Multiple Subchannels Research Articles

Enhancing energy efficiency in MC-NOMA systems through optimized channel and power allocation

Non-orthogonal multiple access (NOMA) has quickly become popular due to its higher spectral efficiency (SE) and is crucial in extending future networks’ capacity. This paper presents an innovative approach to enhancing energy efficiency (EE) in downlink (DL) multi-carrier (MC) NOMA systems, in which a single base station (BS) supports a group of users across multiple sub-channels. A novel least user sum gain-based user assignment (LUSGUA) algorithm is proposed in this paper, which prioritizes users with the lowest channel gains for optimal sub-carrier allocation. Additionally, a novel power allocation (PA) scheme is developed across sub-carriers to further improve energy efficiency (EE), throughput, and fairness. throughput and fairness. Since the PA optimization problem (OP) is constrained and non-convex, to tackle this problem, a penalty approach (PTA) is proposed, which is then addressed by particle swarm optimization (PSO) with sequential optimization of the inter/intra-level PA. Power is allocated across sub-carriers at the inter-level using PSO, while at the intra-level, power is distributed among users via the Bisection Method (BM). Simulation results demonstrate that the proposed algorithm achieves significant enhancements in EE, with improvements ranging from 13.04% to 48.67% compared to existing resource allocation (RA) schemes techniques, while also improving system throughput, fairness, and outage percentage. These results highlight substantial advancements over traditional methods.

HF-NVIS Data Communication: OFDM Transmission Testing and Optimization Strategies

Abstract Orthogonal frequency division multiplexing (OFDM), a technique that uses frequency division multiplexing to allow parallel transmission of data over multiple subchannels, is widely used in various wireless systems. Its effectiveness even extends to HF links, where it has demonstrated superior performance compared to single-carrier schemes. In addition, OFDM’s robustness to multipath fading and its ability to efficiently utilize the available bandwidth make it a preferred choice for modern communication systems, but also for scenarios with complex communication links, such as HF range communications. The objective of this experimental research is to analyze and improve the performance of the OFDM technique adapted to HF communication channels. This study is motivated by the lack of a protocol to regulate HF communications using OFDM. The analysis and optimization method starts by considering the fundamental bandwidths of HF channels (ranging from 3 to 24 kHz). For this purpose, OFDM-specific modifications have been analyzed, including changes in the FFT size (representing the number of subcarriers), adjustment of the number and amplitude of pilots, analysis of the impact of packet length modification (pre-OFDM), and evaluation of guard intervals. In essence, our goal is to implement an OFDM technique commonly used in modern data communications in the context of traditional HF communications systems.

100-Gb/s/λ PAM-4 EAM-Integrated DBR-LD Supporting Multiple Sub-Channels Within 1.29 μm Window

100-Gb/s/λ PAM-4 EAM-Integrated DBR-LD Supporting Multiple Sub-Channels Within 1.29 μm Window

Proportional fair scheduling using water-filling technique for SC-FDMA based D2D communication

The resource allocation in SC-FDMA is constrained by the condition that multiple subchannels should be allocated to a single user only if they are adjacent. Therefore, the scheduling scheme of a D2D-cellular system that uses SC-FDMA must also conform to the so-called adjacency constraint. This paper proposes a heuristic algorithm with low computational complexity that applies proportional fair (PF) scheduling in the D2D-cellular system. The proposed algorithm consists of two main phases: (i) subchannel allocation and (ii) adjustment of data rates, which are executed for both CUEs and DUEs. In the subchannel allocation phase for CUEs (or D2D pairs), the users’ data rates are maximized via optimal power allocation to frequency-contiguous subchannels. In the second phase, a PF scheduling problem is solved to decide the modulation and coding scheme (MCS) of both CUEs and D2D pairs. Both phases of the proposed algorithm benefit from the Water-Filling (WF) technique. The simulation results suggest that the proposed scheme performs similar to optimal PF scheduling from the perspective of users’ data rate and their logarithmic sum. An additional benefit of the proposed scheme is its low computational overhead.

