Throughput Optimization Problem Research Articles

Throughput‐delay tradeoff for opportunistic spectrum access in cognitive radio networks

AbstractThe internet of things accelerates the wireless connections of massive devices to provide all kinds of new services, and thus intensifies the spectrum scarcity and access delay. Cognitive radio (CR) technology brings a solution for the issue. As a typical CR scheme, opportunistic spectrum access (OSA) has been addressed widely in the past decade. However, the tradeoff between two key indexes, the throughput of cognitive radio network (CRN) and the delay of secondary user (SU), is rarely mentioned so far, which ignites the authors’ work in this paper. Taking into account a channel handoff (CH) based multi‐channel OSA scenario, the authors first analyze the opportunistic transmission performance of SU, and model the throughput of CRN as well as the handoff delay of SU. Then, the authors build up a delay‐constraint throughput optimization problem, and thus formulate the throughput‐delay tradeoff for OSA. Finally, the optimal traffic rates of SU for a good throughput‐delay tradeoff are derived according to maximizing the throughput of CRN. Theoretical and simulated results show that to enhance the throughput and to reduce the delay do not conflict always. By well adjusting the traffic rates of SU according to the traffic rates of primary user, the throughput of CRN can be improved while the handoff delay can be kept under a given level.

Backhaul Capacity-Limited Joint User Association and Power Allocation Scheme in Ultra-Dense Millimeter-Wave Networks

Millimeter-wave (mmWave) communication is considered a promising technology for fifth-generation (5G) wireless communications systems since it can greatly improve system throughput. Unfortunately, because of extremely high frequency, mmWave transmission suffers from the signal blocking problem, which leads to the deterioration of transmission performance. In this paper, we solve this problem by the combination of ultra-dense network (UDN) and user-centric virtual cell architecture. The deployment of dense small base stations (SBSs) in UDN can reduce transmission distance of signals. The user-centric virtual cell architecture mitigates and exploits interference to improve throughput by using coordinated multipoint (CoMP) transmission technology. Nonetheless, the backhaul burden is heavy and interbeam interference still severe. Therefore, we propose a novel iterative backhaul capacity-limited joint user association and power allocation (JUAPA) scheme in ultra-dense mmWave networks under user-centric virtual cell architecture. To mitigate interference and satisfy quality of service (QoS) requirements of users, a nonconvex system throughput optimization problem is formulated. To solve this intractable optimization problem, we divide it into two alternating optimization subproblems, i.e., user association and power allocation. During each iteration, a many-to-many matching algorithm is designed to solve user association. Subsequently, we perform power allocation optimization using a successive convex approximation (SCA) algorithm. The results confirm that the performance of the proposed scheme is close to that of the exhaustive searching scheme, which greatly reduces complexity, and clearly superior to that of traditional schemes in improving system throughput and satisfying QoS requirements.

Intelligence Reflecting Surface-Aided Integrated Data and Energy Networking Coexisting D2D Communications

In this paper, we consider an integrated data and energy network and D2D communication coexistence (DED2D) system. The DED2D system allows a base station (BS) to transfer data to information-demanded users (IUs) and energy to energy-demanded users (EUs), i.e., using a time-fraction-based information and energy transfer (TFIET) scheme. Furthermore, the DED2D system enables D2D communications to share spectrum with the BS. Therefore, the DED2D system addresses the growth of energy and spectrum demands of the next generation networks. However, the interference caused by the D2D communications and propagation loss of wireless links can significantly degrade the data throughput of IUs. To deal with the issues, we propose to deploy an intelligent reflecting surface (IRS) in the DED2D system. Then, we formulate an optimization problem that aims to optimize the information beamformer for the IUs, energy beamformer for EUs, time fractions of the TFIET, transmit power of D2D transmitters, and reflection coefficients of the IRS to maximize IUs’ worse throughput while satisfying the harvested energy requirement of EUs and D2D rate threshold. The max-min throughput optimization problem is computationally intractable, and we develop an alternating descent algorithm to resolve it with low computational complexity. The simulation results demonstrate the effectiveness of the proposed algorithm.

