In this paper we study channel estimation for Narrow-band Internet of things (NB-IoT) systems, a low-power radio access technology introduced by the Third Generation Partnership Project (3GPP) as a part of Long Term Evolution (LTE) in Release 13 and recently extended to support Non-Terrestrial Networks (NTN) in Release 17. To improve the quality of channel measurements from pilot signals, we propose a cross-slot smoothing algorithm based on a fixed-lag Kalman smoother (KS). The non-causal and recursive nature of the proposed smoother is suitable for NB-IoT systems, thanks to their delay-tolerant design (due the presence of sub-frame repetitions) and to unitary precoding (which ensures consistency of channel statistics across slots). We propose an efficient implementation of fixed-lag KS, which we call “reduced-redundancy” fixed-lag. Our algorithm eliminates computational redundancies by extending the state vector instead of augmenting it. We tailor our algorithm to practical scenarios, including geostationary (GEO) and low Earth orbit (LEO) satellites. Results show that the proposed algorithm outperforms causal Kalman filter and non-causal moving average or infinite impulse response filters, with comparable computational complexity.

Estimating second-order channel statistics (specifically, frequency-domain covariance matrices) from narrowband reference signals is crucial for channel estimation in 5 G cellular systems. The problem is challenging because of the limited number of available pilot subcarriers. In this work, we reparametrize the problem using a delay-domain characterization. Three algorithms are proposed for estimating the power delay profile (PDP) using least squared error and maximum likelihood estimation frameworks. The algorithms demonstrate a tradeoff between the computational complexity and model insights used to estimate the underlying PDP. The performance of the proposed algorithms is numerically analyzed under different channel conditions and pilot density. The proposed algorithms demonstrate significant gains (up to 3 dB in signal-to-noise ratio), and are robust to noisy delay spread information.

In wireless communication systems where the received signal model is linear with Gaussian-distributed channel and noise, linear minimum mean square error (LMMSE) channel estimation (CE) achieves the best performance in terms of mean square error (MSE). However, LMMSE CE relies on parameters that may be either unavailable at the receiver (e.g., accurate knowledge of the power delay profile (PDP)) or too complex for practical implementation (e.g., the LMMSE filter size). A suboptimal choice of parameters may severely degrade LMMSE CE performance. Motivated by this observation, we investigate machine learning as a tool for refining and improving CE performance. We show that our proposed low-complexity learning-aided LMMSE CE can overcome the impact of suboptimal parameters and approach the ideal LMMSE performance.

In this paper we investigate the problem of precoder selection assuming compressed channel state information (CSI) feedback over sub-bands. Typically (e.g., in NR Rel-16) the problem is approached by a two-step solution: independent precoder optimization for each sub-band, followed by selection of the compression bases. We show that such conventional approach does not perform well in scenarios of medium/high frequency selectivity. Then, we derive an alternative approach, based on wideband capacity maximization, which provides superior performance under frequency-selective channels, while being compatible with the existing NR Rel-16 framework without any additional signaling. We finally propose an adaptive method in which the receiver dynamically selects the wideband or the sub-band optimization strategy, depending on the instantaneous channel condition. The proposed approach achieves consistent gains (up to 3dB) in all the considered test cases.

This paper investigates the problem of noise vari-ance estimation in orthogonal frequency domain multiplexing (OFDM)-based systems such as 5G New Radio (NR). Accurate estimation of the noise variance is critical for the receiver performance, especially when applied with linear minimum mean square error (LMMSE) channel estimation (CE). A commonly used method estimates the noise variance from the power of the residual signal at the CE output. In this paper, we prove that such conventional estimator is biased, resulting in underestimation of the noise variance; then, we derive a bias correction method. Simulation results show that the proposed bias correction can significantly improve LMMSE CE performance, achieving up to 1dB gain in terms of block error rate (BLER).

In this paper, we propose a novel method for selecting the precoding matrix indicator (PMI) from a DFT-based codebook, such as the one specified for LTE full dimension (FD) MIMO. While conventional approaches are based on explicit codebook search, our method directly estimates the best PMI from the singular vectors of the channel matrix. The key idea is to exploit the DFT structure to formulate the PMI selection problem as the estimation of the linear phase ramping of a sequence. The main advantage of the proposed method is that its complexity does not scale with the number of PMI candidates, while achieving performance comparable or superior to that of state-of-the-art search-based approaches.

