Intelligent OFDM telecommunication system. Part 1. Model of complex and quaternion systems

In this paper, we aim to investigate the superiority and practicability of many-parameter transforms (MPTs) from the physical layer security (PHY-LS) perspective. We propose novel Intelligent OFDM-telecommunication systems based on complex and quaternion MPTs. The new systems use inverse MPT (IMPT) for modulation at the transmitter and MPT for demodulation at the receiver. The purpose of employing the MPT is to improve: 1) the PHY-LS of wireless transmissions against to the wide-band anti-jamming and anti-eavesdropping communication; 2) the bit error rate (BER) performance with respect to the conventional OFDM-TCS; 3) the peak to average power ratio (PAPR). Each MPT depends on finite set of independent parameters (angles). When parameters are changed, many-parametric transform is also changed taking form of a set known (and unknown) orthogonal (or unitary) transforms. For this reason, the concrete values of parameters are specific "key" for entry into OFDM-TCS. Vector of parameters belong to multi-dimension torus space. Scanning of this space for find out the "key" (the concrete values of parameters) is hard problem. MPT has the form of the product of the Jacobi rotation matrixes and it describes a fast algorithm for MPT. The main advantage of using MPT in OFDM TCS is that it is a very flexible anti-eavesdropping and anti-jamming Intelligent OFDM TCS. To the best of our knowledge, this is the first work that utilizes the MPT theory to facilitate the PHY-LS through parameterization of unitary transforms. © 2019 IOP Publishing Ltd. All rights reserved

Modulation options for OFDM-based waveforms: classification, comparison, and future directions

This paper provides a comparative study on the performance of different modulation options for orthogonal frequency division multiplexing (OFDM) in terms of their spectral efficiency, reliability, peak-to-average power ratio, power efficiency, out-of-band emission, and computational complexity. The modulation candidates are classified into two main categories based on the signal plane dimension they exploit. These categories are: 1) 2-D signal plane category including conventional OFDM with classical fixed or adaptive QAM modulation and OFDM with differential modulation, where information is conveyed in changes between two successive symbols in the same subcarrier or between two consecutive subcarriers in the same OFDM symbol and 2) 3-D signal plane category encompassing: a) index-based OFDM modulation schemes which include: i) spatial modulation OFDM, where information is sent by the indices of antennas along with conventional modulated symbols and ii) OFDM with index modulation, where the subcarriers’ indices are used to send additional information; b) number-based OFDM modulation schemes which include OFDM with subcarrier number modulation, in which number of subcarriers is exploited to convey additional information; and c) shape-based OFDM modulation schemes which include OFDM with pulse superposition modulation, where the shape of pulses is introduced as a third new dimension to convey additional information. Based on the provided comparative study, the relationship and interaction between these different modulation options and the requirements of future 5G networks are discussed and explained. This paper is then concluded with some recommendations and future research directions.This work was supported in part by the Scientific and Technological Research Council of Turkey (TUBITAK), under Grant 215E316

Spectrum Sharing, Latency, and Security in 5G Networks with Application to IoT and Smart Grid

The surge of mobile devices, such as smartphones, and tables, demands additional capacity. On the other hand, Internet-of-Things (IoT) and smart grid, which connects numerous sensors, devices, and machines require ubiquitous connectivity and data security. Additionally, some use cases, such as automated manufacturing process, automated transportation, and smart grid, require latency as low as 1 ms, and reliability as high as 99.99\%. To enhance throughput and support massive connectivity, sharing of the unlicensed spectrum (3.5 GHz, 5GHz, and mmWave) is a potential solution. On the other hand, to address the latency, drastic changes in the network architecture is required. The fifth generation (5G) cellular networks will embrace the spectrum sharing and network architecture modifications to address the throughput enhancement, massive connectivity, and low latency. To utilize the unlicensed spectrum, we propose a fixed duty cycle based coexistence of LTE and WiFi, in which the duty cycle of LTE transmission can be adjusted based on the amount of data. In the second approach, a multi-arm bandit learning based coexistence of LTE and WiFi has been developed. The duty cycle of transmission and downlink power are adapted through the exploration and exploitation. This approach improves the aggregated capacity by 33\%, along with cell edge and energy efficiency enhancement. We also investigate the performance of LTE and ZigBee coexistence using smart grid as a scenario. In case of low latency, we summarize the existing works into three domains in the context of 5G networks: core, radio and caching networks. Along with this, fundamental constraints for achieving low latency are identified followed by a general overview of exemplary 5G networks. Besides that, a loop-free, low latency and local-decision based routing protocol is derived in the context of smart grid. This approach ensures low latency and reliable data communication for stationary devices. To address data security in wireless communication, we introduce a geo-location based data encryption, along with node authentication by k-nearest neighbor algorithm. In the second approach, node authentication by the support vector machine, along with public-private key management, is proposed. Both approaches ensure data security without increasing the packet overhead compared to the existing approaches

Intelligent OFDM telecommunication system. Part 1. Model of system

In this paper, we aim to investigate the superiority and practicability of many- parameter transforms (MPTs) from the physical layer security (PHY-LS) perspective. We propose novel Intelligent OFDM-telecommunication systems based on MPT. The new systems use Inverse MPT (IMPT) for modulation at the transmitter and Direct MPT (DMPT) for demodulation at the receiver. The purpose of employing the MPT is to improve: 1) the PHY- LS of wireless transmissions against to the wide-band anti-jamming and anti-eavesdropping communication; 2) the bit error rate (BER) performance with respect to the conventional OFDM-TCS; 3) the peak to average power ratio (PAPR). Each MPT depends on finite set of independent parameters (angles), which could be changed independently of one another. When parameters are changed, MPT is also changed taking form of a set known (and unknown) orthogonal (or unitary) transforms. For this reason, the concrete values of parameters are specific “key” for entry into OFDM-TCS. Vector of parameters belong to multi-dimension torus space. Scanning of this space for find out the “key” (the concrete values of parameters) is hard problem. MPT has the form of the product of the sparse Jacobi rotation matrixes and it describes a fast algorithm for MPT. The main advantage of using MPT in OFDM TCS is that it is a very flexible anti-eavesdropping and anti-jamming Intelligent OFDM TCS. To the best of our knowledge, this is the first work that utilizes the MPT theory to facilitate the PHY-LS through parameterization of unitary transforms.This work was supported by the RFBR grant 17-07-00886 and by the Ural State Forest Engineering’s Center of Excellence in «Quantum and Classical Information Technologies for Remote Sensing Systems

Proceedings of the 35th WIC Symposium on Information Theory in the Benelux and the 4th joint WIC/IEEE Symposium on Information Theory and Signal Processing in the Benelux, Eindhoven, the Netherlands May 12-13, 2014

Compressive sensing (CS) as an approach for data acquisition has recently received much attention. In CS, the signal recovery problem from the observed data requires the solution of a sparse vector from an underdetermined system of equations. The underlying sparse signal recovery problem is quite general with many applications and is the focus of this talk. The main emphasis will be on Bayesian approaches for sparse signal recovery. We will examine sparse priors such as the super-Gaussian and student-t priors and appropriate MAP estimation methods. In particular, re-weighted l2 and re-weighted l1 methods developed to solve the optimization problem will be discussed. The talk will also examine a hierarchical Bayesian framework and then study in detail an empirical Bayesian method, the Sparse Bayesian Learning (SBL) method. If time permits, we will also discuss Bayesian methods for sparse recovery problems with structure; Intra-vector correlation in the context of the block sparse model and inter-vector correlation in the context of the multiple measurement vector problem