Throughput Gain Research Articles

Design of an Improved Model for Spectrum Sharing and Resource Management Using Deep Q-Network, Federated Averaging, and Ant Colony Optimization

Indeed, the ever-evolving wireless communication requires DSS and resource management for effectiveness. The old methods of spectrum allocation have a strategic shortfall since they are static and thus not adapted to real-time network conditions. Besides, more important challenges include issues over privacy and inefficiency of resource utilization. In the light of such problems, this paper proposes a new method by bringing together three deep-learning techniques, which are Deep Q-Networks, Federated Averaging, and Ant Colony Optimisation. Each is chosen because of its various competencies and complementary capabilities. First, DQN is chosen because it has the capability to handle high-dimensional action space. DQN applies deep learning to approximate the Q Value function, which provides the optimal decision of spectrum allocation based on real-time network states, such as spectrum usage and interference levels. With this scheme, up to a 20% gain in network throughput and up to a 15% cut in wasted spectrum compared to traditional methods are achievable. The second component FedAvg tackles the model training collaboration issue with privacy. It enables FedAvg for privacy-preserving resource management, where multiple devices update the global model collaboratively by communicating model updates without exchanging raw data samples. For this approach, the resulting method leads to a further increase in data privacy by reducing data leakage as high as 40%, with an improvement in resource management accuracy of up to 15%. ACO was employed to optimize resource allocation and improve spectrum efficiency. ACO emulates the natural foraging behavior of ants to solve combinatorial optimization problems. Consequently, it has led to up to 25% increase in spectral efficiency and up to 20% improvement in resource allocation balance sets. All these methods put together will provide a wholesome solution that not only optimizes spectrum and resource management but also promises privacy and adaptability. This represents severe improvement in overcoming the weaknesses of the existing approaches and will provide a strong foundation for next-generation wireless networks.

Proof-of-Trusted-Work: A lightweight blockchain consensus for decentralized IoT networks

Traditional Internet of Things (IoT) architectures that rely on centralized servers for data management and decision-making are vulnerable to security threats and privacy leakage. To address this issue, blockchain has been advocated for decentralized data management in a tamper-resistance, traceable, and transparent manner. However, a major issue that hinders the integration of blockchain and IoT lies in that, it is rather challenging for resource-constrained IoT devices to perform computation-intensive blockchain consensuses such as Proof-of-Work (PoW). Furthermore, the incentive mechanism of PoW pushes lightweight IoT nodes to aggregate their computing power to increase the possibility of successful block generation. Nevertheless, this eventually leads to the formation of computing power alliances, and significantly compromises the decentralization and security of BlockChain-aided IoT (BC-IoT) networks. To cope with these issues, we propose a lightweight consensus protocol for BC-IoT, called Proof-of-Trusted-Work (PoTW). The goal of the proposed consensus is to disincentivize the centralization of computing power and encourage the independent participation of lightweight IoT nodes in blockchain consensus. First, we put forth an on-chain reputation evaluation rule and a reputation chain for PoTW to enable the verifiability and traceability of nodes' reputations based on their contributions of computing power to the blockchain consensus, and we incorporate the multi-level block generation difficulty as a rewards for nodes to accumulate reputations. Second, we model the block generation process of PoTW and analyze the block throughput using the continuous time Markov chain. Additionally, we define and optimize the relative throughput gain to quantify and maximize the capability of PoTW that suppresses the computing power centralization (i.e., centralization suppression). Furthermore, we investigate the impact of the computing power of the computing power alliance and the levels of block generation difficulty on the centralization suppression capability of PoTW. Finally, simulation results demonstrate the consistency of the analytical results in terms of block throughput. In particular, the results show that PoTW effectively reduces the block generation proportion of the computing power alliance compared with PoW, while simultaneously improving that of individual lightweight nodes. This indicates that PoTW is capable of suppressing the centralization of computing power to a certain degree. Moreover, as the levels of block generation difficulty in PoTW increase, its centralization suppression capability strengthens.

