Orthogonal time-frequency space (OTFS) is a promising waveform for future Sixth Generation (6G) networks and beyond, offering robustness to high-mobility scenarios and enabling seamless integration of sensing and communication services by operating in the delay-Doppler (DD) domain. Reliable channel estimation is essential for both functions; however, conventional embedded-pilot (EP) schemes sustain high pilot overhead and an increased peak-to-average power ratio (PAPR). Superimposed training (ST) methods have been proposed as an alternative to reduce these limitations, since pilots and data symbols share the same DD resources. Nevertheless, existing ST-based techniques typically depend on computationally expensive algorithms or exhibit degraded communication and sensing performance. To overcome these restrictions, this work introduces a novel low-power superimposed pilot design, called sunscape pattern, and a corresponding ST-based channel estimation algorithm specifically tailored to this structure. The core design principle of the sunscape pattern is to distribute pilot energy following a structured layout in the DD domain that resembles a seascape with a sun over the sea. The sun enables accurate delay and Doppler detection (both integer and fractional), while the sea inherently supports interference averaging between pilot and data components, thereby mitigating self-interference and noise without requiring computationally expensive iterative interference cancellation. Simulation results validate the proposed scheme. They show that it achieves accurate channel estimation and reliable DD-domain detection with low computational complexity and manageable PAPR, outperforming existing approaches in both communication and sensing performance.

Integrated sensing and communications (ISAC) is a new feature to be deployed in the evolution of the Sixth Generation (6G) of mobile networks. Currently, this service is planned to use the recently introduced positioning reference signal (PRS) at the expense of further sacrificing the data rate. In this work, we propose the use of a superimposed training (ST)-based simultaneous channel estimation and sensing via the superposition of a pilot Zadoff-Chu (ZC) sequence on the orthogonal frequency division multiplexing (OFDM). The proper design of the parameters of the ZC sequence and the use of matched filtering at the receiver significantly improve the quality of the channel estimates and enable accurate detection of the targets. The ZC pilot signal shares all time-frequency-space resources, thereby achieving zero pilot overhead and eliminating the need for any resources specifically dedicated to reference signals. The analysis and numerical results confirm that the proposed design achieves low-complex and accurate channel estimation and sensing, simplifying and enhancing the implementation of ISAC for 6G applications.

This paper derives an achievable rate for centralized uplink cell-free massive MIMO (CF-mMIMO) networks when using superimposed pilots in combination with advanced combining schemes. This rate relies on a novel channel estimation technique that uncorrelates the combiner applied to any given information symbol from the channel estimate and its corresponding channel estimation error. The performance of powerful combining strategies such as minimum mean square error (MMSE) or zero-forcing (ZF) can then be assessed in the context of superimposed pilot transmission. Results show that despite superimposed training might be suitable when using the simple maximum ratio combining (MRC) technique, its performance when compared with regular pilot transmission falls short when advanced combining strategies are used.

Ultra-dense networks (UDNs) have been proposed to achieve high data rates and energy efficiency, but their performance is limited by the increase of inter-user interference. To overcome this problem, interference alignment (IA) algorithms have been widely researched. However, reported IA techniques neglect the nonlinear distortion (NLD) induced by power amplifiers. Thus, their performance is severely degraded in power-efficient transmissions operating close to the saturation point. Distortion-aware precoding techniques have been studied to reduce NLD in single transmitter scenarios either single-user or multi-user. Nevertheless, its extension to UDNs with multiple transmitters and users is not straightforward. In this work, a novel IA algorithm, named NLD-IA, is proposed to alternatively design the precoding and combining vectors to reduce the interference and NLD by maximizing the sum-rate through the signal-to-interference-plus-noise ratio (SINR). Precoding vectors are obtained via a non-convex optimization problem that models the NLD correlation. An analytical solution based on the Newton method over the complex field is developed to solve this problem with low computational complexity. Simulation results show that the proposed NLD-IA significantly reduces interference and NLD outperforming previous IA algorithms. The proposed method is an attractive solution for UDNs commonly found in the context of Internet of Things applications.

