Frequency Division Multiple Access Research Articles

One Bit Aggregation for Federated Edge Learning With Reconfigurable Intelligent Surface: Analysis and Optimization

As one of the most popular and attractive frameworks for model training, federated edge learning (FEEL) presents a new paradigm, which avoids direct data transmission by collaboratively training a global learning model across multiple distributed edge devices, thus overcoming the disadvantage of centralized machine learning in resource limitations, delay constraints, and privacy issues. However, due to the heavy cost of communicating gradient among edge devices, sharing the parameters of a large-scale neural network can still be time-intensive. To alleviate this bottleneck, an efficient scheme, called SignSGD has been recently proposed, where the one-bit gradient quantization with majority vote is featured at edge devices. Nevertheless, the performance of one-bit aggregation will inevitably deteriorate due to the undesirable propagation error introduced by wireless channels. To address this issue, we propose in this work a novel reconfigurable intelligent surface (RIS)-aided one-bit communication optimization scheme under orthogonal frequency division multiple access (OFDMA) to relieve the negative influence of communication error on the SignSGD-based FEEL. Specifically, a learning convergence analysis is firstly presented to quantitatively characterize the impact of wireless communication error measured by the union bound on pairwise bit error rate (BER) on the performance of SignSGD-based FEEL. Immediately, a unified communication-learning optimization problem is further formulated to jointly optimize the sub-band assignment strategy, the power allocation vector, and the RIS configuration matrix. Numerical experiments show that the proposed design achieves substantial performance improvement compared with the state-of-the-art approaches.

Resource Allocation in Macrocell–Femtocells via Combined Rain and Whale Optimization Algorithm

Femtocell (FC) dense exploitation imposes a novel challenge for the resource allotment in a 2-tier network, like: (1) dynamic resources allotment among the 2 tiers by considering the off-load traffic from macrocell (MC), (2) permitting access to users when ensuring QoS for FC subscribers, and (3) suitable power setting, which discovers cooperation among the overall bandwidth allotted to FCs and system performances. This research work examines the problems existing on cross-tier interfering as well as resource allocation alleviation in “full-duplex (FD) Orthogonal Frequency Division Multiple Access (OFDMA) oriented Heterogeneous Networks (HetNets), which involves macrocell with underlying femtocells”. Here, three primary contributions are concerned: a single objective issue together with power allocation, price allocation & subcarrier allocation of MC-FC networks. In addition, this research establishes a new hybrid algorithm termed Hybrid Whale and Rain Optimization (HW-RO) for solving the optimization problem in MC-FC Networks. Finally, the superiority of the suggested technique is evaluated in comparison to various existing approaches.

A Powerful Joint Modulation and STBC Identification Algorithm for Multiuser Uplink SC-FDMA Transmissions

Owing to the rapid development and broad adoption of multiple antenna communication systems over the past few years, space-time block coding (STBC) identification has emerged as a crucial responsibility for smart radios. The majority of previous analysis of STBC identification assumed that the utilized modulation schemes for single-user and uncoded broadcasts were known. This paper investigates the challenge of joint STBC and modulation identification for uplink transmissions with numerous users in single-carrier frequency division multiple access (SC-FDMA) systems using coded transmissions. Multi-user channel estimation brings us one step closer to implementing the proposed design in real-world systems. We additionally employ the channel decoder’s deliverables, which are common in many real-world systems, to enhance the identifying and estimating procedures. Mathematical findings prove that a recursive approach can be utilized to tackle the maximum likelihood (ML) problem of simultaneous STBC and modulation identification with channel estimation. Distinguishing the superimposed signals that originate at the base-station (BS) is accomplished with the use of the space-alternating generalized expectation-maximization (SAGE) algorithm. After that, an expectation-maximization (EM) engine is deployed to make the necessary adjustments to the parameters being considered for each user. The success of the above-mentioned architecture for usage in practical applications is demonstrated by the simulation results obtained under various conditions.

