Reducing interferences in wireless communication systems by mobile agents with recurrent neural networks-based adaptive channel equalization

Physical layer security (PLS) in wireless communication systems has attracted extensive research attentions in recent years. Unlike cryptography-based methods applied in upper-layer in network, PLS methods are applied in physical layers and can provide information-theoretic security by utilizing the randomness of signals and wireless channels. In this survey, we provide a comprehensive review in the domain of physical layer authentication (PLA) in wireless communication systems, including the concepts, several key techniques of typical PLA architectures as well as future challenges and research trends in more sophisticated communication systems. The survey begins with an overview of the background and basic concepts of PLA, such as the general model of wireless security communication system, typical frameworks of key-based/less PLA systems, and the common attack models. We then discuss the major concerns and key techniques that are applied in PLA systems, where three types of authentication schemes are considered, i.e., the authentication based on channel information, radio-frequency and identity watermarks. Basic models and representative research results about key approaches and techniques applied to the authentication systems above are subsequently covered. Finally, the associated challenges and potential research trends of PLA in future communication systems are presented at the end of the survey paper.

Wireless communication systems have evolved and offered more smart and advanced systems like ad hoc and sensor-based infrastructure fewer networks. These networks are evaluated with two fundamental parameters including data rate and spectral efficiency. To achieve a high data rate and robust wireless communication, the most significant task is channel equalization at the receiver side. The transmitted data symbols when passing through the wireless channel suffer from various types of impairments, such as fading, Doppler shifts, and Intersymbol Interference (ISI), and degraded the overall network performance. To mitigate channel-related impairments, many channel equalization algorithms have been proposed for communication systems. The channel equalization problem can also be solved as a classification problem by using Machine Learning (ML) methods. In this paper, channel equalization is performed by using ML techniques in terms of Bit Error Rate (BER) analysis and comparison. Radial Basis Functions (RBFs), Multilayer Perceptron (MLP), Support Vector Machines (SVM), Functional Link Artificial Neural Network (FLANN), Long-Short Term Memory (LSTM), and Polynomial-based Neural Networks (NNs) are adopted for channel equalization.

Characterizing the energy cost of different physical (PHY) layer building blocks is becoming increasingly important in modern cellular-based communications, considering the cross sector requirements for performance enhancements and energy savings. This paper presents energy profiling metrics of different PHY-layer FPGA implementations encountered in modern wireless communication systems. The results give an insight of the distribution of the consumed energy in different baseband building blocks or configurations before and after applying power optimizations in the FPGA design and implementation.

In modern wireless communication systems, accurate characterization of wireless propagation channels remains a significant challenge. In multiple-input multiple-output (MIMO) wireless systems, a double-directional channel can be achieved by utilizing multipath components' spatial and temporal properties (MPCs). Grouping the MPC can simplify the parameters as a trade-off between complexity and accuracy. This paper implements k-Deep Auto Encoder (k-DAE), AE+k-means, and K Power Means (KPM) clustering approaches and compares their performance in clustering wireless propagation multipaths in indoor and outdoor scenarios. The results show that AE+k-means performs better than k-DAE in indoor scenarios by 25.48%, while k-DAE performs 24.60 % better in outdoor scenarios. The KPM algorithm performs best in all indoor scenarios among the three algorithms, with a significant increase of 4.38% and 11.062% to AE+k-means and k-DAE, respectively. However, both k-DAE and AE+k-means have quite similar performance in outdoor scenarios. The study also highlights the first use of autoencoders in clustering the MPCs. The results indicate that k-DAE can be used as an alternative clustering method in channel modeling. Future works envisioned applying the approach to other wireless channel models.

Random access sequence set design in wireless cellular communication networks

Energy efficiency is always an essential concern in modern wireless communication systems. Energy harvesting is a promising technique and can substantially extend the lifetime of power-critical wireless systems such as wireless sensors. Unlike the traditional battery powered system, the system utilizing energy harvesting usually has a smaller battery or merely a super-capacitor. Rather than the limitation on the maximum energy capacity, it is much more critical to take considerations on the energy harvesting rate when an optimal transmission strategy is proposed for such wireless communication systems. In this paper, we study the above issues, and develop an innovative framework considering the characteristics of the wireless communication systems with energy harvesting. The balance between the available resources such as harvested energy and the output such as successful packets is optimized. The simulation results show that our proposed framework works better in all scenarios compared with the baseline strategy.

