Orthogonal Frequency Division Multiplexing Systems Research Articles

PERFORMANCE IMPROVEMENT OF MULTIPLE-INPUT/MULTIPLE-OUTPUT (MIMO) ORTHOGONAL FREQUENCY-DIVISION MULTIPLEXING (OFDM) SYSTEMS USING MIMO DISCRETE MULTIWAVELET TRANSFORM CODED OFDM

In the present work, we proposed to modify and improve multiple-input/multiple-output (MIMO) fast Fourier transform (FFT) coded orthogonal frequency-division multiplexing (COFDM) by MIMO discrete multiwavelet transform (DMWT) COFDM to reduce the interference level and increase the spectral efficiency and complexity of the system. Combining space division multiplexing with multiwavelet and error correction coding and the diversity of the orthogonal frequency division coding to improve the performance of wireless systems requires mastery of all techniques. This DMWT-COFDM system is new and has many applications in telecommunication systems. In this paper, we present the MIMO-FFT-COFDM and MIMO-DMWT-COFDM system block diagrams. Compared with the MIMO-FFT-COFDM system, the bit error rate of the system can be reduced by more than 10 dB.

Modified Method of PAPR Reduction Using Clipping and Filtering for Image Transmission with OFDM

Due to the capability of OFDM (Orthogonal Frequency Division Multiplexing) to handle difficult channels, the most agreeable modulation for the multi-carrier scheme in present wireless communications is to improve an all-purpose modulation scheme, especially with high data rates. The image in this research article was transmitted and received on a noisy channel using an OFDM simulation technique. Since the average peak power ratio (PAPR) is one of the main disadvantages of OFDM, a new method has been proposed to reduce the PAPR using the clipping and filtering (CF) method. When the OFDM signal has a high PAPR, it means that many subcarrier components will be added through the operation of IFFT. Also, choosing the type of modulation to examine and getting a perfect type of OFDM system that is used for transmitting the image. Furthermore, signal-to-noise ratio (SNR) was considered to find the PAPR effect on the OFDM signal. The new method was tested to get a reduction of PAPR concerning CF and without CF. This method depends on clipping the signal before transmitting it, by using a method to overcome a nonlinear distortion, and therefore, decrease the bit error rate (BER). Then a filter with multi-stages was used to minimize the noise. This whole process was repeated several times to overcome the difficulties of transmitting/receiving the signal including PAPR. BER and SNR show wonderful outcomes when BPSK is chosen. Control over transmission and reception is also considered to be the type of modulation. All simulation results were defined using an Additive White Gaussian Noise (AWGN) channel.

Security in Spatially Correlated MIMO OSFBC OFDM Multicast System over Frequency Selective Fading Channel

The system performance can be improved significantly by employing orthogonal space-frequency block coding (OSFBC) and orthogonal frequency division multiplexing (OFDM) because in this combination the receiver suffers lesser complexity. Recently, the wireless systems are deploying by the increasing number of closely spaced antennas. So the assumption of in-dependency among the antenna brunches is not valid. Realizing the advantages of this combination of system and taking account the effect of antenna correlation, this work demonstrates the insight of secured wireless multicasting network consisting multiple antennas both in transmitter and receiver over spatially correlated frequency selective fading channels. The desired signals have been protected from eavesdropping by useful impact of antenna diversity and antenna correlation. The main contribution of this work is the development of mathematical models of the probability of non-zero secrecy multicast capacity (PNSMC) and the secure outage probability for multicasting (SOPM) to secure the system from eavesdropping. The analytical results are verified via Monte-Carlo simulation to ensure the validity of the developed mathematical model. Moreover, the numerical analysis gives a clear indication that security in OSFBC OFDM system with antenna diversity over spatially correlated frequency selective fading channel upgrades due to the effect of transmit antenna correlation and degrades due to receive antenna correlation. But the loss of this security can be significantly reduced employing antenna diversity with OSFBC-OFDM systems.