Implementation of 802.11ax and cell-free massive MIMO scenario for 6G wireless network analysis extending OMNeT++ simulator

In an era where ubiquitous connectivity and escalating data demands are altering the landscape of wireless communications, our paper proposes a pioneering enhancement for the OMNeT++ simulator to support the advanced features of IEEE 802.11ax high efficiency (HE) alongside cell-free massive multiple-input multiple-output (MIMO) systems. Traditional wireless networks face daunting challenges in sustaining elevated quality of service (QoS), primarily due to fluctuating user densities and signal quality. Cell-free massive MIMO serves as a compelling answer to this predicament by decentralizing the cellular architecture. It eradicates conventional cell boundaries, furnishing uniform QoS regardless of user locations. However, these advancements come at the expense of complex backhaul networks and articulated joint signal processing. The 802.11ax standard, touted for its robustness and efficiency, remains underexplored in this new paradigm. Our research not only dissects the architectural elements and constraints of both 802.11ax and cell-free massive MIMO but also elaborates on the adaptations required to extend OMNeT++ functionalities for these technologies. By doing so, we bridge a crucial gap, enabling the simulator to provide a more precise, detailed, and scalable evaluation of emerging 6G scenarios and directional communications also taking into account the impact of the most known routing protocols such as dynamic source routing (DSR), ad hoc on-demand distance vector routing (AODV), optimized link state routing (OLSR), and dynamic mobile ad hoc network on-demand (DYMO) that were selected for this comparative study. The proposed extensions promise to revolutionize network simulations and lay the foundation for in-depth analyses of wireless systems in complex and dynamic environments. Through extensive simulations, our study demonstrates that cell-free massive MIMO configurations significantly improve network throughput in high-density mobile ad hoc network (MANET) environments, with results indicating an average throughput gain of up to 30% compared with non-cell-free configurations. This improvement highlights the efficacy of cell-free massive MIMO to take advantage of the spatial and frequency multiplexing capabilities inherent in the 802.11ax standard, making it a promising solution for future wireless systems in densely populated areas.

The MIMO Data Transfer Line with Seven-Frequency Octonion Carrier

Currently, with the development of public relations and production systems, there is a need to increase the capacity of communication systems and information transmission. It has been shown theoretically that it is possible to increase throughput by using multidimensional signals in space instead of real signals on a plane. It is now accepted that a multidimensional space, MultipleInput Multiple-Output (MIMO), can be formed using multiple antennas to transmit and receive in physical space. However, as physicists point out, such space is three-dimensional, and with the addition of time it is four-dimensional. It is clear that in such a physical space, when using more than 2 antennas for transmission and 2 for reception, it is impossible to obtain a gain in throughput of more than 4 times, since according to the laws of cybernetics, the diversity at the channel input will not be transmitted to the exit. It follows that it is necessary to reconsider existing views on the dimension of physical space. Previously, in the work the MIMO data transfer line with three-frequency quaternion carrier, it was shown that it is possible to use a hypercomplex quaternion number as a model of physical space. In this case, the dimension of space will be equal to 4 with 3 imaginary (spatial) axes and one scalar axis. In addition, combinations of three quaternion angular frequencies on the imaginary axes formed 4 single-frequency channels. Accordingly, the gain in throughput compared to real signals reached 4 in orthogonal axes and 4 in frequencies. In this work, an octonion with 7 imaginary (spatial) axes and one scalar is used as a mathematical model of physical space. It is shown that the dimension of physical space will be 8 with 64 single-frequency channels in the form of combinations of 7 angular frequencies. Hence, the gain in throughput will be 8 in orthogonal axes and 64 in frequencies.

