status: Published online

What is the optimal base station (BS) resource allocation strategy given a measurement-based power consumption model and a fixed target user rate? Rush-to-sleep in time, rush-to-mute in space, awake-but-whisper in power, or a combination of them? We propose in this paper an efficient solution to the problem of finding the optimal number of active time slots, active antennas, and transmit power at active antennas in a multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) system under per-user rate and per-antenna transmit power constraints. The use of a parametric power consumption model validated on operator measurements of 4G and 5G BSs enhances the interpretation of the results. We discuss the optimal energy-saving strategy at different network loads for three BS configurations. Using as few BS antennas as possible is close to optimal in BSs not implementing time-domain power savings such as micro-discontinuous transmission (μDTX). Energy-saving schemes that jointly operate in the three domains are instead optimal when the BS hardware implements time-domain power-saving modes, with a tendency for rush-to-mute in massive MIMO and for rush-to-sleep in BSs with fewer antennas. Median energy savings up to 30% and energy efficiency improvements up to 50% are achieved at low network loads.

This paper introduces a new approach to physical layer security (PLS) by leveraging power amplifier (PA) nonlinear distortion through distortion-aware precoding. While some conventional PLS techniques inject artificial noise orthogonal to legitimate channels, we demonstrate that inherent PA nonlinearities typically considered undesirable can be exploited to enhance security. The zero 3rdorder (Z3RO) precoder applies a negative polarity to several antennas to cancel the PA distortion at the user location, resulting in distortion being transmitted in non-user locations. Redirecting the distortion to non-user locations creates interference for potential eavesdroppers, lowering their signal-to-noise-and-distortion ratio (SNDR). Numerical simulations reveal that the Z3RO precoder achieves up to a 2.5× improvement in secrecy rate compared to conventional maximum ratio transmission (MRT) precoding under a 10% outage probability, SNR of 32 dB and −5 dB input back-off (IBO) where the PAs enter the saturation regime.

This paper presents an optimal calibration scheme and a weighted least squares (LS) localization algorithm for received signal strength (RSS) based visible light positioning (VLP) systems, focusing on the often-overlooked impact of light-emitting diode (LED) tilt. By optimally calibrating LED tilt and gain, we significantly enhance VLP localization accuracy. Our algorithm outperforms both machine learning Gaussian processes (GPs) and traditional multilateration techniques. Against GPs, it achieves improvements of 58% and 74% in the 50th and 99th percentiles, respectively. When compared to multilateration, it reduces the 50th percentile error from 7.4 cm to 3.2 cm and the 99th percentile error from 25.7 cm to 11 cm. We introduce a low-complexity estimator for tilt and gain that meets the Cramer-Rao lower bound (CRLB) for the mean squared error (MSE), emphasizing its precision and efficiency. Further, we elaborate on optimal calibration measurement placement and refine the observation model to include residual calibration errors, thereby improving localization performance. The weighted LS algorithm’s effectiveness is validated through simulations and real-world data, consistently outperforming GPs and multilateration, across various training set sizes and reducing outlier errors. Our findings underscore the critical role of LED tilt calibration in advancing VLP system accuracy and contribute to a more precise model for indoor positioning technologies.

We study cell-free massive multiple-input multiple-output precoders that minimize the power consumed by the power amplifiers subject to per-user per-subcarrier rate constraints. The power at each antenna is generally retrieved by solving a fixed-point equation that depends on the instantaneous channel coefficients. Using random matrix theory, we retrieve each antenna power as the solution to a fixed-point equation that depends only on the second-order statistics of the channel. Numerical simulations prove the accuracy of our asymptotic approximation and show how a subset of access points should be turned off to save power consumption, while all the antennas of the active access points are utilized with uniform power across them. This mechanism allows to save consumed power up to a factor of 9× in low-load scenarios.