Mobile communication channel resource allocation technology in interference environment based on clustering algorithm

The rapid development of urbanization has led to the gradual increase of urban residential density. Relatively speaking, the spectrum resources are increasingly scarce, which leads to the increasingly serious interference between communities, and the system performance is also greatly limited. Therefore, in order to improve the efficiency of spectrum resources and solve the problem of user interference between cells, the experiment combines the advantages of clustering by fast search and find of Density Peaks Clustering (DPC), and proposes a two-step clustering algorithm. This method is proposed based on the core idea of DPC after in-depth study of the downlink multi-cell orthogonal frequency division multiplexing system architecture. The proposed model is compared with Matching Pursuit (MP) algorithm and Graph-based algorithm with the sum of clustering distance Rs, 1.5⁢Rs. The results show that the two-step clustering algorithm can significantly improve the spectrum efficiency and network capacity while ensuring good quality of service under the condition of channel tension or not. In addition, the minimum SINR value of the two-step clustering algorithm can reach 75 dB. Compared with the 220 dB of the Graph-based algorithm with the clustering distance Rs, it has extremely obvious advantages. Therefore, the two-step clustering algorithm constructed in this study can effectively reduce system interference, and has certain research and application value in solving the problem of mobile communication channel resource shortage.

Performance analysis of OFDM with variations in cyclic prefix and FFT lengths

Orthogonal Frequency Division Multiplexing (OFDM) is a modern and suitable modulation strategy for commercial high-speed broadband wireless communication systems. This is because wireless services and demands have grown so quickly in recent years. While creating wireless communication, the channel environment is crucial. A square grid with identical vertical and horizontal spacing is used for the constellation points in quadrature amplitude modulation (QAM). More bits can be transferred per symbol to higher-order modulation forms. This research has looked at how Orthogonal Frequency Division Multiplexing OFDM systems perform using different modulation techniques on channels with additive white Gaussian noise (AWGN). Using MATLAB software to do an experiment with different modulation methods and parameters such as FFT length and cyclic prefix findings showed that 64-QAM modulation sends the same amount of data faster and with fewer symbols than QPSK 16-QAM. Here, the 64-QAM modulation technique performs better. An extensive literature survey has been carried out. The work done here involved working on many parameters to improve the performance of OFDM, i.e., by reducing BER, changing modulation techniques, and analyzing the trade-off between cyclic prefix length and Bit Error Rate. Compared to the existing work, the proposed system provides better performance.

Coded Orthogonal Frequency Division Multiplexing System : An Overview

This article compares the performance of two modulation techniques—quadrature phase shift keying (QPSK) and M-ary quadrature amplitude modulation (M-QAM) with M=8, 16, 32, and 64—in a coded orthogonal frequency division multiplexing system. As an error-correcting code, convolutional technology is employed. A vehicular channel with additive white gaussian noise (AWGN) is utilized for communication. According to simulation data, for QPSK and M-QAM, a coded orthogonal frequency division multiplexing system performs better than an uncoded one. Additionally, the system performs better with QPSK than it does with M-QAM. Additionally, when M rises, the performance declines.

Efficient PAPR Reduction in Cyclic Prefix with Enhanced Selection Strategy

The OFDM system’s selective mapping (SLM) technique for reducing Peak to Average Power Ratio (PAPR) seems promising. The fundamental principle of SLM is to create unique send sequences from the same data source and choose the transmit signal with the lowest PAPR. This study presents an enhanced cyclic prefix selection system with a lower PAPR by using selective mapping approaches. With the use of a signal multiplexing technique called orthogonal frequency division multiplexing (OFDM), the available bandwidth is divided into a range of frequencies called sounds. OFDMA technology can be employed anywhere that radio waves are used for data transmission. Input data is moved to the transmitter side for channel encoding. Using in-channel encoding, the digital signal is digitized and superfluous binary digits are eliminated. The signal is then connected to add and remove prefixes in a cyclical manner. Faults between these processes are eliminated by the AWGN channel followed by Hilbert Transform to lower the complexity of the entire process. Finally, by combining the clipping and SLM methods, improved outcomes are attaining in terms of spectral efficiency and PAPR reduction value, while maintaining the performance of channel estimation. MATLAB simulation is employed to evaluate the recommended method.

