Channel occupancy-based user association in IEEE 802.11 wireless LANs

In wireless local area networks (WLANs) made up of Extended Service Sets, access point (AP) selection is a key issue to improve the network performance and balance the traffic load. WLANs operating in the shared Industrial, Scientific and Medical band can benefit from the use of cognitive radio (CR) techniques to enable dynamic access to spectrum holes that are free from interference. In this paper, we propose an optimal Green AP Selection (GAPS) scheme, in which AP selection is optimized to maximize the system throughput while minimizing the energy consumption, for multi-rate WLANs enhanced by CRs. Different from most existing AP selection schemes, GAPS takes into account of the state transition tendency of APs and the influence of Automatic Rate Fallback mechanism in the Distributed Coordination Function. The AP selection problem is formulated as a restless bandit problem and solved by the primal-dual index heuristic algorithm based on first order relaxation to yield the GAPS scheme with the "indexability" property and hence a low complexity. GAPS is further divided into offline computation, which accounts for the bulk of the computations, and online selection, with a low complexity to facilitate implementation. Extensive simulation results illustrate the significant performance improvements of GAPS compared with existing AP selection schemes in different scenarios.

PurposeWireless local area networks (WLANs) are the predominant option for broadband wireless access network, and multiple access points (APs) will be much more available for wireless stations (WSTAs). Call admission control (CAC) on AP selection problem over 802.11 WLAN is a critical issue. In the existing architecture, strongest‐signal‐first is the default AP selection mechanism in 802.11 WLAN which uses the single criterion, received signal strength indicator, to select AP. However, this method suffers from bandwidth deficiency and unbalanced load among APs due to the uneven distribution of user load, thus degrading the system throughput. Instead, the purpose of this paper is to propose a multi‐criteria CAC on AP selection algorithms.Design/methodology/approachThe distributed multi‐criteria considered in order are RSSI, minimum required bandwidth of WSTA, estimated effective bandwidth (EEB) and AP‐WSTA distance. A semiMarkov model considering both packet retransmission limit, packet error rate and collision effect is proposed to predict the system throughput and validated through simulation results. Two multi‐criteria AP selection algorithms after EEB is evaluated are proposed and compared based on this analytical model.FindingsThe proposed algorithms outperform the traditional SSF algorithm in terms of the balance index for AP and the average system throughput.Originality/valveThe paper presents performance analysis for multi‐criteria CAC for distributed access point selection in WLANs.

In this paper, we introduce two indoor Wireless Local Area Network (WLAN) positioning methods using augmented sparse recovery algorithms. These schemes render a sparse user's position vector, and in parallel, minimize the distance between the online measurement and radio map. The overall localization scheme for both methods consists of three steps: 1) coarse localization, obtained from comparing the online measurements with clustered radio map. A novel graph-based method is proposed to cluster the offline fingerprints. In the online phase, a Region Of Interest (ROI) is selected within which we search for the user's location; 2) Access Point (AP) selection; and 3) fine localization through the novel sparse recovery algorithms. Since the online measurements are subject to inordinate measurement readings, called outliers, the sparse recovery methods are modified in order to jointly estimate the outliers and user's position vector. The outlier detection procedure identifies the APs whose readings are either not available or erroneous. The proposed localization methods have been tested with Received Signal Strength (RSS) measurements in a typical office environment and the results show that they can localize the user with significantly high accuracy and resolution which is superior to the results from competing WLAN fingerprinting localization methods.

WLAN is now widely used for emergency purposes because of its fast deployment capability. Efficient and intelligent arrangement and selection of access points (AP) are very crucial in emergency situations. Coverage and capacity are two central problems faced when APs are placed in a disaster scene. The paper proposes a power efficient algorithm for AP selection in emergency situations. The current AP selection algorithm only considers received signal strength, and this algorithm arise problems like overloading in APs, congestion and power wastage in networks. But the proposed Dynamic Load Balancing Fairness Algorithm (DLBFA) overcomes all these problems by using a new selection score, obtained by optimizing channel throughput and voice quality. Apart of this score the number of nodes connected to an AP and RSSI are also considered. The main advantage of DLBA is that it can work on both data and voice applications.

Public areas, such as train stations and airports, providing wireless Local Area Network (WLAN) services are increasing and expanding because of the rapid development of WLANs based on IEEE 802.11 standard. Moreover, because of the advances in smartphone tethering technology, portable access points (APs) such as mobile Wi-Fi routers are being utilized more frequently. Consequently, there are increasing circumstances where a user needs to select and connect to one of the many APs. AP selection significantly determines the quality of service of the subsequent communication session. Existing AP selection algorithms consider user movement but not AP movement. We propose an AP selection method that handles both types of movement effectively. Moreover, we show that the proposed method improves the throughput significantly compared to the existing method.

With the announcement of IEEE 802.11 standards and the rapid development of the wireless transmission technology, WLANs (wireless local area networks) have become popular due to its low cost and being easy to set up. To achieve an uninterrupted transmission service, handover mechanisms must be carefully designed. However, current handover mechanisms require completely scanning surrounding channels which can result in long scanning delays and thus severely affect the performance of real-time applications. Apart from this, the handover mechanism in the standard specification chooses the new access point (AP) based only on the information of the received signal strength (RSSI). Since the congestion condition on the new AP is not considered in the selection process, the new AP may not it support the required enough transmission rate if it is heavily loaded. In this thesis, we propose to use the information of the round-trip delay between the probe request and the probe response for handover AP selection. Furthermore, the duration of scanning on a specific channel will be properly designed to reduce the scanning delay. We have verified the proposed method through implementation on the driver of the wireless network card. The experiment results show that the proposed method outperforms the standard mechanism on the handover performance.

