Frequency Resources Research Articles

NON-ORTHOGONAL MULTIPLE ACCESS SCHEMES SURVEY: AN OVERVIEW OF NON-ORTHOGONAL MULTIPLE ACCESS TECHNIQUES

Non-Orthogonal Multiple Access (NOMA) is a promising technology that can significantly improve the performance of wireless communication systems, especially in applications with a high number of users and limited spectral resources. NOMA enables simultaneous transmission and reception of multiple signals at the same time and frequency resources. NOMA can be implemented using different methods, including Power Domain NOMA (PD-NOMA) and Code Domain NOMA (CD-NOMA). The implementation of NOMA requires advanced signal processing techniques and can be challenging in some scenarios. However, with the advancement of technology, NOMA is becoming more practical and is expected to play a significant role in the future of wireless communication systems. This paper will discuss what NOMA and NOMA schemes are. These schemes will be compared in terms of their benefits and drawbacks, and it will be explained which one performs better.

МЕТОД РАЗЛИЧЕНИЯ КООРДИНАТНО-ИНФОРМАТИВНЫХ ПАРАМЕТРОВ АДДИТИВНЫХ КОМПОНЕНТНЫХ СИГНАЛОВ С СОВМЕЩЕНИЕМ НЕСУЩИХ ЧАСТОТ НА ОСНОВЕ СВЕРТКИ СПЕКТРА

The article presents a method for distinguishing the coordinate-informative parameters of additive component signals with the combination of carrier frequencies based on spectrum convolution in the geolocation of subscriber terminals of illegitimate users of the satellite repeater resource. A qualitative assessment of the probabilistic characteristics of the developed method was made, on the basis of which the boundary conditions for the applicability of this method were determined. The results obtained on the basis of simulation modeling can be used for geolocation of subscriber terminals of illegitimate users in satellite communication systems with the reuse of the frequency resource of the repeater satellite using the technology of combining carrier frequencies.

Orthogonal frequency division multiplexing technology based on MATLAB

The subcarriers of an orthogonal frequency division multiplexing (OFDM) system are not identical in the time domain, and their spectra also overlap. As a result, the spectrum is being used effectively. Its effective use of frequency resources and capacity to combat channel fading have made it the de facto technical standard. In wireless communication, numerous pathways in the channel can lead to interference between symbols, known as inter-symbol interference (ISI). This research supplements OFDM systems with some safeguards to eliminate inter-symbol interference. Most traditionally, OFDM symbols have had their post-event sample points duplicated to their beginnings, a process known as Cyclic prefix (CP) padding in the protection interval. In order to further counteract ISI, a protection interval might be added to the beginning of each symbol. An OFDM system's bit error rate (BER) can be decreased by using cyclic prefixes to attenuate inter-code interference. This study explains how OFDM and CP-OFDM work and compares their performance. The primary topic of this study is a contrast between OFDM and CP-OFDM concerning the performance metrics of average symbol error rate (BER) and signal spectrum diagram (PSD).

INCOHERENT DEMODULATION OF TWO SYNCHRONOUS MUTUALLY NON-ORTHOGONAL DIGITAL SIGNALS WITH FREQUENCY SHIFT KEYING

The synthesis method of the incoherent demodulation procedure of two synchronous mutually non-orthogonal digital signals with frequency modulation is considered. In the absence of interference this procedure degenerates into the procedure of classical incoherent demodulation of a digital signal with frequency modulation. When the instantaneous power of one of the signals significantly exceeds the instantaneous power of the other, the interference immunity of the latter approaches the immunity of reception in a channel with additive white Gaussian noise without interference. This procedure can be used in the development of modem compensators that ensure repeated use of the frequency resource as well as in the development of promising interference-protected radio communication devices.