On the performance of delta sigma modulators for DCO-OFDM based NOMA visible light communication systems

The revolution of the solid-state lighting technology and the looming radio frequency (RF) spectrum crisis enabled the rapid evolution of visible light communication (VLC) in the recent times. To furnish contemporaneous illumination and communication, VLC relies on white light emitting diodes (WLEDs). However, the limited modulation bandwidth of WLEDs poses a threat to VLC as the achievable data rates are drastically minimized. Eventually, to enhance the achievable throughput, the pre-eminent way is to apply orthogonal frequency division multiplexing (OFDM) to a VLC system. Furthermore, a VLC system can also exploit non-orthogonal multiple access (NOMA) scheme to bestow with seamless services to the multiple users. However, much similar to the RF-based OFDM system, the OFDM-VLC system also inherits one of the serious disadvantages like the high peak to average power ratio (PAPR). In addition, the rapid fluctuations of the continuous time-domain signal amplitude in DC-biased optical OFDM (DCO-OFDM) results in much complicated design of the LED driver circuitry for driving the LED. Thus, to overcome such drawbacks, we incorporate delta-sigma modulators (DSM) to the NOMA-VLC system which is making use of DCO-OFDM, and we analyze its performance over VLC channel environment. The stunning advantage imparted by the proposed DSM-based DCO-OFDM-NOMA-VLC system is that the continuous amplitude of the time-domain transmitted signal is converted into two levels for driving the LED. Additionally, with the major goal to maximize the sum throughput of the proposed multiuser VLC system by taking into consideration both the user fairness as well as the intensity constraints, we derived the optimal values for the power allocation coefficients corresponding to the fair power allocation algorithm. Furthermore, the dynamically varying fair power allocation algorithm is compared with the fixed/static power allocation algorithm. The major contribution of this work is of two-fold: firstly, the proposed DCO-OFDM-NOMA-VLC system which is making use of DSM exhibits a remarkable reduction in PAPR when compared with the conventional system without the application of DSM, where the proposed system demonstrates a significant gain of 4.78 dB in terms of PAPR reduction. Secondly, from the simulated results it can affirmed that the proposed system not only achieves enhanced sum rates and better outage performance but also imparts a better bit error ratio (BER) performance corresponding to the far user.

FPGA based modified multi-repeat distribution matcher for probabilistic amplitude shaping

The technique of probabilistic amplitude modulation, based on distribution matching, has garnered considerable attention in recent years as a means to enhance spectral efficiency and diminish the constellation energy of coded modulation. This paper introduces the implementation of Probabilistic Amplitude Modulation (PAS) using a Modified Multi-Repeat Distribution Matcher (MMRDM) on a Field Programmable Gate Array (FPGA). The Modified Multiple Repetition Distribution Matcher (MMRDM) is integrated into a 2×2 Multiple Input Multiple Output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) system, realized through the Xilinx System Generator (XSG). Simple Zero-Forced and Minimum Mean Squared Error (MMSE) equalizers are applied to the receiver for signal detection across the MIMO channel. The system incorporates enhanced security through chaos-based scrambling with 16 and 64 Quadrature Amplitude Modulation (QAM). VHDL code files for this system are generated for the Xilinx Kintex-7 (xc7k325t-3fbg676) for hardware implementation. Performance evaluation includes an assessment of required storage capacity, complexity, and bit error rate (BER). Using Vivado 2017.4, the system is successfully routed with resource utilization, for example, 0.67% Block RAM (BRAM), 68.6% Look-Up Tables (LUT), 83% DSP 48s, and 1.5% registers for 64-QAM uniform modulation. Similarly, for 64-QAM 10 level (shaper output 60 bit) shaped modulation, the resource utilization is 0.67% BRAM, 68.8% LUT, 83% DSP 48s, and 1.6% registers on the specified device. Simulation results demonstrate an improvement in the net shaping gain of approximately (2-4 dB) at 1×10^(-4) for different equalizer cases compared to uniform QAM, along with a notable reduction in required storage capacity and computational complexity.