Performance Analysis and Optimal Cooperative Cluster Size for Randomly Distributed Small Cells Under CAN Protocol

The ease of imposing multicell coordination mechanisms to enhancing system spectrum efficiency (SE) and performance estimate is one of the key advantages of cloud/centralized control area networks (CAN). Enormous number of cooperative cells should theoretically result in a higher SE, but they may also cause considerable delays due to extra channel state information (CSI) feedback and joint processing computing needs at the cloud data centre, resulting in performance deficit. I partition the network into numerous clusters of cooperating tiny cells and create a throughput optimization problem to study the impact of delays on throughput gains. As a function of cluster size, I figure various delay factors and the network's sum-rate, treating cluster size as the fundamental optimization variable. For both linear and planar network installations, I treat both base station and user geometric locations as random variables in my study. On the basis of the homogeneous Poisson point processing (PPP)model, the output SINR (signal-to-interference- plus-noise ratio) and ergodic sum-rate are calculated. The sum-rate optimization problem is formulated and solved in terms of cluster size. The suggested analytical framework may be used to precisely evaluate the performance of practical cloud-based small cell networks via clustered cooperation, according to simulated study.

RIS-Aided Zero-Forcing and Regularized Zero-Forcing Beamforming in Integrated Information and Energy Delivery

This paper considers a network of a multi-antenna array base station (BS) and a reconfigurable intelligent surface (RIS) to deliver both information to information users (IUs) and power to energy users (EUs). The RIS links the connection between the IUs and the BS as there is no direct path between the former and the latter. The EUs are located nearby the BS in order to effectively harvest energy from the high-power signal from the BS, while the much weaker signal reflected from the RIS hardly contributes to the EUs’ harvested energy. To provide reliable links for all users over the same time-slot, we adopt the transmit time-switching (transmit-TS) approach, under which information and energy are delivered over different time-slot fractions. This allows us to rely on conjugate beamforming for energy links and zero-forcing/regularized zero-forcing beamforming (ZFB/RZFB) and on the programmable reflecting coefficients (PRCs) of the RIS for information links. We show that ZFB/RZFB and PRCs can be still separately optimized in their joint design, where PRC optimization is based on iterative closed-form expressions. We then develop a path-following algorithm for solving the max-min IU throughput optimization problem subject to a realistic constraint on the quality-of-energy-service in terms of the EUs’ harvested energy thresholds. We also propose a new RZFB for substantially improving the IUs’ throughput.

Throughput Analysis of NOMA-ALOHA

This paper considers from the perspective of MAC layer, a throughput maximization problem of NOMA-ALOHA, ALOHA implemented over NOMA (non-orthogonal multiple access), that can decode simultaneously transmitted multiple packets via successive interference cancellation (SIC) if each of the multiple packets has a different power level from those of others. We start with a throughput optimization problem for two power levels and extend it to a problem for a general number of power levels. For the case of two power levels, we find upper and lower bounds of the maximum throughput and suggest a practical sub-optimal transmission probability whose throughput is close to the maximum. The maximum throughput of NOMA-ALOHA is at least 1.774 times higher than that of traditional slotted ALOHA in our numerical example. We extend the optimization problem into a general one with arbitrary number of power levels. We numerically analyze the performance through a relaxed problem of the original one with high complexity. Our numerical analysis shows that the maximum throughput of NOMA-ALOHA with three power levels is 2.33 times higher than simple ALOHA’s maximum throughput and that adding one power level to NOMA-ALOHA with two power levels enhances throughput at least by more than 28 percent.