Intelligent Reflecting Surface-Assisted Low-Latency Federated Learning Over Wireless Networks

Federated learning (FL) is an emerging technique to support privacy-aware and resource-constrained machine learning, where a base station (BS) will coordinate a set of distributed Internet of Things (IoT) devices to train a shared machine learning model with their local data sets. Nevertheless, due to the frequent interactions between BS and distributed IoT devices for the aggregating/distributing learning model parameters, the performance of FL is fundamentally restricted by the randomness of channel condition. To address this issue, we utilize the intelligent reflecting surface (IRS) to improve the efficiency of learning model aggregation/distribution. In addition, we consider two transmission protocols to enable the model aggregation from IoT devices to BS, i.e., frequency division multiple access (FDMA) and nonorthogonal multiple access (NOMA). For both protocols, we formulate the total training latency minimization problem under the available energy constraints of IoT devices, to jointly optimize the phase shifts of IRS, communication resource scheduling, and transmit power and local computing frequencies of IoT devices. Moreover, we further develop the efficient multidimensional resource management algorithms to solve the formulated training latency minimization problems. Numerical results demonstrate that the proposed IRS-assisted FL systems can achieve significant latency reduction as compared with other benchmark methods, and the NOMA-based model aggregation method exhibits a lower total training latency than the FDMA-based counterpart.

Space/Frequency-Division-Based Full-Duplex Data Transmission Method for Multihop Underwater Acoustic Communication Networks

Underwater acoustic communication networks (UACNs) have been widely utilized in recent years because of the growing interest in interactive information in the deep ocean. Compared with traditional radio wireless networks, UACNs are characterized by complex and dynamic 3-D network topology and longer signal propagation delay. Therefore, recent studies on UACNs usually apply dynamic routes in data communication to adapt to complex UACN structure. However, the approaches are hardly adequate for UACN scenarios with high-traffic requirements. This is because dynamic routing methods, such as opportunistic routing, usually require external contention costs to control the routing paths, which leads to decreased network throughput. Accordingly, this article focuses on enabling high-speed acoustic communications for underwater application scenarios with high-traffic requirements, and proposes an underwater data transmission method using the multichannel full-duplex (FD) communication technique. The proposed method applies the underwater orthogonal frequency division multiple access (OFDM) technique, that uses collision-free channels to relay data in multihop routes for simultaneous transmission and reception. Unlike the traditional communication methods of UACNs with dynamic routing, the proposed one uses static routes when transporting data over hop-by-hop paths to achieve stable and uninterrupted FD communication. The approach also includes a directional forwarding-based route request method and an elimination-based channel evaluation proposal, which purpose to reduce the overheads from exploring routes and enable collision-free FD communications. The performance analysis of the proposed method is under simulated conditions of high-traffic UACN scenarios, and the results show that it has superior performance compare to the classical underwater data transmission methods.

Distinctive analysis of power and frequency allocation for Device to Device (D2D) communication

Wireless communication makes an impeccable advancement from 1 G to 5 G and beyond. Simultaneously, modern technologies like Internet of Things (IoT) and Vehicular Communications also emerged. Improved connectivity between many different devices is provided by 5 G, which maximises data throughput and reduces interference. Non-Orthogonal Multiple Access (NOMA) schemes or Orthogonal Multiple Access techniques (OMA) suit 5 G. It is a NOMA-based device-to-device communication by correlating it with the Orthogonal Frequency Division Multiple Access (OFDMA). NOMA is also utilized to propose an optimal power allocation scheme and duly evaluated. The aim of such a scheme is to achieve a minimized outage and a maximized sum rate. A NOMA based Greedy Interference Algorithm is proposed for interference management by exquisite frequency allocation. The results generated from a comprehensive evaluation justify the algorithm utilization. Simulation results infer that the algorithms proposed improves system throughput and enhance energy efficiency.

