Federated learning (FL) is an emerging distributed learning paradigm and it also finds its applications for the next generation communication systems. Moreover, the performance of the FL depends on the underlying wireless channels, thus establishes a “bidirectional” relationship between the FL and the wireless systems. In this survey article, we provide a comprehensive discussion that highlights this important “bidirectional” relationship between the two scientific fields.

A comprehensive survey of recent advances in UAV-assisted networks is presented with an emphasis on the optimization perspective with different objectives, including coverage area, throughput, energy efficiency, quality of service, delay, and outage probability.

The classification of the cloud health services.

We propose a decentralized prediction and reputation approach. This approach categorizes each vehicle into four possible states. In our approach, the vehicle can predict the future state of other nodes in the vehicular network. In addition, vehicles can decide to interact with reliable vehicles and can detect malicious vehicles.

Here, proposes an enhanced clustering and data aggregation scheme, in which the clusters are formed by finding the correlation of data from the sensor nodes. Neighboring sensor nodes data similarity can be evaluated in terms of correlation value after the deployment of nodes. By assuming the cluster heads are predetermined, the data correlation of CH with each of its neighbor node (based on transmission radius) is calculated. After cluster formation with its neighbor nodes, average correlation value of each cluster can be calculated. After cluster formation with its neighbor nodes, average correlation value of each cluster can be calculated. It is possible to set a suitable threshold value for correlation. If the average correlation metric of a cluster is greater than the threshold value, that cluster is marked as higher similarity cluster, which needs data aggregation. If correlation metric is less than threshold, those clusters are marked as dissimilar and data aggregation is not compulsory for these clusters. Dissimilar data clusters transfer data to cluster head and lastly send to the sink. In the context of higher similarity cluster, a cluster based data aggregation scheme with independent component analysis (ICA) is performed. The CH is responsible for aggregating the data. ICA is one of the effective and computationally efficient data reduction methods. Aggregated data from higher similarity clusters are sending to base station. So it is possible to reduce the computation and energy utilization because of less number of aggregation processes and less number of data transmissions. The figure shows the flow diagram of proposed method. The work is mainly divided into two modules: Data aggregation using ICA and clustering based on data similarity.

This article investigates the handoff optimization in a uplink non-orthogonal multiple access (NOMA)-based small cell networks (SCNs) with successive interference cancellation that takes both the base Station (BS) association and power control into consideration. The formulated problem is solved by adopting the coalition formation game theory, in which the coalition value function is designed by considering the handoff number of each BS in every time frame, transmit power of all MUs and the number of associated MUs to each BS.

In this article, we propose a novel framework to maximize the area spectral efficiency, , by finding the optimal parameters and . As such, two propositions are proved to show that ASE has at least one local maximum w.r.t , at a given , and w.r.t , at a given . We present an iterative approach for obtaining the optimal parameters. We achieve comparable results w.r.t the exhaustive search method, and outperform the single-parameter optimization approach.

Our work targets the management of interference in the MAC sublayer and the network layer of the vehicular environment. A new metric has been realized, depending on the evaluation of the signal-to-interference (SIR) in order to select the best route from a source to a destination.

Introduction of fog computing extends the cloud services nearer to the edge of the network. An efficient resource management scheme is required to proficiently handle fog resources. The profit-driven nature of both the fog service providers and user nodes increases the possibility of malicious activity while resource trading for their advantages or to privilege a bunch of devices. In this article, we designed a trusted and fair incentive mechanism that encourages buyers and sellers to trade by leveraging the benefits of blockchain and smart contracts. Especially, a combinatorial double auction employed market model is proposed which satisfies different economical properties such as individual rationality, budget balance, and truthfulness.

A machine learning (ML) technique with the full-duplex relay-information theoretic links scheduling spectrum sharing method in device-to-device communications is employed. Transmission powers of transmitters and corresponding relays are determined with a power control (PC) mechanism employing genetic algorithm (GA). The objective functions are maximizing the average sum rate, energy efficiency, and addressing two objectives simultaneously. Due to the ML approaches has more flexibility and lower computational complexity in comparison to the traditional optimization methods, we use ML to determine the transmission powers of transmitters and their corresponding relay pairs. The proposed neural network considers the channel coefficients as inputs and optimal power for transmitters and relays as outputs. The simulation results show that the PC mechanism with ML approach has almost the same performance as GA, while the calculation time and system complexity are reduced.

In this research, an energy and spectrum efficient optimized resource allocation model is developed. The joint contribution of the maximization of energy efficiency (EE) and spectral efficiency (SE) with its constraints is formulated as a multiobjective optimization problem. The resource allocation (RA) issue in the network is handled by the hybrid deep bidirectional battle royale long short term memory (Deep bi-BRLSTM) model. Fuzzy based energy efficient adaptive atom search optimizer (FEEAAS) is instigated to optimize the trade-off of EE and SE consecutively also it improves the overall throughput.