Impact of Self-Energy Recycling and Cooperative Jamming on SWIPT-Based FD Relay Networks With Secrecy Constraints

This paper investigates the secrecy performance of a power splitting-based simultaneous wireless information and power transfer cooperative relay network in the presence of an eavesdropper. The relay is considered to operate in full-duplex (FD) mode to perform both energy harvesting and information decoding simultaneously. To accomplish that, the relay is assumed to employ two rechargeable batteries, which switch between power supplying mode and charging mode at each transmission block. We also assume that the self-interference inherent of the FD mode is not completely suppressed. Therefore, it is assumed that, after some stages of passive and active self-interference cancellation, there is still a residual self-interference (RSI). A portion of this RSI (remaining after passive cancellation) is recycled for energy harvesting. In order to improve the system secrecy performance, it is considered that the relay can split its transmit power to send the information signal and to emit a jamming signal to degrade the eavesdropper’s channel. The secrecy performance is evaluated in terms of the secrecy outage probability and the optimal secrecy throughput. Tight-approximate and asymptotic expressions are obtained for the secrecy outage probability, and the particle swarm optimization method is employed for addressing the secrecy throughput optimization problem. From numerical results, we show that the secrecy performance can be increased depending on the self-energy recycling channel condition. Finally, our derived expressions are validated via Monte Carlo simulations.

An Algorithmic Study to Maximize 5G Network Throughput Based on the Markov Decision Process

In this article, the network throughput optimization problem is investigated based on the theory of Markov decision process, combining the device-to-device direct selection problem with the finite stage discount MDP model problem. First, models for the device-to-device communication selection using MDP are built; second, the optimal mode selection strategy is derived using a finite stage backward iterative algorithm; and finally, the given mode selection strategy is evaluated by conducting a large number of simulation experiments. The results show that the MDP-based mode selection method proposed in this article has better performance in maximizing throughput and can yield better mode selection strategies with the advantage of obtaining larger system throughput.

Achieving per-flow satisfaction with multi-path D2D

Device-to-Device (D2D) communication allows for users placed in a cell to establish direct connections with each other using several connection modes. In this paper, we propose Multi-Path D2D (MPD2D), a mathematical optimization framework that accounts for the availability of D2D modes under the requirements dictated by a process of flow requests. MPD2D selects the combination of cellular and D2D links that boosts network performance as much as possible. We consider Underlay and Overlay as Inband D2D modes reusing cellular frequencies with scheduled resources and the Outband D2D mode exploiting WLAN frequencies and the 802.11 random access scheme to complement cellular resources. We model throughput, energy consumption, interference, and per-flow network requirements, so to define a network utility function that accounts for throughput and power efficiency. Moreover, we formulate a user satisfaction metric that accounts for the history of users within the cell. Integrating such a metric in a throughput optimization problem is lightweight yet very effective to drive towards almost complete fairness. Our optimization scheme is formulated as a Binary Non-Linear Program, which results in higher throughput performance in comparison to other state-of-the-art solutions we have tested. Finally, we propose two effective heuristics, whose performance is near-optimal, whereas their complexity scales polynomially with the number of users.

Throughput Optimization for Cognitive UAV Networks: A Three-Dimensional-Location-Aware Approach

An effective throughput optimization problem is studied in cognitive unmanned aerial vehicle networks (CUAVNs), where a leading unmanned aerial vehicle (LUAV) serving as an aerial mobile access point and sensing node receives data from a group of following unmanned aerial vehicles (FUAVs). For the case of single FUAV, the joint optimizing of three-dimensional location and spectrum sensing duration of LUAV is formulated to maximize FUAV’s throughput. The original non-convex problem is tackled by iteratively optimizing two convex sub-problems. For the case of multiple FUAVs, the preferable LUAV location of each FUAV can be individually calculated by using the algorithm developed for the single FUAV case, and then the final LUAV location is obtained by solving a well-formulated weighted sum problem. Numerical results show that our proposed single FUAV algorithm and throughput weighted sum based on multiple FUAVs algorithm can approach the optimal one with only 2% performance loss. Moreover, compared to the only time-dimension spectrum sensing scheme, the effective throughput of FUAV rises about 500% with the proposed schemes.

Max-Min Throughput in Hybrid of Wireless Powered NOMA and Backscatter Communications

This article proposes a hybrid of backscatter communication (BackCom) and wireless powered non-orthogonal multiple access (NOMA) network for Internet of Things (IoT) applications, which consists of one power beacon (PB), multiple energy-constrained IoT devices, and one information receiver (IR). In the proposed network, the IoT devices harvest energy and backscatter their information to the IR in turn when PB broadcasts energy signals, and then use the harvested energy to transmit information to the IR via uplink NOMA when PB keeps silent. Considering the non-overflowing energy constraint, a max-min throughput optimization problem is formulated to ensure the throughput fairness among different IoT devices by jointly optimizing the time resource for operating BackCom and uplink NOMA, the reflection coefficient of BackComs, the transmit power for uplink NOMA and the transmit power of the PB. Although the formulated problem is non-convex and challenging to solve, we first transform the original non-convex problem into an equivalent convex one with the aid of an inequality transformation approach and introducing several kinds of auxiliary variables, and then devise a two-layer iterative algorithm to obtain the optimal resource allocation. Simulation results are provided to verify the convergence of the devised iterative algorithm and validate that the proposed scheme achieves the highest max-min throughput by comparing it with three baseline schemes.

Minimum Throughput Maximization for Multi-UAV Enabled WPCN: A Deep Reinforcement Learning Method

This paper investigates joint unmanned aerial vehicle (UAV) trajectory planning and time resource allocation for minimum throughput maximization in a multiple UAV-enabled wireless powered communication network (WPCN). In particular, the UAVs perform as base stations (BS) to broadcast energy signals in the downlink to charge IoT devices, while the IoT devices send their independent information in the uplink by utilizing the collected energy. The formulated throughput optimization problem which involves joint optimization of 3D path design and channel resource assignment with the constraint of flight speed of UAVs and uplink transmit power of IoT devices, is not convex and thus is extremely difficult to solve directly. We take advantage of the multi-agent deep Q learning (DQL) strategy and propose a novel algorithm to tackle this problem. Simulation results indicate that the proposed DQL-based algorithm significantly improve performance gain in terms of minimum throughput maximization compared with the conventional WPCN scheme.

SFDE: Shuffled Frog-Leaping Differential Evolution and Its Application on Cognitive Radio Throughput

Differential Evolution (abbreviation for DE) is showing many advantages in solving optimization problems, such as fast convergence, strong robustness, and so on. However, when DE faces a complex target space, the diversity of its population will degenerate in a small scope; even sometimes it is premature to fall into the local minimum. All things contend in beauty in the world; a Shuffled Frog Leaping Algorithm (abbreviation for SFLA) has a strong global ability; unfortunately, its convergence speed is also slow. In order to overcome the shortcoming, this article suggests a Shuffled Frog-leaping Differential Evolution (abbreviation for SFDE) algorithm in a cognitive radio network, which combines Differential Evolution with Shuffled Frog Leaping Algorithm. This proposed method hikes its local searching for a certain number of subgroups, and their individuals join together and share their mutual information among different subgroups, which improves the population diversity and achieves the purpose of fast global search during the whole Differential Evolution. The SFDE is examined by 20 well-known numerical benchmark functions, and those obtained results are compared with four other related algorithms. The experimental simulation in solving the problem of effective throughput optimization for cognitive users shows that the proposed SFDE is effective.

Optimal Resource Management and Binary Power Control in Network-Assisted D2D Communications for Higher Frequency Reuse Factor.

Device-to-device (D2D) communications can be adopted as a promising solution to attain high quality of service (QoS) for a network. However, D2D communications generates harmful interference when available resources are shared with traditional cellular users (CUs). In this paper, network architecture for the uplink resource management issue for D2D communications underlaying uplink cellular networks is proposed. We develop a fractional frequency reuse (FFR) technique to mitigate interference induced by D2D pairs (DPs) to CUs and mutual interference among DPs in a cell. Then, we formulate a sum throughput optimization problem to achieve the QoS requirements of the system. However, the computational complexity of the optimization problem is very high due to the exhaustive search for a global optimal solution. In order to reduce the complexity, we propose a greedy heuristic search algorithm for D2D communications so as to find a sub-optimal solution. Moreover, a binary power control scheme is proposed to enhance the system throughput by reducing overall interference. The performance of our proposed scheme is analyzed through extensive numerical analysis using Monte Carlo simulation. The results demonstrate that our proposed scheme provides significant improvement in system throughput with the lowest computational complexity.