Superimposed training (ST) is one of the most appealing channel estimation techniques for orthogonal frequency division multiplexing (OFDM), to be possibly exploited in the 6G. The data and pilot symbols are sharing the same time and frequency resources, and the overhead is significantly reduced. Besides, the superimposed pilots can be also used for the reduction of the peak-to-average power ratio (PAPR). However, joint channel estimation and PAPR reduction procedures have never been addressed. In this work, a novel scheme denoted as dual layers-superimposed training (DL-ST) is proposed for this joint purpose. The training sequence (TS) of the first layer is targeted to perform channel estimation, while the TS of the second layer is designed for PAPR reduction and is made transparent to the first one. Both layers can be independently processed, which implies a reduced complexity. To verify the performance of the proposed technique, the analytical expression of the channel estimation mean squared error (MSE) is derived. Finally, several numerical results further illustrate the performance of the proposal, showing how the MSE is improved while significant PAPR reductions are attained with negligible complexity.

Fully digital (FD) beamforming strategies become prohibitive in massive multiple-input multiple-output (mMIMO) systems due to their hardware complexity. Therefore, hybrid digital-analog beamforming (HB) is considered in millimeter waves (mmWaves) to reduce the number of radio frequency (RF) chains. When the analog stage is implemented with low-resolution phase shifters (PSs), which implies quantized phase constraints, the spectral efficiency (SE) is severely affected. To overcome this degradation, the reported designs in the literature improve performance at the cost of adding more RF chains than data streams. However, this solution reduces the energy-efficiency of the system. This work proposes a novel HB method to achieve a near-optimal FD performance for mMIMO systems with the minimum required number of RF chains. The reduction of RF chains arises as a key procedure to obtain an energy-efficient design. The proposed HB scheme is based on a quantization-aware matrix composition (QMC) to systematically compensate for the residual quantization errors. Furthermore, a quantization strategy, called sum Euclidean quantization (SEQ), is introduced to improve the accuracy of the analog stage. Numerical results reveal that unlike previous reported HB methods, a near-optimal SE can be achieved with our proposal while minimizing the number of RF chains and thus, the hardware cost and power consumption.

Constant envelope orthogonal frequency division multiplexing (CE-OFDM) is a multi-carrier waveform with 0 dB peak-to-average power ratio (PAPR). This property enables the exploitation of multi-carrier waveforms with non-linear power amplifiers, avoiding the undesirable clipping effects. However, the existing channel estimation techniques designed for OFDM cannot be reused, since the use of a phase modulator makes CE-OFDM a non-linear waveform. Previous works assumed that the channel estimation process relies on the transmission of preambles, and the data symbols are equalized using a frequency domain equalizer (FDE). To avoid the overhead induced by preambles, a phase-domain injected training (PIT) is proposed, where the pilot sequence is embedded in the phase dimension of the data symbols. This novel approach does not waste time and/or frequency resources as in preamble-based schemes. Moreover, it does not require additional power for the training. The received symbols are averaged with a dual procedure, and owing to the particular structure of CE-OFDM, the channel estimates are recovered. Also, the analytical expression of the channel estimation mean squared error (MSE) is derived. Finally, several numerical results illustrate the performance of the proposal, showing that the MSE, bit error rate (BER) and achievable rate are improved, as compared to the existing works.

Cyclic block filtered multi-tone (CB-FMT) is a waveform that can be efficiently synthesized through a filter-bank in the frequency domain. Although the main principles have been already established, channel estimation has not been addressed yet. This is because of assuming that the existing techniques based on pilot symbol assisted modulation (PSAM), implemented in OFDM-like schemes, can be reused. However, PSAM leads to an undesirable loss of data-rate. In this paper, an alternative method inspired by the superimposed training (ST) concept, namely pilot pouring ST (PPST), is proposed. In PPST, pilots are superimposed over data taking advantage of the particular spectral characteristics of CB-FMT. Exploiting the sub-channel spectrum, the pilot symbols are poured in those resources unused for data transmission. This spectral shaping of pilots is also exploited at the receiver to carry out channel estimation, by enhancing those channel estimates that exhibit a low data interference contribution. Furthermore, a frequency domain resource mapping strategy for the data and poured pilot symbols is proposed to enable an accurate estimation in strongly frequency-selective channels. The parameters of the proposed scheme are optimized to minimize the channel estimation mean squared error (MSE). Finally, several numerical results illustrate the performance advantages of the proposed technique as compared to other alternatives.