Recurrent neural networks for enhanced joint channel estimation and interference cancellation in FBMC and OFDM systems: unveiling the potential for 5G networks

FBMC is a pivotal system in 5G, serving as a cornerstone for efficient use of available bandwidth while simultaneously meeting stringent requirements for high spectral efficiency. Notably, FBMC harnesses the power of multicarrier modulation (MC), a good alternative to orthogonal frequency division multiplexing (OFDM) technology that supports fourth-generation (4G) systems. The wireless communications field is full of challenges, the most important of which are channel estimation and interference cancellation, both of which deserve comprehensive study to increase the efficiency of data transmission. In this paper, our investigation takes a deliberate step towards the convergence of two prominent modulation models: OFDM and FBMC. We specifically contrast these modulation techniques with the intricate field of joint channel estimation and interference cancellation (JCEIC). In this research study, we take advantage of recurrent neural networks' (RNNs') efficiency as a vehicular channel to perform precise channel estimation and recovery of uncorrupted transmitted signals, thereby lowering the bit error rate (BER). Our channel estimation for a dual selective channel is based on the thoughtful placement of pilots scattered over the temporal and frequency dimensions, and is further improved by the interference cancellation method of low complexity that was selected. Our JCEIC proposal aims to integrate RNNs carefully, using the output sequences of JCEIC algorithms as useful inputs to this neural architecture. By clearly demonstrating a decrease in BER as compared to traditional approaches, it is evident that the performance of the novel approach is near to that of a perfect channel. Additionally, a comparison of the performance of FBMC and OFDM systems at various signal-to-noise ratios reveals a clear performance divide that favors the former in terms of system efficiency. The BER is restricted by FBMC to a commendable threshold of less than 0.1 at a modest 5 dB, continuing the higher trend started by its improved RNN-based channel estimate. The accuracy of channel estimation is clearly improved by this paradigm shift, and the computing complexity typical of 5G networks is also clearly reduced.

A novel PAPR reduction scheme based on time-delay neural network for CO-OFDM systems

In this paper, we propose a low-complexity peak-to-average power ratio (PAPR) reduction scheme based on time-delay neural network (TDNN) for coherent optical orthogonal frequency division multiplexing (CO-OFDM) systems to reduce the limitation of PAPR on the total power of laser diodes and the impact of fiber nonlinear effects. The proposed scheme is jointly trained on traditional iterative clipping and filtering (ICF) scheme and simplified clipping and filtering (SCF) scheme, and changes the objective function by adjusting the given training weight α. Compared to the original orthogonal frequency division multiplexing (OFDM) signal of existing system, our method achieves an adjustable PAPR reduction of 5.31 dB to 6.12 dB for 10−4 complementary cumulative distribution function (CCDF). When bit error rate (BER) Pe=10−3, the proposed scheme (α=0.5) achieves additional transmission distances of 220 km, 55 km, 105 km, and 100 km, compared to the original OFDM signal, partial transmission sequence (PTS) scheme, ICF scheme, and feedforward neural network (FNN)-based ICF scheme, respectively. Besides, our method achieves 86.3% and 80.0% floating-point operations (FLOPs) reductions, compared with PTS scheme and ICF scheme, respectively, which effectively reduces the complexity of PAPR reduction module, saving the system computational time.