Visible light positioning (VLP) offers a cost-effective and accurate method for indoor localization. Gaussian processes (GPs), a data-driven method widely used in the received signal strength (RSS)-based VLP systems, face difficulties when training data are scarce or when forced to extrapolate. In this work, we propose a novel hybrid model, PhyGP, which integrates physics-based models into GPs through Bayesian active learning to enhance extrapolation capabilities without decreasing the interpolation accuracy of GPs. Experimental results, validated on real-world data, demonstrate significant improvements in extrapolation accuracy compared to GPs and in computational efficiency compared to physics-based models. Our approach achieves an improvement in extrapolation accuracy ranging from 32% to 83%, reducing the $\mathbf{P 5 0}$ error from 72 cm to 12 cm at its best performance. Additionally, the PhyGP model offers a four-order magnitude gain in computational efficiency compared with physics-based models.

The increasing energy consumption of mobile networks has emerged as a critical concern for mobile telecommunication operators, requiring measures to curb or reverse the historical upward trajectory in order to align with the sector’s decarbonization target. Meanwhile, 5G-NR is massively deployed in the networks to improve quality of service, with the hope of simultaneously improving energy efficiency thanks to enhanced power-saving features. However, up-to-date 5G-enabled base stations, that support higher bandwidths with more transceivers at higher frequencies, raise concerns about their absolute power consumption, especially at low traffic loads. Proper power models are therefore needed to identify the key levers for energy savings in mobile networks under real traffic loads. This paper addresses this challenge by first providing a parametric power consumption model applicable to commercial sub- 6 GHz cellular base stations. Then, numerical model parameters are estimated by combining on-site measurements from operators with radio equipment documentation from manufacturers. The uncertainty of model predictions is assessed to be in the range of $\mathbf{1 0 - 2 0 \%}$. Moreover, we show that the proposed average power model aligns well with measurements of equipment that use existing power-saving features. Estimates of power consumption are also provided for typical 3-sector single-band macro base stations in active mode, e.g., 1-4 kW when equipped with traditional radio units, and 2-4 kW when using active antenna units.

Massive multiple input multiple output (MIMO) systems are typically designed under the assumption of linear power amplifiers (PAs). However, PAs are typically most energy-efficient when operating close to their saturation point, where they cause non-linear distortion. Moreover, when using conventional precoders, this distortion coherently combines at the user locations, limiting performance. As such, when designing an energy-efficient massive MIMO system, this distortion has to be managed. In this work, we propose the use of a neural network (NN) to learn the mapping between the channel matrix and the precoding matrix, which maximizes the sum rate in the presence of this non-linear distortion. This is done for a third-order polynomial PA model for both the single and multi-user case. By learning this mapping a significant increase in energy efficiency is achieved as compared to conventional precoders and even as compared to perfect digital pre-distortion (DPD), in the saturation regime.

In indoor localization, Received Signal Strength (RSS)-based Visible Light Positioning combined with Multi Layer Perceptrons (MLPs) or Gaussian processes (GPs) has attracted much attention due to its high accuracy. However, there is a lack of detailed investigation on the advantages, disadvantages, and applicability of MLPs and GPs in large datasets collected from representative industrial environments. In this paper, we present a comprehensive comparison and analysis of MLPs and GPs from theoretical and experimental perspectives, focusing on model parameters, complexity, and interpretability. Our study demonstrates that while GPs outperform MLPs on small datasets, they exhibit drawbacks such as high computational cost on larger datasets. Furthermore, our investigation reveals that including Batch Normalization (BN) layers in MLPs enhances their generalization and suppresses outliers in prediction. To address the issues of scalability and interpretability, we introduce the Deep Kernel Learning (DKL) model as a solution, supported by both theoretical and experimental findings.

Massive multiple-input multiple-output (MIMO) precoders are typically designed by minimizing the transmit power subject to a quality-of-service (QoS) constraint. However, current sustainability goals incentivize more energy-efficient solutions and thus it is of paramount importance to minimize the consumed power directly. Minimizing the consumed power of the power amplifier (PA), one of the most consuming components, gives rise to a convex, non-differentiable optimization problem, which has been solved in the past using conventional convex solvers. Additionally, this problem can be solved using a proximal gradient descent (PGD) algorithm, which suffers from slow convergence. In this work, in order to overcome the slow convergence, a deep unfolded version of the algorithm is proposed, which can achieve close-to-optimal solutions in only 20 iterations as compared to the 3500 plus iterations needed by the PGD algorithm. Results indicate that the deep unfolding algorithm is three orders of magnitude faster than a conventional convex solver and four orders of magnitude faster than the PGD.

The number of wireless devices is drastically increasing, resulting in many devices contending for radio resources. In this work, we present an algorithm to detect active devices for unsourced random access, i.e., the devices are uncoordinated. The devices use a unique, but non-orthogonal preamble, known to the network, prior to sending the payload data. They do not employ any carrier sensing technique and blindly transmit the preamble and data. To detect the active users, we exploit partial channel state information (CSI), which could have been obtained through a previous channel estimate. For static devices, e.g., Internet of Things nodes, it is shown that CSI is less time-variant than assumed in many theoretical works. The presented iterative algorithm uses a maximum likelihood approach to estimate both the activity and a potential phase offset of each known device. The convergence of the proposed algorithm is evaluated. The performance in terms of probability of miss detection and false alarm is assessed for different qualities of partial CSI and different signal-to-noise ratio.

Large array systems use a massive number of antenna elements and clever precoder designs to achieve an array gain at the user location. These precoders require linear front-ends, and more specifically linear power amplifiers (PAs), to avoid distortion. This reduces the energy efficiency since PAs are most efficient close to saturation, where they generate most nonlinear distortion. Moreover, the use of conventional precoders can induce a coherent combining of distortion at the user locations, degrading the signal quality. In this work, novel linear precoders, simple to compute and to implement, are proposed that allow working close to saturation, while cancelling the third-order nonlinearity of the PA without prior knowledge of the signal statistics and PA model. Their design consists in saturating a single or a few antennas on purpose together with an negative gain with respect to all other antennas to compensate for the overall nonlinear distortion at the user location. The performance gains of the designs are significant for PAs working close to saturation, as compared to maximum ratio transmission (MRT) precoding and perfect per-antenna digital pre-distortion (DPD) compensation.

Large array-based transmission uses the combination of a massive number of antenna elements and clever precoder designs to achieve array gain and spatially multiplex different users. These precoders require linear front-ends, and more specifically linear power amplifiers (PAs). However, this reduces energy efficiency since PAs are most efficient close to saturation, where they generate most nonlinear distortion. Moreover, the use of conventional precoders, such as maximum ratio transmission (MRT), induces a coherent combining of distortion at the user location, degrading the signal quality. In this work, a linear precoder is proposed that allows working close to saturation while canceling the coherent combining of the third order nonlinear PA distortion at the user location. In contrast to other solutions, the zero third-order distortion (Z3RO) precoder does not require prior knowledge of the signal statistics and the PA model. The design consists of saturating a single or a few antennas on purpose together with an opposite phase shift to compensate for the distortion of all other antennas. The resulting array gain penalty becomes negligible as the number of base station antennas grows large.

In multiple input multiple output (MIMO) systems, precoding allows the base station to spatially focus and multiplex signals towards each user. However, distortion introduced by power amplifier nonlinearities coherently combines in the same spatial directions when using a conventional precoder such as maximum ratio transmission (MRT). This can strongly limit the user performance and moreover create unauthorized out-of-band (OOB) emissions. In order to overcome this problem, the zero third-order distortion (Z3RO) precoder was recently introduced. This precoder constraints the third-order distortion at the user location to be zero. In this work, the performance of the Z3RO precoder is validated based on real-world channel measurement data. The results illustrate the reduction in distortion power at the UE locations: an average distortion reduction of 6.03 dB in the worst-case single-user scenario and 3.54 dB in the 2-user case at a back-off rate of -3dB.