Energy Efficient Resource Allocation for Uplink MC-NOMA Based Heterogeneous Small Cell Networks With Wireless Backhaul

In this paper, we investigate energy efficient resource allocation for uplink multi-carrier non orthogonal multiple access (MC-NOMA) based two-tier heterogeneous networks (HetNets) with wireless backhaul. We have exploited channel diversity by assigning multiple subchannels to various MC-NOMA users (NUs), while allowing multiple NUs to use the same subchannel. We formulate a joint NU clustering, and power and subchannel allocation problem for system's energy efficiency (EE) maximization under the constraints of minimum quality of service (QoS) requirement, maximum allowable transmit power for each NU, and successive interference cancelation. The formulated problem is non-convex with fractional objective function, and therefore, to convert it into a tractable from, it is divided into two subproblems, i.e., subchannel allocation and NU clustering subproblem, and power allocation subproblem. The subchannel allocation and NU clustering subproblem is solved through a suboptimal algorithm, which exploits the channel diversity by maintaining maximum possible difference in transmit power levels of clustered NUs. Whereas for the power allocation subproblem, the EE optimization problem is convexified through Dinkelbach and successive convex approximation techniques and a closed-form suboptimal solution is obtained. The simulation results show that the proposed energy efficient algorithm outperforms the existing NOMA and orthogonal multiple access (OMA) algorithms. The proposed approach also guarantees user fairness by giving priority to the NUs with unfulfilled throughput requirements.

Topological MIMO Optical Signal Processing

An important advantage of optical signal processing is the possibility of performing various operations in parallel. Among the techniques proposed for parallel optical signal processing, frequency division multiplexing (FDM) is a well-known approach, in which different operations are performed in multiple sub-channels associated with different frequency bands. Here, we demonstrate the possibility of performing parallel optical signal processing by frequency division multiplexing of the edge modes of a topological photonic structure. This leads to the realization of a multiple-input-multiple-output (MIMO) topological optical signal processor that is capable of performing several analog functionalities simultaneously, enhancing the speed of computation significantly. Our theoretical and experimental findings open up the venue towards the realization of a new generation of multi-functional topological optical signal processing systems that are ultra-fast, consume little power, and enjoy strong protection against disorder.

Design and Analysis of an Energy-Efficient Load Balancing and Bandwidth Aware Adaptive Multipath N-Channel Routing Approach in MANET

Mobile Ad Hoc Network (MANET) is a collection of spontaneous nodes that form a dynamic network without any centralized administration. Routing decisions for mobile communication is a challenging task because the continuous movement of nodes increases the routing overhead and energy consumption. Numerous studies have been conducted in the field of MANET to reduce the routing decision overhead and energy consumption. Their concepts are good for improving efficiency in terms of load balancing, congestion control, and energy consumption. This article provides new concepts that are able to give better outcomes. This paper, proposes an adaptive Multipath Multichannel (N-channel) Energy Efficient (MMEE) routing approach in which route selection strategies are dependent on predictive energy consumption per packet (calculated accustomed data delivery), available bandwidth, queue length, and channel utilization. Multipath provides multiple routes and balances network load, whereas multichannel reduces network collisions via a channel ideal assignment mechanism. In the multichannel mechanism, link bandwidth is divided into multiple sub-channels. Multiple source nodes access the channel bandwidth in a simultaneous manner that minimizes the network collision. The collaborative multipath multichannel mechanism provides more than one path between a single source or multiple sources to the destination without collision and congestion. The path selection is based on the MMEE routing approach. In the proposed MMEE, a load and bandwidth aware routing method selects the path, based on node energy and predicts their lifetime, which increases the network reliability. The result shows the comparative analysis between various multichannel medium access techniques such as MMAC, Parallel Rendezvous Multi Channel Medium Access Protocol (PRMMAC), Quality of Service Ad hoc On Demand Multipath Distance Vector (QoS-AOMDV), Q-learning based Multipath Routing (QMR), Topological Change Adaptive Ad hoc On-demand Multipath Distance Vector (TA-AOMDV), and the proposed MMEE method. The outcome concludes that the MMEE method performs better than other schemes. The proposed MMEE protocol provides better performance in terms of percentage of data received, throughput, and minimum energy utilization.