Utility Maximization for IRS Assisted Wireless Powered Mobile Edge Computing and Caching (WP-MECC) Networks

This paper exploits an intelligent reflecting surface (IRS) assisted wireless powered mobile edge computing and caching (WP-MECC) network. In particular, an IRS is utilized to reflect energy signals from a power station (PS) to various IoT devices for energy harvesting during uplink wireless energy transfer (WET). These devices collect energy to support their own partially local computing for computational tasks and their offloading capabilities to an access point (AP), with the help of IRS via time or frequency division multiple access (TDMA or FDMA). The AP is equipped with a local cache connected with a MEC server via a backhaul link, which prefetches the data to facilitate edge computing capabilities. The maximization of a utility function is formulated to evaluate the overall network performance, which is defined as the difference between the sum of computational bits (offloading bits and local computing bits) and total backhaul cost. Due to multiple coupled variables, we first design the optimal caching strategy. Then, an auxiliary vector is introduced to coordinate the energy consumption of local computing and offloading, where its optimal solution can be achieved by an exhaustive search. Moreover, we utilize the Lagrange dual method and the Karush-Kuhn-Tucker (KKT) conditions to derive the optimal time scheduling for the TDMA scheme or the optimal bandwidth allocation for the FDMA counterpart in closed form. The IRS phase shifts are iteratively designed by employing the quadratic transformation (QT) and the Riemannian Manifold Optimization (RMO). Finally, simulation results are demonstrated to validate the network utility performance and confirm the advantage of the employment of IRS, the optimal IRS phase shift design and caching strategy, in comparison to the benchmark schemes.

UAV-Assisted Multi-Access Computation Offloading via Hybrid NOMA and FDMA in Marine Networks

With the rapid development of marine networks, there have been growing demands for computation-intensive and delay-sensitive marine applications and services. However, the limited underwater energy supply and the acoustic channels result in the low efficiency for computing tasks and high transmission delay. In this paper, we investigate the unmanned aerial vehicles (UAVs)-assisted multi-access computation offloading in marine networks, with the objective of minimizing the energy consumption of ocean devices. Specifically, for the underwater segment, we consider the scenario that multiple underwater sensor nodes (USNs) covered by the unmanned surface vehicle (USV) upload their sensing data via non-orthogonal multiple access (NOMA) for improving the channel utilization. For the radio frequency segment, we consider the scenario that multiple UAVs hovering in the air act as the aerial edge servers for providing computing services, in which the USV offloads the workloads to UAVs via frequency division multiple access (FDMA) for avoiding their co-channel interference, while taking into account that a malicious node overhears the USV's offloading transmission. To improve the computation offloading efficiency, we formulate an optimization problem for USNs and USV to minimize the total energy consumption by jointly optimizing the USN's uploading time, USV's computation offloading, USV's offloading time, and the secrecy provisioning. Despite the non-convexity of the formulated joint optimization problem, we exploit a layered structure to decompose the problem, and propose efficient algorithms to obtain the optimal solutions. Finally, we conduct simulations to validate the effectiveness and efficiency of the proposed algorithms. Numerical results demonstrate that our algorithms can significantly reduce the energy consumption in comparison with the benchmark schemes.

Optimal Resource Allocation for NOMA Wireless Networks

The non-orthogonal multiple access (NOMA) method is a novel multiple access technique that aims to increase spectral efficiency (SE) and accommodate enormous user accesses. Multi-user signals are superimposed and transmitted in the power domain at the transmitting end by actively implementing controllable interference information, and multi-user detection algorithms, such as successive interference cancellation (SIC), are performed at the receiving end to demodulate the necessary user signals. Although its basic signal waveform, like LTE baseline, could be based on orthogonal frequency division multiple access (OFDMA) or discrete Fourier transform (DFT)-spread OFDM, NOMA superimposes numerous users in the power domain. In contrast to the orthogonal transmission method, the non-orthogonal method can achieve higher spectrum utilization. However, it will increase the complexity of its receiver. Different power allocation techniques will have a direct impact on the system’s throughput. As a result, in order to boost the system capacity, an efficient power allocation mechanism must be investigated. This research developed an efficient technique based on conjugate gradient to solve the problem of downlink power distribution. The major goal is to maximize the users’ maximum weighted sum rate. The suggested algorithm’s most notable feature is that it converges to the global optimal solution. When compared to existing methods, simulation results reveal that the suggested technique has a better power allocation capability.