Non-terrestrial networks (NTN) can provide connectivity in unreachable or remote areas. The massive multiple-input multiple-output (MIMO) is a promising architecture for future NTN networks through different platforms, such as earth orbit satellites or airborne vehicles. The long transmission distance and large coverage area challenge the physical layer design in a massive MIMO system. In particular, there is a clear trade-off between power amplifier (PA) efficiency and linearity: PAs are most efficient close to saturation, generating the most nonlinearities and degrading the achievable rate. In this paper, we study the power allocation and array selection in a distributed LoS massive MIMO system with maximum ratio transmission (MRT), by taking PA nonlinearity characteristics into account. With the objective to maximize the sum spectral efficiency (SE) with total power constraints, we first formulate the power allocation problem as nonlinear programming. Then, we propose an iterative power allocation algorithm based on the multiplier punitive method. Simulation results corroborate that the proposed power allocation can maximize spectral efficiency with awareness of the PA nonlinearity and significantly prevents it from degrading performance.

One important problem in spectrum sensing is to detect a noisy and unknown signal, while keeping the risk of detection error as low as possible. This problem may increase in mobile environments due to fast situation changes. In this paper, we consider a mobile cognitive radio scenario, and try to evaluate whether some knowledge about the environment and the mobility parameters of the user can help in improving the detection of changes in the spectrum occupancy. To do so, we assume that the mobility parameters can be summarized in some a priori knowledge on the average time of spectrum change and we use Bayesian changepoint detection methods. Considering that the power of the signal to be detected is usually unknown, a low-complexity algorithm is proposed that does not rely on this knowledge. It is then compared with the existing algorithms in the literature. Finally, a new metric is introduced to jointly evaluate the costs of interference and spectrum waste induced by the changepoint detection algorithms, in a time-limited communication context. Results reveal that the derived algorithm outperforms its non-Bayesian equivalent at low signal to noise ratio (SNR).

Iterative localization is currently seen as an attractive solution to localize a transmitter in a cellular network. It has been shown that, by iterating between a range estimation step and a multi-lateration step, it is possible to refine the estimation in the first step, where only local information is used at iteration one. The iterative approach gets close to the performance of direct localization; nevertheless, it does not seem to converge to the direct localization performance for medium and low signal-to-noise-ratio values, due to the fact that it still suffers from loss of information due to projections and data representation. In this work, we propose to approximate the range log-likelihood at the base station with a Dirichlet kernel and to perform all the processing in a common xy-domain so that projections are no longer needed. We numerically show that our approach brings significant performance gains as compared to the time-of-arrival based iterative position estimation algorithm, getting really close to the performance of direct localization.

A distributed antenna system (DAS) consists of several interconnected access points (APs) which are distributed over an area. Each AP has an antenna array. In previous studies, the DAS has been demonstrated great potential to improve capacity and power efficiency compared to a centralized antenna system (CAS) which has all the antennas located in one place. The existing research also has shown that the placement of the APs is essential for the performance of the DAS. However, most research on AP placement does not take into account realistic constraints. For instance, they assume that APs can be placed at any location in a region or the array radiation pattern of each AP is isotropic. This paper focuses on optimizing the AP placement for the DAS with massive MIMO (D-mMIMO) in order to reduce the transmit power. A square topology for the AP placement is applied, which is reasonable for deploying the D-mMIMO in urban areas while also offering theoretically interesting insights. We investigate the impact of the radiation pattern, signal coherence, and region size on the D-mMIMO's performance. Our results suggest that (i) among the three factors, the array radiation pattern of each AP is the most important one in determining the optimal AP placement for the D-mMIMO; (ii) the performance of the D-mMIMO is highly impacted by the placement and array radiation pattern of each AP, the D-mMIMO with unoptimized placement may perform even worse than the CAS with massive MIMO (C-mMIMO); (iii) with the consideration of patch antennas and the mutual coupling effect, the optimized D-mMIMO can potentially save more than 7dB transmit power compared to the C-mMIMO. Furthermore, our analytical results also provide an intuition for determining an adequate AP placement for the D-mMIMO in practice.