Multi-task deep learning based hybrid precoding for mmWave massive MIMO system

Due to the different signal-to-noise ratio (SNR) of each subchannel, the bit error rate (BER) of hybrid precoding based on singular value decomposition (SVD) decreases. In this paper, we propose a multi-task learning based precoding network (PN) model to solve the BER loss problem caused by SVD based hybrid precoding under imperfect channel state information (CSI). Specifically, we firstly generate a dataset including imcomplete CSI input channel matrix and corresponding output labels to train the PN model. The output labels are designed based on uniform channel decomposition (UCD) which decomposes the channel into multiple subchannels with same gain, while the vertical-bell layered space-time structure (V-BLAST) signal processing technology is combined to eliminate the inner interference of the subchannels. Then, the PN model is trained to design the analog and digital precoding/combining matrix simultaneous. Simulation results show that the proposed scheme has only negligible gap in spectrum efficiency compared with the fully digital precoding, while achieves better BER performance than SVD based hybrid precoding.

Orbital Angular Momentum-Based Multiple-Input-Multiple- Output with Receive Antenna Shift Keying for 6G

Spectral efficiency is a major concern for future 6G wireless communication systems. Thus, an appropriate scheme is needed to provide channel capacity improvement for multiple transmitters and receiver-based wireless communication systems without consuming extra resource for communication (e.g., frequency/time/code) or causing interference. Therefore, to fulfill the mentioned requirements for the future 6G wireless network, orbital angular momentum-based multiple-input-multiple-output (OAM-MIMO) multiplexing technique is incorporated with the receive antenna shift keying (RASK) technique in this study (termed as the OAM-MIMO-RASK scheme). OAM-MIMO-RASK can transfer multiple symbols from multiple transmitters to different receivers simultaneously by using multiple subchannels using the OAM and RASK techniques without any interference or additional resource (frequency/time/code). The numerical results illustrated that the proposed OAM-MIMO-RASK can achieve almost double capacity than the existing OAM-MIMO scheme and significantly higher capacity than the existing RASK-based scheme for different values of signal-to-noise ratio. Moreover, the simulation result is validated by the theoretical result which is also shown by the numerical result. In addition, due to different normalized distances from the transmitters and receivers, the proposed OAM-MIMO-RASK scheme can achieve almost double capacity than the existing OAM-MIMO scheme by using OAM-MIMO and RASK technique effectively which is also depicted by the numerical results.

Analysis of Precoder Decomposition Algorithms for MIMO System Design

ABSTRACT Wireless communication over Multiple Input and Multiple Output (MIMO) channel achieve increased transmission rate by dividing the input stream into a multitude of parallel data streams which are transmitted in parallel. Precoding at the transmitter aims to decompose the channel into an uncorrelated multiple subchannels using the channel decomposition technique so that data streams will be sent in parallel independent subchannels. The proposed analysis is to analyze the performances of the MIMO precoder using various channel decomposition algorithms are compared and its computational complexity is analyzed. The channel decomposition schemes considered are Singular Value Decomposition, Geometric Mean Decomposition, LDLH, LU, Schur, QR and Jordan decomposition. The simulation and analytical results confirm that precoding for the MIMO channel decomposed by the QR scheme outperforms all the other precoding methods based on channel decomposition in the light of Bit Error Rate performance and involves a relatively lesser number of Floating Point Operations.

A Low-Latency Random Access Scheme by Multichannel SIC for Industrial IoT

Low latency is a prerequisite for real-time applications, and random access is almost an inevitable choice for large-scale wireless networks. In this article, we consider how to decrease access delay in random access manners for the industrial Internet of Things. We propose a novel scheme by integrating multiple subchannels on the user equipment (UE) side with message-level successive interference cancellation (SIC) on the sink side. Specifically, for any UE that starts an uplink transmission, it just stochastically chooses parts of its subchannels and then simultaneously transmits the same message on them without any power control. On the sink side, the sink decodes signals from UEs using SIC across multiple subchannels. Based on the proposed multichannel SIC scheme, minimizing the access delay in the random access manner is equivalent to minimizing the probability of decoding deadlock. Based on the mathematical method of density evolution and the help from the differential evolution algorithm, an optimal distribution for choosing subchannels is presented. With the optimal distribution, the average access delay is minimized.

A transfer learning model with multi-source domains for biomedical event trigger extraction

BackgroundAutomatic extraction of biomedical events from literature, that allows for faster update of the latest discoveries automatically, is a heated research topic now. Trigger word recognition is a critical step in the process of event extraction. Its performance directly influences the results of the event extraction. In general, machine learning-based trigger recognition approaches such as neural networks must to be trained on a dataset with plentiful annotations to achieve high performances. However, the problem of the datasets in wide coverage event domains is that their annotations are insufficient and imbalance. One of the methods widely used to deal with this problem is transfer learning. In this work, we aim to extend the transfer learning to utilize multiple source domains. Multiple source domain datasets can be jointly trained to help achieve a higher recognition performance on a target domain with wide coverage events.ResultsBased on the study of previous work, we propose an improved multi-source domain neural network transfer learning architecture and a training approach for biomedical trigger detection task, which can share knowledge between the multi-source and target domains more comprehensively. We extend the ability of traditional adversarial networks to extract common features between source and target domains, when there is more than one dataset in the source domains. Multiple feature extraction channels to simultaneously capture global and local common features are designed. Moreover, under the constraint of an extra classifier, the multiple local common feature sub-channels can extract and transfer more diverse common features from the related multi-source domains effectively. In the experiments, MLEE corpus is used to train and test the proposed model to recognize the wide coverage triggers as a target dataset. Other four corpora with the varying degrees of relevance with MLEE from different domains are used as source datasets, respectively. Our proposed approach achieves recognition improvement compared with traditional adversarial networks. Moreover, its performance is competitive compared with the results of other leading systems on the same MLEE corpus.ConclusionsThe proposed Multi-Source Transfer Learning-based Trigger Recognizer (MSTLTR) can further improve the performance compared with the traditional method, when the source domains are more than one. The most essential improvement is that our approach represents common features in two aspects: the global common features and the local common features. Hence, these more sharable features improve the performance and generalization of the model on the target domain effectively.

Joint Transmit Precoding and Reconfigurable Intelligent Surface Phase Adjustment: A Decomposition-Aided Channel Estimation Approach

Reconfigurable intelligent surfaces (RISs), consisting of many low-cost elements that reflect the incident waves by an adjustable phase shift, have attracted sudden attention for their potential of reconfiguring the signal propagation environment and enhancing the performance of wireless networks. The passive nature of RISs is indeed beneficial, but the lack of radio frequency (RF) chains at the RIS has made channel estimation extremely challenging. We face this challenge by proposing a joint channel estimation and transmit precoding framework for RIS-aided multiple-input multiple-output (MIMO) systems. Specifically, the effective cascaded channel of the reflected transmitter-RIS-receiver link is decomposed into multiple subchannels, each of which corresponds to a single RIS element. Then our joint RIS-transmitter precoding model is formulated for the individual subchannels of each reflecting element. Finally, we develop a two-stage precoding design for successively determining the required phase shifts of each reflecting element of the RIS and the digital baseband precoder of the transmitter, only relying on the channel state information (CSI) of the subchannels. The performance of the proposed subchannel estimation and joint precoding method is evaluated by extensive simulations. Our numerical results show that the proposed designs provide an attractive solution to RIS-aided MIMO systems.