In this paper, we utilize parallel computing of multivehicle cooperation, to provide low-delay computation services without exceeding the energy constraint. Furthermore, a cooperative vehicles assisted task offloading strategy based on double deep Q-network (DDQN) is proposed to obtain the optimal task offloading ratio after selecting cooperative vehicles. Simulation results indicate that our proposed strategy can effectively decrease the total system delay.

We introduce a hybrid precoding algorithm to improve the spectral efficiency and reduce the computational complexity of switch network-based dynamic partially connected for millimeter wave multiple-input multiple-output system. Invoking compressed sensing, the proposed algorithm alternately optimizes the precoding matrices and enjoys satisfactory computational complexity.

A multiuser steered SQSM (MUS-SQSM) scheme is introduced in this article. The MUS-SQSM utilizes the Frobenius norm-based user selection algorithm and applies a fully digital GMD-based precoding technique.

This figure represents the graphical abstract of the proposed work. Energy efficiency of the UDN networks are achieved by using hybrid optimization technique. The problem is formulated as convex linear problem. The closed form as a factor has been considered as a factor of the density of SBSs. After this the Poisson point process and heuristic algorithms are finally formulated and analyzed to achieve energy aware networks.

This article proposes the exact and asymptotic closed-form expressions of NOMA users' average symbol error probability (ASEP) for dual-hop cooperative DF maritime communication systems over TWDP fading using Appell's and Lauricella's hypergeometric functions for various modulations sets, that is, BPSK-BPSK and QPSK-BPSK. The outage probability of the aforementioned system is also investigated utilizing performance metrics such as distance, path loss exponent, and Rician factor.

This article proposes cooperative spectrum sensing (CSS) with filtered bank multi carrier (FBMC) in cognitive radio network (CRN) for mitigating the interference with proficient bandwidth utilization. Here, five primary users (PU) and one secondary user (SU) are considered. The secondary user only occupies the slot in the absence of primary user.

• Proposing a lightweight mutual authentication protocol between sensors and servers in the IoT. The sensor nodes were placed in groups. Each node is using static and continuous authentications.
• Using only hash and XOR operators in authentication so that our scheme has low computational costs and can be used in sensors with limited resources.
• Reducing the communications cost, resisting against security attacks, privacy-preserving, providing forward secrecy, and allowing key agreement.
• Using BAN logic and AVISPA tool to verify for security.

Compared with the latest controller placement algorithms based on CO, CSSA, VBO, and TLBO, the controller placement scheme obtained by the improved FA has a lower synthetical delay and more balanced controller load. Simultaneously, the time consumption of the improved FA is acceptable.

In this article, we propose an enhanced signal detection scheme for the receiver of ambient backscatter communications adopting differential energy detection. We discovered that the universal approximate threshold in classic detection will not provide accurate results in universal scenarios, thus double-threshold detection is proposed, integrating with differential energy detection and maximum likelihood method. Results show that the detector with double thresholds will offer performance improvement in diverse scenarios, especially when the system is in a poor channel status environment.

B3CF-tree construction in the cloud-fog computing level.

In this article, the DNN inference task offloading problem in queue-based multi-device and multi-server collaborative edge computing is investigated. A deep reinforcement learning based task offloading algorithm, named LSTM-TD3, is proposed to minimize the average inference delay and maximizes average inference accuracy.

A 28 GHz two stage variable gain low noise amplifier (LNA) is proposed with envelope detection technique for power reduction (21.62%) and tunable negative feedback capacitor for gain variation in 40 nm CMOS technology. A 6.22 GHz of bandwidth is achieved with the tunable gain from 20.3 dB to 26.7 dB.

This article investigates the two-way information exchange process by using the power-domain non-orthogonal multiple access strategy. The investigation considers a dual-hop amplify-and-forward based full-duplex bi-directional relay-assisted network in the system model. The transmitters exchange the superimposed signals and utilize the echo-cancellation and successive interference cancellation at the receivers.

This article investigates a resource allocation method to improve energy efficiency in heterogeneous cloud radio access networks using a user relay-aided. A multilayer network consisting of macrocells, femtocells, and D2D communications is used to target BBU and RRH traffic offloading to femtocells. The simulation results show that the suggested technique can effectively decrease energy consumption of BBUs through switching off under-utilized BBUs and offloading the traffic from remote radio heads and macro-cell to FAPs.

