to those structured datasets. We built a machine learning model based on a realistic scenario where the Channel State Information (CSI) of the channels (legitimate users and eavesdroppers) is unknown. We exploit the massive MIMO technique features to improve the performance of the detection models. The work presented here provides insights into the design of DNN and new machine learning-based secure transmission schemes in Massive MIMO.

The long term evolution (LTE) has already been commercially implemented for nearly a decade. Despite the fact that it is constantly being updated throughout new releases, the establishment of the fifth generation (5G) mobile networks has been started. In this research, we study the performance of our LTE/5G testbed using software defined radio (SDR) at the Technical University of Chemnitz (TUC) in Germany. The goal is to discover certain critical performance metrics and provide thoughtful considerations in the context of remote/autonomous driving and vehicle-to-everything (V2X) communication. Investigating the channel conditions of the testbed in terms of the link-budget and delay-spread, we evaluate and compare the latency and data-rate performance of the system. We present a review with a discussion of the limitations that must be carefully considered to meet the requirements in beyond-5G and future mobile networks technology.

In this paper, we evaluate and make a comparison of the channel estimation performance for three different frame structures of IEEE 802.11p, IEEE 802.11bd-draft and a unique-word (UW)-based physical layer (PHY). As in vehicle-to-everything communication the wireless channel conditions may vary significantly depending on the environment and vehicle velocity, severe fading in both time and frequency domains may occur. Through simulation results, we show that the UW-based PHY achieves an interference-free performance of channel estimation via a low complexity technique, whereas the 802.11bd would need to employ a high complexity approach in order to achieve a comparable estimation performance.

In vehicular communications, reliable channel estimation is critical for the system performance due to the doubly-dispersive nature of vehicular channels. IEEE 802.11p standard allocates insufficient pilots for accurate channel tracking. Consequently, conventional IEEE 802.11p estimators suffer from a considerable performance degradation, especially in high mobility scenarios. Recently, deep learning (DL) techniques have been employed for IEEE 802.11p channel estimation. Neverthe-less, these methods suffer either from performance degradation in very high mobility scenarios or from large computational complexity. In this paper, these limitations are solved using a long short term memory (LSTM)-based estimation. The proposed estimator employs an LSTM unit to estimate the channel, followed by temporal averaging (TA) processing as a noise alleviation technique. Moreover, the noise mitigation ratio is determined analytically, thus validating the TA processing ability in improving the overall performance. Simulation results reveal the performance superiority of the proposed schemes compared to the recently proposed DL-based estimators, while recording a significant reduction in the computational complexity.

In conventional multicarrier systems, a cyclic prefix (CP) is added to the transmission block in order to protect it from multi-path propagation of the wireless channel. Nonetheless, due to the random nature of the CP, it is usually discarded at the receiver side, and from a synchronization perspective, this energy is wasted. Unique Word (UW) is a promising concept for CP replacement, because, in addition to protecting the signal from multi-path propagation, it allows per-block synchronization. Considering a multiple-input-multiple-output (MIMO) system, the state-of-the-art (SoA) data-aided synchronization approaches are mainly preamble based, while, on the other hand, the synchronization techniques for UW sequences are being applied to single-input-single-output systems in low mobility scenarios. In this paper, we investigate time and frequency synchronization of UW-based MIMO systems in high mobility conditions where the wireless channel is both frequency selective and fast fading. Through theoretical derivations as well as extensive simulations, we show that the proposed UW-based synchronization approach for MIMO outperforms the SoA MIMO synchronization techniques.

In this paper, we design a frame structure for unique word (UW) based transmission of multiple-input-multiple-output (MIMO) systems under doubly-dispersive wireless channel conditions. We elaborate an energy and spectral efficiency analysis of a MIMO UW-based system vs. a conventional MIMO cyclic prefix (CP)-based system. Considering the UW-based transmission for a MIMO multi-carrier, we derive its signal processing algorithms for channel estimation and joint channel-equalization-and-demodulation. Through theoretical derivations as well as extensive simulations, we show that the proposed MIMO UW-based system significantly outperforms the state-of-the-art approaches.

