Big generative models have significantly impacted several domains like natural language processing, computer vision, and drug discovery. These developments, large-scale generative foundation models exemplified by architectures such as GPT-4 have emerged to address a broad spectrum of generalized tasks. These models are trained to capture the underlying general correlations from a large training dataset. Although big generative AI techniques have been explored in various wireless communication applications, such as channel generation, the integration of generalized foundation models into this domain remains limited. A core component of these models is the masked autoencoder. This work investigates the suitability of masked autoencoders for massive multiple-input multiple-output (MIMO) systems, focusing on their capacity to capture spatial and temporal correlations in massive MIMO channels. To this end, a massive MIMO scenario with user mobility is considered, where the channel state information (CSI) varies with both spatial and temporal correlation. A masked autoencoder is trained in a self-supervised manner using channel state information (CSI) from multiple users. The trained model is then tested for its performance in tasks of feedback compression, channel interpolation, and channel prediction. Experimental results demonstrate that masked autoencoders effectively capture inherent correlations within massive MIMO channels, underscoring their potential to advance foundational model-based approaches in wireless communications.

In mobile communication multiple-input multiple-output (MIMO) systems, angle estimates are invaluable for positioning, initial beam acquisition and interference mitigation. In the context of true-time delay (TTD) systems, angle estimates can be obtained by established signal processing routines at the receiver. Similar signal processing approaches are applicable to frequency diverse array (FDA) systems. However, real-world implementations suffer from hardware impairments such as sample-time-offset (STO) and carrier frequency offset (CFO), which can degrade estimation accuracy.This work compares TTD and FDA arrays in regards of their resilience to STO and CFO impairments using the root mean square error (RMSE) of angle estimates as metric. Furthermore, the impact of implementing the time/frequency shifts in base-/pass-band is examined. The investigation shows that even under typical STO and CFO impairments, estimation errors of 1° can be achieved at 0 dB signal-to-noise ratio (SNR), highlighting the robustness of these techniques.

Current orthogonal frequency division multiplexing (OFDM) standards specify limited options for cyclic prefix (CP) duration, regardless of the wireless channel characteristics. These fixed options can result in significant overhead when the channel delay spread is very short. To address this, a more flexible approach to CP selection is needed, allowing for CP lengths that may be shorter than the delay spread. In this paper, we revisit the classical issue of waveform optimization for channels with short delay spreads, and investigate the potential to reduce the CP duration in OFDM. Building on our prior work in [1], we extend the analysis to multi-antenna systems and assess the impact of number of antennas on multiple-input single-output (MISO)-OFDM system with reduced CP durations. We first derive closed-form expressions for the desired signal power and inter-symbol interference (ISI) power in MISO-OFDM where the CP duration is shorter than the length of channel impulse response (CIR). Then, conditioned on the radio link reliability, defined by the link outage probability, we formulate an optimization problem to jointly determine the minimum CP duration and SNR values required for the system to satisfy that reliability condition. To solve the optimization problem, we use a weighted-sum approach combined with the Bisection method. Our analysis demonstrates that energy efficiency comparable to conventional OFDM systems can be maintained, while achieving increased spectral efficiency (SE) due to the reduced CP duration.

Recent transition in mobile communication standards suggests massive multiple-input multiple-output (MIMO) to be an integral part of the foreseeable future. However, as antenna elements increase to hundreds in the fifth-generation (5G) and beyond, traditional signal processing methods become prone to significant hardware impairments compound from multiple chains, leading to a substantial performance degradation. This paper explores the effectiveness of generative artificial intelligence (AI) techniques in addressing these challenges within massive MIMO systems. For this purpose, the conditional generative adversarial network (CGAN), a special class of generative AI algorithms, is employed to enhance the accuracy of channel state information (CSI) estimation in a hardware-impaired transceiver setup. This problem is treated as an image-denoising task, where the noise is introduced by the hardware impairments and LS estimation error. Through simulations conducted across various antenna array sizes, the potential of generative AI to improve CSI estimation accuracy under hardware impairments is demon-strated. This highlights its capacity to address critical signal processing challenges in the next-generation wireless systems.

