Dense Wireless Local Area Networks Research Articles

A Study of the Active Access-Point Configuration Algorithm under Channel Bonding to Dual IEEE 802.11n and 11ac Interfaces in an Elastic WLAN System for IoT Applications

Currently, Internet of Things (IoT) has become common in various applications, including smart factories, smart cities, and smart homes. In them, wireless local-area networks (WLANs) are widely used due to their high-speed data transfer, flexible coverage ranges, and low costs. To enhance the performance, the WLAN configuration should be optimized in dense WLAN environments where multiple access points (APs) and hosts exist. Previously, we have studied the active AP configuration algorithm for dual interfaces using IEEE802.11n and 11ac protocols at each AP under non-channel bonding (non-CB). In this paper, we study the algorithm considering the channel bonding (CB) to enhance its capacity by bonding two channels together. To improve the throughput estimation accuracy of the algorithm, the reduction factor is introduced at contending hosts for the same AP. For evaluations, we conducted extensive experiments using the WIMENT simulator and the testbed system using Raspberry Pi 4B APs. The results show that the estimated throughput is well matched with the measured one, and the proposal achieves the higher throughput with a smaller number of active APs than the previous configurations.

Collision-Based Window-Scaled Back-Off Mechanism for Dense Channel Resource Allocation in Future Wi-Fi

Wireless local area networks (WLANs), known as Wi-Fi, are widely deployed to meet the enhanced needs of data-centric internet applications, such as wireless docking, unified communications, cloud computing, interactive multimedia gaming, progressive streaming, support of wearable devices, up-link broadcasts and cellular offloading. Wi-Fi networks typically adopt the Distributed Coordination Function (DCF)-based Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA), which uses the Binary Exponential Back-off (BEB) algorithm at the MAC layer mechanism to access channel resources. Currently deployed Wi-Fi networks face huge challenges towards efficient channel access for denser environments due to the blind exponential increase/decrease of a contention window (CW) procedure that is inefficient for a higher number of contending stations. Several modifications and amendments have been proposed to improve the performance of the MAC layer channel access based on a fixed or variable CW size. However, a more realistic network density-based channel resource allocation solution is still missing. An efficient channel resource allocation is one of the most critical challenges for future highly dense WLANs, such as High-Efficiency WLAN (HEW). In this paper, we propose a Channel Collision-based Window Scaled Back-off (CWSB) mechanism for channel resource allocation in HEW. In our proposed CWSB, all contending stations select an optimized CW size for each back-off stage for collided or successfully transmitted data frames. We affirm the performance of the proposed CWSB mechanism with the help of an Iterative Discrete Time Markov Chain (I-DTMC) model. This paper evaluates the performance of our proposed CWSB mechanism in HEW Wi-Fi networks using an NS3 simulator in terms of the normalized throughput and channel access delay compared to the state-of-the-art BEB and a recently proposed mechanism.

Interference-Aware Two-Level Differentiated Transmission for Improving Downlink Spatial Reuse in Dense WLANs.

In this study, we address the problem of downlink throughput degradation in dense wireless local area networks (WLANs) based on the IEEE 802.11ax standard. We demonstrate that this problem essentially results from the asymmetric characteristic of carrier sense multiple access between downlink and uplink transmissions in infrastructure WLANs, and it is exacerbated by a dynamic sensitivity control algorithm that aims to improve spatial reuse (SR) in IEEE 802.11ax. To solve this problem, we propose the interference-aware two-level differentiation mechanism consisting of the dual channel access (DCA) and supplemental power control (SPC) schemes. The proposed mechanism introduces a new measure called a spatial reusability indicator, which roughly estimates the signal-to-interference ratio from the received signal strength of beacon frames. Based on this measure, stations (STAs) are classified into the following two categories: spatial reusable STAs (SR-STAs) and non-spatial reusable STAs (NSR-STAs). Because SR-STAs are more robust to interference than NSR-STAs, the DCA scheme prioritizes transmissions to SR-STAs over those to NSR-STAs by using differentiated carrier sensing thresholds. Moreover, the SPC scheme selectively increases the transmission power to NSR-STAs to compensate for transmission failure due to interference. By combining the SPC and DCA schemes, the proposed mechanism effectively differentiates the downlink transmissions to SR-STAs and NSR-STAs in terms of channel access and transmission power, and it can boost the possibility of successful SR. The proposed mechanism can be easily implemented in IEEE 802.11ax without any complex calculation or significant signaling overhead. Moreover, we provide a practical guideline to determine appropriate parameter values for use in the proposed mechanism. The extensive simulation results obtained in this study confirm that the proposed mechanism increases the downlink throughput by more than several times without decreasing the overall throughput, compared to the existing mechanisms, and it maintains fairness between SR-STAs and NSR-STAs in terms of the ratio of successful transmission.