Deep Learning-Driven Interference Perceptual Multi-Modulation for Full-Duplex Systems

In this paper, a novel data transmission scheme, interference perceptual multi-modulation (IP-MM), is proposed for full-duplex (FD) systems. In order to unlink the conventional uplink (UL) data transmission using a single modulation and coding scheme (MCS) over the entire assigned UL bandwidth, IP-MM enables the transmission of UL data channels based on multiple MCS levels, where a different MCS level is applied to each subband of UL transmission. In IP-MM, a deep convolutional neural network is used for MCS-level prediction for each UL subband by estimating the potential residual self-interference (SI) according to the downlink (DL) resource allocation pattern. In addition, a subband-based UL transmission procedure is introduced from a specification point of view to enable IP-MM-based UL transmission. The benefits of IP-MM are verified using simulations, and it is observed that IP-MM achieves approximately 20% throughput gain compared to the conventional UL transmission scheme.

Deep reinforcement learning aided secure UAV communications in the presence of moving eavesdroppers

Integrating unmanned aerial vehicles (UAVs) with wireless systems is widely anticipated to bring enormous benefits to network users, and is considered as a key enabling technology for next generation wireless networks. However deployment of UAVs raises security concerns, as they are prone to eavesdropping attacks. In this paper we investigate secure data transmissions in a UAV-aided communication system from the physical layer security (PLS) perspective. The UAV is employed as a flying base station and transmit confidential information to a ground user. Specifically, an eavesdropper is moving in the vicinity of the ground user, attempting to intercept legitimate data transmission. We aim to optimize UAV trajectory to maximize secrecy throughput and due to the fast changing environment with unpredictable movements of the eave, this nonconvex problem is difficult to solve. Instead, we propose a deep reinforcement learning framework by reformulating this problem as a Markov Decision Process (MDP). By carefully designing state spaces, state-dependent action sets and time-dependant reward signals, we aim to help the agent learn an optimized trajectory and complete the flight task within time limit. We adapt the deep Q network (DQN) algorithm and extend it with prioritized experience replay (PER) and importance sampling method to ensure a stable and effective learning outcome. Simulation results validate the effectiveness of the learning algorithm and confirm that the agent is able to learn an effective policy with desirable and robust learning outcomes. Compared with benchmark schemes, our proposed scheme is shown to achieve considerable performance gain in secrecy throughput.

PDASTSGAT: An STSGAT-Based Multipath Data Scheduling Algorithm

How to select the transmitting path in MPTCP scheduling is an important but open problem. This paper proposes an intelligent data scheduling algorithm using spatiotemporal synchronous graph attention neural networks to improve MPTCP scheduling. By exploiting the spatiotemporal correlations in the data transmission process and incorporating graph self-attention mechanisms, the algorithm can quickly select the optimal transmission path and ensure fairness among similar links. Through simulations in NS3, the algorithm achieves a throughput gain of 7.9% compared to the PDAA3C algorithm and demonstrates improved packet transmission performance.

Best sum-throughput evaluation of cooperative downlink transmission nonorthogonal multiple access system

In cooperative simultaneous wireless information and power transfer (SWIPT) nonorthogonal multiple access (NOMA) downlink situations, the current research investigates the total throughput of users in center and edge of cell. We focus on creating ways to solve these problems because the fair transmission rate of users located in cell edge and outage performance are significant hurdles at NOMA schemes. To enhance the functionality of cell-edge users, we examine a two-user NOMA scheme whereby the cell-center user functions as a SWIPT relay using power splitting (PS) with a multiple-input single-output. We calculated the probability of an outage for both center and edge cell users, using closed-form approximation formulas and evaluate the system efficacy. The usability of cell edge users is maximized by downlink transmission NOMA (CDT-NOMA) employing a SWIPT relay that employs PS. The suggested approach calculates the ideal value of the PS coefficient to optimize the sum throughput. Compared to the noncooperative and single-input single-output NOMA systems, the best SWIPT-NOMA system provides the cell-edge user with a significant throughput gain. Applying SWIPT-based relaying transmission has no impact on the framework’s overall throughput.