In the context of joint communication and sensing (JCAS), communication systems can be used for sensing, where parameters such as time delay can be determined from channel estimation. These time delays enable applications such as ranging and localization. Various studies show that environmental conditions significantly impact sensing performance. Thus practical measurements within the actual environment are essential to overcome the limitations of simulation models. To accurately evaluate the real-world performance of sensing algorithms, a software-defined radio (SDR)-based experimental platform was developed in previous work for indoor measurements with co-located user equipments (UEs). This paper introduces extensions to the platform to enable long-range measurements. Using the enhanced platform, two-way ranging (TWR) measurements are performed in a vehicle-to-vehicle (V2V) setup to evaluate the realistic accuracy using cross-correlation (CCR)-and superresolution path delay estimation (SPDE) algorithms for ranging applications. The measurement results confirm that SPDE can improve accuracy in scenarios where the signal-to-noise ratio (SNR) and signal integrity are assured.

5G sidelink technology recently presented its unique potential for precise positioning using time delay estimates, although environmental conditions and the reference signal used highly influence the performance. Given the complexity of simulating all potential environmental impacts in a particular scenario, carrying out measurements within the specific environment becomes necessary. This paper introduces a flexible experimental platform to support research in designing the protocol, waveform, and time delay estimation algorithms, offering configurable transmitting signals and radio frequency (RF) parameters. Furthermore, this platform can emulate potential timing errors due to hardware constrains, offering a more practical understanding of 5G sidelink ranging applications.

This work presents an investigation on the scalability of a deep leaning (DL)-based blind transmitter positioning system for addressing the multi transmitter localization (MLT) problem. The proposed approach is able to estimate relative coordinates of non-cooperative active transmitters based solely on received signal strength measurements collected by a wireless sensor network. A performance comparison with two other solutions of the MLT problem are presented for demonstrating the benefits with respect to scalability of the DL approach. Our investigation aims at highlighting the potential of DL to be a key technique that is able to provide a low complexity, accurate and reliable transmitter positioning service for improving future wireless communications systems.

Sub-THz communications have been recently considered as an alternative to increase the data rate for the 6th generation (6G) of mobile systems. Since maintaining a reasonable link budget becomes more difficult in higher frequencies, the DFTs-OFDM waveform has been considered as a candidate for sub-THz transmissions, because it has low power-to-average peak ratio (PAPR) in comparison to waveforms with higher PAPR, e.g., orthogonal frequency division multiplexing (OFDM). Additionally, recent channel measurements at 140 GHz have demonstrated that the channel is frequency-selective. This fact motivated us to investigate the DFTs-OFDM link-level performance under an empirical sub-THz channel with the employment of an iterative receiver, since it is known that iterative equalization can mitigate the effects of inter-symbol interference. For this purpose, we consider the minimum mean squared error with parallel interference cancellation (MMSE-PIC) iterative receiver, with convolutional and low-density parity-check (LDPC) codes. The results show that for medium frequency selectivity, LDPC codes provide best performance in terms of frame error rate, but for high selectivity, the convolutional code system has the best performance.

Accurate indoor positioning for wireless communication systems represents an important step towards enhanced reliability and security, which are crucial aspects for realizing Industry 4.0. In this context, this paper presents an investigation on the real-world indoor positioning performance that can be obtained using a deep learning (DL)-based technique. For obtaining experimental data, we collect power measurements associated with reference positions using a wireless sensor network in an indoor scenario. The DL-based positioning scheme is modeled as a supervised learning problem, where the function that describes the relation between measured signal power values and their corresponding transmitter coordinates is approximated. We compare the DL approach to two different schemes with varying degrees of online computational complexity. Namely, maximum likelihood estimation and proximity. Furthermore, we provide a performance comparison of DL positioning trained with data generated exclusively based on a statistical path loss model and tested with experimental data.

Several waveforms have been recently proposed in the literature as alternatives to orthogonal frequency division multiplexing (OFDM) for frequency selective channels. However, in order to achieve a superior performance, it is necessary to employ iterative equalization. In this paper, we consider the sparse Walsh-Hadamard (SWH) waveform with maximum a-Posterior (MAP) equalization. We show that the inherent structure of the SWH matrix allows a significant reduction in the number of multiplications required for the MAP equalizer implementation. The proposed solutions is compared with the zero padding single carrier (ZP-SC) with MAP equalization. We show that SWH with MAP equalization achieves a good trade-off performance vs complexity compared with ZP-SC. In particular, for 16-QAM under the Proakis C channel, ZP-SC is not even feasible while SWH with MAP equalization has manageable complexity.