A User Prioritisation Algorithm for Horizontal Handover in Dense WLANs

This paper proposes an algorithm that enhances horizontal handover (HO) in dense wireless local area networks (WLANs), which is implemented in a software-defined wireless networking (SDWN)-based architecture. The algorithm considers the concept of user prioritisation, classifying the WLAN stations (STAs) into two categories representing high and low priorities respectively, and always attempts to guarantee the best quality of experience (QoE) to the high priority users. The architecture that implements the algorithm leverages the flexibility, programmability, and centralised nature of SDWN to efficiently manage the HO process. Moreover, the paper presents a performance evaluation campaign that demonstrates significant achievements against a state-of-the-art solution in terms of the provided QoE, throughput and delay. Finally, we discuss the importance of considering user prioritisation in a HO algorithm for dense WLANs.

A centralized architecture for autonomic quality of experience oriented handover in dense networks

This paper presents an Optimised Handover (HO) Algorithm for Dense Wireless Local Area Networks (WLANs) based on a novel architecture of Software Defined Wireless Network (SDWN). The work has been designed to be effective in large network environments with a high density of Access Points (APs) and stations, which increase the chances of the Ping-Pong HO effect. Specifically, it considers Quality of Experience (QoE) by applying an optimised HO algorithm for WLANs, which relies on Fuzzy Logic Control Theory (FLCT) combined with Adaptive Hysteresis Values (AHVs). SDWN allows to monitor and manage the networks and to autonomously programme the APs through a centralized controller. The paper includes also a detailed performance analysis of the algorithm developed in an SDWN-based simulator implemented through OPNET. Specifically, our algorithm achieved promising performance results compared to the state of the art in terms of QoE, throughput, delay and reduction of the ping-pong HO effect.

An Efficient Radio Access Resource Management Scheme Based on Priority Strategy in Dense mmWave Cellular Networks

In millimeter wave (mmWave) communication systems, beamforming-enabled directional transmission and network densification are usually used to overcome severe signal path loss problem and improve signal coverage quality. The combination of directional transmission and network densification poses a challenge to radio access resource management. The existing work presented an effective solution for dense mmWave wireless local area networks (WLANs). However, this scheme cannot adapt to network expansion when it is applied directly to dense mmWave cellular networks. In addition, there is still room for improvement in terms of energy efficiency and throughput. Therefore, we firstly propose an efficient hierarchical beamforming training (BFT) mechanism to establish directional links, which allows all the small cell base stations (SBSs) to participate in the merging of training frames to adapt to network expansion. Then, we design a BFT information-aided radio access resource allocation algorithm to improve the downlink energy efficiency of the entire mmWave cellular network by reasonably selecting beam directions and optimizing transmission powers and beam widths. Simulation results show that the proposed hierarchical BFT mechanism has the smaller overhead of BFT than the existing BFT mechanism, and the proposed BFT information-aided radio access resource allocation algorithm outperforms the existing corresponding algorithm in terms of average energy efficiency and throughput per link.

SDN-Based Centralized Channel Assignment Scheme Using Clustering in Dense WLAN Environments

IEEE WLAN 802.11 uses a contention-based medium access control protocol. Adjacent WLAN access points (APs) and stations (STAs) sharing the same channels causes interference, and affect network performance. Existing distributed channel selection schemes are prone to channel oscillation and ripple effect problems. Additionally, centralized schemes may provide optimal or sub-optimal performance but the computation time complexity increases with number of APs. In this paper, we propose a clustering-based centralized channel selection scheme implemented using software-defined networking architecture to overcome these limitations. In the proposed scheme, we first group the APs using a similarity-based clustering algorithm using proximity information. The channel assignment problem is then formulated using an integer linear programming model. Performance evaluation using simulations indicate that the proposed scheme outperforms the common least congested channel selection (LCCS), in terms of throughput and required number of channels switching. Additionally, for N number of APs, the computation complexity is reduced from $$\mathcal {O}\left( 3^{N}\right)$$ to $$\mathcal {O}\left( (N)^2\right)$$ , in comparison to the common centralized channel selection scheme. The test bed results validate simulation results by achieving better data rates for the STAs compared with the LCCS scheme.

Dynamic Control for On-Demand Interference-Managed WLAN Infrastructures

In order to handle a high traffic demand, dense wireless local area networks (WLANs) have been deployed rapidly in the past years. However, dense WLANs cause two critical issues: wastage of energy and severe interference. To address these issues, the centralized management of dense WLANs has been emerged as a powerful paradigm for improving energy efficiency as well as avoiding severe interference. In this paper, we study the joint optimization problem of power-operation modes in access points (APs), channel selections and user-AP associations for improving energy efficiency and avoiding interference without sacrificing users’ demands. To this end, we first formulate it as a mixed-integer programming using the popular Lyapunov approach, but it turns out to be computationally intractable, i.e., NP-hard. To address the issue, we propose a polynomial-time approximation algorithm and prove that it achieves a constant-factor approximation guarantee under mild assumptions. The main novelty underlying our algorithm design is based on a linear programming relaxation combining with two different greedy rounding schemes, where each achieves a constant-factor approximation in different regimes of parameters. We verify the performance of the proposed algorithm via extensive simulations and also demonstrate its practicability by implementing it at commercial APs using a Software-defined Networking framework. Results from our experiments show that it reduces the wasted energy significantly while maintaining even higher throughput.

Decoupling-Based Channel Access Mechanism for Improving Throughput and Fairness in Dense Multi-Rate WLANs

Legacy IEEE 802.11 Medium Access Control (MAC) adopts the Distributed Coordination Function (DCF) mechanism, which provides the same access opportunity for all contenders. However, in dense multi-rate Wireless Local Area Networks (WLANs), the pure distributed control mechanism will cause high collision rate and performance anomaly, which results in low network utilization and wasting valuable channel resources. In this paper, we present a decoupling MAC mechanism (DMAC) based on the idea of contention/reservation to reduce collision and realize collision free data transmission. In proposed mechanism, the channel access time is partitioned into channel contention process and data transmission process. The proposed algorithm makes full use of the distributed random channel access mechanism and performs a centralized collision-free data transmission. Wherein, we also design an adaptive algorithm to adjust the length of the contention period to improve the channel utilization. Furthermore, we further propose two airtime fairness algorithms Improve-DMAC1 (I-DMAC1) and Improve-DMAC2 (I-DMAC2) for delay sensitive network and high throughput network scenarios, respectively, to solve the performance anomaly in multi-rate WLANs, based on DMAC. We verify the effectiveness of these decoupling algorithms through extensive simulations. Moreover, the simulation results show that the proposed algorithms achieve better performance than the 802.11 standard and other protocols.

Q-learning-enabled channel access in next-generation dense wireless networks for IoT-based eHealth systems

One of the key applications for the Internet of Things (IoT) is the eHealth service that targets sustaining patient health information in digital environments, such as the Internet cloud with the help of advanced communication technologies. In eHealth systems, wireless networks, such as wireless local area networks (WLAN), wireless body sensor networks (WBSN), and wireless medical sensor networks (WMSNs), are prominent technologies for early diagnosis and effective cures. The next generation of these wireless networks for IoT-based eHealth services is expected to confront densely deployed sensor environments and radically new applications. To satisfy the diverse requirements of such dense IoT-based eHealth systems, WLANs will have to face the challenge of assisting medium access control (MAC) layer channel access in intelligent adaptive learning and decision-making. Machine learning (ML) offers services as a promising machine intelligence tool for wireless-enabled IoT devices. It is anticipated that upcoming IoT-based eHealth systems will independently access the most desired channel resources with the assistance of sophisticated wireless channel condition inference. Therefore, in this study, we briefly review the fundamental models of ML and discuss their employment in the persuasive applications of IoT-based systems. Furthermore, we propose Q-learning (QL) that is one of the reinforcement learning (RL) paradigms as the future ML paradigm for MAC layer channel access in next-generation dense WLANs for IoT-based eHealth systems. Our goal is to contribute to refining the motivation, problem formulation, and methodology of powerful ML algorithms for MAC layer channel access in the framework of future dense WLANs. This paper also presents a case study of next-generation WLAN IEEE 802.11ax that utilizes the QL algorithm for intelligent MAC layer channel access. The proposed QL-based algorithm optimizes the performance of WLAN, especially for densely deployed devices environment.