WiFi6 Dynamic Channel Optimization Method for Fault Tolerance in Power Communication Network

As the scale of power networks has expanded, the demand for multi-service transmission has gradually increased. The emergence of WiFi6 has improved the transmission efficiency and resource utilization of wireless networks. However, it still cannot cope with situations such as wireless access point (AP) failure. To solve this problem, this paper combines orthogonal frequency division multiple access (OFDMA) technology and dynamic channel optimization technology to design a fault-tolerant WiFi6 dynamic resource optimization method for achieving high quality wireless services in a wirelessly covered network even when an AP fails. First, under the premise of AP layout with strong coverage over the whole area, a faulty AP determination method based on beacon frames (BF) is designed. Then, the maximum signal-to-interference ratio (SINR) is used as the principle to select AP reconnection for the affected users. Finally, this paper designs a dynamic access selection model (DASM) for service frames of power Internet of Things (IoTs) and a scheduling access optimization model (SAO-MF) based on multi-frame transmission, which enables access optimization for differentiated services. For the above mechanisms, a heuristic resource allocation algorithm is proposed in SAO-MF. Simulation results show that the method can reduce the delay by 15% and improve the throughput by 55%, ensuring high-quality communication in power wireless networks.

Analysis of the differences between Wi-Fi 6 and Wi-Fi 5

While Wi-Fi 5 is still sufficient for many organizations and businesses, it is important to consider and upgrade to Wi-Fi 6. This article discusses the main differences between Wi-Fi 5 and Wi-Fi 6. The goal is to identify key metrics transmission of information content of signals, a method for determining the best use of Wi-Fi signals for certain categories of use, an analysis and study of the influence of frequencies on the quality of transmitted information, and also analyzes the growth rate of an organization using these technologies. Using the Orthogonal Frequency Division Multiple Access connection principle, Wi-Fi 6 technology provides the main advantage of performing multiple receptions and transmissions at the same time. Multiple User Multiple Input Multiple Output technology allows routers to communicate with all devices at the same time, resulting in maximum packet exchange. Using the Basic Service Set (BSS) color scheme, we can see that Wi-Fi 6 is far ahead of previous technologies.

Computation rate maximization in multi-user cooperation-assisted wireless-powered mobile edge computing with OFDMA

In the era of Internet of Things (IoT), mobile edge computing (MEC) and wireless power transfer (WPT) provide a prominent solution for computation-intensive applications to enhance computation capability and achieve sustainable energy supply. A wireless-powered mobile edge computing (WPMEC) system consisting of a hybrid access point (HAP) combined with MEC servers and many users is considered in this paper. In particular, a novel multiuser cooperation scheme based on orthogonal frequency division multiple access (OFDMA) is provided to improve the computation performance, where users can split the computation tasks into various parts for local computing, offloading to corresponding helper, and HAP for remote execution respectively with the aid of helper. Specifically, we aim at maximizing the weighted sum computation rate (WSCR) by optimizing time assignment, computation-task allocation, and transmission power at the same time while keeping energy neutrality in mind. We transform the original non-convex optimization problem to a convex optimization problem and then obtain a semi-closed form expression of the optimal solution by considering the convex optimization techniques. Simulation results demonstrate that the proposed multi-user cooperation-assisted WPMEC scheme greatly improves the WSCR of all users than the existing schemes. In addition, OFDMA protocol increases the fairness and decreases delay among the users when compared to TDMA protocol.

Secrecy Analysis of Uplink IM-OFDMA Systems in the Presence of IQ Imbalance

This paper introduces the secrecy analysis of the index modulation-based orthogonal frequency division multiple access (IM-OFDMA) schemes by considering the in-phase and quadrature imbalance (IQI) presence at the users' RF front ends. In particular, by assuming the existence of an eavesdropper, the secrecy performance of the considered uplink multiple-access system is investigated through the strictly positive secrecy capacity and secrecy outage probability, and closed-form expressions are derived for both quantities. Moreover, the secrecy performance is conducted regarding IQI compensation based on a preamble-based estimation method. Further, analysis of the impact of the different parameters of IM-OFDMA, IQI, and estimation method is included via analytical and simulation-based results. The results reveal that IM-OFDMA is more reliable in secrecy performance than OFDMA, regardless of whether IQI is present. Also, IQI compensation can lead to a better secrecy performance compared to the case of no IQI.