Physical-layer security (PLS) has the potential to strongly enhance the overall system security as an alternative to or in combination with conventional cryptographic primitives usually implemented at higher network layers. Secret-key generation relying on wireless channel reciprocity is an interesting solution as it can be efficiently implemented at the physical layer of emerging wireless communication networks, while providing information-theoretic security guarantees. In this article, we investigate and compare the secret-key capacity based on the sampling of the entire complex channel state information (CSI) or only its envelope, the received signal strength (RSS). Moreover, as opposed to previous works, we take into account the fact that the eavesdropper's observations might be correlated and we consider the high signal-to-noise ratio (SNR) regime where we can find simple analytical expressions for the secret-key capacity. As already found in previous works, we find that RSS-based secret-key generation is heavily penalized as compared to CSI-based systems. At high SNR, we are able to precisely and simply quantify this penalty: a halved pre-log factor and a constant penalty of about 0.69 bit, which disappears as Eve's channel gets highly correlated.

Estimating downlink (DL) channel state information (CSI) in frequency division duplex (FDD) massive multi-input multi-output (MIMO) systems generally requires downlink pilots and feedback overheads. Accordingly, this paper investigates the feasibility of zero-feedback FDD massive MIMO systems based on channel extrapolation. We use the high-resolution parameter estimation (HRPE), specifically the space-alternating generalized expectation-maximization (SAGE) algorithm, to extrapolate the DL CSI based on the extracted parameters of multipath components in the uplink channel. We apply the HRPE to two different channel models: the vector spatial signature (VSS) model and the direction of arrival (DOA) model. We verify these methods through real-world channel data acquired from channel measurement campaigns with two different types of channel sounders: a) a switched array-based, real-time, time-domain, outdoors setup at 3.5 GHz, and b) a virtual array-based, high-accuracy, frequency-domain, indoors setup at 2.4 and 5-7 GHz. The performance metrics of the extrapolated channels that we evaluate include the mean squared error, beamforming efficiency, and spectral efficiency in multiuser MIMO scenarios. The results show that the HRPE-based channel extrapolation performs best under the simple VSS model, which does not require array calibration, and if the BS is in an open outdoor environment having line-of-sight (LOS) paths to well-separated users.

While mm-wave systems are a mainstay for 5G communications, the inexorable increase of data rate requirements and user densities will soon require the exploration of next-generation technologies. Among these, Terahertz (THz) band communication seems to be a promising direction due to availability of large bandwidth in the electromagnetic spectrum in this frequency range, and the ability to exploit its directional nature by directive antennas with small form factors. The first step in the analysis of any communication system is the analysis of the propagation channel, since it determines the fundamental limitations it faces. While THz channels have been explored for indoor, short-distance communications, the channels for wireless access links in outdoor environments are largely unexplored. In this paper, we present the - to our knowledge - first set of double-directional outdoor propagation channel measurements for the THz band. Specifically, the measurements are done in the 141 - 148.5 GHz range, which is one of the frequency bands recently allocated for THz research by the Federal Communication Commission (FCC). We employ double directional channel sounding using a frequency domain sounding setup based on RF-over-Fiber (RFoF) extensions for measurements over 100 m distance in urban scenarios. An important result is the surprisingly large number of directions (i.e., direction-of-arrival and direction-of-departure pairs) that carry significant energy. More generally, our results suggest fundamental parameters that can be used in future THz Band analysis and implementations.