GaN HEMT With Convergent Channel for Low Intrinsic Knee Voltage

When the gated channel region of a GaN high-electron-mobility transistor (HEMT) is configured into multiple sub-channels in parallel and separated by embedded isolating patterns, the effective resistance of the access regions could be reduced, and consequently, the knee voltage ( ${V}_{{\text {K}}}$ ) of the transistor could be lowered. In this work, each sub-channel is defined as a convergent funnel-like shape, with its width gradually shrunk from the source side to the drain side. Different from conventional channels with uniform width under the entire gate, the funnel-shaped channel could converge electrons as they transport from source side to drain side, which facilitates electrons’ acceleration toward saturation velocity under a smaller drain-to-source bias, leading to a reduced intrinsic ${V}_{{\text {K}}}$ in the gated channel. Thus, more desirable ${I}$ - ${V}$ characteristics and more balanced performance enhancement in RF linearity and power added efficiency are achieved at a low supply voltage, making the convergent-channel HEMT attractive for power amplifiers in mobile terminals.

Optimal Rate-Diverse Wireless Network Coding Over Parallel Subchannels

This paper derives the maximum achievable sum-rate and presents the optimal encoding/decoding framework for rate-diverse wireless network coding (RD-WNC) over broadband channels consisting of multiple parallel subchannels. RD-WNC applies to a communication scenario in which a base station wants to deliver two different messages with different rates to two users. The base station combines the two separate messages into one network-coded message and broadcasts the network-coded message to both users. Each user then extracts its desired message from the network-coded message by subtracting from it the other message, which we assume to be side information available to the user. Deriving the maximum achievable sum-rate for RD-WNC is challenging when the channel consists of multiple parallel subchannels with different channel coefficients (e.g., the subcarrier channels of OFDM systems), since apart from the rate allocation between the two users, optimal power allocation among multiple subchannels needs to be identified. The first contribution of this paper is a new “mountain-leveling” power allocation algorithm to achieve the maximum sum-rate. With the resulting power allocation, we can then achieve the corresponding optimal sum-rate by having a separate encoding/decoding mechanism for each subchannels, but doing so is cumbersome and complex when the number of subchannels is large. The second contribution of this paper is a practical encoding/decoding framework using only one encoder-decoder mechanism for all subchannels without sacrificing sum-rate optimality. We provide numerical results to corroborate our theoretical findings and to demonstrate the benefits of our encoding/decoding framework.

Design of a Narrow Transition Band Dynamic Digital Channelized Receiver withoutMerging Adjacent Sub-channels

In electronic reconnaissance, effective channelization of several input signals with unknown parameters is the key requirement of the broadband receiver. Generally, since the prototype filter must be perfect, the dynamic channelized receiver based on merging adjacent sub-channels to reconstruct non-uniform filter Banks occupies too many resources and is difficult to realize. This paper proposed a sharp wideband channelized receiver based on DFT filter banks and interpolated filter banks. The proposed receiver consists of an analysis section, channel detection and arbitration section, and a synthesis section. The analysis section divides the wideband signal into multiple sub-channels. Channel detection and arbitration section output the center frequencies and the bandwidths of the input signals. In the synthesis section, after being converted to baseband, the input signals are filtered and decimated to achieve the low rate signals. The proposed method released the design of the prototype filter from the requirement of perfect reconstruction. So it is computationally efficient. Besides, time-multiplexed and interpolated filter techniques can be used to further reduce the hardware complexity. Simulation results verify the validity of the proposed method.

A Novel Resource Allocation Scheme in NOMA-Based Cellular Network with D2D Communications