Dendrogram-based Heterogeneous Learners for Automatic Modulation Classification in DSTBC-OFDM Systems

Software-Defined Radios (SDRs) have a crucial role in dynamic spectrum sensing and sharing as they can quickly adapt to changes in their environment. Digital Modulation Recognition (DMR) enables this process by giving SDR the ability to recognize the received signal modulation format to make intelligent and autonomous decisions to switch on the optimal modulation scheme. However, with the deployment of the Distributed Space-Time Block Coding Orthogonal Frequency Division Multiplexing (DSTBC-OFDM) signals that support MIMO systems, that provides spatio-temporel multiplexing, new and challenging signal identification problems have emerged.This paper proposes a robust DMR method for relaying Space-Time Block Coding (STBC) Orthogonal Frequency Division Multiplexing (OFDM) systems. The proposed classifier is built using heterogeneous base classifiers namely, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Random Forest (RFC), based on a Dendrogram or decision tree structure.The Dendrogram-based Heterogeneous Learners (DHL) classifier were obtained via Ascendant Hierarchical Clustering (AHC) and by employing Higher-Order Moment (HOMs) and Higher-Order Cumulants (HOCs) as features. Moreover, we propose a grid search-based hyperparameter tuning for DHL to produce the best structure and optimize the performance.

Enhancing channel estimation accuracy in polar-coded MIMO–OFDM systems via CNN with 5G channel models

In this paper, a novel channel estimation technique for multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO–OFDM) systems in fifth generation (5G) networks is proposed, which leverages the power of convolutional neural networks (CNNs) along with the application of polar coding. By employing polar encoding at the transmitter and polar decoding at the receiver, the proposed system enhances error correction performance, reliability, and data transmission rate in 5G networks. A novel optimized CNN-based channel estimation technique, named CNN-CENet, has been integrated into the polar-coded MIMO–OFDM receiver. This integration aims to address the interference and noise challenges in 5G systems. The proposed channel estimation method utilizes multiple layers of CNN to accurately characterize the properties of the real channel by incorporating least squares (LS) estimations. Hence, the proposed CNN-CENet provides an accurate estimation of the channel coefficients, enabling efficient signal detection at the receiver. The MATLAB simulation has been conducted using deep learning and 5G toolboxes to obtain results for various channel parameters. These results are then compared with the LS and minimum mean square error (MMSE) methods for performance evaluation. The proposed model surpasses the performance of the LS and MMSE methods, achieving a significant 95.6% reduction in mean square error (MSE) compared to LS and a substantial 59.7% reduction compared to MMSE. Additionally, it delivers notable bit error rate (BER) reductions of 59.8% and 53.2% for LS and MMSE, respectively, in low mobility scenarios, and 57.4% and 48.5% reductions in high mobility scenarios. This improvement leads to enhanced reliability and overall system efficiency.

Improved PAPR Reduction Technique in 5G Systems: Addressing Limitations and Challenges with Computational Techniques

A high peak-to-average power ratio (PAPR) in the Orthogonal Frequency Division Multiplexing (OFDM) system reduces the efficiency of the power amplifier (PA) at the transmitter. This flaw lowers the efficiency of the OFDM system overall and results in excessive energy usage. The OFDM signal is preprocessed to lower its peak power before transmission in order to address this problem. For 4G and 5G waveforms, we investigate several crest-factor reduction (CFR) approaches in this work. We emphasize the importance of processing OFDM signals before transmission in order to address the significant PAPR issue. In-depth analysis of the CFR technique known as "clipping," which is frequently utilized in real-time applications, is conducted. We go over the difficulties brought on by the high PAPR of OFDM signals and how they affect power amplifier effectiveness. We evaluate the suggested PAPR reduction technique's performance in terms of PAPR reduction capability, signal distortion, computational overhead, and bit error rate (BER) using simulation models and MATLAB-based evaluations. We contrast the efficiency of our suggested method with the PAPR reduction strategies currently in use, such as the partial transmit sequence (PTS), selected mapping (SLM), and crest factor reduction (CFR) methods. Additionally, we confirm the suggested method using a hardware testbed, highlighting its usefulness in actual 5G systems. Additionally, we assess the suggested method's ability to lower the cost and complexity of hardware design for 5G systems in order to assess the economic benefits.