Reconfigurable Intelligent Surface Enabled Over-the-Air Uplink NOMA

Innovative reconfigurable intelligent surface (RIS) technologies are rising and recognized as promising candidates to enhance 6G and beyond wireless communication systems. RISs acquire the ability to manipulate electromagnetic signals, thus, offering a degree of control over the wireless channel and the potential for many more benefits. Furthermore, active RIS designs have recently been introduced to combat the critical double fading problem and other impairments passive RIS designs may possess. In this paper, the potential and flexibility of active RIS technology are exploited for uplink systems to achieve virtual non-orthogonal multiple access (NOMA) through power disparity over-the-air rather than controlling transmit powers at the user side. Specifically, users with identical transmit power, path loss, and distance can communicate with a base station sharing time and frequency resources in a NOMA fashion with the aid of the proposed hybrid RIS system. Here, the RIS is partitioned into active and passive parts and the distinctive partitions serve different users aligning their phases accordingly while introducing a power difference to the users’ signals to enable NOMA. First, the end-to-end system model is presented considering two users. Furthermore, outage probability calculations and theoretical error probability analysis are discussed and reinforced with computer simulation results.

Phase-Domain Injected Training for Channel Estimation in Constant Envelope OFDM

Constant envelope orthogonal frequency division multiplexing (CE-OFDM) is a multi-carrier waveform with 0 dB peak-to-average power ratio (PAPR). This property enables the exploitation of multi-carrier waveforms with non-linear power amplifiers, avoiding the undesirable clipping effects. However, the existing channel estimation techniques designed for OFDM cannot be reused, since the use of a phase modulator makes CE-OFDM a non-linear waveform. Previous works assumed that the channel estimation process relies on the transmission of preambles, and the data symbols are equalized using a frequency domain equalizer (FDE). To avoid the overhead induced by preambles, a phase-domain injected training (PIT) is proposed, where the pilot sequence is embedded in the phase dimension of the data symbols. This novel approach does not waste time and/or frequency resources as in preamble-based schemes. Moreover, it does not require additional power for the training. The received symbols are averaged with a dual procedure, and owing to the particular structure of CE-OFDM, the channel estimates are recovered. Also, the analytical expression of the channel estimation mean squared error (MSE) is derived. Finally, several numerical results illustrate the performance of the proposal, showing that the MSE, bit error rate (BER) and achievable rate are improved, as compared to the existing works.

Polarization adaptation based on a UHF triorthogonal element

Currently, the problem of further increasing the efficiency of using the frequency resource and the bandwidth of UHF radio links remains relevant, including on the basis of operational adaptation of the characteristics of antenna-feeder devices. The article considered approaches to building a UHF antenna system based on a triorthogonal antenna element with the possibility of polarization adaptation. The proposed antenna system will effectively compensate for the effects of depolarization on the propagation paths of radio waves and increase the signal/(noise+interference) ratio, including in a difficult interference environment.

Deep-Learning-Based Resource Allocation for Transmit Power Minimization in Uplink NOMA IoT Cellular Networks

For Internet of Things (IoT) networks, it is important to develop energy-efficient communication schemes to extend the operating life of battery-powered IoT devices. Additionally, non-orthogonal multiple access (NOMA) can utilize frequency resources more efficiently than orthogonal multiple access, making it more suitable to support massive connectivity of IoT users. Motivated by these facts, we consider uplink NOMA IoT cellular networks and develop two novel algorithms that jointly optimize sub-band allocation and transmit power control to minimize the total transmit power of all users and the maximum transmit power among all users’ transmit power, respectively, while meeting the minimum required data rate for all users. Specifically, we propose a novel two-step approach that sequentially performs sub-band assignment and transmit power control for each IoT user, in which a genetic algorithm-based method is applied for sub-band assignment whereas unsupervised learning (USL) implemented as deep neural network (DNN) models is utilized for transmit power control. Moreover, we propose loss functions that can achieve an appropriate balance between power minimization and rate constraint satisfaction in the process of training. Extensive simulations are performed to evaluate the performance of the proposed algorithm in various aspects, and we show that our proposed two-step algorithm can approach the optimal performance achievable through an exhaustive search with much lower computational complexity.

Efficient Resource Allocation for Beam-Hopping-Based Multi-Satellite Communication Systems

With the rapid growth of data traffic, low earth orbit (LEO) satellite communication networks have gradually ushered in a new trend of development due to its advantages of low latency, wide coverage, and high capacity. However, as a result of the limited on-board resources and rapidly changing traffic demand, it is increasingly urgent to design an efficient resource-allocation scheme to satisfy the traffic demand. In this paper, we propose two resource allocation algorithms in the multi-satellite system based on beam-hopping technology. In the offline case, it is assumed that the channel gains in all time-slots are known in advance, and we propose a heuristic algorithm to allocate time and frequency resources, and a successive convex approximation (SCA) algorithm to allocate power resources. In the online case, it is assumed that only the instant channel gains information is known; therefore, we apply the dynamic programming (DP) algorithm to maximize the system throughput. The simulation results prove that the proposed resource-allocation algorithms based on beam-hopping technology have better performance than the traditional average allocation method, and the online algorithm has acceptable performance loss compared with the offline algorithm.