Cross Layer Resource Allocation in H-CRAN With Spectrum and Energy Cooperation

5G and beyond wireless networks are the upcoming evolution for the current cellular networks to provide the essential requirement of future demands such as high data rate, low energy consumption, and low latency to provide seamless communication for the emerging applications. Heterogeneous cloud radio access network (H-CRAN) is envisioned as a new trend of 5G that uses the advantages of heterogeneous and cloud radio access networks to enhance both spectral and energy efficiency. In this paper, building on the notion of effective capacity (EC), we propose a framework in orthogonal frequency division multiple access (OFDMA)- non-orthogonal multiple access (NOMA) based H-CRAN to meet these demands simultaneously. Our proposed approach is to maximize the effective energy efficiency (EEE) while considering spectrum and power cooperation between a macro base station (MBS) and radio remote heads (RRHs). To solve the formulated problem and to make it more tractable, we transform the original problem into an equivalent subtractive form via Dinkelbach algorithm. Afterward, the combinational framework of distributed stable matching and successive convex algorithm (SCA) is then adopted to obtain the solution of the equivalent problem. Hereby, we propose an efficient resource allocation scheme to maximize energy efficiency while maintaining the delay quality of service (QoS) requirements for all users. The simulation results show that the proposed algorithm can provide a non-trivial trade-off between delay and energy efficiency in OFDMA-NOMA based H-CRAN systems in terms of EC and EEE and the spectrum and power cooperation improves EEE of the proposed network. Moreover, our proposed solution complexity is much lower than the optimal solution and it suffers a very limited gap compared to the optimal method.

Data Collection Maximization in IoT-Sensor Networks via an Energy-Constrained UAV

In this paper, we study sensing data collection of IoT devices in a sparse IoT-sensor network, using an energy-constrained Unmanned Aerial Vehicle (UAV), where the sensory data is stored in IoT devices while the IoT devices may or may not be within the transmission range of each other. We formulate two novel data collection problems to fully or partially collect data stored from IoT devices using the UAV, by finding a closed tour for the UAV that consists of hovering locations and the sojourn duration at each of the hovering locations such that the accumulative volume of data collected within the tour is maximized, subject to the energy capacity on the UAV, where the UAV consumes energy on both hovering for data collection and flying from one hovering location to another hovering location. To this end, we first propose a novel data collection framework that enables the UAV to collect sensory data from multiple IoT devices simultaneously if these IoT devices are within the coverage range of the UAV, through adopting the orthogonal frequency division multiple access (OFDMA) technique. We then formulate two data collection maximization problems to deal with full or partial data collection from IoT devices at each hovering location, and show that both defined problems are NP-hard. We instead devise approximation and heuristic algorithms for the problems. We finally evaluate the performance of the proposed algorithms through experimental simulations. Simulation results demonstrated that the proposed algorithms are promising.

An Uplink Random Access Scheme Based on ALOHA System Assisted by Gain Division Multiple Access

Gain division multiple access (GDMA) is a non-orthogonal multiple access (NOMA) technique for uplink communications. In GDMA, the receiver employs both the magnitudes and phases of channel gains associated to the users to separate users. In the power-domain NOMA, the receiver employs the power levels associated to users to separate users.We apply the GDMA concept together with orthogonal frequency division multiple access (OFDM) to combat the collision problem in the ALOHA system. Our goal is to minimize the complexity of the devices (users) required for coordination and to obtain high throughput simultaneously in the random access protocol. For the proposed unslotted OFDM-GDMA-ALOHA, each user randomly chooses a Zadoff-Chu (ZC) sequence as its preamble for the transmitted packet so that the receiver can simultaneously obtain the information for timing and the information for coarse channel estimation. Moreover, fine channel estimation is obtained by using a cluster-based method. Simulation for a coded unslotted OFDM-GDMA-ALOHA system with BPSK modulation demonstrates that the obtained throughput performance can be better than that obtained by the slotted ALOHA system without using GDMA.We also provide simulation for slotted OFDM-GDMA-ALOHA, which shows that very high throughput can be achieved if the synchronization of slots can be obtained.