Adaptive infinite-impulse response (IIR) filters can be used for a variety of digital communication and digital signal processing problems, such as, linear prediction, adaptive notch filtering, adaptive differential pulse code modulation, channel equalization, and adaptive array processing [1 ] . In such applications an adaptive IIR filter can provide a better performance than the standard adaptive FIR filters because hR filters can track both zeroes and poles. Also to achieve a specified level of performance an IIR filter requires fewer coefficients than the corresponding FIR filters [1]. Many types of optoelectronic adaptive FIR filters based on fiber optics or acousto optics have been developed over the last few years [2,3 ]. Design and fabrication of optoelectronic adaptive IIR signal processors is a goal of our research.

In the modern wireless communication system, digital technology has tremendous growth, and all the communication channels are slowly moving towards digital form. Wireless communication has to provide the reliable and efficient transfer of information between transmitter and receiver over a wireless channel. The channel coding technique is the best practical approach to delivering reliable communication for the end-users. Many conventional encoder and decoder units are used as error detection and correction codes in the digital communication system to overcome the multiple transient errors. The proposed convolutional encoder consists of both Recursive Systematic Convolutional (RSC) Encoder and Adaptive Variable-Rate Convolutional (AVRC) encoder. Adaptive Variable-Rate Convolutional encoder improves the bit error rate performance and is more suitable for a power-constrained wireless system to transfer the data. Recursive Systematic Convolutional encoder also reduces the bit error rate and improves the throughput by employing the trellis termination strategy. Here, AVRC encoder ultimately acquires the channel state information and feeds the data into a fixed rate convolutional encoder and rate adaptor followed by a buffer device. A hybrid encoder combines the AVRC encoder and RSC encoder output serially and parallel, producing the solid encoded data for the modulator in the communication system. A modified turbo code is also obtained by placing interleaver between the two encoder units and building the stronger code word for the system. Finally, the conventional encoder system is compared and analyzed with the proposed method regarding the number of LUT’s, gates, clock cycle, slices, area, power, bit error rate, and throughput.

Modern wireless communication systems are limited to line-of-sight (LoS) links due to high path loss and blockage issues in millimeter wave (5G) and beyond in optical/visible light communication networks. This letter proposes utilizing (optical) reconfigurable intelligent surface (RIS)-equipped UAV (RISeUAV) to support indirect aerial LoS (ALoS) links for mobile vehicles that deliver critical metropolitan emergency/security services. The RISeUAV performs as an aerial transponder and reflects optical and wireless communication signals in dense urban areas. The navigation problem of the RISeUAV is nontrivial where RISeUAV should be autonomously navigated through an energy-efficient obstacle-free path. Notably, the flight altitude should be relatively low to ensure the quality of ALoS service while the maximum possible ALoS links for vehicles are provided in an obstructed environment. However, designing the flight path for rendering valid ALoS service is an NP-hard problem that is not feasible in real-time for autonomous navigation. We model the RISeUAV navigation as an optimization problem and propose an efficient technique to make the problem computationally tractable in real-time using Benders’ decomposition method and sequential convex programming. Simulation results validate the effectiveness of the proposed method.

Modern wireless communication systems are expected to provide improved latency and reliability. To meet these expectations, a short packet length is needed, which makes the first-order Shannon rate an inaccurate performance metric for such communication systems. A more accurate approximation of the achievable rates of finite-block-length (FBL) coding regimes is known as the normal approximation (NA). It is therefore of substantial interest to study the optimization of the FBL rate in multi-user multiple-input multiple-output (MIMO) systems, in which each user may transmit and/or receive multiple data streams. Hence, we formulate a general optimization problem for improving the spectral and energy efficiency of multi-user MIMO-aided ultra-reliable low-latency communication (URLLC) systems, which are assisted by reconfigurable intelligent surfaces (RISs). We show that an RIS is capable of substantially improving the performance of multi-user MIMO-aided URLLC systems. Moreover, the benefits of RIS increase as the packet length and/or the tolerable bit error rate are reduced. This reveals that RISs can be even more beneficial in URLLC systems for improving the FBL rates than in conventional systems approaching Shannon rates.