Validation hybrid filter detection for Multi-User multiple input multiple output F-OFDM by Universal software radio Peripheral

The present communication system is not enough efficient for the data rate requirement for the real-time applications like video conferencing and file sharing. To meet the requirement of the data rate, the concept of F-OFDM (Filter- Orthogonal Frequency Division Multiplexing) has been proposed. To further improve the efficiency of the system, Cognitive Radio is used. Cognitive Radio is capable of recognizing the spectrum utilization of the licensed users and accordingly switches the frequency bands. The aim of the paper is to detect the primary users in an efficient and novel way. Therefore, a hybrid filter detection technique is proposed for the Multi-User Multiple Input Multiple Output (MU-MIMO) F-OFDM system where multiple users are served with the same time and frequency resource and improve signal strength. The proposed technique uses the combination of cosine filtering, Bartlett segmenting, and Hamming windowing. The cosine filtering differentiated between the 5G traffic and the noise. Then, the filtered signals are segmented with the help of the Bartlett Segmenting for decreasing the noise variance then every segment is windowed using the Hamming windowing for maintaining the signal resolution. This paper proposes a Validation Hybrid Filter Detection for Multi-User Multiple Input Multiple Output (MIMO) Frequency-Division Multiplexing Orthogonal Frequency Division Multiplexing (F-OFDM) by Universal Software Radio Peripheral (USRP) based on Cognitive Radio (CR). Detection performance dropped to its least value for M−ary = 256 and 10 user SUs. Further, false alarm probability at −24 dB SNR was less than 0.01, which is excellent considering system characteristics of less than 0 dB SNR, 0.092% and > 98% global system error and detection likelihoods, respectively. The study also evaluated spectral efficiency to offer optimum detection at acceptable complexity levels for low SNR signals. The performance of the proposed technique is compared with the existing techniques and it is found that the proposed technique better and can effectively improve the detection accuracy of the signal with low SNR.

QoS for Device-to-Device communication in 4G LTE-A network

Device to device (D2D) communication is a technology model that enables mobile devices to communicate directly with each other without the intervention of a base station or enabled Node B (eNB). It helps the reuse of LTE-Advanced radio frequency resources of the network. With this paper, we simulated two metrics in delay and throughput. According to simulation results, D2D mode is the best option for communication when two mobile devices are close to one another but far from the eNB.

Joint Optimization for Cooperative Computing Framework in Double-IRS-Aided MEC Systems

This letter investigates a cooperative task computing framework, where the source node partially offloads its computational task to multiple user equipments (UEs) aided by double intelligent reflecting surfaces (IRSs). With the aim of maximizing the total amount of computing task subject to latency and power constraints, we highlight an interesting tradeoff between the transmit power and the computing power at the source node and optimize the computing frequency resources as well as phase shift matrices for double IRSs. Numerical results verify the power allocation tradeoff and demonstrate the superiority of our double-IRS-aided solution in terms of maximizing the total amount of computing task over other benchmark strategies.

Revisiting Model Order Selection: A Sub-Nyquist Sampling Blind Spectrum Sensing Scheme

Wideband spectrum sensing based on sub-Nyquist sampling is an attractive approach to advance dynamic spectrum sharing (DSS), which can improve frequency resource utilization while overcoming sampling bottlenecks. Under the Compressive Sensing (CS) framework, finding occupied subbands can be equivalent to computing the support set for the Multiple Measurement Vectors (MMV) problem. To guarantee the performance of joint support recovery in noisy environments, different kinds of prior information are required, one of which is sparsity, a time-varying parameter. To address the dependence of recovery performance on signal sparsity, this paper proposed a two-step scheme for blind wideband spectrum sensing using a Modulated Wideband Converter (MWC) sub-Nyquist sampling front-end. The scheme first adopts the model order selection (MOS) method to estimate sparsity from the compressed covariance matrix, and then uses the estimates to dynamically adjust joint support recovery. The complete theoretical derivation innovatively applies MOS to sub-Nyquist sampling and presents a design method for MOS penalty constant. Extensive simulation results show that the proposed scheme can not only achieve blind sensing under the spectrum occupancy up to 40%, reduce the overall computational complexity of iterative SOMP, but also significantly improve the false alarm performance, meeting the requirements of the IEEE 802.22 standard.

