Experimental and Analytical Investigations of an Optically Pre-Amplified FSO-MIMO System With Repetition Coding Over Non-Identically Distributed Correlated Channels

This paper presents theoretical and experimental bit error rate (BER) results for a freespace optical (FSO) multiple-input-multiple-output system over an arbitrarily correlated turbulence channel. We employ an erbium-doped fiber amplifier at the receiver (Rx), which results in an improved Rx’s sensitivity at the cost of an additional non-Gaussian amplified spontaneous emission noise. Repetition coding is used to combat turbulence and to improve the BER performance of the FSO links. A mathematical framework is provided for the considered FSO system over a correlated non-identically distributed Gamma-Gamma channel; and analytical BER results are derived with and without the pre-amplifier for a comparative study. Moreover, novel closed-form expressions for the asymptotic BER are derived; a comprehensive discussion about the diversity order and coding gain is presented by performing asymptotic analysis at high signal-tonoise ratio (SNR). To verify the analytical results, an experimental set-up of a 2 × 1 FSO-multiple-inputsingle-output (MISO) system with pre-amplifier at the Rx is developed. It is shown analytically that, both correlation and pre-amplification do not affect the diversity order of the system, however, both factors have contrasting behaviour with respect to coding gain. Further, to achieve the target forward error correction BER limit of 3.8 × 10−3 , a 2 × 1 FSO-MISO system with a pre-amplifier requires 6.5 dB lower SNR compared with the system with no pre-amplifier. Moreover, an SNR penalty of 2.5 dB is incurred at a higher correlation level for the developed 2×1 experimental FSO set-up, which is in agreement with the analytical findings

Broadcasting scalable video with generalized spatial modulation in cellular networks

This paper considers the transmission of scalable video via broadcast and multicast to increase spectral and energy efficiency in cellular networks. To address this problem, we study the use of generalized spatial modulation (GSM) combined with non-orthogonal hierarchical M-QAM modulations due to the capability to exploit the potential gains of large scale antenna systems and achieve high spectral and energy efficiencies. We introduce the basic idea of broadcasting/multicasting scalable video associated to GSM, and discuss the key limitations. Non-uniform hierarchical QAM constellations are used for broadcasting/multicasting scalable video while user specific messages are carried implicitly on the indexes of the active transmit antennas combinations. To deal with multiple video and dedicated user streams multiplexed on the same transmission, an iterative receiver with reduced complexity is described. 5G New Radio (NR) based link and system level results are presented. Two different ways of quadruplicating the number of broadcasting programs are evaluated and compared. Performance results show that the proposed GSM scheme is capable of achieving flexibility and energy efficiency gain over conventional multiple input multiple output (MIMO) schemes.info:eu-repo/semantics/publishedVersio

Passive detection of correlated subspace signals in two MIMO channels

In this paper, we consider a two-channel multiple-input multiple-output passive detection problem, in which there is a surveillance array and a reference array. The reference array is known to carry a linear combination of broadband noise and a subspace signal of known dimension, but unknown basis. The question is whether the surveillance channel carries a linear combination of broadband noise and a subspace signal of the same dimension, but unknown basis, which is correlated with the subspace signal in the reference channel. We consider a second-order detection problem where these subspace signals are structured by an unknown, but common, p-dimensional random vector of symbols transmitted from sources of opportunity, and then received through unknown M × p matrices at each of the M-element arrays. The noises in each channel have spatial correlation models ranging from arbitrarily correlated to independent with identical variances. We provide a unified framework to derive the generalized likelihood ratio test for these different noise models. In the most general case of arbitrary noise covariance matrices, the test statistic is a monotone function of canonical correlations between the reference and surveillance channels.I. Santamaría and J. Vía have received funding from Ministerio de Economía y Competitividad (MINECO) of Spain, and AEI/FEDER funds of the E.U. under projects TEC2013-47141-C4-3-R (RACHEL), TEC2016-75067-C4-4-R (CARMEN) and TEC2016-81900-REDT (KERMES). The research of Haonan Wang was partially supported by NSF grant DMS-1521746

A Survey of Blind Modulation Classification Techniques for OFDM Signals

Blind modulation classification (MC) is an integral part of designing an adaptive or intelligent transceiver for future wireless communications. Blind MC has several applications in the adaptive and automated systems of sixth generation (6G) communications to improve spectral efficiency and power efficiency, and reduce latency. It will become a integral part of intelligent software-defined radios (SDR) for future communication. In this paper, we provide various MC techniques for orthogonal frequency division multiplexing (OFDM) signals in a systematic way. We focus on the most widely used statistical and machine learning (ML) models and emphasize their advantages and limitations. The statistical-based blind MC includes likelihood-based (LB), maximum a posteriori (MAP) and feature-based methods (FB). The ML-based automated MC includes k-nearest neighbors (KNN), support vector machine (SVM), decision trees (DTs), convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory (LSTM) based MC methods. This survey will help the reader to understand the main characteristics of each technique, their advantages and disadvantages. We have also simulated some primary methods, i.e., statistical- and ML-based algorithms, under various constraints, which allows a fair comparison among different methodologies. The overall system performance in terms bit error rate (BER) in the presence of MC is also provided. We also provide a survey of some practical experiment works carried out through National Instrument hardware over an indoor propagation environment. In the end, open problems and possible directions for blind MC research are briefly discussed

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig