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Through the integration of Network Function Virtualization (NFV), Software-Defined Networks, cloud service providers can realize cost reduction, performance enhancement, and support for advanced application scenarios. Within this framework, Service Function Chain (SFC) has emerged as a pivotal approach for delivering network services, offering flexibility and cost-effectiveness in provisioning Virtual Network Functions (VNFs) dynamically and elastically across a network of physical devices. This paper concentrates on optimizing VNF placement within NFV-enabled networks to elevate the number of SFC Requests (SFCRs) successfully processed. Emphasizing the often-overlooked factors of time-varying workloads and VNF sharing via multi-tenancy technology, we present a novel Integer Linear Programming (ILP) formulation for the strategic placement of VNFs. To effectively solve this ILP, we introduce a Throughput Optimization Heuristic Solution (TOHS), comprising an algorithm based on correlation for assigning SFCRs to nodes, alongside a VNFR aggregation algorithm aimed at optimizing node resource utilization by consolidating VNFRs requiring identical VNF types. Through detailed numerical simulations and comparative analysis with existing literature, we demonstrate that TOHS approaches the performance of ILP solvers for small-scale problems, outperforming other solutions in diverse scenarios. Specifically, TOHS achieves higher network throughput and more efficient utilization of network resources, underscoring its efficacy in practical NFV-enabled network deployments. [ABSTRACT FROM AUTHOR]

In this paper, we optimize the instantaneous and average throughput when wireless communications uses Reconfigurable Intelligent Surfaces (RIS) and the source harvests power from sun using a Photo-Voltaic (PV) system. Instantaneous throughput optimization offers up to 6 dB gain versus average throughput optimization. Average throughput optimization offers a higher throughput than any packet length. Performance gain is up to 1 dB gain using average throughout optimization versus other values of packet length. [ABSTRACT FROM AUTHOR]

This research deals with improving signal processing techniques for largescale multiple-input, multiple-output (MIMO) antenna systems with the goal of high data rate transmission, and low interference in the seventh generation of wireless communication networks. In the current society, MMIMO has shown enhanced improvements in spectral efficiency, capacity and coverage due to its large number of antennas. However, it also posess disadvantages which are an enhanced computational complexity and an interference between the users. This paper inventories linear precoding, minimum mean square error (MMSE) detection and interference alignment to eliminate the interference and to optimize the through put of data. Further, machine learning for adaptive beamforming and channel estimation in the contexts of signal transmission in environments that can vary from one time instance to another is explored. The performance of these algorithms is then analyzed in simulations and theoretically by comparing their: spectral efficiency, power consumption, and bit error rate (BER). This is shown in the results, which point out that the adoption of advanced signal processing strategies can indeed mitigate interference and improve the system's throughput of envisaged environments such as 5G and beyond, more specifically, densely urbanized environments with high user density. This work has implications for the creation of better and more efficient massive MIMO systems to allow networks in the future to support the ever-growing need for enhanced and improved communication. [ABSTRACT FROM AUTHOR]

In this paper, we study an unmanned aerial vehicle (UAV)-assisted communication system, where the UAV is dispatched to implement simultaneous transmission and reception (STR) in the existence of multiple malicious jammers. Two schemes are investigated, namely frequency band-division-duplex (FDD) and time-fraction (TF). Based on the FDD scheme, the UAV can transmit information by using the portion of the bandwidth and receive information within the remaining portion of the bandwidth simultaneously. To perform the STR within the whole bandwidth, the TF-based scheme is considered by using a fraction of a time slot for the downlink, while the remaining fraction of the time slot is allocated for the uplink. We aim to maximize the worst-case throughput by optimizing the UAV three-dimensional (3D) trajectory and resource allocation for each scheme. The optimization problem is non-convex and thus computationally intractable. To handle the nonlinear problem, we use the block coordinate decomposition method to disaggregate the optimization problem into four subproblems and adopt the successive convex approximation technique to tackle non-convex problems. The simulation results demonstrate the performance of the TF-based scheme over the benchmark schemes. [ABSTRACT FROM AUTHOR]

Existing works on caching in multi-access edge computing focus on service caching and request routing. However, loading cost and execution time influenced by resource sharing have not been well exploited. To fill this gap, we investigate the joint optimization problem over deep neural network (DNN) model caching and DNN request routing with edge collaboration in edge-enabled wireless sensor networks. A problem is formulated, with the objective of maximizing throughput, under constraints of budget, accuracy and latency etc. The proof of NP-hardness for the formulated problem is provided. To solve the problem, an approximation algorithm based on randomized rounding is presented. In addition, the approximation ratio for the presented algorithm is proved to be |$1/(1-\sqrt{4\ln S/\xi^\dagger})$|⁠ , where |$S$| is the number of edge servers and |$\xi^\dagger$| is the objective value from linear relaxation. Extensive experiments demonstrate that the system throughput for the presented algorithm can be improved by 58.8% on average, compared with that of the baseline algorithm. [ABSTRACT FROM AUTHOR]

In new wireless network scenarios, such as the future Internet of Things, it is necessary to transmit information and energy simultaneously, and the communication environment is complex and variable, with high maintenance costs for communication equipment. On the one hand, IRS can improve the performance of wireless communication and wireless power transfer by intelligent adjustment of the wireless channel. On the other hand, providing power supply to IRS by wireless power transfer can solve the problem of inconvenient power supply of IRS and reduce the maintenance costs. This paper investigated the throughput optimization of a dual wireless powered IRS-assisted wireless communication system, in which the multi-antenna base station (BS) first transmitted energy to two IRS, and then transmitted information to multiple single-antenna users with the help of the IRS. For the cases without and with channel blocking between the BS and the IRS, this paper proposed two-phase and three-phase transmission modes, respectively. It maximized the throughputs of these modes by optimizing the transmit beamforming of the BS, the reflect beamforming of the IRS, and the time allocation of different phases, subject to the constraints of the maximum transmit power of the BS, the minimum rate of the users, the duration of each phase, and the unit-module reflection coefficients of the IRS. Since the resulting optimization problems were convex and the optimization variables were highly coupled, it was difficult to find their optimal solution directly. This paper proposed two optimization algorithms based on the alternating optimization, successive convex approximation, and semi-definite relaxation techniques to obtain high-quality sub-optimal solutions. Simulation results show that the proposed algorithms achieve significantly better throughput performance compared to some benchmark algorithms and demonstrate the suitable application scenarios of the two-phase and three-phase transmission modes. [ABSTRACT FROM AUTHOR]

Cognitive radio (CR) is a technological invention to improve the spectrum performance in wireless networks linked to a cooperative spectrum sensing strategy. This performance, however, can be degraded by adversary secondary nodes which intentionally aim to falsify the decisions on the presence of primary nodes. The purpose of this paper is to model and investigate the impact of adversary secondary nodes in the context of cooperative spectrum sensing in CRs in terms of false alarms and missed detection probabilities. The impact of these probabilities is then included analytically in the throughput formula accordingly, under the assumption of adopting Slotted-ALOHA protocol by the secondary nodes. Then an intrusion detection system is proposed with the goal to maximize the network throughput with adversary secondary users. Simulations are carried out to test the validation of the proposed method and to evaluate the achieved results with the well-known decision rules. [ABSTRACT FROM AUTHOR]

In this paper, we derive the performance of wireless communications using wind energy harvesting. We study the single-user and multi-user scenarios using Non Orthogonal and Orthogonal Multiple Access (NOMA and OMA). We derive the statistics of SINR when energy harvesting uses the wind.

Recently, the combination of cognitive radio networks with the nonorthogonal multiple access (NOMA) approach has emerged as a viable option for not only improving spectrum usage but also supporting large numbers of wireless communication connections. However, cognitive NOMA networks are unstable and vulnerable because multiple devices operate on the same frequency band. To overcome this drawback, many techniques have been proposed, such as optimal power allocation and interference cancellation. In this paper, we consider an approach by which the secondary transmitter (STx) is able to find the best licensed channel to send its confidential message to the secondary receivers (SRxs) by using the NOMA technique. To combat eavesdroppers and achieve reasonable performance, a power allocation policy that satisfies both the outage probability (OP) constraint of primary users and the security constraint of secondary users is optimized. The closed-form formulas for the OP at the primary base station and the leakage probability for the eavesdropper are obtained with imperfect channel state information. Furthermore, the throughput of the secondary network is analyzed to evaluate the system performance. Based on that, two algorithms (i.e., the continuous genetic algorithm (CGA) for CR NOMA (CGA-CRN) and particle swarm optimization (PSO) for CR NOMA (PSO-CRN)), are applied to optimize the throughput of the secondary network. These optimization algorithms guarantee not only the performance of the primary users but also the security constraints of the secondary users. Finally, simulations are presented to validate our research results and provide insights into how various factors affect system performance.

Low power wide area network (LPWAN) technologies have become an integral part of Internet-of-Things (IoT) applications due to their ability to meet key requirements such as long range, low cost, massive device numbers, and low energy consumption. Among all available LPWAN technologies, LoRaWAN has garnered significant interest from both industry and academia. Due to wide communication range of LoRaWAN, the coverage of gateway (GW) may overlap and the packet transmissions from nodes in the overlapping area would collide, which deteriorates the network performance. How to tune the backoff parameter settings to reduce collision and maximize the network throughput in multi-GW LoRaWAN is still an open issue. To solve this issue, in this paper, we first propose a low-complexity model for multi-GW LoRaWAN, which divides nodes into different groups based on GWs they can communicate with. Key performance metrics, i.e., the network throughput, access delay and the probability of successful transmission, are derived as functions of backoff parameter and input rate. As the throughput performance crucially depends on whether each group is saturated or not, we propose an iterative algorithm to analyze the stability of the multi-GW LoRaWAN, based on which we further develop an iterative algorithm to tune the backoff parameter of each group iteratively for the network throughput optimization. The simulation results validate the effectiveness of the proposed algorithms and reveal the trade-off between fairness and efficiency.

Hardware is often required to support fast search and high-throughput applications. Consequently, the performance of search algorithms is limited by storage bandwidth. Hence, the search algorithm must be optimized accordingly. We propose a CostCounter (CC) algorithm based on cuckoo hashing and an Improved CostCounter (ICC) algorithm. A better path can be selected when collisions occur using a cost counter to record the kick-out situation. Our simulation results indicate that the CC and ICC algorithms can achieve more significant performance improvements than Random Walk (RW), Breadth First Search (BFS), and MinCounter (MC). With two buckets and two slots per bucket, under the 95% memory load rate of the maximum load rate, CC and ICC are optimized on read-write times over 20% and 80% compared to MC and BFS, respectively. Furthermore, the CC and ICC algorithms achieve a slight improvement in storage efficiency compared with MC. In addition, we implement RW, MC, and the proposed algorithms using fine-grained locking to support a high throughput rate. From the test on field programmable gate arrays, we verify the simulation results and our algorithms optimize the maximum throughput over 23% compared to RW and 9% compared to MC under 95% of the memory capacity. The test results indicate that our CC and ICC algorithms can achieve better performance in terms of hardware bandwidth and memory load efficiency without incurring a significant resource cost. [ABSTRACT FROM AUTHOR]

In this paper, we derive the performance of wireless communications using wind energy harvesting. We study the single-user and multi-user scenarios using Non Orthogonal and Orthogonal Multiple Access (NOMA and OMA). We derive the statistics of SINR when energy harvesting uses the wind. [ABSTRACT FROM AUTHOR]

We introduce three optimized scheduling algorithms for dispersed computing and present JupiterTP, a real-world system built on k8s and the prior Jupiter system, enabling end-to-end computation on distributed clusters. Distinguishing itself from traditional throughput optimization approaches that focus on theory and simulations, our work is the first implementation of such an end-to-end system capable of handling arbitrary DAGs across diverse computing networks, including public clouds, IoT systems, and edge networks. Beyond mere scheduling, JupiterTP integrates profilers, execution, and orchestration engines, offering unified interfaces for additional scheduling algorithm integrations. The system's performance is tested on real clusters and real applications, compared to prior work that relied on simulations alone. We make JupiterTP available to the community as open-source software at https://github.com/ANRGUSC/JupiterTP. • Presented three innovative scheduling algorithms designed to enhance throughput in dispersed computing. • Introduced a real-world system implementation—JupiterTP, extending beyond theoretical studies. • JupiterTP can handle any Directed Acyclic Graph (DAG) structures, ensuring adaptability across various network architectures. It is designed to operate on diverse platforms, including public/private clouds, IoT networks, and edge networks. • JupiterTP includes profilers, execution engines, and orchestration engines, supporting end-to-end pipelined computations. The system is built to incorporate other scheduling algorithms, providing a flexible platform for future advancements. • Demonstrated JupiterTP's effectiveness through rigorous testing on actual computing clusters, enhanced credibility by moving beyond simulations, made the system available as an open-source tool, promoting community engagement and collaboration. [ABSTRACT FROM AUTHOR]

We investigate a location-allocation-routing problem where trucks deliver goods from a central production facility to a set of warehouses with fixed locations and known demands. Due to limited capacities congestion occurs and results in queueing problems. The location of the center is determined to maximize the utilization of the given resources (measured in throughput) and the minimal number of trucks is determined to satisfy the overall demand generated by the warehouses. Main results for this integrated decision problem on strategic and tactical/operational level are: (i) The location decision is reduced to a standard Weber problem with weighted distances. (ii) The joint decision for location and fleet size is separable. (iii) The location of the center is robust against perturbations of several system parameters on the operational/tactical level. Additionally, we consider minimization of travel times as optimization target. By numerical examples we demonstrate the consequences of neglecting available information on long-term (rough) demand structure.

With the development of the Internet of Things (IoT), the amount of transmitted data is getting huge. Traditional passive UHF CRFID Tag currently adopts 2ASK or 2PSK backscatter modulation, and each symbol conveys 1 bit of information. Still, this modulation has the problems of low throughput, poor real-time, and low energy utilization. This paper proposes an optimal 4 Quadrature Amplitude Modulation (4QAM) backscatter scheme at 920 MHz, which can increase the throughput and simultaneously improve energy utilization. In this optimal scheme, the reflection coefficient modulus | Γ | is closely related to the energy. The low power of the passive tag requires that the modulation circuit's design consider how to choose | Γ | o p t (optimal reflection coefficient modulus). Therefore, we build the energy model of CRFID to explore the | Γ | o p t at different symbol rates and use the | Γ | o p t to design a more reasonable constellation diagram for passive tags. Finally, this paper analyzes the decoding of multinary backscatter signals, and an improved Kmeans algorithm can solve the cluster offset problem at high symbol rates. After experiments, | Γ | o p t is around 0.67, the maximum throughput of 4QAM backscatter modulation can reach 16 Mbit/s, and the power consumption is 4.38 mW. Compared with the traditional binary modulation, the tag in this paper can double the throughput at the same symbol rate. [ABSTRACT FROM AUTHOR]

The semiconductor etch cluster facility is the most used facility platform in the semiconductor manufacturing process. Optimizing cluster facilities can depend on production schedules and can have a direct impact on productivity. According to the diversity of semiconductor processes, the complexity of optimization is also increasing. Various optimization methods have been studied in many papers for optimizing such a complex cluster facility. However, there is a lack of discussion of how these methods can apply to practical semiconductor manufacturing fabs and the actual performance results. Even now, data analysis and optimal parameter derivation for maximizing the productivity of cluster manufacturing in semiconductor manufacturing fabs are continuing. In this study, we propose an automated method for data collection and analysis of the cluster, which used to be done manually. In addition, the derivation of optimization parameters and application to facilities are addressed. This automated method could improve the manual analysis methods, such as simulation through data analysis using machine learning algorithms. It could also solve the inefficiency caused by manual analysis performed within the network inside the semiconductor manufacturing fabs. [ABSTRACT FROM AUTHOR]

Network densification has led to a renewed emphasis on means to improve aggregate network throughput for next-gen (High Efficiency) WLANs. The introduction of BSS color feature in support of enhanced spatial reuse sets IEEE 802.11ax apart from legacy WLAN. BSS color enables stations within a network to identify whether a transmission originates from an overlapped BSS and provides an option for mitigating the exposed node problem by allowing it to transmit (over the on-going OBSS transmission) albeit with reduced transmit power. As a result, distances between networks can be reduced, thereby improving the area spectral efficiency. To the best of our knowledge, this is the first work to develop an analytical model for IEEE 802.11ax spatial reuse that provides useful rules for optimizing network area throughput. We show that the spatial reuse gain is tightly linked to the interference range properties of each BSS in the network. Further, the analytical model predictions are validated and expanded via definitive ns-3 simulation results exploiting a recently concluded upgrade to 802.11 WLAN network stack (that implemented the necessary spatial reuse features). [ABSTRACT FROM AUTHOR]

Nowadays, network softwareization is an emerging techno-economic transformation trend that significantly impacts how enterprises deploy their network services. As an essential technology in this trend, Network Function Virtualization (NFV) enables scalable and inexpensive network services by flexibly instantiating Virtualized Network Functions (VNFs) on commercial-off-the-shelf devices. In this paper, we focus on the VNF placement problem in NFV-enabled networks, aiming to maximize the number of accepted Service Function Chain Requests (SFCRs) while guaranteeing their delay requirements. To improve resource utilization efficiency, we take account of Fundamental Resource Overheads (FROs) and the shareability of VNF instances. We mathematically formulate the VNF placement problem and propose the Throughput Optimization and Delay Guarantee (TO-DG) heuristic solution, consisting of an affinity-based SFCR mapping algorithm and a VNF request adjustment algorithm. The evaluation results show that the performance of TO-DG is near to results derived by ILP solver for small scale problems. Moreover, TO-DG obtains higher network throughput than contrasting schemes in different scenarios and significantly improves network resource utilization. [ABSTRACT FROM AUTHOR]

Timing violations in high performance communication channels in system-on-chips (SoC) may occur in the late stages of the physical design process. To address that, latency insensitive systems (LISs) employ pipelining in the communication channels through the insertion of relay stations. Although the functionality of an LIS is robust with respect to the communication latencies, imbalances in relay station insertion may degrade the throughput of the system. While having a large number of buffer queues can eliminate such performance loss, the system may not have adequate area to accommodate these buffers. The problem of buffer queue sizing for maximizing throughput while meeting buffer area constraints has been solved using a mixed-integer linear program (MILP) formulation; however, such an approach is not scalable. In this work, we formulate the buffer queue sizing problem as a parameterized graph optimization problem where for every communication channel there is a parameterized edge with buffer counts as the edge weight. We then use a minimum cycle mean algorithm to determine from which edges buffers can be removed safely. Experimental results on large LISs suggest that the proposed approach is scalable. Moreover, quality of the solutions, in terms of the throughput and the size of buffer queues, is observed to be as good as that of the MILP-based approach. [ABSTRACT FROM AUTHOR]

The paper is the first work that considers a constrained feedback control strategy to navigate an unmanned aerial vehicle (UAV) from a given starting point to a given terminal point while harvesting solar energy and providing a wireless communication service for ground users. Wireless communication channels are stochastic and cannot be known off-line, making the problem of off-line UAV path planning for wireless communication as considered in most existing works less meaningful. We consider the problem of navigating a solar-powered UAV from a starting point to a terminal point to harvest solar energy while serving the two-way communication between multiple pairs of ground users in a complex terrain. The objective is to jointly optimize the UAV’s flight time and its flight path by trading-off between the harvested energy and power consumption subject to the ground users’ minimum throughput requirement. We develop a new model predictive control (MPC) technique to address this problem. Namely, based on the well-known statistics of the air-to-ground (A2G) and ground-to-air (G2A) wireless channels, a predictive control model is proposed at each time-instant, which leads to an optimization problem over a receding horizon for the control design. This problem is non-convex due to the involvement of various optimization variables, which is then solved via novel convex iterations. Simulation results show the merits of the proposed algorithm. The results obtained by the proposed algorithm match with the benchmark non-MPC and offline-MPC approaches. [ABSTRACT FROM AUTHOR]

This paper studies a full-duplex symbiotic radio network, where a full-duplex access point (FAP) transmits downlink orthogonal frequency division multiplexing signals to a legacy user (LU) and simultaneously receives signals backscattered from multiple passive backscatter devices (BDs) with capability of radio-frequency energy harvesting. A non-orthogonal-multiple-access enhanced dynamic-time-division-multiple-access (NOMA-DTDMA) transmission scheme is proposed to exploit the channel dynamics and further improve the spectrum efficiency. In order to maximize the throughput performance and ensure BD fairness, we maximize the minimum throughput among all BDs by jointly optimizing the FAP's subcarrier power allocation, the BDs' backscatter time allocation and power reflection coefficients, subject to the LU's throughput requirement, the BDs' harvested energy requirements, and other practical constraints. An efficient iterative algorithm is proposed to solve the formulated non-convex problem, by utilizing the block coordinated descent and successive convex optimization techniques. The convergence and complexity of the proposed algorithm are also analyzed. Numerical results show that the proposed NOMA-DTDMA scheme significantly outperforms the benchmark scheme of dynamic TDMA in terms of both throughput performance and BD fairness. Also, the trade-off performances between the BDs' throughput and the LU's throughput requirement as well as the BDs' harvested energy requirements are numerically verified.

A significant number of processing cores in any many-core systems nowadays and likely in the future have to be switched off or forced to be idle to become dark cores, in light of ever increasing power density and chip temperature. Although these dark cores cannot make direct contributions to the chip's throughput, they can still be allocated to applications currently running in the system for the sole purpose of heat dissipation enabled by the temperature gradient between the active and dark cores. However, allocating dark cores to applications tends to add extra waiting time to applications yet to be launched, which in return can have adverse implications on the overall system performance. Another big issue related to dark core allocation stems from the fact that application characteristics are prone to undergo rapid changes at runtime, making a fixed dark core allocation scheme less desirable. In this paper, a runtime dark core allocation and dynamic adjustment scheme is thus proposed. Built upon a dynamic programming network (DPN) framework, the proposed scheme attempts to optimize the performance of currently running applications and simultaneously reduce waiting times of incoming applications by taking into account both thermal issues and geometric shapes of regions formed by the active/dark cores. The experimental results show that the proposed approach achieves an average of 61% higher throughput than the two state-of-the-art thermal-aware runtime task mapping approaches, making it the runtime resource management of choice in many-core systems.

The semiconductor etch cluster facility is the most used facility platform in the semiconductor manufacturing process. Optimizing cluster facilities can depend on production schedules and can have a direct impact on productivity. According to the diversity of semiconductor processes, the complexity of optimization is also increasing. Various optimization methods have been studied in many papers for optimizing such a complex cluster facility. However, there is a lack of discussion of how these methods can apply to practical semiconductor manufacturing fabs and the actual performance results. Even now, data analysis and optimal parameter derivation for maximizing the productivity of cluster manufacturing in semiconductor manufacturing fabs are continuing. In this study, we propose an automated method for data collection and analysis of the cluster, which used to be done manually. In addition, the derivation of optimization parameters and application to facilities are addressed. This automated method could improve the manual analysis methods, such as simulation through data analysis using machine learning algorithms. It could also solve the inefficiency caused by manual analysis performed within the network inside the semiconductor manufacturing fabs.

Interference management has always been a research hotspot in the field of wireless communications. Currently, researches based on interference management mostly focus on an interference management technology, and few studies have combined multiple interference management technologies. This paper proposes an interference management technology that combines Interference Alignment (IA) and Successive Interference Cancellation (SIC) technologies. First, we determine the routing path of the session with the goal to minimize hops and interference and establish the IA-SIC mathematics model in multi-hop networks. Based on this model, a cross-layer optimization framework for multi-hop networks is developed. The goal is to make full use of the advantages of IA and SIC to improve the end-to-end data transmission rate of multi-hop sessions. To evaluate the performance of our algorithm in multi-hop networks, we compare the performance of the network using IA-SIC with that without SIC. Simulation results show that using IA-SIC can significantly increase the throughput of the communication sessions. [ABSTRACT FROM AUTHOR]

The 802.11ax standard final specification is expected in 2019, however first parameters are just released. The target of the new standard is four times improvement of the average throughput within the given area. This standard is dedicated for usage in dense environment such as stadiums, means of municipal communication, conference halls and others. The main target is to support many users at the same time with the single access point. The question arises if the new standard will have higher throughput then previous ones in the single user mode. The author calculated the maximal theoretical throughput of the 802.11ax standard and compared the results with the throughput of older 802.11 standards such as 802.11n and 802.11ac. The new he-wifi-network example included in the ns-3.27 release of the NS-3 simulator was used to simulate the throughput between the access point and the user terminal. The results indicate that in some conditions the 802.11ac standard has higher throughput than the new 802.11ax standard.

Unmanned aerial vehicle (UAV)-aided two-way relaying networks are designed, where a UAV is deployed to assist multiple pairs of users in their information exchange. There are two basic approaches for the user pairs’ information exchange within a single time slot via the UAV relay. The first approach is based on full-duplex, where all participants operate in the full-duplex mode to transmit and receive signals simultaneously. However, all transceivers have to operate in the face of severe self-interference, which cannot be completely suppressed. The second approach is based on conventional half-duplex, where the users send their information to the UAV within a certain fraction of the time slot, and the UAV relays them within the remaining fraction to avoid the self-interference. In either approach, the joint bandwidth and power allocation maximizing the sum information exchange throughput under realistic resource and user throughput constraints poses a complex nonconvex problem. New inner approximations are proposed for developing path-following algorithms for their computation. Our numerical results show that the time-fraction-based half-duplex approach clearly outperforms the high-complexity full-duplex approach. [ABSTRACT FROM AUTHOR]

In this paper, we derive the throughput of non-orthogonal multiple access NOMA with adaptive transmit power for cognitive radio networks (CRN). The secondary source and relay adapt their power to not generate high interference at primary destination. We evaluate the packet error probability and the throughput at the packet level, while previous studies compute it at the symbol level. We also optimize the powers allocated to near and far users to maximize the throughput of CRN-NOMA. Besides, optimal power allocation of CRN-NOMA with adaptive transmit power has not been yet suggested and previous studies deal with fixed transmit power. [ABSTRACT FROM AUTHOR]

In this letter, we propose an energy harvesting (EH) based incremental relaying cooperative non-orthogonal multiple access (IR-EH-NOMA) protocol, considering two user downlink network. We derive analytical expressions for the system throughput of IR-EH-NOMA network and conventional cooperative relaying NOMA network with EH (CR-EH-NOMA network) in the delay limited transmission mode, considering imperfect successive interference cancellation (i-SIC) and maximal ratio combining (MRC). We also derive expressions for the optimal power splitting (OPS) factors at the relay that maximizes the asymptotic throughput of the considered systems. Numerical and simulation results are described to establish the supremacy of IR-EH-NOMA system over CR-EH-NOMA system in terms of outage and throughput. [ABSTRACT FROM AUTHOR]

D2D communications are considered as effective means to achieve vehicle-to-vehicle (V2V) communications, which can be used to improve the performance in vehicular ad hoc networks (VANETs). In this paper, we investigate the resource allocation and interference management based on clustering mechanism in the D2D communications under-laying VANETs aiming to optimize resource utilization efficiency. Firstly, given the transmission resources of intra-cluster for D2D communications, we design a vehicle clustering algorithm to improve intra-cluster resource efficiency and nullify the intra-cluster interference. Secondly, by analyzing the wireless service in VANETs, a cooperative communication scheme with optimized resource utilization is proposed to maximize intra-cluster spectral efficiency. Thirdly, when uplink spectrums are shared, an interference neutralization (IN) model is developed to manage inter-cluster interference, while each vehicle is equipped with one antenna and there is no extra antenna for interference cancellation. In accordance with the IN model, a cross-layer optimization frame for multi-hop VANETs is proposed to maximize the end-to-end throughput of multiple coexisting communication sessions. To evaluate the performance, we select the method without cooperative communication and IN as a benchmark and compare it against the method with cooperative communication only and the method with both cooperative communication and IN. The simulation results show that the method that uses cooperative communication and IN can dramatically increase the throughput. Furthermore, the throughput gains increase along with vehicle density growing in VANETs. [ABSTRACT FROM AUTHOR]

Network coding has been proposed to increase network throughput by combining packets at intermediate nodes. In this paper, we propose a joint throughput optimization method for a multirate multichannel multiradio wireless network with network coding. We propose algorithms for congestion control, joint distributed rate control, and heuristic resource allocation to solve the utility maximization problem in the wireless network environment with network coding. To minimize performance degradation generated by packet loss, we propose a congestion control algorithm. Furthermore, we present a sophisticated resource allocation algorithm which finds a node for the worst traffic jam and selects a session that needs to be first transferred in the node. Then, the node is allocated with the channels and radios for the packets of the selected session. Furthermore, a rate selection method is presented to determine the rate of each node. The simulation results show that the proposed method achieves the optimal performance in a multirate multichannel multiradio wireless network. [ABSTRACT FROM AUTHOR]

LoRa is an extremely flexible low-power wide-area technology that enables each IoT node to individually adjust its transmission parameters. Consequently, the average per-node throughput of LoRa-based networks has been mathematically formulated and the optimal network-level configuration derived. For end nodes to update their transmission parameters, this centrally computed global configuration must then be disseminated by LoRa gateways. Unfortunately, the regional limitations imposed on the usage of ISM bands—especially those related to the maximum utilization of the band—pose a potential handicap to this parameter dissemination. To solve this problem, a set of tools from the machine learning field have been used. Precisely, the updating process has been formulated as a reinforcement learning (RL) problem whose solution prescribes optimal disseminating policies. The use of these policies together with the optimal network configuration has been extensively analyzed and compared to other well-established alternatives. Results show an increase of up to 147% in the accumulated per-node throughput when our RL-based approach is employed. [ABSTRACT FROM AUTHOR]

Recently, the mobile social networks, which consider both social relationship between users and mobile communication networks, have been received great attention. In this paper, we consider the trust degree of node as the social relationship for the cooperative communication networks. In contrast to the existing works that consider the case of the perfect trust degree information, for the case that transmitter has an imperfect trust degree information, we propose an imperfect trust degree based cooperative communication technique that maximizes a throughput. We first model the imperfect trust degree information as a probability distribution and derive the outage probability using the probability distribution. Then, we propose the transmission scheme that maximizes the throughput, which consider both outage probability and transmission rate. The simulation results show that the proposed cooperative transmission scheme outperforms the conventional scheme in terms of the throughput. [ABSTRACT FROM AUTHOR]

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Obtaining optimal data transfer performance is of utmost importance to today’s data-intensive distributed applications and wide-area data replication services. Tuning application-layer protocol parameters such as pipelining, parallelism, and concurrency can significantly increase efficient utilization of the available network bandwidth as well as the end-to-end data transfer performance. However, determining the best settings for these parameters is a challenging problem, as network conditions can vary greatly between sites and over time. Poor protocol tuning can cause either under- or over-utilization of network resources and thus degrade transfer performance. In this paper, we present three novel algorithms for application-layer parameter tuning and transfer scheduling to maximize transfer throughput in wide-area networks. Our algorithms use heuristics to tune the level of control channel pipelining (for small file optimization), the number of parallel data streams per file (for large file optimization), and the number of concurrent file transfers to increase I/O throughput (for all types of files). The proposed algorithms improve the transfer throughput up to 10x compared to the baseline and 7x compared to the state-of-the-art solutions. We also propose adaptive tuning to adjust the values of parameters based on real-time observations. The results show that adaptive tuning can further improve transfer throughput by up to 24% compared to the heuristic approach. [ABSTRACT FROM AUTHOR]