Neural Network Aided Computation of Generalized Spatial Modulation Capacity (original) (raw)
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Neural Network Aided Computation of Mutual Information for Adaptation of Spatial Modulation
IEEE Transactions on Communications
Index Modulations, in the form of Spatial Modulation or Polarized Modulation, are gaining traction for both satellite and terrestrial next generation communication systems. Adaptive Spatial Modulation based links are needed to fully exploit the transmission capacity of time-variant channels. The adaptation of code and/or modulation requires a real-time evaluation of the channel achievable rates. Some existing results in the literature present a computational complexity which scales quadratically with the number of transmit antennas and the constellation order. Moreover, the accuracy of these approximations is low and it can lead to wrong Modulation and Coding Scheme selection. In this work we apply a Multilayer Feedforward Neural Network to compute the achievable rate of a generic Index Modulation link. The case of two antennas/polarizations is analyzed throughly showing the neural network not only a one-hundred fold decrement of the Mean Square Error in the estimation of the capacity compared with existing analytical approximations, but it also reduces fifty times the computational complexity. Moreover, the extension to an arbitrary number of antennas is explained and supported with simulations. More generally, neural networks can be considered as promising candidates for the practical estimation of complex metrics in communication related settings.
3rd Annual Communication Networks and Services Research Conference (CNSR'05), 2005
Neural network (NN) based channel estimation method has been proposed for identifying the parameters of a nonlinear time varying satellite channel. A multipath time-varying Ricean-fading channel has been considered in the analysis for a down link scenario. To study the flexibility and performance of the proposed method, the channel has been varied over a reasonable range of Doppler frequencies, and the estimation for each case has been made by employing 16-quadrature amplitude modulation (16-QAM) technique. Back propagation (BP) and natural gradient (NG) algorithms have been studied for the channel identification technique. Based on different learning rates and normalized Doppler frequencies, a comparative analysis between the algorithms has been provided. Finally, a NN maximum likelihood sequence estimator (NN-MLSE) based receiver has been studied for the addressed system. Simulation results show that the NN-MLSE receiver performs close to that of the ideal MLSE receiver in terms of symbol error rate (SER).
This paper presents the adaptive linearisation of a non- linear digital satellite communication down link. That link is made up a 16-QAM modulator, followed by a non-linear High Power Amplifier, on board the satellite. When using the amplifier with low input back-off for a maximum power efficiency, two kinds of distortions occur on the input signal: amplitude (AM/AM conversion) and phase (AM/PM conversion). The satellite payload is regenerative. So, we use a predistortion on board to linearize the amplifier. We present the predistortion architecture realized with Multi-Layer Perceptron (MLP) Neural Networks (NN). Two algorithms associated to that predistorter are shown and compared: the ordinary and the natural gradient. The major problem to implement that predistorter is to get enough bandwidth (100 Mbits/s data rate). A mixed analog/digital implementation is one solution to solve it. We analyze the implementation imperfections effects in comparison with the theoretical algorithm.
2018
The deployment of Long Term Evolution (LTE) cellular systems started in Nigeria some fey years ago, with the aim of enhancing the existing cellular communication systems such as Universal Mobile Telecommunication System (UMTS), Global Systems for Mobile Communication (GSM) and HighSpeed Packet Access (HSPA). LTE is designed to provide improved cellular communication systems, like superior sector capacity and coverage, flexible bandwidth operation, enriched user experience with full mobility, enhanced end-user throughputs, compact user plane latency, robust multi-antenna support, equitable operating costs, and seamless integration with existing systems [1]. Accordingly, the LTE can provide up to 50 Mbps peak data rates for uplink and 100 Mbps for downlink, at 20 MHz bandwidth (BW)). In terms of spectral efficiency, it can provide up to 2.5 bps/Hz for uplink and 5 bps/ Hz for downlink [1, 2].LTE is also designed to provide better cell edge coverage performance and scalable BW capacity...
Capacity Analysis of Index Modulations over Spatial, Polarization and Frequency Dimensions
IEEE Transactions on Communications, 2017
Determining the capacity of a modulation scheme is a fundamental topic of interest. Index Modulations (IM), such as Spatial Modulation (SMod), Polarized Modulation (PMod) or Frequency Index Modulation (FMod), are widely studied in the literature. However, finding a closed-form analytical expression for their capacity still remains an open topic. In this paper, we formulate closed-form expressions for the instantaneous capacity of IM, together with its 2nd and 4th order approximations. We show that, in average, the 2nd approximation error tends to zero for low Signal to Noise Ratio (SNR) and is o (SNR). Also, a detailed analysis of the ergodic capacity over Rayleigh, Rice and Nakagami-m channel distributions is provided. As application of the capacity analysis, we leverage the proposed expressions to compute the ergodic capacities of SMod for different antenna configuration and correlations, PMod for different channel components and conditions, and FMod for different frequency separations.
