We consider the transmission of spatially correlated analog information in a wireless sensor network (WSN) through fading single-input and multiple-output (SIMO) multiple access channels (MACs) with low-latency requirements. A lattice-based analog joint source-channel coding (JSCC) approach is considered where vectors of consecutive source symbols are encoded at each sensor using an$n$-dimensional lattice and then transmitted to a multiantenna central node. We derive a minimum mean square error (MMSE) decoder that accounts for both the multidimensional structure of the encoding lattices and the spatial correlation. In addition, a sphere decoder is considered to simplify the required searches over the multidimensional lattices. Different lattice-based mappings are approached and the impact of their size and density on performance and latency is analyzed. Results show that, while meeting low-latency constraints, lattice-based analog JSCC provides performance gains and higher reliability with respect to the state-of-the-art JSCC schemes.

The design of zero-delay Joint Source-Channel Coding (JSCC) schemes for the transmission of correlated information over fading Multiple Access Channels (MACs) is an interesting problem for many communication scenarios like Wireless Sensor Networks (WSNs). Among the different JSCC schemes so far proposed for this scenario, Distributed Quantizer Linear Coding (DQLC) represents an appealing solution since it is able to outperform uncoded transmissions for any correlation level at high Signal-to-Noise Ratios (SNRs) with a low computational cost. In this paper, we extend the design of DQLC-based schemes for fading MACs considering sphere decoding to make the optimal Minimum Mean Squared Error (MMSE) estimation computationally affordable for an arbitrary number of transmit users. The use of sphere decoding also allows to formulate a practical algorithm for the optimization of DQLC-based systems. Finally, non-linear Kalman Filtering for the DQLC is considered to jointly exploit the temporal and spatial correlation of the source symbols. The results of computer experiments show that the proposed DQLC scheme with the Kalman Filter decoding approach clearly outperforms uncoded transmissions for medium and high SNRs.

This paper focuses on distributed linear precoding when users transmit correlated information over a fading multiple-input and multiple-output multiple access channel. The precoders are optimized in order to minimize the sum-mean square error (MSE) between the source and the estimated symbols. When sources are correlated, minimizing the sum-MSE results in a non-convex optimization problem. The precoders for an arbitrary number of users and transmit and receive antennas are thus obtained via a projected steepest-descent algorithm and a low-complexity heuristic approach. For the more restrictive case of two single-antenna users, a closed-form expression for the minimum sum-MSE precoders is derived. Moreover, for the scenario with a single receive antenna and any number of users, a solution is obtained by means of a semi-definite relaxation. Finally, we also consider precoding schemes where the precoders are decomposed into complex scalars and unit norm vectors. Simulation results show a significant improvement when source correlation is exploited at precoding, especially for low signal-to-noise ratios and when the number of receive antennas is lower than the number of transmitting nodes.

Joint source-channel coding for discrete-time analog sources is an appealing transmission approach because of its extremely low delay and complexity. When the users access the channel orthogonally, analog transmission of correlated information over fading multiple access channels (MACs) using modulo-like mappings provides better performance than uncoded transmission. In this paper, we propose a simplified decoder for modulo mappings in possibly non-orthogonal MAC scenarios with a single-antenna users and a multiple-antenna receiver. Sphere decoding is investigated to reduce the computational complexity when the number of users is large. In addition, affordable strategies are proposed to optimize the mapping parameters according to the channel conditions and the source correlation. The obtained results show that the use of modulo mappings is suitable when the number of antennas at the receiver is larger than the number of users and for high correlation between user data.

Analog transmission of correlated information over Gaussian broadcast channels (BCs) using analog joint source- channel coding (JSCC) is addressed. This communication strategy has attractive advantages, such as low complexity, minimal delay, graceful degradation, and adaptation to time-varying environments. In this paper, we focus on the optimization of parametric continuous mappings that satisfy individual quality of service requirements over Gaussian BCs. This optimization is based on the balancing of the user distortions to ensure the feasibility of the resulting optimization problems. An analysis of the overall distortion corresponding to the considered mappings is carried out to design algorithms that determine the optimal values for the mappings parameters. Results show that the proposed analog JSCC scheme provides near optimal performance and an adequate balancing of the individual distortions.

We address the transmission of bivariate Gaussian sources using analog Joint Source Channel Coding (JSCC). The analog mappings are specifically designed to exploit the correlation between the source symbols. A parametric mapping based on sinusoidal functions is proposed and its performance is compared to that of the optimal non parametric mappings and other applicable analog JSCC mappings, and also to the theoretical bound. The obtained results show that the performance of the parametric mapping closely approaches the optimal performance, and it is very similar to that of the non parametric one.

Long Term Evolution (LTE) is expected to substitute the Global System for Mobile Communications (GSM) as the radio access technology for railway communications. Recently, especial attention has been devoted to high-speed trains since this particular environment poses challenging problems in terms of performance simulation and measurement. In order to severely decrease the cost and complexity of high-speed measurement campaigns, we have proposed a technique to induce effects caused by highly-time varying channels on Orthogonal Frequency-Division Multiplexing (OFDM) signals while conducting measurements at low speeds. In this work, we evaluate this technique by comparing the results of LTE measurements at different velocities as well as by simulations. Additionally, we use this technique to show the performance of LTE for high-speed train scenarios up to 600 km/h. To accomplish this, we use both a controlled high-speed measurement setup as well as a channel model developed according to the guidelines of the ITU Radiocommunication Sector (ITU-R) for the evaluation of radio interface technologies for IMT- Advanced systems.

This paper describes a multicore Software Defined Radio (SDR) architecture devised to implement a fully reconfigurable downlink for WiMAX transceivers. The proposed architecture is made up of Commercial-Off-The-Shelf (COTS) modules available in the market and includes a DSP, three different models of FPGAs, DACs and ADCs. We show that the architecture is capable of supporting all the functionalities of the downlink sub frame of the Orthogonal Frequency Division Multiple Access (OFDMA) WiMAX physical layer, including Partial Usage of Sub carriers (PUSC) symbol structure and Forward Error Correction (FEC). The primary advantage of the design is the full reconfigurability at different levels: bandwidth, size of the FFT, modulation, code rate, etc. without modifying or restarting the system. We show that the five downlink profiles defined by the WiMAX Forum can be successfully implemented with the proposed architecture.