Power-domain asynchronous non-orthogonal multiple access (ANOMA) is a novel radio access technique with non-orthogonal resource allocation that enables asynchronous transmissions and has an enhanced spectrum efficiency compared to orthogonal multiple access. In this work, an iterative receiver is derived for linearly modulated waveforms. Orthogonal frequency division multiplexing (OFDM), single-carrier (SC) and orthogonal chirp division multiplexing (OCDM) are investigated. The receiver is based on triangular successive interference cancellation (T-SIC) in combination with a minimum mean square error parallel interference cancellation (MMSE-PIC) detector. It is advantageous to utilize a waveform which spreads the data symbols in the frequency domain as OCDM or SC in order to exploit the multipath diversity in frequency-selective channels. However, it is numerically shown that OCDM performs the best due to its additional time-spreading property, which is desirable for the time-dependent interference that occurs in an ANOMA system. Furthermore, for the considered scenario of two users and four blocks, we show that all the studied waveforms achieve the best performance in terms of block error rate with the derived receiver when the blocks overlap halfway.

The channel impulse response (CIR)-based localization requires estimating the channel path delays at a certain position. Several state-of-the-art (SoA) approaches have been proposed to estimate the delays based on eigendecomposition. However, they are limited by the assumption of uncorrelated paths, which is not accurate in the context of localization. The attempt to mitigate the rank deficits problem by means of frequency smoothing (FS) requires a considerable amount of measurements on different center frequencies. Another solution with single snapshot multiple signal classification (MUSIC) lacks the accuracy in resolving fractional path delays and requires high computational complexity. In this paper, we propose a new approach to construct the eigendecomposition model by means of a simple arrangement of the frequency-domain estimated channel gains. Our method has lower computational complexity and is able to outperform the SoA techniques in terms of estimation accuracy and resolution. The performance is evaluated with numerical simulations and supported by measurements using realistic radio frequency (RF) hardware.

Power-domain non-orthogonal multiple access (NOMA) is a promising radio access technique with non-orthogonal resource allocation that provides a greater spectrum efficiency than the conventional orthogonal multiple access (OMA). In this paper, an iterative receiver is derived for NOMA. It is based on soft-information successive interference cancellation (SIC) combined with a minimum mean square error parallel interference cancellation (MMSE-PIC) detector. Orthogonal frequency division multiplexing (OFDM) is usually the typical waveform employed. However, with the proposed receiver design, any linear modulation can be used. In addition to OFDM, single-carrier (SC) and the recently proposed sparse Walsh-Hadamard (SWH) are investigated. The NOMA scheme is analysed in a multi-path fading channel, where two users have different power ratios and waveforms. Simulation results show that mixing OFDM and SWH for a two-user NOMA gives the best performance with low receiver complexity.

This paper describes a blind transmitter localization technique based on the deep neural network (DNN) framework. Blind localization assumes no previous knowledge on the transmit signal. It is shown that DNN based location approaches the maximum likelihood solution with reduced computational complexity. Moreover, the maximum likelihood, least squares and radio environment map localization estimators are presented in order to compare the design and performance of the proposed DNN algorithm. The system model is built based on a wireless sensor network that collects received signal strength measurements assuming disturbances of distance dependent correlated shadowing noise. Performance evaluation using numerical simulations shows that the proposed DNN scheme achieves location accuracy similar to the optimum maximum likelihood estimator while presenting computational complexity reduction of more than 90%.

Communication using orbital angular momentum (OAM) modes has recently received a considerable interest in free space optical (FSO) communications. Propagating OAM modes through free space may be subject to atmospheric turbulence (AT) distortions that cause signal attenuation and crosstalk which degrades the system capacity and increases the error probability. In this paper, we propose to enhance the OAM FSO communications in terms of bit error rate and spectral efficiency, for different levels of AT regimes. The performance gain is achieved by introducing orthogonal frequency division multiplexing (OFDM) with index modulation technique to the OAM FSO system.