Optimal Oblivious Routing With Concave Objectives for Structured Networks

Oblivious routing distributes traffic from sources to destinations following predefined routes with rules independent of traffic demands. While finding optimal oblivious routing with a concave objective is intractable for general topologies, we show that it is tractable for structured topologies often used in datacenter networks. To achieve this, we apply graph automorphism and prove the existence of the optimal automorphism-invariant solution. This result reduces the search space to targeting the optimal automorphism-invariant solution. We design an iterative algorithm to obtain such a solution by alternating between convex optimization and a linear program. The convex optimization finds an automorphism-invariant solution based on representative variables and constraints, making the problem tractable. The linear program generates adversarial demands to ensure the final result satisfies all possible demands. Since the construction of the representative variables and constraints are combinatorial problems, we design polynomial-time algorithms for the construction. We evaluate the iterative algorithm in terms of throughput performance, scalability, and generality over three potential applications. The algorithm i) improves the throughput up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$87.5\%$</tex-math> </inline-formula> for partially deployed FatTree and achieves up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2.55\times$</tex-math> </inline-formula> throughput gain for DRing over heuristic algorithms, ii) scales for three considered topologies with a thousand switches, iii) applies to a general structured topology with non-uniform link capacity and server distribution.

Machine Learning in RIS-Assisted NOMA IoT Networks

A reconfigurable intelligent surface (RIS)-assisted downlink non-orthogonal multiple access (NOMA) Internet-of-Things (IoT) network is proposed, where a quality-of-service (QoS)-based NOMA clustering scheme is conceived to effectively utilize the limited wireless resources among IoT devices. A throughput maximization problem is formulated by jointly optimizing the phase shifts of the RIS and the power allocation of the base station (BS) from the short-term and long-term perspectives. We aim to investigate and compare the performance of deep learning (DL) and deep reinforcement learning (DRL) algorithms for solving the formulated problems. In particular, the DL method utilizes model-agnostic-meta-learning (MAML) to enhance the generalization capability of the neural network and to accelerate the convergence rate. For the DRL method, the deep deterministic policy gradient (DDPG) algorithm is employed to incorporate continuous phase-shift variables. It shows that the DL method only focuses on the maximization of the instantaneous throughput, whereas the DRL method can coordinate the power consumption over different time slots to maximize the long-term throughput. Numerical results demonstrate that 1) the proposed QoS-based NOMA clustering scheme achieves higher IoT throughput than the conventional channel-based scheme; 2) the implementation of RISs induces approximately 5% to 25% throughput gain as the number of RIS elements increases from 8 to 64; 3) DL and DRL achieve a similar throughput performance for the short-term optimization, while DRL is superior for the long-term optimization, especially when the total transmit power is limited.

Multi-objective distributed cooperative spectrum sensing for cognitive radio networks using greedy-based coalitional game

In this work, we propose a distributed cooperative spectrum sensing (CSS) scheme for cognitive radio networks applying a greedy-based coalitional game. We formulate the cooperative spectrum sensing problem as a multi-objective optimization problem using the weighted global criteria method. The objective of the formulated problem is to jointly consider the relative impacts of the three key performance metrics in CSS: throughput gain, penalty cost, and energy overhead. By integrating these three distinct metrics into a global optimization problem, we aimed to achieve a well-balanced trade-off, essential for deploying the CSS scheme across diverse network and application environments. We prove that the formulated CSS problem is non-deterministic polynomially hard (NP-hard). The computational complexity of the formulated CSS problem is decreased by proposing a heuristic scheme that produces a sub-optimal solution in polynomial time. We develop the heuristic scheme by putting forward a greedy-based coalitional game theoretic model. The proposed scheme exploits the spatial diversity of secondary users (SUs) in forming distributed coalitions to minimize the effects of attenuation and distortion on the transmitted signal. Further sensing energy overhead is reduced by dynamically deciding the sensing duration for the SUs based on their received signal strength. To establish the stability of the coalitional game model, we carry out proof which shows that the proposed heuristic algorithm converges to a stable coalition structure. Simulation-based results show the effectiveness of the proposed scheme in terms of average detection accuracy, utility, and energy overhead compared to conventional schemes.

Deep-Reinforcement-Learning-Based NOMA-Aided Slotted ALOHA for LEO Satellite IoT Networks

The low earth orbit (LEO) satellites have received extensive attention as an essential supplement to the terrestrial network for supporting global Internet of Things (IoT) services. Considering the rapid growth of IoT devices and the significant satellite-to-ground latency, proposing low-latency, low-overhead access protocols for LEO satellite IoT systems is challenging. In this paper, we propose a multi-beam random access (RA) framework and deploy the deep reinforcement learning (DRL) algorithm to control the non-orthogonal multiple access (NOMA) aided random access strategy. First, we divide the satellite coverage region into multiple beams and assume that the adjacent beams share parts of regions. Hence, the devices in the sharing region are allowed to transmit packets in two periods allocated for the two beams. Then, packets in multiple beams can be decoded jointly by an inter-slot successive interference cancellation (SIC) decoder. In addition, we consider the heterogeneity among devices and assign different power levels for heterogeneous types of devices, which enables power-domain NOMA and the intra-slot SIC decoder in this system to mitigate the collision resolution. To maximize the average throughput, the deep deterministic policy gradient (DDPG) algorithm is adopted to achieve an online decision to optimize the random access protocol where the packet repetition strategies of devices are adjusted dynamically. The simulation results show that the proposed scheme outperforms the traditional benchmark schemes with significant throughput gain.

Performance analysis of distributed coordination function with early collision detection in In-band Full-duplex wireless networks

In-band Full-duplex (IB-FD) wireless has the potential to double the spectral efficiency offering its innate capability to resolve conventional problems emerge owing to the imperfect medium access control (MAC) in In-band half-duplex (IB-HD) wireless networks. IEEE 802.11 distributed coordination function (DCF), a binary slotted exponential back-off with carrier sense multiple access scheme, has been used extensively in literature as the fundamental contention resolution technique to devise MAC protocols for IB-FD wireless networks. Unlike IB-HD, an IB-FD network can contain two simultaneously active transmission links within a single collision domain. This phenomenon invalidates the applicability of existing IB-HD models of the DCF to compute its performance (i.e., saturation throughput) accurately in IB-FD wireless networks. We have proposed two MAC protocols for two types of network scenarios taking early collision detection capability of an IB-FD wireless transceiver into consideration. This paper also presents a distinct analytical model to quantify the performance of the DCF in the context of IB-FD wireless assuming finite number of stations and idle channel conditions. We have applied the concept of primary–secondary transmission links and, thoroughly derived the probability that a station transmits either as primary or secondary in a randomly chosen time-slot taking secondary transmitter’s back-off counter into account. We have also investigated the effect of inter-station interference free IB-FD link on the performance of the DCF. Our protocol, when applied to network without hidden stations, has obtained the highest throughput gain of 1.59, 1.61, 1.68 while the DCF is configured with CWmin of 16, 32, 64, respectively than IEEE 802.11 basic access scheme. We have observed increased throughput gain with hidden stations, thanks to inter-station interference free transmission region, and achieved 2.12, 2.14, 2.16 times higher throughput, respectively than that of IEEE rts-cts access scheme.

Joint Precoding and Array Design for Broadcast in the Internet of Unmanned Aerial Vehicles

To promote energy and spectrum efficient communications in multi-antenna channels, broadcast can provide a substantial gain in system throughput. However, the hardware constraints and strong line-of-sight (LoS) limit the implementation and performance of multi-antenna broadcast in the Internet of unmanned aerial vehicles (UAVs). Based on the pseudo-Doppler principle, we propose a joint precoding and antenna array design to reduce the number of radio frequency (RF) chains required by the broadcast and free the LoS path from the inter-stream interference. The reduction of RF chains is realised by designing the precoding matrix that makes at least one of the transmit antennas have null inputs during any broadcast and, moreover, the LoS path is formed to match the obtained precoding matrix through antenna array design at the broadcasting UAV. The algorithms with low computational complexity for optimising this design are developed to minimise the transmit power within the UAV broadcast paradigms. Theoretical formulation and numerical results in the metrics of sum data rate and bit error rate substantiate the validity of our proposed design, specifically in the Internet of UAVs with strong LoS.