Network densification is one of the most promising solutions to address the high data rate demands in 5G and beyond (B5G) wireless networks while ensuring an overall adequate quality of service. In this scenario, most users experience significant interference levels from neighbouring mobile stations (MSs) and access points (APs) making the use of advanced interference management techniques mandatory. Clustered interference alignment (IA) has been widely proposed to manage the interference in densely deployed scenarios with a large number of users. Nonetheless, the setups considered in previous works are still far from the densification levels envisaged for 5G/B5G networks that are considered in this paper. Moreover, prior designs of clustered‐IA systems relied on oversimplified channel models and/or enforced single‐stream transmission. In this paper, we explore an ultradense deployment of small cells (SCs) to provide coverage in 5G/B5G wireless networks. A novel cluster design based on a size‐restricted k‐means algorithm to divide the SCs into different clusters is proposed taking into account path loss and shadowing effects, thus providing a more realistic solution than those available in the current literature. Unlike previous works, this clustering method can also cater for spatial multiplexing scenarios. Also, several design parameters such as the number of transmit antennas, multiplexed data streams, and deployed APs are analyzed in order to identify trade‐offs between performance and complexity. The relationship between density of network elements per area unit and performance is investigated, thus allowing to illustrate that there is an optimal coverage area value over which the network resources should be distributed. Moreover, it is shown that the spectral‐efficiency degradation due to the intercluster interference in ultradense networks (UDNs) points to the need of designing an interference management algorithm that accounts for both intracluster and intercluster interferences. Simulation results provide key insights for the deployment of small cells in interference‐limited dense scenarios.

Energy harvesting (EH) emerges as a novel technology to promote green energy policies. Based on Cognitive Radio (CR) paradigm, nodes are designed to operate with harvested energy from radio frequency signals. CR-EH systems state several strategies based on sensing and access policies to maximize throughput and protect primary users from interference, simultaneously. However, reported solutions do not consider to maximize detection performance to detect spectrum holes which represent a major drawback whenever available energy is not efficiently used. In this concern, this paper addresses optimal sensing policies based on energy harvesting schemes to maximize probability of detection of available spectrum. These novel policies may be incorporated to previous reported solutions to maximize performance. Optimal processing scheduling schemes are proposed for offline and online scenarios based on convex optimization theory, Dynamic Programming (DP) algorithm and heuristic solutions (Constant Power and Greedy policies). Performance of proposed policies are validated by simulations for common detection techniques such as Matched Filter (MF), Quadrature Matched Filter (QMF) and Energy Detector (ED). As a result, it is shown that the best detection scheme theoretically addressed by MF, does not always perform better than the poorest detection scheme, given by the ED, in an energy harvesting scenario.

Visible Light Communications (VLC) is considered one of the most promising technologies to guarantee the future demand of user wireless data rate. Since a DC value is needed for illumination, Direct Current-biased Optical Orthogonal Frequency Division Multiplexing (DCO-OFDM) is adopted as modulation technique in these environments. Channel estimation in VLC is quite different from the conventional radiofrequency (RF) counterparts because the indoor optical wireless channel model significantly differs from the traditional RF one. Previous proposals rely on dedicated pilot symbols to perform channel estimation what leads to a loss in efficiency. In this paper, we propose a novel approach of Superimposed Training (ST) for channel estimation in VLC scenarios.

Wireless broadband communication systems are always requiring higher data rates. In order to achieve this goal, we should provide advanced schemes which are capable of improving the spectral efficiency and reusing, in a better way, the available radio spectrum resources. Filter Bank Multi-Carrier Offset Quadrature Amplitude Modulation (FBMC-OQAM) combined with Superimposed Training (ST) is a promising technique with a very high spectral efficiency. This improvement is due to the low out-of-band emissions of FBMC-OQAM, because it uses a well-designed prototype filter, and the lack of dedicated pilot tones owing to ST scheme. However, this combination is not straightforward due to the appearance of the self-interference at receiver side in FBMC-OQAM. In this paper, we provide a novel channel estimation which is capable of dealing with this self-interference in the context of combining these two techniques.