This paper addresses the problem of precoding matrix selection in large-scale MIMO cellular systems, where traditional codebook search methods may result in high complexity for the receiver due to the increased codebook size. For this reason, we propose to first compute an unconstrained reference codeword, and then search the codebook for the best approximation of such reference codeword, using low-complexity distance metrics. We consider two possible choices for the distance metric: the chordal distance, previously used in the literature for codebook design, and a newly defined distance function based on the sum of phase differences between the elements of two complex matrices. We show that the proposed method can match or outperform state-of-the-art approaches (based on capacity or mutual information) with significantly lower per-candidate complexity.

In this paper, semi-blind class of spectrum sensing algorithms, Energy Detection (ED) and Roy's Largest Root Test (RLRT), are considered under a typical flat fading channel scenario. The knowledge of the noise variance is imperative for the optimum performance of ED and RLRT. Unfortunately, the variation and unpredictability of noise variance is unavoidable. An idea of auxiliary noise variance estimation is introduced in order to cope with the absence of prior knowledge of the noise variance, thus a hybrid approach of signal detection is set forth for each considered method. The detection performance of the methods are derived and expressed by closed form analytical expressions. The impact of noise estimation accuracy on the the performance of ED and RLRT is compared in terms of Receiver Operating Characteristic (ROC) curves and performance curves (Probability of Detection/Miss-detection as a function of SNR by fixing the false alarm probability). It is concluded that optimum performance of ED and RLRT can be achieved even with the use of estimated noise variance by using a large number of slots for variance estimation. Finally, it is also found out that the impairment due to noise uncertainty is reduced on RLRT w. r. t. ED.

We present a statistical approach for estimating the interference coupling coefficients in an LTE network based on a set of various measurements available at the network and terminal level. The proposed approach combines the measurements with prior information (spatial correlation among interference links) and takes into account measurement uncertainty. The result is a simple closed-form estimator that allows for fast realtime interference estimation.

This paper presents an optimization framework for energy-aware relay selection and user association in cellular networks aided by nomadic relays. The framework of sparse optimization is used to minimize network energy consumption for desired service provisioning of the terminals. We show that some constraints in the underlying optimization are of quadratic form due to the assumption of relays with wireless backhaul links. Hence, previously proposed algorithms for activation of network elements with wired backhaul links are not applicable. In this paper, therefore, novel algorithms based on different relaxations of the quadratic constraints are proposed and evaluated for energy savings. Simulation results confirm that the proposed algorithms may significantly reduce the overall energy consumption of cellular networks compared with conventional cell selection schemes.

Low density parity check (LDPC) codes can be decoded with a variety of decoding algorithms, offering a trade-off in terms of complexity, latency, and performance. We describe seven distinct LDPC decoders and provide a performance comparison for a practical regular LDPC code. Our simulations indicate that the best performance/latency trade-off is achieved by one version of the reweighted max-product decoder. When latency is not an issue, the traditional sum-product decoder yields the best performance.

Modern vehicles will have strong requirements with regard to seamless mobility support in future cellular systems, in order to enable advanced cooperative driver assistance and infotainment systems that guarantee traffic safety and efficiency. In this work, we introduce street-specific handover parameters for vehicular terminals. In particular, we propose an adaptive optimization algorithm that exploits vehicle context information in order to tune the handover parameters. Simulation results confirm that the proposed concept has the potential to improve handover performance significantly.

The mobility robustness optimization (MRO) problem in LTE self-organizing networks (SON) is a multi-objective optimization problem; it involves a set of non-convex contradicting objective functions that depend on multiple variables such as handover (HO) parameters and user mobility classes. This paper exploits the framework of stochastic processes to develop a novel method of successively choosing a sequence of multi-variate training points for multi-objective optimization. Combined with the collected statistics and a priori knowledge, the proposed method is used in the design of an efficient MRO algorithm. The performance of the algorithm is evaluated by simulations to illustrate significant improvements with respect to both HO-related ratio link failures (RLFs) and unnecessary HOs.