BAR: Blockwise Adaptive Recoding for Batched Network Coding.

Multi-hop networks have become popular network topologies in various emerging Internet of Things (IoT) applications. Batched network coding (BNC) is a solution to reliable communications in such networks with packet loss. By grouping packets into small batches and restricting recoding to the packets belonging to the same batch; BNC has much smaller computational and storage requirements at intermediate nodes compared with direct application of random linear network coding. In this paper, we discuss a practical recoding scheme called blockwise adaptive recoding (BAR) which learns the latest channel knowledge from short observations so that BAR can adapt to fluctuations in channel conditions. Due to the low computational power of remote IoT devices, we focus on investigating practical concerns such as how to implement efficient BAR algorithms. We also design and investigate feedback schemes for BAR under imperfect feedback systems. Our numerical evaluations show that BAR has significant throughput gain for small batch sizes compared with existing baseline recoding schemes. More importantly, this gain is insensitive to inaccurate channel knowledge. This encouraging result suggests that BAR is suitable to be used in practice as the exact channel model and its parameters could be unknown and subject to changes from time to time.

Hybrid detection techniques for 5G and B5G M-MIMO system

Massive-Multiple Inputs and Multiple Outputs (M−MIMO) will have a greater impact in the advanced radio framework. It efficiently increases the capacity, spectral access, and data speed of the framework. However, the detection of the signal becomes complicated due to the use of several antennas at the microcell. Separating such a vast range of connected devices is necessary to enable the detection of transmit antennas in response to various available data sources. In the presented work, novel hybrid algorithms such as QR-maximum likelihood detection (QR-MLD), QR-minimum means square error (MMSE), QR-zero forcing equaliser (ZFE), and QR-beam forming (QR-BF) are implemented for 16x16, 64x64, and 256x256 MIMO structures. The hybrid algorithms obtained an efficient bit error rate (BER) of 10-3 at the SNR of 2.9 dB with trivial complexity. Further, the proposed algorithms are compared with conventional methods. It is be noted that the QR-MLD achieves a gain of 3 dB when compared to the MMSE. It is concluded that the QR-MLD provided optimal performance and significantly enhanced the throughput gain of the framework.

Joint Optimization of Trajectory and Discrete Reflection Coefficients for UAV-Aided Backscatter Communication System with NOMA

Backscatter communication is a promising technology for the Internet of Things (IoT) systems with low-energy consumption, in which the data transmission of the backscatter devices relies on reflecting the incident signal. However, limited by the low power characteristic of the reflected signal from backscatter devices, achieving efficient data collection for the widely distributed backscatter devices is a thorny problem. Considering that unmanned aerial vehicles (UAVs) have flexible deployment capability, employing UAVs in a backscatter communication network can achieve feasible data collection for backscatter devices. In this paper, we consider a UAV-aided backscatter system and introduce Non-orthogonal multiple access (NOMA) to enable the UAV to collect signals from multiple backscatter devices simultaneously. We formulate an optimization problem to maximize the communication throughput of the considered system by jointly designing the backscatter device matching, the trajectory of the UAV, and the reflection coefficients of the backscatter devices, which is a non-convex optimization problem and challenging to solve. Hence, we decouple the original problem into three sub-problems and propose an efficient iterative algorithm based on Block Coordinate Descent (BCD) to solve them. In detail, a game-based matching algorithm is designed to ensure the transmission needs of remote backscatter devices. The UAV trajectory and reflection coefficients of backscatter devices are optimized through the Successive Convex Approximation (SCA) algorithm and relaxation algorithm. By iterative optimization of the sub-problems, the original problem is solved. The simulation results show that the proposed scheme can obtain a significant throughput gain compared to benchmark schemes.

Accelerated MRI using intelligent protocolling and subject-specific denoising applied to Alzheimer's disease imaging.