Indoor localization can be based on a matrix of pairwise distances between nodes to localize and reference nodes. This matrix is usually not complete, and its completion is subject to distance estimation errors as well as to the noise resulting from received signal strength indicator measurements. In this paper, we propose to use convolutional neural networks in order to denoise the completed matrix. A trilateration process is then applied on the recovered euclidean distance matrix (EDM) to locate an unknown node. This proposed approach is tested on a simulated environment, using a real propagation model based on measurements, and compared with the classical matrix completion approach, based on the adaptive moment estimation method, combined with trilateration. The simulation results show that our system outperforms the classical schemes in terms of EDM recovery and localization accuracy.

In this paper, we investigate three different concepts for robust link-level performance under doubly-dispersive wireless channels, namely, i) channel estimation, ii) cyclic prefix (CP)-free transmission, and iii) waveform design. We employ a unique word-based channel estimation, where we decouple the channel related errors into channel estimation error (CEE) and Doppler error (DE). Then, we show that a trade-off between CEE and DE emerges in the frame design, where the system can be optimized to achieve the minimum composite channel error. Another strategy to improve the link-level performance is to suppress the CP of the sub-blocks. This allows for better channel estimation due to the reduced transmission time, with the penalty of requiring the CP-restoration processing at the receiver. Furthermore, we propose the waveform design based on the equal-reliability criterion (ERC), leading to the block multiplexing-orthogonal chirp division multiplexing (BM-OCDM). This waveform is advantageous in the CP-free transmission mode, where the data symbols have equally distributed interference from adjacent sub-blocks. Our framework is a generalization of the recently proposed orthogonal time frequency space (OTFS), which fails to achieve the ERC. The link-level simulations show that at high modulation and coding scheme, the proposed BM-OCDM provides superior link-level performance than OTFS.

This paper describes a coherent chirp spread spectrum (CSS) technique based on the Long-Range (LoRa) physical layer (PHY) framework. LoRa PHY employs CSS on top of a variant of frequency shift keying (FSK), and non-coherent detection is employed at the receiver for obtaining the transmitted data symbols. In this paper, we propose a scheme that encodes information bits on both in-phase and quadrature components of the chirp signal, and rather employs a coherent detector at the receiver. Hence, channel equalization is required for compensating the channel induced phase rotation on the transmit signal. Moreover, a simple channel estimation technique exploits the LoRa reference sequences used for synchronization to obtain the complex channel coefficient used in the equalizer. Performance evaluation using numerical simulation shows that the proposed scheme achieves approximately 1 dB gain in terms of energy efficiency, and it doubles the spectral efficiency when compared to the conventional LoRa PHY scheme. This is due to the fact that the coherent receiver is able to exploit the orthogonality between in-phase and quadrature components of the transmit signal.

Least square (LS) channel estimation employed in various communications systems suffers from performance degradation especially in low signal-to-noise ratio (SNR) regions. This is due to the noise enhancement in the LS estimation process. Minimum mean square error (MMSE) takes into consideration the noise effect and achieves better performance than LS with higher complexity. This paper proposes to correct the LS estimation error using deep learning (DL). Simulation results show that the proposed DL-based schemes perform better than both LS and MMSE channel estimation scheme, with less complexity than accurate MMSE.

Channel state information is very critical in various applications such as physical layer security, indoor localization, and channel equalization. In this paper, we propose an adaptive channel estimation based on deep learning that assumes the signal-to-noise power ratio (SNR) knowledge at the receiver, and we show that the proposed scheme highly outperforms linear minimum mean square error based channel estimation in terms of normalized minimum square error, with similar order of online computational complexity. The proposed channel estimation scheme is also evaluated for an imperfect estimation of the SNR and showed to be robust for a high SNR estimation error.

Lack of coordination between network layers limits the performance of most proposed solution for new challenges posed by wireless networks. To overcome such limitations, cross-layer physical and medium access (PHY-MAC) design for multi-input-multi-output orthogonal frequency division multiple access system in heterogeneous networks (HetNETs) is proposed. In this paper, we formulate an optimization problem for hybrid beamforming, in a multi-user HetNET scenario aiming to maximize the total system throughput. Furthermore, analog beamforming is selected from a codebook containing a limited number of candidates for steering vectors. The proposed problem is non-convex and hard to solve. Thus it is relaxed by transforming it into a subtraction form of two convex funcions. Afterward we apply a group of well-known metaheuristic algorithms to calculate the normalized hybrid beamforming vectors. The optimal solution is obtained using an exhaustive search (ES) algorithm that provides an ideal solution, but with high complexity. In addition, zero-forcing-based approach (ZFA), matched filter (MF), and QR-based approach (QR) are applied to get quick sub-optimal solutions. Hence, we analyze the performance of our systems using the throughput metric. The simulation results show that QR algorithm outperforms ZFA and MF in low and middle signal-tonoise ratio (SNR) regime, while ZFA outperforms QR and MF at higher SNRs. Moreover, QR is close to the optimal solution ES.