On Performance Evaluation of Dynamic Sensitivity Control Techniques in Next-Generation WLANs

Dynamically adapting carrier sensing range, also known as dynamic sensitivity control (DSC), could be an attractive proposition for improving the spatial reuse in dense wireless local area networks. This paper shows the promise and pitfalls of a DSC technique proposed in the IEEE 802.11ax standard and highlights a solution to mitigate these issues. An analytical framework is also presented for the performance evaluation of DSC techniques.

A user application-based access point selection algorithm for dense WLANs.

The current commercial access point (AP) selection schemes are mostly based on received signal strength, but perform poorly in many situations. To address this problem, a number of alternative schemes collect and analyze the actual load of every candidate AP. However, these schemes may incur significant latency and signaling overhead in dense wireless local area networks (WLANs). To mitigate such overhead, we propose a user application-based AP selection scheme that considers historical information about AP performance. Without inducing any signaling activity, our scheme monitors the amount of network traffic used by applications and estimates the achievable throughput of APs. Our scheme employs the characteristics of application traffic with the intent of accurately predicting AP performance. Using a measurement study in dense WLAN environments, we show that our scheme achieves higher throughput and lower association latency than those of existing schemes in places highly accessible to the user.

Deep Reinforcement Learning Paradigm for Performance Optimization of Channel Observation–Based MAC Protocols in Dense WLANs

The potential applications of deep learning to the media access control (MAC) layer of wireless local area networks (WLANs) have already been progressively acknowledged due to their novel features for future communications. Their new features challenge conventional communications theories with more sophisticated artificial intelligence-based theories. Deep reinforcement learning (DRL) is one DL technique that is motivated by the behaviorist sensibility and control philosophy, where a learner can achieve an objective by interacting with the environment. Next-generation dense WLANs like the IEEE 802.11ax high-efficiency WLAN are expected to confront ultra-dense diverse user environments and radically new applications. To satisfy the diverse requirements of such dense WLANs, it is anticipated that prospective WLANs will freely access the best channel resources with the assistance of self-scrutinized wireless channel condition inference. Channel collision handling is one of the major obstacles for future WLANs due to the increase in density of the users. Therefore, in this paper, we propose DRL as an intelligent paradigm for MAC layer resource allocation in dense WLANs. One of the DRL models, Q-learning (QL), is used to optimize the performance of channel observation-based MAC protocols in dense WLANs. An intelligent QL-based resource allocation ( ${i}$ QRA) mechanism is proposed for MAC layer channel access in dense WLANs. The performance of the proposed mechanism is evaluated through extensive simulations. Simulation results indicate that the proposed intelligent paradigm learns diverse WLAN environments and optimizes performance, compared to conventional non-intelligent MAC protocols. The performance of the proposed ${i}$ QRA mechanism is evaluated in diverse WLANs with throughput, channel access delay, and fairness as performance metrics.

Joint Power Control and Channel Allocation for Interference Mitigation Based on Reinforcement Learning

In dense Wireless Local Area Networks (WLANs), high-density Access Points (APs) bring severe interference that seriously affects the experience of users, resulting in lower throughput and poor connection quality. Due to the heavy computation workload raised by the sizable networking systems and the difficulty in estimating instantaneous Channel State Information (CSI), existing works are hard to solve interference problem. In this paper, we propose a Joint Power control and Channel allocation based on Reinforcement Learning (JPCRL) algorithm combining with statistical CSI to reduce interference adaptively. Firstly, we analyze the correlation between transmit power and channel, and formulate the interference optimization as a Mixed Integer Nonlinear Programming (MINLP) problem. Secondly, we use the statistical CSI method to take the power and channel state as the state and action space, the overall throughput increment as the reward function of Q-learning, and obtain the optimal joint optimization strategy through off-line training. Moreover, for the periodic reinforcement learning process leading to resource consumption, we design an event-driven mechanism of Q-learning, which triggers online learning to refresh the optimal policy by event-driven condition and the consumption of computing resources can be reduced. The evaluation results show that the proposed algorithm can effectively improve the throughput compared with the existing scheme.