Secret-key generation exploiting the channel reciprocity between two legitimate parties is an interesting alternative solution to cryptographic primitives for key distribution in wireless systems as it does not rely on an access infrastructure and provides information-theoretic security. Many works in the literature assume that the eavesdropper gets no side information about the key from her observations provided that: (i) it is spaced more than a wavelength away from a legitimate party and (ii) the channel is rich enough in scattering. In this paper, we show that this condition is not always verified under realistic propagation conditions and we study the resulting secret-key capacity.

Designing beamforming techniques for the downlink (DL) of frequency division duplex (FDD) massive MIMO is known to be a challenging problem due to the difficulty of obtaining channel state information (CSI). Indeed, since the uplink-downlink bands are disjoint, the system cannot rely on channel reciprocity to estimate the channel from uplink (UL) pilots as in time division duplexing (TDD) system. Still, in this paper, we propose original designs for robust beamformers that do not require any feedback from the users and only rely on the transmission of UL pilots. The price to pay is that the beamformer is non-coherent in the sense that it does not leverage full knowledge of the phase of each multipath component. A large variety of novel designs are proposed under different criterion and partial phase knowledge.

Development of a comprehensive channel propagation model for high-fidelity design and deployment of wireless communication networks necessitates an exhaustive measurement campaign in a variety of operating environments and with different configuration settings. As the campaign is time-consuming and expensive, the effort is typically shared by multiple organizations, inevitably with their own channel-sounder architectures and processing methods. Without proper benchmarking, it cannot be discerned whether observed differences in the measurements are actually due to the varying environments or to discrepancies between the channel sounders themselves. The simplest approach for benchmarking is to transport participant channel sounders to a common environment, collect data, and compare results. Because this is rarely feasible, this paper proposes an alternative methodology - which is both practical and reliable - based on a mathematical system model to represent the channel sounder. The model parameters correspond to the hardware features specific to each system, characterized through precision, in situ calibration to ensure accurate representation; to ensure fair comparison, the model is applied to a ground-truth channel response that is identical for all systems. Five worldwide organizations participated in the cross-validation of their systems through the proposed methodology. Channel sounder descriptions, calibration procedures, and processing methods are provided for each organization as well as results and comparisons for 20 ground-truth channel responses.

Channel estimation for the downlink of frequency division duplex (FDD) massive multiple-input-multiple output (MIMO) systems is well known to generate a large overhead as the amount of training generally scales with the number of transmit antennas in a MIMO system. In this paper, we consider the solution of extrapolating the channel frequency response from uplink pilot estimates to the downlink frequency band. This drastically reduces the downlink pilot overhead and completely removes the need for a feedback from the users. The price to pay is a degradation in the quality of the channel estimates, which reduces the downlink spectral efficiency. We first show that conventional estimators fail to achieve reasonable accuracy. We propose instead to use high-resolution channel estimation. We derive the Cramer-Rao lower bound (CRLB) of the mean squared error (MSE) of the extrapolated channel. Furthermore, a relationship between the imperfect channel state information (CSI) and the downlink user performance is derived. The extrapolation-based FDD massive MIMO performance is validated through numerical simulations and compared to a corresponding time division duplex (TDD) system. Considered figures of merit for extrapolation performance include channel MSE, beamforming efficiency, extrapolation range, spectral efficiency and uncoded symbol error rate. Our main conclusion is that channel extrapolation is a viable solution for FDD massive MIMO systems.

Downlink channel estimation in massive MIMO systems is well known to generate a large overhead in frequency division duplex (FDD) mode as the amount of training generally scales with the number of transmit antennas. Using instead an extrapolation of the channel from the measured uplink estimates to the downlink frequency band completely removes this overhead. In this paper, we investigate the theoretical limits of channel extrapolation in frequency. We highlight the advantage of basing the extrapolation on high-resolution channel estimation. A lower bound (LB) on the mean squared error (MSE) of the extrapolated channel is derived. A simplified LB is also proposed, giving physical intuition on the SNR gain and extrapolation range that can be expected in practice. The validity of the simplified LB relies on the assumption that the paths are well separated. The SNR gain then linearly improves with the number of receive antennas while the extrapolation performance penalty quadratically scales with the ratio of the frequency and the training bandwidth. The theoretical LB is numerically evaluated using a 3GPP channel model and we show that the LB can be reached by practical high-resolution parameter extraction algorithms. Our results show that there are strong limitations on the extrapolation range than can be expected in SISO systems while much more promising results can be obtained in the multiple-antenna setting as the paths can be more easily separated in the delay-angle domain.