Design and performance investigation of a coherent optical system based multi-band OFDM with various indexes

Abstract In this work, a comprehensive study of a multi-band optical orthogonal frequency division multiplexing (MB-OOFDM) system is investigated. Different OFDM indexes (128,256,512, and 1024) are utilized with the coherent detection CD technique. High-order modulation 16-quadrature amplitude modulation (16-QAM) is used to obtain high spectral efficiency. Transmission studies indicated that 128 subcarriers with 2-bands exhibit a higher optical signal-to-noise ratio (OSNR) of 23 dB with lower bit error rate (BER) of 1 × 10−4, while 1024 subcarriers with 8-bands shows a lower OSNR of 22 dB with a higher BER of 3 × 10−3. Even though the higher subcarrier type has the lowest BER, it provides a superior solution when combined with wavelength-division multiplexing (WDM) in the presence of forward error correction (FEC). The performance of the system with 40 Gbps along 800 km of uncompensated fiber has designed and simulated using Optisystem software.

OFDM-Based Massive Connectivity for LEO Satellite Internet of Things

Low earth orbit (LEO) satellite has been considered as a potential supplement for the terrestrial Internet of Things (IoT). In this paper, we consider grant-free non-orthogonal random access (GF-NORA) in the orthogonal frequency division multiplexing (OFDM) system to increase access capacity and reduce access latency for LEO satellite-IoT. We focus on the joint device activity detection (DAD) and channel estimation (CE) problem at the satellite access point. The delay and the Doppler effect of the LEO satellite channel are assumed to be partially compensated. We propose an OFDM-symbol repetition technique to better distinguish the residual Doppler frequency shifts, and present a grid-based parametric probability model to characterize channel sparsity in the delay-Doppler-user domain, as well as to characterize the relationship between the channel states and the device activity. Based on that, we develop a robust Bay esian message-passing algorithm named modified variance state propagation (MVSP) for joint DAD and CE. Moreover, to tackle the mismatch between the real channel and its on-grid representation, an expectation–maximization (EM) framework is proposed to learn the grid parameters. Simulation results demonstrate that our proposed algorithms significantly outperform the existing approaches in both activity detection probability and channel estimation accuracy.

Viewing Channel as Sequence Rather Than Image: A 2-D Seq2Seq Approach for Efficient MIMO-OFDM CSI Feedback

In this paper, we aim to design an effective learning-based channel state information (CSI) feedback scheme for the multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems from a physics-inspired perspective. We first argue that the CSI matrix of a MIMO-OFDM system is physically closer to a two-dimensional (2-D) sequence rather than an image due to its apparent unsmoothness, non-scalability, and translational variance within both the spatial and frequency domains. On this basis, we introduce a 2-D long short-term memory (LSTM) neural network to represent the CSI and propose a 2-D sequence-to-sequence (Seq2Seq) model for CSI compression and reconstruction. Specifically, one two-layer 2-D LSTM is used for CSI feature extraction, and the other is used for CSI representation and reconstruction. The proposed scheme can not only fully utilize the unique 2-D characteristics of CSI but also preserve the index information and unsmooth features of the CSI matrix compared with current convolutional neural network (CNN) based schemes. We show that the computational complexity of the proposed scheme is linear in the number of transmit antennas and subcarriers. Its key performances, like reconstruction accuracy, convergence speed, generalization ability after short-term training, and robustness to lossy feedback, are comprehensively compared with existing popular convolutional networks. Experimental results show that our scheme can bring up to nearly 7 dB gain in reconstruction accuracy under the same overhead and reduce feedback overhead by up to 75% under the same accuracy compared with the conventional CNN-based approaches.