Magnetic Resonance Imaging (MR Imaging) is routinely employed in diagnosing Alzheimer's Disease (AD), which accounts for up to 60-80% of dementia cases. However, it is time-consuming, and protocol optimization to accelerate MR Imaging requires local expertise since each pulse sequence involves multiple configurable parameters that need optimization for contrast, acquisition time, and signal-to-noise ratio (SNR). The lack of this expertise contributes to the highly inefficient utilization of MRI services diminishing their clinical value. In this work, we extend our previous effort and demonstrate accelerated MRI via intelligent protocolling of the modified brain screen protocol, referred to as the Gold Standard (GS) protocol. We leverage deep learning-based contrast-specific image-denoising to improve the image quality of data acquired using the accelerated protocol. Since the SNR of MR acquisitions depends on the volume of the object being imaged, we demonstrate subject-specific (SS) image-denoising. The accelerated protocol resulted in a 1.94 × gain in imaging throughput. This translated to a 72.51% increase in MR Value-defined in this work as the ratio of the sum of median object-masked local SNR values across all contrasts to the protocol's acquisition duration. We also computed PSNR, local SNR, MS-SSIM, and variance of the Laplacian values for image quality evaluation on 25 retrospective datasets. The minimum/maximum PSNR gains (measured in dB) were 1.18/11.68 and 1.04/13.15, from the baseline and SS image-denoising models, respectively. MS-SSIM gains were: 0.003/0.065 and 0.01/0.066; variance of the Laplacian (lower is better): 0.104/-0.135 and 0.13/-0.143. The GS protocol constitutes 44.44% of the comprehensive AD imaging protocol defined by the European Prevention of Alzheimer's Disease project. Therefore, we also demonstrate the potential for AD-imaging via automated volumetry of relevant brain anatomies. We performed statistical analysis on these volumetric measurements of the hippocampus and amygdala from the GS and accelerated protocols, and found that 27 locations were in excellent agreement. In conclusion, accelerated brain imaging with the potential for AD imaging was demonstrated, and image quality was recovered post-acquisition using DL-based image denoising models.

Resource allocation and user assignment schemes in cellular supported industrial IoT networks

AbstractIndustrial Internet of Things (IIoT) deployment underlying cellular networks has been drawing increasing attention in recent years. In this work, we consider group based resource allocation for industrial IoT networks where cellular‐IoT (C‐IoT) devices support uplink transmission for multiple IoT groups/clusters. The joint group and subcarrier optimization problem is formulated for maximizing the cell/group throughput under the optimal group member selection, subcarrier and minimum data rate constraints. Depending on the interference information, the interference aware group allocation (IA‐GA) is proposed to find the cellular user and cellular‐IoT device grouping for each subcarrier. However, to achieve the maximizing the cell/group throughput, another iterative algorithm, namely genetic algorithm based group allocation (GA‐GA) method is proposed, which provides an optimal solution for the user grouping in the most of the cases where an iterative technique is used for the sub‐carrier allocation. Simulations results show that the proposed IA‐GA and GA‐GA methods provide enhanced cell throughput gain and accessibility of the C‐IoTs.

Ambient LoRa Backscatter System With Chirp Interval Modulation

Ambient LoRa backscatter enables battery-free wireless communication with long-range connectivity for the Internet of Things. However, current efforts mainly focus on symbol-level modulation, which trades considerable data rates for long backscatter ranges. To enhance the data rate, this paper presents and prototypes Pacim, which fully explores the potential of long-period LoRa chirps and conveys additional information by varying symbol lengths. Specifically, we propose the chirp interval modulation scheme that modulates multiple data bits in each time interval between two chirp-based anchor symbols. Moreover, we design a twin-chirp cancellation method at the receiver that eliminates the frequency discontinuity within anchor symbols, and propose a fine-grained detection algorithm to measure the arrival time of anchor symbols in the frequency domain. We further propose three reliable methods to improve transmission reliability and analyze the symbol error rate (SER) performance. We also build a hardware prototype and perform comprehensive evaluations. Our experiment results show that Pacim can achieve up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$8.6 \times $ </tex-math></inline-formula> throughput gain while keeping long-range, compared with the state-of-the-art ambient LoRa backscatter design.