Hybrid Precoding and Combining Based on Rate Balancing for Wideband mmWave Multiuser MIMO Systems

Considering wideband millimeter wave (mmWave) multiuser multiple-input multiple-output (MIMO) systems, we propose a new hybrid precoding and combining method for rate balancing among users. The orthogonal frequency division multiple access (OFDMA) scheme is employed for multiuser transmission, and a greedy-based algorithm is developed for subband allocation under the max-min user rate criterion. A new algorithm is derived that optimizes the radio frequency (RF) precoder and combiners under the constant-modulus constraints as well as the baseband precoders and combiners under the rate balancing power allocation criterion. The proposed algorithm iteratively updates the RF precoder and combiners using the conjugate gradient method, and baseband precoders and combiners through the singular value decomposition and numerical gradient-based power allocation among users and subcarriers. The complexity of the proposed method is compared to existing wideband hybrid processing schemes through analysis and numerical runtime measurement. The convergence of the proposed algorithm is verified via numerical simulations in mmWave MIMO-OFDMA systems. It is also shown through numerical simulations that the proposed wideband hybrid processing method outperforms the existing wideband hybrid techniques in terms of the minimum user rate, regardless of the signal-to-noise ratio, the number of antennas, and the number of users. Moreover, simulation results present that the proposed scheme is more advantageous than the conventional hybrid processing methods under channel uncertainty.

Coded Pulse Stream LiDAR Based on Optical Orthogonal Frequency-Division Multiple Access

Coded Pulse Stream LiDAR Based on Optical Orthogonal Frequency-Division Multiple Access

Performance Analysis of Zero-Padded Sequences for Joint Communications and Sensing

Self-interference (SI) is a known but critical issue at the receiver side of a monostatic radar in joint communications and sensing (JCAS) systems, and this is particularly true for high spectral-efficient waveforms. Full-duplex transceivers are usually assumed in the literature, but it is arguable if they can be taken for granted in many devices. We propose a different approach, by eliminating the SI using zero-padded (ZP) orthogonal frequency-division multiplexing (OFDM) and single-carrier frequency-division multiple access (SC-FDMA), instead of the more widespread cyclic-prefix (CP)-OFDM. ZP-sequences do not need full-duplex for the monostatic radar operation, as there is no SI between the transmit and receive antennas during the guard interval (GI), which can be used for radar detection. We derive the required radar receiver processing for ZP-sequences and CP-OFDM in time and frequency domains, respectively, to show that when the SI in JCAS is high, ZP-sequences can be beneficial, depending on the target range. Furthermore, we prove analytically that the low peak-to-average power ratio (PAPR) of ZP-SC-FDMA, when compared to ZP-OFDM, is beneficial in time-domain radar processing. This is demonstrated also by means of numerical simulation with ROC curves, for all the candidate waveforms in both coherent and incoherent receiver processing.

Hybrid NOMA/OFDMA visible light communication system with coordinated multiple point transmission.

In this study, a hybrid non-orthogonal multiple access / orthogonal frequency division multiple access (NOMA/OFDMA) scheme is proposed for multi-cell multi-user visible light communication (VLC) systems. Each cell is divided into several sub-cells that are multiplexed using OFDMA. Users within the same sub-cell are multiplexed by NOMA. Thus, the hybrid NOMA/OFDMA scheme takes advantage of both NOMA and OFDMA, which not only improves the spectral efficiency of OFDMA but also avoids the co-channel interference of NOMA. Moreover, coordinated multiple-point transmission based on repeated coding is introduced to eliminate multiuser interference, which also improves the received signal-to-noise ratio of edge users. In this manner, spectrum resources are fully utilized, where the frequency reuse factor is equal to 1. Furthermore, we propose a two-dimensional power-allocation algorithm for the proposed hybrid NOMA/OFDMA VLC system. Based on the fixed power allocation strategy, power is allocated jointly among sub-cells and users within sub-cells to minimize the average symbol error rate (SER). The performance of the proposed system was investigated in detail by simulation, where the SERs were evaluated under different power ratios. Simulation results also show that the SER performance of the proposed hybrid NOMA/OFDMA VLC system is significantly improved compared to the traditional NOMA and OFDMA VLC systems in different VLC networks. Finally, the proof-of-concept experiment was set up, clearly validating the superiority of the proposed system further.