Efficient energy resource utilization in a wireless sensor system for monitoring water quality

In this paper, a new approach to energy harvesting and data transmission optimization in a heterogeneous-based multi-class and multiple resource wireless transmission wireless sensor network system that focus on monitoring water and its quality is presented. Currently, energy is a scarce resource in wireless sensor networks due to the limited energy budget of batteries, which are typically employed for powering sensors. Once the available energy of a particular sensor node battery is depleted, such sensor node becomes inactive in a network. As a consequence, such node may not be able to participate in the transmission of the application signal in the uplink stage of the network, resulting in a lack of ability to communicate vital signals in a timely manner. Energy scarcity has been a long standing problem in wireless sensor network applications. To address this problem, energy harvesting from intended radiofrequency power source is considered in this work. However, wirelessly powered wireless sensor network systems are confronted by unfairness in resource allocation problem, as well as interference problem in multiple energy resource transmissions. These problems adversely impact the performance of the system in the context of the harvested energy by the sensor nodes, sensors information transmission rates, and the overall system throughput rate. These problems are tackled in this paper by formulating a sum-throughput maximization problem to reduce system energy consumption and enhance the system overall throughput rate. The throughput optimization problem is formulated as a non-convex function. Through the exploitation of the problem structure, it is converted to a convex function. The mathematical models of the optimization problem are validated through numerical simulations. The simulation results reveal that the proposed wireless powered sensor network system outperforms an existing wireless powered sensor network system, by comparison of the numerical simulations of this work to the numerical simulations of the existing WPSN system, regardless of the distances of the sensor nodes to the IPS and the base station. Also, the newly proposed method performs efficiently using parameters that include path-loss exponent impact, performance comparison of systems, convergence based on iteration, comparison based on unequal network distances to the BS, transmission power impact on the attainable throughput and on fraction of energy consumed on information transmissions, and influence of different number of nodes in the network classes.

Joint Time and Power Allocation in Multi-Cell Wireless Powered Communication Networks

This paper investigates the joint time and power allocation in a multi-cell wireless powered communication network (WPCN) with successive interference cancellation. In the WPCN, the hybrid access points broadcast wireless energy to all users and then the users utilize the harvested energy to transmit information simultaneously in a spectrum-sharing fashion. The max-min fairness throughput optimization and max-sum throughput optimization problems are formulated to analyze the system performance. However, both of the two optimization problems are nonconvex due to the complicated co-channel interference and the coupled time and power variables. To deal with this challenge, efficient two-stage approaches are proposed to transform them into more tractable ones. Specifically, for the max-min fairness throughput optimization problem, we first convert the power allocation problem into geometric programming (GP) problem for given time allocation and then optimize the time allocation. For the max-sum throughput optimization problem, we first transform the power allocation problem into a signomial programming problem with fixed time allocation, which is further approximated as a GP one. Then, time allocation is optimized. The simulation results validate the effectiveness of the two proposed approaches. In addition, it is shown that the “doubly near-far” issue can be well addressed by the max-min fairness throughput optimization.

An Optimal Stopping Approach to Cell Selection in 5G Networks

Initial cell search and selection is one of the first few essential steps that a mobile device must perform to access a mobile network. The traditional maximum received power based initial cell selection may result in unbalanced loads across the cells in multi-tier heterogeneous 5G networks with millimeter wave. The objective of this work is to improve the initial cell search and selection scheme to achieve a more balanced load distribution in 5G networks. We propose to augment the existing pure maximum received power based scheme with a new load factor broadcast as part of system information. To study the theoretical performance of the proposed initial cell search and selection scheme, we formulate a throughput optimization problem and solve it by using optimal stopping theory. We characterize the throughput optimal stopping strategy and the attained maximum throughput. We prove that the proposed initial cell search and selection scheme is throughput optimal with a carefully optimized connection threshold. The optimal connection threshold and maximum throughput can be found by solving a fixed point equation. We further develop an iterative algorithm to compute the solution to the fixed point equation and prove the convergence of the proposed iterative algorithm. We also provide simulation results to demonstrate the analytical results and obtain insights. Our analysis and results show that the proposed initial cell search and selection scheme can help achieve a more balanced load distribution in 5G networks.