Analog-Circuit Domain Cancellation with Optimal Feedback Path Selection on Full-Duplex Relay Systems

This paper proposes a scheme that reduces residual self-interference significantly in the analog-circuit domain on wireless full-duplex relay systems. Full-duplex relay systems utilize the same time and frequency resources for transmission and reception at the relay node to improve spectral efficiency. Our proposed scheme measures multiple responses of the feedback path by changing the direction of the main beam of the transmitter at the relay, and then selecting the optimal direction that minimizes the residual self-interference. Analytical residual self-interference is derived as the criterion to select the optimal direction. In addition, this paper considers the target of residual self-interference power before the analog-to-digital converter (ADC) dependent on the dynamic range in the analog-circuit domain. Analytical probability that the residual interference exceeds the target is derived to help in determining the number of measured responses of the feedback path. Computer simulations validate the analytical results, and show that in particular, the proposed scheme with ten candidates improves the residual self-interference by approximately 6 dB at the probability of 0.01 that the residual self-interference exceeds target power compared with a conventional scheme with the feedback path modeled as Rayleigh fading.

An integrated dependability guarantee provisioning for cluster‐based IoV networks with slicing

AbstractHigh‐dependability Internet of Vehicle (IoV) is essential for urban transit, since it contributes to the safe operation of vehicles, and even to the safety of the entire city. This paper introduces the IoV cluster model with network slicing. Based on this model, an integrated vehicles clustering and network slicing scheme for IoV networks is introduced to guarantee the dependability of different traffics in IoV. The network slicing scheme is proposed to divide the wireless frequency resource of the IoV network into separate logical networks. A analysis model, based on age of information, is utilized as an integrated indicator for evaluating the dependability of the IoV system in urban transit. Asynchronous reinforcement learning with advantage actor‐critic is employed to optimize the integrated dependability of IoV. It is demonstrated in simulations that the proposed IoV cluster incorporating the network slicing scheme can guarantee the integrated dependability of the IoV system. Moreover, by the application of reinforcement learning, the system dependability can be further improved.

Machine Learning-Based Solutions for Handover Decisions in Non-Terrestrial Networks

The non-terrestrial network (NTN) is a network that uses radio frequency (RF) resources mounted on satellites and includes satellite-based communications networks, high altitude platform systems (HAPS), and air-to-ground networks. The fifth generation (5G) and NTN may be crucial in utilizing communication infrastructure to provide 5G services in the future, anytime and anywhere. Based on the outcome of the Rel-16 study, the 3rd generation partnership project (3GPP) decided to start a work item on an NTN in 5G new radio (NR) Rel-17, and the focus of the study was on mobility management procedures, due to the movements of NTN platforms; especially, low earth orbit (LEO) satellites. Handover enhancements were discussed to tackle the frequent handover due to the fast satellite movement. Therefore, the major problem of handover in LEO satellite systems was the signaling storm created by handing over all user equipment (UE) in a cell to a new cell because when the UE crosses the boundary between the neighboring cell of a satellite, an intra-satellite or cell handover occurs; thus, all users in a cell are expected to experience a change of cell due to handover every few seconds. In addition, UE location is not easy to define due to moving cell/beam situations. In this study, we propose machine learning-based solutions for handover decisions in non-terrestrial networks for cell handovers or intra-satellite handovers to reduce signaling storms during handovers where the handover requests will be executed by clustered users. First, the dataset was generated by the simulator that simulates communication between users and satellites. Second, we preprocessed the data, and also used the feature creation technique to create the distance feature using the Haversine formula, and then applied clustering and classification algorithms. The experimental results show that the distance between a user and its cell center is an important parameter for handover decisions in NTN, and the random forest outperforms all models with a higher accuracy of 99% along with a better F1-score of 0.9961.