RF/Microwave filters represent an important component in most modern wireless communication systems since they reject unwanted signals (noise) and pass the required frequencies. Emerging new generations and development of communication systems need for good performance and miniaturized Band Pass Filters (BPFs). Minkowski fractals used as a miniaturization technique has been applied to slotted hexagonal loop resonator for designing BPF operate at a 3.5GHz resonance frequency which is suitable for WiMAX applications. The proposed filter has been designed using FR-4 substrate with dielectric constant 4.3 and thickness 1.6mm. More than one structure has been simulated. The overall dimensions of the second iteration Minkowski fractal proposed BPF are 7.07mm x 9.88mm which is demonstrates 80.46% reduction in size as compared with conventional closed hexagonal loop resonator BPF. CST microwave studio suite software package has been used in the simulation.

For the last decade, recurrent neural networks (RNNs) have been commonly applied to communications channel equalization. The major problems of gradient-based learning techniques, employed to train recurrent neural networks are slow convergence rates and long training sequences. In high-speed communications system, short training symbols and fast convergence speed are essentially required. In this paper, the derivative-free Kalman filter, so called the unscented Kalman filter (UKF), for training a fully connected RNN is presented in a state-space formulation of the system. The main features of the proposed recurrent neural equalizer are fast convergence speed and good performance using relatively short training symbols without the derivative computation. Through experiments of nonlinear channel equalization, the performance of the RNN with a derivative-free Kalman filter is evaluated.

In modern wireless communication systems different services with different bit rates and individual error protection requirements are supported by the same network. Rate matching by repetition is often employed after convolutional turbo encoding in order to achieve flexibly the required decrease of the code rate. This ensures near error-free transmission down to a specific target channel quality. We propose the optimization of the rate matching using EXIT charts. The rate for random repetition required for error-free decoding is determined semi-analytically avoiding extensive simulations. This includes the analytical optimization of the random repetition scheme and the derivation of the overall EXIT chart taking into account the convolutional turbo encoding and rate matching.

Now a days, the demand for wireless communication systems with high data rates and high capacity has dramatically increased. CDMA (Code Division Multiple access) plays an important role in modern wireless communication systems. MIMO refers to links with multiple antennas at the transmitter and receiver side. CDMA with MIMO is a very promising technique beyond 3G and 4G wireless communications. The BER performance of MIMO-CDMA system depends on its spreading strategy. In this paper MIMO-CDMA system is designed with STBC (Space-Time Block Code) matrices for spreading. The proposed technique outperforms the tdesign permutation spreading method and also the conventional method. Simulation results shows that gain improvement with STBC approach as compared to other existing techniques.

Adaptive techniques play a key role in modern wireless communication systems. The concept of adaptation is emphasized in the Adaptation in Wireless Communications Series through a unified framework across all layers of the wireless protocol stack ranging from the physical layer to the application layer, and from cellular systems to next-generation wireless networks. Adaptation and Cross Layer Design in Wireless Networks is devoted to adaptation in the data link layer, network layer, and application layer. The book presents state-of-the-art adaptation techniques and methodologies, including cross-layer adaptation, joint signal processing, coding and networking, selfishness in mobile ad hoc networks, cooperative and opportunistic protocols, adaptation techniques for multimedia support, self organizing routing, and tunable security services. It presents several new theoretical paradigms and analytical findings which are supported with various simulation and experimental results. Adaptation in wireless communications is needed in order to achieve high capacity and ubiquitous communications. The current trend in wireless communication systems is to make adaptation dependent upon the state of the relevant parameters in all layers of the system. Focusing on simplified cross layer design approaches, this volume describes advanced techniques such as adaptive resource management, adaptive modulation and coding, 4G communications, QoS, diversity combining, and energy and mobility aware MAC protocols. The first volume in the series, Adaptive Signal Processing in Wireless Communications (cat no.46012) covers adaptive signal processing at the physical layer.