Throughput Optimization Using Metaheuristic-Tabu Search in the Multicast D2D Communications Underlaying LTE-A Uplink Cellular Networks

The sum throughput of a cellular network can be improved when nearby devices employ direct communications using a resource sharing technique. Multicast device-to-device (M-D2D) communication is a promising solution to accommodate higher transmission rates. In an M-D2D communication, a multicast group is formed by considering a transmitter that can transmit the same information to multiple receivers by considering the transmission link conditions. In this paper, we focus on the uplink interference generated due to the non-orthogonal sharing of resources between the cellular users and M-D2D groups. To mitigate the interference, we propose a spectrum reuse-based resource allocation and power control scheme for M-D2D communication underlaying an uplink cellular network. We formulate the throughput optimization problem by considering the fractional frequency reuse (FFR) method within a multicell cellular network. In addition, a metaheuristic-tabu search algorithm is developed that maximizes the probability of finding optimal solutions by minimizing uplink interference. To analyze fairness resource distribution among users, we finally consider Jain’s fairness index. Simulation results show that the proposed scheme can improve the coverage probability, success rate, spectral efficiency, and sum throughput of the network, compared with a random resource allocation scheme without a metaheuristic-tabu search algorithm.

Cellular throughput optimization by game-based power adjustment and outband D2D communication

Device-to-device (D2D) communication is a promising option for the fifth generation (5G) mobile communication network to reduce energy consumption and increase throughput, which makes high throughput applications possible. Also, the improvement for energy and spectrum efficiency is critical in such applications. Without occupation of cellular spectrum resources, outband D2D communication is increasingly applied to high throughput applications to increase spectrum supply. However, energy efficiency is still a key issue that needs to be addressed. Moreover, the overall energy efficiency in cellular networks is severely limited by cell-edge devices. Therefore, in this paper, we apply multi-hop relay-aided outband D2D communication to cellular networks and propose a game-based power adjustment method to address throughput optimization problem. Firstly, we model an interaction relationship of power adjustment for each transmission path as a potential game, where a new utility function is designed for each player (i.e., the receiving end of a transmission path) to evaluate its action gain and to determine whether taking action or not. And then, it is proved that the utility function is an ordinal potential function (OPF) and the game of power adjustment is an ordinal potential game (OPG), which guarantees the convergence of game decision process. Moreover, we propose a new game decision algorithm, which has quicker convergence speed than the existing typical algorithm. In addition, we design a network-assisted distributed processing architecture for solving throughput optimization problem, including receiving mode selection, verification for relay selection, and transmission power adjustment, which can ease the burden of centralized processing. The experimental results show that our scheme is superior to the existing typical work in terms of throughput, delay, energy efficiency, continuous service ability, and convergence performance.

Distance-Constrained Outage Probability Analysis for Device-to-Device Communications Underlaying Cellular Networks with Frequency Reuse Factor of 2

Device-to-device (D2D) communication is affirmed as one of the dynamic techniques in improving the network throughput and capacity and reducing traffic load to the evolved Node B (eNB). In this paper, we propose a resource allocation and power control technique in which two-pairs of D2D users can simultaneously share same uplink cellular resource. In this case, interference between D2D users and cellular users is no longer insignificant so it must be properly handled. The proposed scheme considers fractional frequency reuse (FFR) scheme as a promising method that can relatively reduce the intra-cell interference. The main objective of the proposed scheme is to maximize the D2D communication throughput and overall system throughput by minimizing outage probability. Hence, we formulate an outage probability problem and overall system throughput optimization problem while guaranteeing minimum allowable signal-to-interference-plus-noise ratio (SINR). For fair distribution of cellular resources to multiple D2D pairs, we used Jain's fairness index (JFI) method. Simulation is conducted in MATLAB and our simulation results demonstrate that the proposed scheme achieves remarkable system performance as compared with existing methods.