Multi-sensor signal detection based on the the largest eigenvalue of the received sample covariance matrix is known to be optimal (asymptotically in the sample size and under Gaussian assumption) in the Neyman-Pearson sense. In this paper we propose two decentralized algorithms to implement this type of signal detector in distributed wireless networks without fusion center. The proposed solutions are based on iterative numerical algorithms (power method and Lanczos algorithm), implemented in a decentralized manner with matrix and vector products computed via average consensus. Numerical results show that such methods, in particular the decentralized Lanczos method, outperform the recently proposed decentralized energy detector after a very small number of iterations.

In this paper we propose a decentralized approach for cooperative signal detection, based on peer-to-peer collaboration among sensor nodes. The proposed method combines belief propagation, implemented in a distributed fashion through the exchange of local messages to and from neighboring nodes, with a Neyman-Pearson framework, that allows control over the false-alarm rate of each node. At the same time, nodes gradually learn their degree of correlation with neighbors, and clusters of nodes under homogeneous conditions are formed automatically. The performance of the resulting va Neyman-Pearson belief propagation" (NP-BP) algorithm is shown to be nearly equivalent to that of cooperative energy detection applied separately at each cluster. Thanks to its decentralized structure, NP-BP provides improved robustness, flexibility, and scalability compared to traditional, centralized schemes. In addition, its ability to adaptively form clusters makes the algorithm suitable for heterogeneous or time-varying radio environments.

In this paper, we propose a new inference algorithm, suitable for distributed processing over wireless networks. The algorithm, called uniformly reweighted belief propagation (URW-BP), combines the local nature of belief propagation with the improved performance of tree-reweighted belief propagation (TRW-BP) in graphs with cycles. It reduces the degrees of freedom in the latter algorithm to a single scalar variable, the uniform edge appearance probability ρ. We provide a variational interpretation of URW-BP, give insights into good choices of ρ, develop an extension to higher-order potentials, and complement our work with numerical performance results on three inference problems in wireless communication systems: spectrum sensing in cognitive radio, cooperative positioning, and decoding of a low-density parity-check (LDPC) code.

Direction-of-arrival (DoA)-based localization is a suitable approach for estimating the position of primary users in cognitive radio networks, as it does not require knowledge of transmission power or propagation channel. In this paper we consider a scenario where secondary users obtain DoA estimates by multiple antennas or virtual antenna arrays. We express the Cramer Rao bound of the localization error as a function of the problem geometry and of array-specific parameters. Then, we investigate how the bound scales with the number of sensors and the number of antennas per sensor, and we address the following question: is it better to have more sensors with fewer antennas, or fewer sensors with more antennas?

In this paper we address the problem of statistical spectrum sensing attacks, where misbehaving nodes falsify their sensing reports with a certain probability in order to artificially increase or reduce the throughput of a cognitive network. Instead of trying to identify unreliable nodes and exclude them from the decision process, we propose a novel approach where spectrum sensing and estimation of type/probability of the attacks are performed jointly. Our method is based on a Bayesian formulation and is implemented using belief propagation on factor graphs. The performance of the proposed method is then evaluated by analytical results and by simulations.

Nonparametric belief propagation (NBP) is a well-known particle-based method for distributed inference in wireless networks. NBP has a large number of applications, including cooperative localization. However, in loopy networks NBP suffers from similar problems as standard BP, such as over-confident beliefs and possible non-convergence. Tree-reweighted NBP (TRW-NBP) can mitigate these problems, but does not easily lead to a distributed implementation due to the non-local nature of the required so-called edge appearance probabilities. In this paper, we propose a variation of TRW-NBP, suitable for cooperative localization in wireless networks. Our algorithm uses a fixed edge appearance probability for every edge, and can outperform standard NBP in dense wireless networks.

In this paper we consider signal detection in cognitive radio networks, under a non-parametric, multi-sensor detection scenario, and compare the cases of known and unknown noise level. The analysis is focused on two eigenvalue-based methods, namely Roy's largest root test, which requires knowledge of the noise variance, and the generalized likelihood ratio test, which can be interpreted as a test of the largest eigenvalue vs. a maximum-likelihood estimate of the noise variance. The detection performance of the two considered methods is expressed by closed-form analytical formulas, shown to be accurate even for small number of sensors and samples. We then derive an expression of the gap between the two detectors in terms of the signal-to-noise ratio of the signal to be detected, and we identify critical settings where this gap is significant (e.g., low number of sensors and signal strength). Our results thus provide a measure of the impact of noise level knowledge and highlight the importance of accurate noise estimation.

