Abstract-Emerging cellular networks integrate the user terminal geo-localization function besides the communication function. The conventional positioning approach is to estimate the terminal location in two-steps: first the distance to all connected base stations is assessed based on signal time-of-flight measurements, then the location is deduced from the distances by multilateration. The two-step approach incurs a performance degradation because information is lost from the received signal when the multi-lateration is performed. In this paper, we propose to iterate between the two conventional steps to progressively refine the distance estimates based on the knowledge of the position estimate obtained from the previous iterations. The information exchanged between the two-steps not only consists in the mean of the estimates (distance or position) but also of their variance that convey information about the reliability of the estimates. Simulation results show that the achievable performance after a few iterations is close to the performance of the optimal approach that directly estimates the position based on the observation of the received signal.

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Offset-QAM-based filterbank multicarrier (FBMC-OQAM) has been shown to be a promising alternative to cyclic prefix-orthogonal frequency division multiplexing (CP-OFDM). More recently, the use of FBMC-OQAM has been proposed in combination with massive MIMO communications. In this context, it is interesting to study the overall effect of massive MIMO on the FBMC-OQAM intrinsic interference and its interaction with channel frequency selectivity. In this paper, the performance of an FBMC-OQAM uplink massive MIMO system is theoretically characterized in terms of the output mean squared error (MSE) of the estimated transmitted symbols and for three types of linear receivers, namely, zero forcer (ZF), linear minimum mean squared error (LMMSE) and matched filter (MF). Using random matrix theory, the output MSE of these receivers is asymptotically characterized as the number of base station (BS) antennas N and the number of users K grow large, while keeping a finite ratio N/K. The obtained expressions allow to draw many conclusions, some of which were already noticed in the literature but not yet theoretically proven. First, the MSE becomes uniform across the frequency band as a result of the channel hardening effect. Secondly, it is shown that a good synchronization of the users is crucial in a massive MIMO scenario. Finally, if the users are well synchronized, the different terms that compose the MSE, such as noise, inter-user interference (IUI) and the distortion caused by the channel frequency selectivity, become negligible for large values of the ratio N/K. This effect was previously referred to as "self-equalization" in the literature.

In this paper, the joint support recovery of several sparse signals whose supports present similarities is examined. Each sparse signal is acquired using the same noisy linear measurement process, which returns fewer observations than the dimension of the sparse signals. The measurement noise is assumed additive, Gaussian, and admits different variances for each sparse signal that is measured. Using the theory of compressed sensing, the performance of simultaneous orthogonal matching pursuit (SOMP) is analysed for the envisioned signal model. The cornerstone of this paper is a novel analysis method upper bounding the probability that SOMP recovers at least one incorrect entry of the joint support during a prescribed number of iterations. Furthermore, the probability of SOMP failing is investigated whenever the number of sparse signals being recovered simultaneously increases and tends to infinity. In particular, convincing observations and theoretical results suggest that SOMP committing no mistake in the noiseless case does not guarantee the absence of error in the noisy case whenever the number of acquired sparse signals scales to infinity. Finally, simulation results confirm the validity of the theoretical results

Abstract-In this paper, we develop a new low-complexity linear frequency domain equalization (FDE) approach for continuous phase modulated (CPM) signals. As a CPM signal is highly correlated, calculating a linear minimum mean square error (MMSE) channel equalizer requires the inversion of a nondiagonal matrix, even in the frequency domain. In order to regain the FDE advantage of reduced computational complexity, we show that this matrix can be approximated by a block-diagonal matrix without performance loss. Moreover, our MMSE equalizer can be simplified to a low-complexity zero-forcing equalizer. The proposed techniques can be applied to any CPM scheme. To support this theory we present a new polyphase matrix model, valid for any block-based CPM system. Simulation results in a 60 GHz environment show that our reduced-complexity MMSE equalizer significantly outperforms the state of the art linear MMSE receiver for large modulation indices, while it performs only slightly worse for small ones.Index Terms-Continuous phase modulation (CPM), frequency domain equalization (FDE), minimum mean square error (MMSE) equalization, complexity reduction, polyphase representation

Offset-QAM-based filterbank multicarrier (FBMC/OQAM) is an attractive candidate to improve the spectral containment of optical fiber communication systems, especially when considering a sufficiently high number of subcarriers. As for other multicarrier modulations, the chromatic dispersion (CD) compensation is simplified in FBMC/OQAM systems since it is performed in the frequency domain. Unfortunately, FBMC/OQAM systems are sensitive to the laser phase noise (PN). The PN becomes difficult to mitigate when the number of subcarriers increases due to the increased symbol period. It results in inter-carrier interference (ICI) and inter-symbol interference (ISI) due to the loss of OQAM orthogonality. In this paper, we consider the use of moderate numbers of subcarriers to allow for simpler PN tracking. Consequently, more advanced CD compensation methods are required and a trade-off between CD Abstract-Offset-QAM-basedfilterbank multicarrier (FBMC/OQAM) is an attractive candidate to improve the spectral containment of optical fiber communication systems, especially when considering a sufficiently high number of subcarriers. As for other multicarrier modulations, the chromatic dispersion (CD) compensation is simplified in FBMC/OQAM systems since it is performed in the frequency domain. Unfortunately, FBMC/OQAM systems are sensitive to the laser phase noise (PN). The PN becomes difficult to mitigate when the number of subcarriers increases due to the increased symbol period. It results in inter-carrier interference (ICI) and inter-symbol interference (ISI) due to the loss of OQAM orthogonality. In this paper, we consider the use of moderate numbers of subcarriers to allow for simpler PN tracking. Consequently, more advanced CD compensation methods are required and a trade-off between CD and PN compensations needs to be studied. In this paper, the frequency sampling equalizer is used for the CD compensation, whereas an innovative adaptive maximum likelihood estimator is used for the PN compensation. A methodology is then presented to analyze this performance trade-off between CD and PN compensations, and design the desirable system parameters such as the number of subcarriers and the equalizer length. This is illustrated in the case of a terrestrial long-haul FBMC/OQAM transmission system, with 400-kHz laser linewidth and a 1000 km optical link.

Abstract-Several exact recovery criteria (ERC) ensuring that orthogonal matching pursuit (OMP) identifies the correct support of sparse signals have been developed in the last few years. These ERC rely on the restricted isometry property (RIP), the associated restricted isometry constant (RIC) and sometimes the restricted orthogonality constant (ROC). In this paper, three of the most recent ERC for OMP are examined. The contribution is to show that these ERC remain valid for a generalization of OMP, entitled simultaneous orthogonal matching pursuit (SOMP), that is capable to process several measurement vectors simultaneously and return a common support estimate for the underlying sparse vectors. The sharpness of the bounds is also briefly discussed in light of previous works focusing on OMP.