RIS-Aided Multiuser MIMO-OFDM With Linear Precoding and Iterative Detection: Analysis and Optimization

In this paper, we consider a reconfigurable intelligent surface (RIS) aided uplink multiuser multi-input multi-output (MIMO) orthogonal frequency division multiplexing (OFDM) system, where the receiver is assumed to conduct low-complexity iterative detection. We aim to minimize the total transmit power by jointly designing the precoder of the transmitter and the passive beamforming of the RIS. This problem can be tackled from the perspective of information theory. But this information-theoretic approach may involve prohibitively high complexity since the number of rate constraints that specify the capacity region of the uplink multiuser channel is exponential in the number of users. To avoid this difficulty, we formulate the design problem of the iterative receiver under the constraints of a maximal iteration number and target bit error rates of users. To tackle this challenging problem, we propose a groupwise successive interference cancellation (SIC) optimization approach, where the signals of users are decoded and canceled in a group-by-group manner. We present a heuristic user grouping strategy, and resort to the alternating optimization technique to iteratively solve the precoding and passive beamforming sub-problems. Specifically, for the precoding sub-problem, we employ fractional programming to convert it to a convex problem; for the passive beamforming sub-problem, we adopt successive convex approximation to deal with the unit-modulus constraints of the RIS. We show that the proposed groupwise SIC approach has significant advantages in both performance and computational complexity, as compared with the counterpart approaches.

CFLIT: Coexisting Federated Learning and Information Transfer

Future wireless networks are expected to support diverse mobile services, including artificial intelligence (AI) services and ubiquitous data transmissions. Federated learning (FL), as a revolutionary learning approach, enables collaborative AI model training across distributed mobile edge devices. By exploiting the superposition property of multiple-access channels, over-the-air computation allows concurrent model uploading from massive devices over the same radio resources, and thus significantly reduces the communication cost of FL. In this paper, we study the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">coexistence</i> of over-the-air FL and traditional information transfer (IT) in a mobile edge network, where an access point (AP) coordinates a set of devices for over-the-air FL and serves multiple devices for information transfer in the meantime. We propose a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">coexisting federated learning and information transfer (CFLIT)</i> communication framework, where the FL and IT devices share the wireless spectrum in an orthogonal frequency division multiplexing (OFDM) system. Under this framework, we aim to maximize the IT data rate and guarantee a given FL convergence performance by optimizing the long-term radio resource allocation. A key challenge that limits the spectrum efficiency of the coexisting system lies in the large overhead incurred by frequent communication between the server and edge devices for FL model aggregation. To address the challenge, we rigorously analyze the impact of the computation-to-communication ratio on the convergence of over-the-air FL in wireless fading channels. The analysis reveals the existence of an optimal computation-to-communication ratio that minimizes the amount of radio resources needed for over-the-air FL to converge to a given error tolerance. Based on the analysis, we propose a low-complexity online algorithm to jointly optimize the radio resource allocation for both the FL devices and IT devices. We further derive an analytical expression of the achievable data rate of IT users. Extensive numerical simulations verify the superior performance of the proposed design for the coexistence of FL and IT devices in wireless cellular systems.

Filter bank multi-carrier based offset ternary phase shift keying(FBMC/OTPSK)

Filter bank multi-carrier (FBMC) is a popular researched technique for wireless communications that offers several advantages over traditional orthogonal frequency-division multiplexing (OFDM) systems. In this paper, we propose a FBMC system using offset Triangle phase shift keying (TPSK) modulation (FBMC/OTPSK), analyze its bit error rate (BER) performance via additive white Gaussian noise (AWGN) channel, and compare it to that of conventional FBMC using offset quadrature amplitude modulation (FBMC/OQAM) system. Simulation results show that the proposed FBMC/OTPSK system considerably improves BER performance over FBMC/OQAM system. Additionally, we explore the impact of using ternary convolutional coding (TCC) with TPSK and binary convolutional coding (BCC) with QAM. The presented TPSK modulation encoded by TCC offers better error rate performance and minimum amplitude fluctuation compared to QAM encoded by BCC; therefore, TCC-coded OTPSK is proposed to be used with FBMC instead of BCC-coded OQAM to benefit from its lower error rates without bandwidth expansion, as well increase information carried by symbol, hence increases throughput.