To meet the requirements of new 5G use cases, two methodologies have been proposed. The first considers additional processing on orthogonal frequency division multiplexing (OFDM), e.g. windowing, filtering, and precoding. The other aims at the design of new modulation schemes. Essentially, any block-based linear modulation, such as generalized frequency division multiplexing (GFDM), can be seen as precoded-OFDM, where the precoded data result from the frequency domain modulation. This representation has a significant benefit for the implementation of new waveforms. Namely, the available OFDM transceiver techniques can be reused, whereas the required signal features can be fulfilled by the configuration of the GFDM modem. In this paper, we propose flexible OFDM precoding based on the GFDM framework. As a particular case, we focus on orthogonal precoding to enhance the performance in fading channels. By means of closed-form expressions, we show that all the sybsymbols within the same GFDM subcarrier attain the same signal-to-interference-plus-noise ratio (SINR) in frequency selective channel. In special precoding cases, all symbols achieve equal SINR. This feature is important to enhance the performance without a need for power allocation.

The conventional receiver designs of generalized frequency division multiplexing (GFDM) system assume full subcarrier allocation. In this case, the optimal linear receivers can be implemented with low-complexity channel equalization followed by zero-forcing (ZF) demodulation. In some use cases, e.g. multiuser, only a subset of the subcarriers is active for data transmission. Therefore, the optimal receiver design needs to consider the effective joint channel and modulation matrix, which complicates the practical implementation. To maintain low-complexity realization in these cases, full allocation can still be assumed, however, the performance loss is remarkable. In this paper, we propose an efficient transceiver design for non-fully allocated GFDM system. In the proposed approach, the frequency-domain (FD) sparsity of GFDM is exploited to represent the transmitted signal by means of an effective small-size GFDM model with one non-active subcarrier. Therefore, the assumption of full allocation becomes more realistic. Moreover, the received signal can be further reformulated with fully allocated system, but at the cost of altering the effective channel gains. The proposed design significantly reduces the computation cost of the practical GFDM receiver, whereas the performance still approaches the counterpart optimal linear receiver.

Orthogonal time frequency space modulation (OTFS) has been recently proposed to achieve time and frequency diversity, especially in linear time-variant (LTV) channels with large Doppler frequencies. The idea is based on the precoding of the data symbols using symplectic finite Fourier transform (SFFT) then transmitting them by mean of orthogonal frequency division multiplexing (OFDM) waveform. Consequently, the demodulation and channel equalization can be coupled in one processing step. As a distinguished feature, the demodulated data symbols have roughly equal gain independent of the channel selectivity. On the other hand, generalized frequency division multiplexing (GFDM) modulation also employs the spreading over the time and frequency domains using circular filtering. Accordingly, the data symbols are implicitly precoded in a similar way as applying SFFT in OTFS. In this paper, we present an extended representation of GFDM which shows that OTFS can be processed as a GFDM signal with simple permutation. Nevertheless, this permutation is the key factor behind the outstanding performance of OTFS in LTV channels, as demonstrated in this work. Furthermore, the representation of OTFS in the GFDM framework provides an efficient implementation, that has been intensively investigated for GFDM, and facilitates the understanding of the OTFS distinct features.

In this paper we study the achievable rate region of an FBMC based two-way decode-and-forward relaying system. Unlike a CPOFDM system, the FBMC based systems experience inter-carrier interference and inter-symbol interference especially in a highly frequency selective channel. To calculate the resulting rate region, we have to solve a joint optimization of the per-subcarrier multitap filters at the relay as well as at the users, which is nonconvex. Therefore, we resort to a two-step approach. First, we design closed-form solutions for the per-subcarrier pre-equalizers and equalizers at all nodes. Then we derive an optimal power allocation scheme to maximize the achievable rate. Simulation results show that the achievable sum rate increases as the number of taps used for pre-equalization and equalization at the subcarriers increases.