Https://sifisheriessciences.com/journal/index.php/journal/article/view/1294

Wireless experts worldwide have become interested in using Autoencoders (AEs) for modelling communication systems as an end-to-end reconstruction task. This approach optimizes both the transmitter and receiver components simultaneously, offering flexibility and convenience for representing complex channel models. Traditional communication systems rely on conventional models and assumptions that limit their utilization of limited frequency resources and hinder their ability to adapt to new wireless applications. However, with the rise of Artificial Intelligence, new wireless systems are capable of learning from wireless spectrum data and optimizing their performance. In this paper, the use of deep learning with autoencoders is explored to create an end-to-end communication system that replaces traditional transmitter and receiver activities. The autoencoder architecture effectively addresses channel impairments and enhances overall performance. Simulation results indicate that autoencoders surpass conventional communication systems in terms of Block Error Rate performance, even when facing impairments in the autoencoder's channel layer and using different neural network optimization algorithms.

Block Distributed Joint Temporal-Frequency-Phase Modulation for Steady-State Visual Evoked Potential Based Brain-Computer Interface With a Limited Number of Frequencies.

How to encode as many targets as possible with limited frequency resources is a grave problem that restricts the application of steady-state visual evoked potential (SSVEP) based brain-computer interfaces (BCIs). In the current study, we propose a novel block-distributed joint temporal-frequency-phase modulation method for a virtual speller based on SSVEP-based BCI. A 48-target speller keyboard array is virtually divided into eight blocks and each block contains six targets. The coding cycle consists of two sessions: in the first session, each block flashes at different frequencies while all the targets in the same block flicker at the same frequency; in the second session, all the targets in the same block flash at different frequencies. Using this method, 48 targets can be coded with only eight frequencies, which greatly reduces the frequency resources required, and average accuracies of 86.81 ±9.41% and 91.36 ±6.41% were obtained for both the offline and online experiments. This study provides a new coding approach for a large number of targets with a small number of frequencies, which can further expand the application potential of SSVEP-based BCI.

Multigateway precoded NOMA in multibeam satellite multicast systems

In this paper, we consider multigateway-based multibeam satellite non-orthogonal multiple access (NOMA) multicast systems. We investigate the improvement in spectral efficiency and accessibility by superimposing multiple signals on each beam at the same frequency and time resource employing NOMA, where multiple gateways transmit precoded signals to alleviate inter-beam interference caused by full frequency reuse. We formulate an optimization problem of maximizing the sum rate while satisfying the gateways and satellite transmission power constraints, where the precoding vector and power allocation for superposition coding as well as the decoding order for successive interference cancellation are optimized. This optimization problem is challenging to solve due to its non-convex mixed-integer nonlinear programming. However, a suboptimal solution can be obtained using a block coordinate descent algorithm. The simulation results of the proposed NOMA technique are compared with those of the orthogonal multiple access (OMA) technique. The proposed technique outperforms the OMA technique. We also investigate the impact of channel imperfection and decoding capability of the proposed algorithm through some selected simulation results.

A survivable multipath resource allocation strategy based on fragmentation-sensitive fragmentation-aware in space division multiplexing elastic optical networks

The space division multiplexing elastic optical network (SDM-EONs) can effectively supply the large-capacity for the current optical network. If a link failure occurs, it will bring huge data losses or disruptions for the operator. We propose a survivable multipath fragmentation-sensitive fragmentation-aware routing, core and spectrum assignment (SM-FSFA-RSCA) scheme to solving the problem of survivability of the request in SDM-EONs and spectrum fragmentation that caused by the dynamic allocation and release of spectrum resources. We design a fragmentation-sensitive fragmentation metric (FSFM) to estimate the spectrum fragmentation in the established lightpaths, and design a dynamic routing and core selection strategy for the request to control spectrum fragmentation in the core according to FSFM. We consider survivable multipath routing strategy for saving spectrum resources, each path of the multipath can be used as a working path or a protection path. In addition, for spectrum allocation, a survivable multipath with shared spectrum allocation strategy is adopted to maximize the utilization of frequency resources. The simulation results show that the algorithm proposed in this paper significantly reduces the bandwidth blocking probability,fragmentation ratio and also improves the spectrum utilization.