In this paper it is presented a proposal to increase the capacity of Down-Link (DL) transmissions in Long Term Evolution (LTE) like systems based on Multiple-Input Multiple-Output (MIMO)-Orthogonal Frequency Division Multiplexing (OFDM). The augment of the data rate is achieved with the total or partial suppression of the Cyclic Prefix (CP), which requires the use of a variable number of samples without conveying any information. The proposal is based on an iterative cancellation of the main impairments that the CP suppression supposes, the Inter Symbol and Inter Carrier Interferences. The interference mitigation procedure demands adequate channel estimations obtained in two different stages. Firstly, this new scheme requires the utilization of a preamble symbol appended to the beginning of the data transmission, which enables an initial Maximum Likelihood channel estimation. Secondly, time-variant channels will be estimated using a Least Squares estimator by the use of scattered pilots within the LTE frame structure. Through simulations it has been demonstrated that, despite the interferences arisen due to the CP suppression, our proposal attains adequate channel estimations which converge to theoretical bounds and the overall system obtains values of Bit Error Rate similar to the ideal situation of not suppressing the CP. It is also presented that considering the typical values of CP in the LTE standard the increment of the capacity employing this strategy can range between 7% and 25%.

A new support vector machine (SVM) algorithm for coherent robust demodulation in orthogonal frequency-division multiplexing (OFDM) systems is proposed. We present a complex regression SVM formulation specifically adapted to a pilots-based OFDM signal. This novel proposal provides a simpler scheme than an SVM classification method. The feasibility of our approach is substantiated by computer simulation results obtained for IEEE 802.16 broadband fixed wireless channel models. These experiments allow to scrutinize the performance of the OFDM-SVM system and the suitability of the epsiv-Huber cost function, in the presence of non-Gaussian impulse noise interfering with OFDM pilot symbols

In this paper, a novel peak-to-average power reduction approach for orthogonal frequency division multiplexing (OFDM) has been addressed. Two-dimensional pilot-symbol assisted modulation (2D-PSAM) is employed in coherent OFDM for channel estimation, and it is based on inserting known symbols spread throughout the 2D time-frequency grid. These pilot symbols are employed to simultaneously perform distortionless peak power reduction with a suboptimum technique named orthogonal pilot sequences (OPS), which reduces additional system complexity and side information compared to optimum pilot values. This proposal attains a further step over other previous works, since this set of sequences allows blind detection at the receiver without prior knowledge of any side information.

Maximum likelihood (ML) joint detection of multi-carrier code division multiple access (MC-CDMA) systems can be efficiently implemented with a sphere decoding (SD) algorithm. In this paper, we examine the application of complex instead of real SD to detect MC-CDMA, which solves many problems in a more elegant manner and extends SD adaptability to any constellation. We first propose a new complex SD algorithm whose efficiency is based on not requiring an estimate of the initial search radius but selecting the Babai point as the initial sphere radius instead; also, efficient strategies regarding sorting the list of possible lattice points are applied. Indeed, complex SD allows complex matrix operations which are faster than real counterparts in double dimension. Next, a novel lattice representation for the MC-CDMA system is introduced, which allows optimum multiuser detection directly from the received signal. This avoids noise whitening operation, and also despreading and equalization procedures are not required further at the receiver side

A new peak-to-average power reduction approach for OFDM has been addressed. Two-dimensional pilot-symbol assisted modulation (2D-PSAM) is employed in coherent OFDM for channel estimation and it is based on inserting known symbols spread throughout the 2D time-frequency grid. We show that these scattered symbols can also be employed to perform distortionless peak power reduction. To reduce additional system complexity and side information, a finite set of orthogonal pilot sequences is proposed, and also blind detection can be performed without prior knowledge of redundancy at the receiver.

A new frequency offset tracking approach for OFDM has been addressed. Two-dimensional pilot-symbol assisted modulation (2D-PSAM) is employed in coherent OFDM for channel estimation and it is based on inserting known symbols spread out through the 2D time-frequency grid. We show that these scattered symbols can also be employed to perform frequency synchronization. Maximum likelihood estimation has been derived for both time and frequency domains. Also, if a decision-guided strategy is used jointly with the 2D-PSAM signalling, an improvement in performance can be attained.