This paper addresses the problem of estimating the location of Internet of Things (IoT) Long Range Wide Area Networks (LoRaWAN) devices from time of arrival differences measured at gateways. An Extended Kalman Filter (EKF) based approach is considered to aggregate the measurements obtained at different time instants. Particular attention is paid to the processing of outliers. Based on experimental data obtained from field measurements conducted on a real LoRaWAN™ network an insight into the realistic localization accuracy of the considered localization approach is provided.

This paper focuses on the practical implementation of a Cloud-RAN architecture in the context of future 5G systems, with particular emphasis on different aspects of the functional split between the cloud platform and the radio access points. First, we provide a comprehensive overview of implementation aspects and how different hardware options impact the implementation of RAN functionality. We further discuss a virtualized infrastructure which may have a significant impact on how algorithms are implemented, how they interact with each other, and how they can be scaled within the RAN. We also analyze implementation constraints to be considered to provide backwards compatibility with 3GPP LTE; such constraints on the computing platforms result from the RAN requirements in terms of latency and throughput. Finally, the level of flexibility achievable by the proposed architecture is described from a practical point of view.

In this paper, two semi-analytical performance prediction methods are proposed and compared for multiuser MIMO transmission over block-fading multipath channels and iterative MMSE successive interference cancellation based joint decoding. In the first method, we develop a completely analytical parameterisation of the whole turbo receiver up to the users' channel decoders. The method which follows the classical framework of extrinsic information transfer charts tracks the evolution of the average mutual information defined at coded bit level and circulating between the multiuser detector and the bank of channel decoders. The modelling of the demapping output in the multiuser detector is based on an explicit calculation of the (conditional) mutual information for each symbol digit in a symbol label under arbitrary (possibly non-uniform) a priori probability distributions on others. Then, shifting in viewpoint and considering user demapping and decoding as a joint process, we present an alternative method which tracks the evolution of the average mutual information defined at symbol level and circulating between the MMSE successive interference based interface and the bank of joint demappers and channel decoders. This allows to avoid the critical issue of parameterising the demapping. Numerical results demonstrate the superiority of the second method for high-order non-linear mapping.

Abstract-This paper presents a semi-analytical method to predict the performance of iterative interference cancellers used in advanced multiuser MIMO CDMA systems. At the receiver side, the orthogonality between codes is restored by a chip rate equalization allowing the transmitted symbols to be subsequently estimated by a simple correlations. The proposed prediction method is based on Signal to Interference-plus-Noise Ratio (SINR) tracking and Average Mutual Information (AMI) compression. Therefore, the SINR at symbol level after despreading is analytically derived and injected into the algorithm. Simulations confirm that the method provides accurate performance prediction and is fast enough to be integrated in radio resource management and link adaptation mechanisms of advanced MIMO CDMA/MC-CDMA systems using powerful interference cancellation techniques.

This paper presents a novel fast performance prediction method for iterative minimum-mean square error interference cancellation based joint decoding in a very general multiuser MIMO setting considering a transmission over block-fading frequency-selective channels. A Gaussian approximation is made for all propagated messages between the outer codes and the space-time multiuser detector. The method relies on a signal-to-interference-plus-noise ratio compression, by means of the mutual information effective signal-to-noise metric (MIESM), which allows a one-dimensional AWGN look-up table to capture the soft decoding result of each distinct user. Simulations show that this method while being extremely simple and fast keeps very accurate and is thus adequate for radio resource management and link adaptation purposes.

A new space-time block code family for two transmitters, called the space-time error correcting codes (STECCs), is presented. As it is built from any linear forward error correcting code (FEC), a STECC is able to correct errors while achieving high spectral efficiencies. The key principle is the FEC linearity which is exploited to transmit linear combinations of FEC codewords to create a space-time redundancy. The proposed code exhibits interesting performance on an ISI-free block fading channel. This result is all the more true as the number of FEC codewords considered in the combination is high.

Most of multiple-input multiple-output (MIMO) coding schemes can be sorted into two families. The first one includes the space-time block codes (STBCs) and in particular, the orthogonal STBCs (OSTBCs) as they can be optimally decoded with a low complexity. They improve the transmission quality, the communication range or the receiver robustness. The second family is based on the layered space-time (LST) architecture and aims at increasing the data rate without bandwidth nor power extra cost. To guarantee a given transmission quality, the use of a forward error correcting code (FEC) is mandatory like in the space-time bit interleaved coded modulation (ST-BICM) case. The space-time error correcting codes (STECCs) we introduced, which are based on any linear FEC, appear to be a good compromise between both families. They achieve better performance than the usual Alamouti code associated with the same FEC and propose higher data-rate with space-time puncturing techniques. However, the bottleneck of these codes is the detection stage which complexity grows exponentially with the number of FEC codewords involved in the creation of one STECC codeword. By applying the turbo equalization principle, we can significantly reduce this complexity, while achieving equivalent level of performance.

Since the introduction of the block turbo code (BTC) except, several soft-input/soft-output (SISO) algorithms have been used in order to softly decode product codes. The classical Chase-Pyndiah algorithm seems to be one with the best trade-off between complexity and performance, especially for low error correction capability t (typically 1 or 2) where it is nearly optimal. However, as an algebraic decoding-based algorithm, the lack of codeword diversity is one of its weakness for BTCs with higher error correction capability and/or non binary BTCs. In this paper, we propose an improved iterative decoding algorithm for BTCs. We present a simple sliding encoding-window (SEW) based decoding algorithm which exploits the cyclic and systematic properties of RS and BCH codes. By adding the SEW algorithm to a classical algebraic decoding method, the proposed decoder can easily generate a list of codewords that are close to the decoded codeword. With the codeword diversity, we can compute more reliable soft output necessary in the turbo decoding process, Monte-Carlo simulations of binary and non-binary BTCs are carried out on Gaussian channels. The results show that the algorithm can improve the error performance up to 1.5 dB relative to the conventional Chase-Pyndiah decoder, while the increase in complexity due to the encoding is minor since it is a low-cost process compared to that of algebraic decoding. Compared to the other encoder-based decoding algorithms in the literature, the proposed algorithm has the advantage that there is no requirement to recompute the generator of parity-check matrix by using Gaussian elimination operations, thus a lower computational complexity

In this paper, we present an original use of Reed-Solomon (RS) codes as Space-Time Block Codes (STBCs) and compare them to orthogonal STBCs (OSTBCs). Due to their symbol structure, they can have a representation close to that of an STBC. RS codes are admittedly more complex to decode as they have no orthogonal properties. However, they offer much higher coding rates. In addition, a wide range of these codes can be easily generated. To decode them, we have developed a new algorithm using a sliding encoding-window (SEW), which is combined with the decoding-based Chase algorithm. We compare the RS(15,7) code to the Alamouti one, both having an equivalent coding rate. Its performance compared to Alamouti gets better as the receive diversity increases. Moreover, RS codes do not require the channel to remain constant over the code duration. In fact, they offer better results if the channel changes rapidly, which can be obtained virtually by introducing interleaving.