International Journal of Intelligent Systems and Applications
The impact of rain-influenced attenuation (RIA) has a more pronounced effect as frequency increases, especially in the tropical zones with heavier rainfall than the temperate zones. The International Telecommunication Union (ITU) has recommended a universal model which may not fit well in this tropical region due to the temperate data used to develop the model. It is therefore necessary to adopt locally measured data to develop a suitable model for each region, as also recommended by ITU recommendation 618-13. The experimental site for this study is at the Department of Physics, Federal University of Technology, Akure, Nigeria (7.299° N, 5.147° E) in the tropical rainforest region of Nigeria. In the present work, the backpropagation neural network (BPNN) of the artificial neural network (ANN) is trained based on time-series rain rates data collected between 2015 and 2019 to predict time-series RIA. Based on four inputs (rain rate, rain heights, elevation angle, and polarization angl...
Modeling and Performance Evaluation of Dual Polarized MIMO Land Mobile Satellite Channels
International Journal of Computer Applications, 2014
The paper is concerned with generating of narrow-band dual polarized Multi Input Multi Output (MIMO) over land Mobile Satellite (LMS) fading channels. In the absence of accurate experimental results of MIMO-LMS channels, a statistical model for the characterization of MIMO-LMS is proposed based on available experimental results for Single Input Single output (SISO) LMS and MIMO wireless channels as well as on their extrapolation to the MIMO-LMS case of interest. In this paper a step-bystep methodology for transforming SISO model (Loo model) to MIMO model with detailed description and block diagrams for the simulation and time series signal generation for MIMO-LMS, with the desired power, probability distribution, covariance relations and spectrum. Moreover, based on the proposed channel model, the channel capacity statistics as well as the error performance of a SIMO-LMS diversity system, assuming both maximal ratio combining (MRC) and selection combining (SC), and MIMO-LMS using Space-Time Block Code are evaluated through simulations. Useful numerical results on the capacity are also provided taking into account several operational system parameters. The simulation model and results are useful for LMS-MIMO physical layer researches and system designer, who need an easy to implement and realizable model, representative of typical MIMO-LMS communication systems.
2021
Free-space communication is a leading component in global communications. Its advantages relate to a broader signal spread, no wiring, and ease of engagement. Satellite communication services became recently attractive to mega-companies that foresee an excellent opportunity to connect disconnected remote regions, serve emerging machine-to-machine communication, Internet-of-things connectivity, and more. Satellite communication links suffer from arbitrary weather phenomena such as clouds, rain, snow, fog, and dust. In addition, when signals approach the ground station, it has to overcome buildings blocking the direct access to the ground station. Therefore, satellites commonly use redundant signal strength to ensure constant and continuous signal transmission, resulting in excess energy consumption, challenging the limited power capacity generated by solar energy or the fixed amount of fuel. This research proposes LTSM, an artificial recurrent neural network technology that provides ...
Sensors
Fifth-generation (5G) networks have been deployed alongside fourth-generation networks in high-traffic areas. The most recent 5G mobile communication access technology includes mmWave and sub-6 GHz C-bands. However, 5G signals possibly interfere with existing radio systems because they are using adjacent and co-channel frequencies. Therefore, the minimisation of the interference of 5G with other signals already deployed for other services, such as fixed-satellite service Earth stations (FSS-Ess), is urgently needed. The novelty of this paper is that it addresses issues using measurements from 5G base stations (5G-BS) and FSS-ES, simulation analysis, and prediction modelling based on artificial neural network learning models (ANN-LMs). The ANN-LMs models are used to classify interference events into two classes, namely, adjacent and co-channel interference. In particular, ANN-LMs incorporating the radial basis function neural network (RBFNN) and general regression neural network (GRN...
Statistical Modeling of Dual-Polarized MIMO Land Mobile Satellite Channels
2010
This Letter addresses the statistical modeling of dual-polarized MIMO-LMS fading channels. In the absence of accurate experimental results, a statistical model for the characterization of MIMO-LMS channels is proposed based on consolidation of available experimental results for SISO-LMS and MIMO wireless channels as well as on their extrapolation to the MIMO-LMS case of interest. Moreover, a step-by-step methodology for the simulation and time-series generation of the proposed MIMO-LMS channel model is provided, which is useful for the design and performance assessment of MIMO-LMS transmission systems. The proposed model incorporates the effects of all relevant critical channel aspects in a flexible and fully-parameterized way. Index Terms-Fading channels, land mobile satellite (LMS), multiple-input multiple-output (MIMO), polarization diversity.