Channel observation‐based scaled backoff mechanism for high‐efficiency WLANs

A channel observation-based scaled backoff (COSB) mechanism for the carrier sense multiple access with collision avoidance of high efficiency wireless local area networks (WLANs) is devised. The proposed protocol modifies the blind scaling of contention window (W) in binary exponential backoff (BEB) scheme of currently deployed WLANs. COSB is employed to adaptively scale-up and scale-down the W size during the backoff mechanism for collided and successfully transmitted data frames, respectively. It can achieve higher throughput and shorter delay compared to the conventional BEB mechanism in highly dense WLANs.

Traffic differentiation in dense collision-free WLANs using CSMA/ECA

The ability to perform traffic differentiation is a promising feature of the current Medium Access Control (MAC) in Wireless Local Area Networks (WLANs). The Enhanced Distributed Channel Access (EDCA) protocol for WLANs proposes up to four Access Categories (AC) that can be mapped to different traffic priorities. High priority ACs are allowed to transmit more often than low priority ACs, providing a way of prioritising delay sensitive traffic like voice calls or video streaming. Further, EDCA also considers the intricacies related to the management of multiple queues, virtual collisions and traffic differentiation. Nevertheless, EDCA falls short in efficiency when performing in dense WLAN scenarios. Its collision-prone contention mechanism degrades the overall throughput to the point of starving low priority ACs, and produce priority inversions at high number of contenders.Carrier Sense Multiple Access with Enhanced Collision Avoidance (CSMA/ECA) is a compatible MAC protocol for WLANs which is also capable of providing traffic differentiation. Contrary to EDCA, CSMA/ECA uses a contention mechanism with a deterministic backoff technique which is capable of constructing collision-free schedules for many nodes with multiple active ACs, extending the network capacity without starving low priority ACs, as experienced in EDCA. This work analyses traffic differentiation with CSMA/ECA by describing the mechanisms used to construct collision-free schedules with multiple queues. Additionally, evaluates the performance under different traffic conditions and a growing number of contenders. Furthermore, it introduces a way to eliminate Virtual Collisions (VC), which also contributes to the throughput degradation in EDCA WLANs. Simulation tests are performed using voice and video packet sources that emulate commonly used codecs. Results show CSMA/ECA outperforming EDCA in different commonly-found scenarios with high number of users, including when both MAC protocols coexist in the same WLAN.

Traffic differentiation in dense collision-free WLANs using CSMA/ECA

The ability to perform traffic differentiation is a promising feature of the current Medium Access Control (MAC) in Wireless Local Area Networks (WLANs). The Enhanced Distributed Channel Access (EDCA) protocol for WLANs proposes up to four Access Categories (AC) that can be mapped to different traffic priorities. High priority ACs are allowed to transmit more often than low priority ACs, providing a way of prioritising delay sensitive traffic like voice calls or video streaming. Further, EDCA also considers the intricacies related to the management of multiple queues, virtual collisions and traffic differentiation. Nevertheless, EDCA falls short in efficiency when performing in dense WLAN scenarios. Its collision-prone contention mechanism degrades the overall throughput to the point of starving low priority ACs, and produce priority inversions at high number of contenders. Carrier Sense Multiple Access with Enhanced Collision Avoidance (CSMA/ECA) is a compatible MAC protocol for WLANs which is also capable of providing traffic differentiation. Contrary to EDCA, CSMA/ECA uses a contention mechanism with a deterministic backoff technique which is capable of constructing collision-free schedules for many nodes with multiple active ACs, extending the network capacity without starving low priority ACs, as experienced in EDCA. This work analyses traffic differentiation with CSMA/ECA by describing the mechanisms used to construct collision-free schedules with multiple queues. Additionally, evaluates the performance under different traffic conditions and a growing number of contenders. (arXiv's abstract field is not large enough for the paper's abstract, please download the paper for the complete abstract.)