Non-orthogonal multiple access (NOMA) has become a promising technology for 5G. With the support of effective resource allocation algorithms, it can improve the spectrum resource utilization and system throughput. In this article, a new resource allocation algorithm in the NOMA-enhanced cellular network with device-to-device (D2D) communications is proposed, in which we use two new searching methods and an optimal link selection scheme to maximize the system throughput and limit the interferences of the NOMA-based cellular network. In the proposed joint user scheduling, tree-based search power allocation and link selection algorithm, we simplify the solving process of previous methods and set up the optimization function, which does not need to be derivable. With successive interference cancellation (SIC) technology, we give conditions for the D2D devices accessing into the network. We also propose a suboptimal scheme to schedule cellular users and D2D devices into multiple subchannels, which reduces the complexity of the exhaustive search method. Through consistent tree-based searching for the power allocation coefficients, we can get the maximum arithmetic average of the system sum rate. Meanwhile, for the existence of the part of interferences from larger power users which can be canceled by the SIC in NOMA systems, the search options are decreased for increasing the search rate of the power allocation algorithm. Moreover, we propose a distance-aware link selection scheme to guarantee the quality of communications. In summary, the proposed algorithm can improve the system throughput, has a low complexity cost and potentially increases spectral utilization. Numerical results demonstrate that the proposed algorithm achieves a higher data transmission rate than some of the traditional methods and we also investigate the convergence and the computational complexity cost of the joint algorithm.

Energy-Efficient Resource Allocation in Multicarrier NOMA Systems With Fairness

Non-orthogonal multiple access (NOMA) has attracted both academic and industrial interest since it has been considered as one of the promising 5G technologies in order to increase connectivity and spectral efficiency. In this paper, we focus on a downlink multicarrier (MC) NOMA network, where a single base station serves a set of users through multiple subchannels. The goal is to jointly optimize energy efficiency (EE) and fairness among users with respect to the subcarrier and power allocation parameters. To achieve this with acceptable complexity, a novel greedy subcarrier assignment scheme based on the worst-user first principle is proposed. Due to the fractional form of the EE expression and the existence of interference, the power allocation problem is non-convex and NP-hard. To this end, we first transform this into an equivalent subtractive form, which is then solved by using fractional programming with sequential optimization of the inter/intra-subchannel power allocation vectors. Simulation results reveal the effectiveness of the proposed scheme in terms of EE and fairness among users compared to baseline schemes. Finally, the proposed algorithms are of fast convergence, low complexity, and insensitive to the initial values.

Optical frequency comb-based multichannel parallel continuous-variable quantum key distribution.

Continuous-variable quantum key distribution (CVQKD) provides an approach for secure communication in optical fiber communication systems. However, its practical implementation has been hindered by low secret key bit rates that are usually limited to several bits/s to hundreds of kbits/s at distances of more than 25 kilometers. In this paper, we use a pair of optical frequency combs (OFCs) for both multiple parallel transmission and coherent reception, which assign multiple sub-channels involving multiple independent secret keys in a single fiber to increase the key bit rate. The first and last sub-channels are selected for propagating phase references to compensate the phase offset between two free-running combs. We analyze possible excess noise caused by dispersive walk-off in the transmission, imperfect phase compensation in the reception and photon leakage from the phase references. Compared to the previous single-channel CVQKD method, simulation results show more than a factor of 20 increase in the secret key rate at a transmission distance of 35 km and the number of comb lines of 35.

A Bi-Directional Carrier Sense Collision Avoidance Neighbor Discovery Algorithm in Directional Wireless Ad Hoc Sensor Networks.

From the perspective of media protocol control and routing of directional wireless ad hoc sensors networks, neighbor discovery protocol is an important problem to be solved first. In the past period of time, some methods have been studied on neighbor discovery protocol, but they have a common defect of link collision. The collision is caused by mutual interference of multiple transmitting nodes which are in one reception beam of the receiving node. To solve this problem, we propose a neighbor discovery algorithm using a bi-directional carrier sense collision avoidance and multi subchannels based on a scan-based algorithm (BD-SBA). Based on a scan-based algorithm (SBA), bi-directional carrier sense of the BD-SBA algorithm is performed in the first broadcast step which can reduce the collision of broadcasting the scanning request (SREQ) frames. In the second step (the reply step), the mechanism of multiple subchannels and multiple slots is applied to reduce the collision of the scanning response (SRES) frames. From the analysis and simulation, we can see that nodes using proposed algorithm can discover their neighbor nodes in fewer time. Moreover, the proposed algorithm has better performance for different beamwidths and densely distributed scenes. So it has great significance in engineering application.