Tree-reweighted belief propagation is a message passing method that has certain advantages compared to traditional belief propagation (BP). However, it fails to outperform BP in a consistent manner, does not lend itself well to distributed implementation, and has not been applied to distributions with higher-order interactions. We propose a method called uniformly-reweighted belief propagation that mitigates these drawbacks. After having shown in previous works that this method can sub-stantially outperform BP in distributed inference with pairwise interaction models, in this paper we extend it to higher-order interactions and apply it to LDPC decoding, leading performance gains over BP.

This study addresses the problem of detecting signals characterised by a discontinuous presence in the detection window. This situation is of particular interest for applications of spectrum sensing in cognitive radio networks where, on the one hand, the sampling frequency may be limited by hardware capabilities and, on the other hand, the signal to be detected may be bursty with a burst duration comparable to or lower than the minimum sampling period. Modified expressions are derived for the detection probability of two popular detectors – energy detector and largest eigenvalue test – in the presence of discontinuous signals. The new expressions, adapted as a function of the signal occupancy rate, provide an accurate estimation of the detection probability under this scenario and allow a more careful selection of the decision threshold.

We propose a novel cooperative positioning algorithm that fuses information from satellites and terrestrial wireless systems, suitable for \acs{GPS}-challenged scenarios. The algorithm is fully distributed over an unstructured network, does not require a fusion center, does not rely on fixed terrestrial infrastructure, and is thus suitable for ad-hoc deployment. The proposed message passing algorithm, named \acf{H-SPAWN}, is described and analyzed. A novel parametric message representation is introduced, to reduce computational and communication overhead. Through simulation, we show that \ac{H-SPAWN} improves positioning availability and accuracy, and outperforms hybrid positioning algorithms based on conventional estimation techniques.

Cooperative positioning algorithms have been recently introduced to overcome the limitations of traditional methods, relying on GNSS or other terrestrial infrastructure. In particular, SPAWN (Sum- Product Algorithm over a Wireless Network) was shown to provide accurate position estimate even in challenged indoor environments, thanks to exchange of local information among peers. In this paper we extend the SPAWN framework by considering a hybrid scenario, where agents combine satellite and peer-to-peer terrestrial measurements. The novel hybrid SPAWN (H-SPAWN) approach allows increased availability and robustness compared to GNSS- only positioning in light and deep indoor scenarios, while keeping the advantages of a distributed implementation of the original SPAWN. A parametric message representation is proposed to reduce the communication overhead, and to improve the estimation accuracy. Simulation results show that the proposed solution outperforms traditional algorithms such as cooperative least squares and the extended Kalman filter.

This paper addresses the problem of spectrum sensing for cognitive radio, in the case of a primary signal characterized by a discontinuous channel occupation within the considered sensing window. Under such conditions, the performance of two different energy detectors is investigated. The first one (energy average detector) decides whether the channel is free or busy on the basis of the energy sample average; the second one (energy threshold detector) performs a soft estimation of the channel occupation probability by comparing the energy samples with a proper threshold. For both the detectors, an analytical model is derived and validated through simulation, and the optimal decision threshold is found as a function of the primary signal parameters.

As a consequence of the diffusion of wireless systems operating in the 2.4 GHz ISM band, harmful interference among heterogeneous networks is becoming a serious issue for their performance. This paper is focused on IEEE 802.15.4 WSNs undergoing the interference of co-located IEEE 802.11b/g WLANs or Bluetooth piconets. On the basis of energy measurements carried out using a standard-compliant testbed, the statistics of the interfering energy are characterized for an extensive set of traffic conditions. Then the impact of each interference pattern onto a practical WSN application is analyzed in terms of packet loss rate at the application layer. The last part of the paper introduces a channel selection algorithm based on the estimation of the information-theoretic capacity of the considered channels.

Eigenvalue-based detection is an efficient signal detection technique, recently introduced in the Cognitive Radio context to guarantee a reliable identification of primary users. In this paper we contribute to the theoretical analysis of this detection scheme by deriving a mathematical expression for the probability of missed detection as a function of the number of cooperating receivers, the number of samples and the signal-to-noise ratio of the primary user. The analysis is referred to a detector using as test statistic the ratio between the largest and the smallest eigenvalue of the covariance matrix. Along with previous results on the probability of false alarm, this contribution completes the performance evaluation of this type of detector.