Probe/PreAck: A Joint Solution for Mitigating Hidden and Exposed Node Problems and Enhancing Spatial Reuse in Dense WLANs

In dense wireless local area networks (WLANs), the primary causes of interference and hindrance to spatial reuse are the well-known hidden node problem (HNP) and the exposed node problem (ENP). In this paper, we propose a joint solution to these problems. The proposed mechanism, referred to as Probe/PreAck (PR/PA), utilizes the concept of a two-way handshake for efficient channel reservation and spatial reuse by means of two control frames called PR and PA. The PR frame is designed for the semi-reservation of a channel initiated by a transmitting node, while the PA frame is used for the receiver-oriented permission of a channel reservation. Once a transmitting node advertises a PR frame, the channel is temporarily reserved, and the channel reservation is finally completed after the node receives the corresponding PA frame. Otherwise, the channel reservation is immediately released. Unlike the ready-to-send/clear-to-send mechanism, which is a well-known solution to HNP, the proposed mechanism does not unnecessarily prevent nodes from accessing the channel but selectively blocks transmitting and receiving nodes when they overhear the PA and PR frames transmitted by neighboring nodes, respectively. In this way, the proposed PR/PA mechanism effectively deals with both HNP and ENP in a unified framework. We further enhance the PR/PA mechanism by devising an immediate destination switching scheme, which is implemented in access points (APs) to improve the downlink throughput. If an AP fails to complete the exchange of PR and PA frames with a specific destination, it sends another PR frame to a different destination node without performing a new back-off procedure. Moreover, we adopt the transmission time control scheme to assure successful spatial reuse in multiple basic service set (BSS) WLANs. By adjusting the transmission time of the data frames simultaneously transmitted in different BSSs, severe interference between the data and acknowledgment frames can be avoided. The results of a simulation study confirmed that the proposed mechanism outperformed conventional mechanisms in dense multi-BSS WLANs; the downlink throughput was increased by more than 10 times while the overall network throughput was increased by approximately 50%.

Enhanced Dual Carrier Sensing With Transmission Time Control for Fair Spatial Reuse in Heterogeneous and Dense WLANs

In this paper, we focus on fairness and spatial reuse in heterogeneous and dense wireless local area networks (WLANs) where stations (STAs) have different capabilities of carrier sensing and basic service sets (BSSs) are densely deployed. First, we show a serious problem of unfairness arising when STAs with different carrier sensing threshold (CSTH) coexist and compete for channel access. To cope with this problem, a dual carrier sensing mechanism has been proposed in the on-going IEEE 802.11ax standardization. This mechanism uses two differentiated CSTHs: a conservative CSTH for fairness among intra-BSS STAs and an aggressive CSTH for spatial reuse by inter-BSS STAs. This dual carrier sensing mechanism may be effective in improving fairness. However, we show that its gain is marginal in terms of spatial reuse because an acknowledgment (ACK) frame is liable to be corrupted due to interference from a data frame transmitted by an inter-BSS STA. In order to avoid ACK corruption, we enhanced the dual carrier sensing mechanism by controlling the transmission time of the data frame. Depending on the receiving signal strength of data frame transmitted by an inter-BSS STA, the proposed mechanism selectively adjusts the concurrent transmission time of the data frame so that ACK frames can be successfully delivered. Results from simulation confirmed the outstanding performance of the proposed mechanism in heterogeneous and dense WLANs; compared with conventional mechanisms, the total throughput was considerably increased by more than two-fold while the fairness among the STAs was improved.

A State Transition-Aware Energy-Saving Mechanism for Dense WLANs in Buildings

With the explosive growth of smart terminals, access points (APs) are densely deployed in the buildings of enterprise, campus, hotel, and so on, to provide sufficient coverage and capacity for peak user demands. However, existing studies show that during the off-peak periods, not all the capacity is needed and a large fraction of low-utilization or idle APs cause a great deal of energy waste in these buildings. Although many solutions have been proposed to switch on/off the APs according to the user needs, few works consider the energy cost by state transition. In this paper, we propose a state transition-aware energy-saving mechanism for dense wireless local area networks, which can dynamically switch the APs’ states to meet the user needs while controlling the switching frequency and balancing the number of associated users of each AP. First of all, we analyze the most recent user behaviors, which are used to design the energy-saving mechanism. Then, we model the proposed mechanism in order to set relevant parameters reasonably. Finally, evaluation results show that comparing with a typical static strategy, the energy consumption is reduced by 24.3%, and the average available bandwidth is increased by 27.8%. Meanwhile, the switching frequency is reduced by 14.3% as well.