Application of massive multiple-input multipleoutput (MIMO) systems to frequency division duplex (FDD) is challenging mainly due to the considerable overhead required for downlink training and feedback. Channel extrapolation, i.e., estimating the channel response at the downlink frequency band based on measurements in the disjoint uplink band, is a promising solution to overcome this bottleneck. This paper presents measurement campaigns obtained by using a wideband (350 MHz) channel sounder at 3.5 GHz composed of a calibrated 64 element antenna array, in both an anechoic chamber and outdoor environment. The Space Alternating Generalized Expectation- Maximization (SAGE) algorithm was used to extract the parameters (amplitude, delay, and angular information) of the multipath components from the attained channel data within the â€training†(uplink) band. The channel in the downlink band is then reconstructed based on these path parameters. The performance of the extrapolated channel is evaluated in terms of mean squared error (MSE) and reduction of beamforming gain (RBG) in comparison to the â€ground truthâ€, i.e., the measured channel at the downlink frequency. We find strong sensitivity to calibration errors and model mismatch, and also find that performance depends on propagation conditions: LOS performs significantly better than NLOS.

Offset-QAM-based filterbank multicarrier (FBMC-OQAM) modulations are known to progressively loose their orthogonality as the channel gets more selective in time and frequency. The effect of channel frequency selectivity on FBMC-OQAM systems has been extensively studied in the literature. Many compensations methods have been proposed to combat it. However, most of them have a significant implementation complexity and do not take into account the time selective nature of the channel. In this paper, we propose a MIMO equalizing structure for doubly selective channel based on a simple single-tap per-subcarrier decoding matrix. The decoding matrices are designed to minimize the mean squared error of the symbol estimate. This decoder exploits the degrees of freedom offered by the extra antennas at the receiver to compensate for the distortion induced by time and frequency selectivity. Simulation results demonstrate the performance gain of the proposed design with respect to classical designs.

This paper addresses the carrier phase recovery problem in Offset-QAM-based filterbank multicarrier (FBMC-OQAM) systems. The combined phase noise coming from the transmit and receive lasers, is known to induce a phase rotation of the demodulated symbols at the receiver. Several approaches have been proposed to recover the phase in FBMC-OQAM communication systems on optical fiber. Most of them have a significant complexity and do not make use of all information at disposal. In this paper, we propose two new estimators, obtained by minimizing a maximum likelihood (ML) and a maximum a posteriori (MAP) criteria. They use an error model formulation which allows to easily use priors on the phase noise statistics. By linearization of the error, an analytical solution is found for the phase error, which avoids the need for multiple phase tests. Simulation results demonstrate the better performance of the proposed estimators with respect to state of the art solutions in the low signal-to-noise (SNR) regime and for a small number of subcarriers.

This paper investigates the optimal design of precoders or decoders under a channel inversion criterion for multi-user (MU) MIMO filterbank multicarrier (FBMC) modulations. The base station (BS) is assumed to use a single tap precoding/decoding matrix at each subcarrier in the downlink/uplink, resulting in a low complexity of implementation. The expression of the asymptotic mean squared error (MSE) for this precoding/decoding design in the case of strong channel selectivity is recalled and simplified. Optimizing the MSE under a channel inversion constraint, the expression of the optimal precoding/decoding matrix is found. It is shown that as long as the number of BS antennas is larger than the number of users, the optimized precoder and decoder can compensate for the channel frequency selectivity and even restore the system orthogonality for a large enough number of BS antennas.