In this paper, we have analyzed the impact of Fractional Power Control on Downlink Uplink Decoupled-based HetNets in varying Path Loss Exponent environment. To the best of our knowledge, such an analysis of DUDe performance using stochastic geometry have not been published before. We have developed a generalized system model and derived generalized expressions for uplink network performance indicators like decoupled uplink coverage probability and average spectral efficiency. Moreover, we have performed a baseline comparison of obtained results with the ones obtained while considering coupled association criteria based on maximum downlink received signal power. We have gauged, compared and observed interesting results regarding DUDe performance under different system model parameters.

Main revenue factors are categorized as service level agreement and penalty management, resource scalability, customer satisfaction and management, resource utilization and provision, cost and price management, and advertising and auction. These parameters are investigated in detail and new dynamics for researchers in the field of the cloud are discovered.

The performance improvement methodology using ADO-OFDM and MIMO in various design scenarios is presented. The BER performance is enhanced for MIMO-ADO-OFDM with maximum ratio combining, and the spectral efficiency is maximized by utilizing spatial multiplexing. Also, multi-user MIMO is developed by adopting ADO-OFDM with zero-forcing interference cancellations, allowing users to consume all available bandwidth.

Aiming at the intelligent inspection scenario in industry, an algorithm based on game theory is designed considering the mobility of terminal equipment.

In this paper, a railway communication system with the help of intelligent surface technology is proposed for high mobility scenario. Results demonstrate the significant mobile service enhancement introduced by the intelligent surface in railway communication system and show the effectiveness of our proposed scheme.

This article proposes a low complexity detection technique suitable for small scale and massive MIMO applications. In addition, a low complexity preprocessing technique is also developed. Finally, an efficient symbol ordering and soft symbol generation method is developed to reduce computational overhead.

Proposed framework

Message authentication code access control multi-channel attack detection model.

The main contributions of this paper are as follows: (1) Some studies use a part of the roadside units (RSUs) in the RSU plane as the underlying controllers to assist network management in reducing the vehicle communication delay in the edge network. Given the lack of in-depth research on how the RSU plane works, the roles and tasks of the domain controller and the RSU controllers in processing the flow rule request sent by the on board units (OBUs) are clearly explained, and the flow setup delay is also clearly defined. (2) Formulate an optimization objective model of the RSU plane in software-defined vehicular networks. Based on the high mobility of the vehicle network, the increase of flow setup delay caused by RSU node remapping is reduced by reducing the state fluctuation of the RSU plane. (3) By adding a traffic flow trajectory prediction module for the domain controller, issue the flow rules in advance to the underlying RSU controller covering the arrival position of the vehicle, to further reduce the flow setup delay.

This paper proposes a joint spectrum sensing and resource allocation scheme for overlay CRNs. Based on the overlay CRN model and hard combining, we propose a selective majority algorithm to exploit the malicious behavior based on Byzantine identification, which can maintain excellent performance in various attack scenarios. In addition, our proposed spectrum resource allocation scheme further deters Byzantine attack, forming a complete closed-loop for Byzantine defense scheme.

V2X, aided with cellular D2D communication, has a great potential in enhancing the road safety and transportation. However, due to the fast variations of channel state information (CSI) caused by high mobility of vehicles, the accurate CSI is difficult to be obtained in real time. Therefore, we consider a more realistic channel state model that only partial CSI is available and propose the corresponding resource allocation scheme.

In this manuscript, blockchain enabled joint trust (MF-WWO-WO) algorithm for clustered based energy efficient routing protocol on wireless sensor network is proposed for secure data transmission by finding the optimum CHs under network. Initially, MayflyWater Wave Optimization (MF-WWO) algorithm is used for the selection of CH accurately. After the selection of CH, arrival of malicious node is occurred in the cluster. Hence, multi objective whale optimization algorithm is suggested to find the trust path from the several paths. At last, the optimized selected trust paths are given to the blockchain for communications in the network that is safe and more trustworthy.

In this work, we proposed a hybrid attention convolution network to classify different modulations to develop a robust automatically learning method for radio signals with harsh impairments (oscillator drift, clock drift, noise) and the lightweight network is further optimized.

We investigate resource allocation in MC-NOMA systems and formulate an optimization problem to jointly optimize subcarrier and power allocation with proportional rate constraints. By using variable substitution, we convert the original problem into a convex optimization problem and develop joint subcarrier and power allocation algorithms.

The uniform circular frequency diverse array (FDA) element is excited by the designed signal to generate OAM beams related to the range, elevation and azimuth. OAM beams generated by FDA with the random frequency offset can maintain the helical phase and the intensity pattern characteristics at the desired range and two-dimensional direction information of OAM beam radiation pattern can be modulated into the signal to generate the waveform of the angle-dependent direction modulation. The single antenna can employ the phase compensation and helical phase factor to restore composite dual-mode OAM signal in the desired position.