We consider the channel estimation (CE) of a non-orthogonal multi-carrier system where the wireless channel is both frequency-selective and time-variant. In non-orthogonal multi-carriers e.g. generalized frequency division multiplexing (GFDM), the reference signals for channel estimation become contaminated by the data symbols, which consequently, limits the transceiver performance. On the other hand, the well time-localization of the pilot symbols in GFDM, allows a more efficient use of cyclic prefix (CP). Particularly, by localizing the energy of the pilot symbols to the end of block, it is possible to use the pilot's information also from CP for channel estimation. Moreover, since in a non-orthogonal waveform, the energy concentration of the pilots might not be uniform over the transmit block duration, the CE algorithm that relies solely on block-fading assumptions might have its best performance at a specific time sample within the block duration. The knowledge of such time sample is specifically important for deriving the channel autocorrelation for adaptive filtering in time-variant situations. In this paper, we first propose an approach to efficiently use the whole transmission block for channel estimation including its CP, and then, we derive the well-known adaptive Wiener-Hopf filters for CE of the non-orthogonal interference-limited GDFM system. From the simulation results, we observe that using CP information for channel estimation and applying the Wiener-Hopf filters achieves up to 1.45 dB smaller frame error rate in comparison to an orthogonal frequency division multiplexing system.

Motivated by 5G application requirements that challenge the use of orthogonal frequency division multiplexing (OFDM), non-orthogonal multi-carriers are being investigated. Unlike OFDM that takes advantage of orthogonal pilot observation, in non-orthogonal waveforms, pilots are contaminated by interference from multiple dimensions, i.e., inter-subsymbol-, inter-carrier-, and inter-antenna-interference, when multiple-input-multiple-output (MIMO) is also part of the transmission. Employing cyclic-prefix (CP) in multi-carrier systems not only protects the signal from inter-symbol-interference but also allows circular interpretations of the channel, which simplifies the estimation and equalization techniques. Nevertheless, the CP information is usually discarded at the receiver side. In this paper, by considering the fact that non-orthogonal waveforms suffer from multiple dimensions of interference, we derive a MIMO linear-minimum-mean-squared-error (LMMSE)-based parallel-interference-cancellation (PIC) method for joint channel estimation and equalization of non-orthogonal waveforms. Unlike the common practice, by properly localizing the pilots in time domain, we also use the pilots' information from CP. We apply our proposed algorithm to a flexible non-orthogonal waveform known as generalized frequency division multiplexing (GFDM). Taking advantage of block-circularity of GFDM, we investigate the complexity aspects for such CP-aided LMMSE-PIC channel estimation. Through simulation results, we show that using CP information of pilots for GFDM gains up to 2.4-dB better frame error rate performance than an OFDM signal.

Generalized Frequency Division Multiplexing (GFDM) is a flexible non-orthogonal waveform. Due to its flexibility it can be served as a framework to emulate diverse multi-carrier waveforms including orthogonal frequency division multiplexing (OFDM) and single-carrier frequency domain equalization (SC- FDE). Nevertheless, inter-symbol- and inter-carrier- interference may arise in GFDM if the filter roll-off factor is larger than zero. In multiple-input multiple-output (MIMO) scenarios, also inter-antenna- interference further challenges the receiver design. In this paper, we focus on pilot-aided channel estimation for GFDM. In contrast to our prior works, we propose a technique to insert the pilot symbols in a manner such that they are orthogonal to the data symbols in the frequency domain. Based on this design, frequency-domain channel estimation algorithms initially developed for OFDM become straightforwardly applicable. We also examine the impact of such pilot design on the signal properties, including power spectral density (PSD) and peak-to- average-power ratio (PAPR). At the end of the paper, the performance of a MIMO-GFDM system is investigated and compared with the conventional MIMO-OFDM systems.

New waveform candidates are being investigated for the fifth generation wireless systems. Among the promising candidates, generalized frequency division multiplexing (GFDM) offers the flexibility to address a wide range of requirements (e.g. low latency, coarse synchronization, etc.). Due to the non- orthogonality of GFDM, the transmit signal subjects to inter-symbol and inter-carrier interference. In this paper, the problem of GFDM channel estimation with the aid of reference signals (pilots) is investigated. In GFDM, the receive signal is a combination of pilots, data and the noise part. Hence, when utilizing the conventional estimation techniques, degradation of channel estimation performance due to interference from data symbols further challenges the receiver design for GFDM. We show that if we employ multiple pilots per subcarrier within a single GFDM block, different pilot patterns have significant impact on the resulting interference term and thus, the quality of the channel estimation in GFDM. Such results are then compared with the performance of channel estimation in orthogonal frequency division multiplexing (OFDM) which takes advantage of clear pilot observation.