Traffic diversion and load balancing are critical issues in Wireless Local Area Network (WLAN), such as Wi-Fi. When multi hop ad-hoc nodes are introduced between two WLANs, the performance becomes more inevitable, which brings new challenging issues, such as how to reduce the packet dropping rate, end-to-end delay, how to control traffic in more dense WLAN, how to take more advantage of multi hop relay nodes for better resource management. In this paper we proposed a traffic diversion scheme for high dense Infrastructure based WLAN. This proposed scheme consists of two parts. In the first part, we have focused on Access Point (AP) selection policy, which utilizes multi-hop adhoc nodes to divert traffic of high dense AP to less dense AP with non-overlapping coverage. In the second part, we provided a framework for routing scheme in multi hop ad-hoc nodes for exchange of information. Ongoing ns2 simulations demonstrate that the scheme that we proposed is more effective than traditional one in terms of packet drop and end to-end delay.

Location estimation using received signal strength (RSS) in pervasively available Wi-Fi infrastructures has been considered as a popular indoor positioning solution. However, accuracy deterioration due to uncertainty of RSS and offline manual calibration cost limit the deployment of Wi-Fi positioning systems. This paper proposes a signal perturbation technique to enhance existing support vector regression (SVR) based Wi-Fi positioning. By signal perturbation, more RSS training samples are generated, thus enhancing the generalization ability of SVR. In addition, access point (AP) selection method is applied to reduce the input dimension by discarding the redundant APs. The proposed method is compared with previous classical methods in a real wireless indoor environment. Experimental results show that the proposed method improves accuracy while reducing calibration cost.

Throughput maximization in a network is one of the major key challenges in wireless LANs (WLANs) and it could be achieved when the load of access points (APs) is balanced. The AP load in WLANs is very often found to be uneven. To overcome such imbalance of load, a load balancing technique have been proposed. The load balancing techniques commonly require proprietary software or hardware at the user side for controlling the user-AP association. A new load balancing technique by controlling the size of WLAN cells (i.e., AP’s coverage range), which is conceptually similar to cell breathing in cellular networks. The proposed scheme does not require any modification from the user side nor may any changes be required to the IEEE 802.11 standard [1]. It only requires the ability of dynamically changing the transmission power of the AP beacon messages. We develop a newer load balancing algorithm that will find the optimal beacon power settings which will thereon help to minimize the load of the most congested AP. The load may become shared and this may be performed with minimal delay and also maximized throughput is to be achieved. Keywords: Network throughput, Access points, WLAN, Beacon Messages, Load balancing, Throughput, Wireless LANs.

The idea of smart and sustainable cities is incomplete without using information and communication technologies. Wireless Local Area Network (WLAN) plays a vital role in connecting different services, transferring data and providing indoor localization. WLAN fingerprinting technique has become the most common approach for indoor localization in smart cities. This technique utilizes received signal strength (RSS) and channel status from the installed WLAN Access Points (AP). Currently, WLAN planning relies on the site survey, which is laborious and uneconomical for large areas. The WLAN has a limited number of channels assigned in the frequency reuse strategy. An inefficient channel assignment strategy will lead towards increased interference, thus degrading the overall service. This paper proposes a WLAN channel assignment strategy for large and crowded indoor areas. The proposed scheme assigns the channels to installed APs for indoor localization in fingerprinting framework. The proposed technique ensures that the proposed AP channel assignment algorithm in our network-planning scheme mitigates interference.

Indoor positioning methods using location-sensitive features of available wireless signals can achieve high accuracy. In particular, many state-of-the-art methods exploit quite unique sets of location-dependent received signal strength (RSS) measurements from multiple wireless local area network (WLAN) access points (APs), also called as wireless fingerprints. However, reception of signals from WLAN APs is not often stable, and RSS measurements tend to be unavailable due to WLAN card or AP transient effects, limited sensitivity of WLAN cards, and fluctuating attenuation and reflection of signals due to a multipath environment, structural changes and moving objects. In certain hostile scenarios, bogus APs may be installed to disorient WLAN localisation algorithms. In this study, two approaches are proposed to mitigate the impact of faulty signal strength measurements. Performance figures are provided for both simulated and empirical environments in order to support conclusions.

Light Fidelity (LiFi) is an emerging technology for future high-speed indoor wireless communications. Co-channel interference (CCI) caused by the dense deployment of LiFi access points (APs) can be effectively mitigated by using angle diversity receivers (ADRs). ADRs require signal combining where the combining weights depend on the selection of serving APs. In this paper, the AP selection (APS) strategy considering handover is studied. A novel APS scheme based on evolutionary game theory (EGT) is proposed for the LiFi network using ADRs. The performance of the proposed scheme is comprehensively analysed and compared with the APS scheme based on signal strength strategy (SSS). The result shows that, in terms of ADRs with SBC/MRC, the EGT-based APS scheme achieves more than 5% improvement in quality of service (QoS) compared with the SSS-based APS scheme. With the sub-optimum weights of maximum ratio combining (MRC) for ADRs, the EGT-MRC scheme can achieve more than 20 Mbps data rate improvement compared with LiFi systems using single photodiode (PD) receiver.

In this paper, we study the problem of enhancing the fault tolerance of IEEE 802.11 wireless local area networks in the design stage. Our goal is to place minimal number of APs (access points), such that the system can tolerate AP failures while guaranteeing QoS requirement. Given a set of clients (end-users) in a region, each client has a traffic demand for Internet access. Our concern is to find out minimal number of APs and their locations, so that both fault tolerance and QoS constraints can be satisfied. That is, when there is no AP failure, all clients' traffic demands should be met. When one AP fails, the clients it serves shall switch to other APs and a certain percentage of traffic demands of these clients affected shall still be met. We proposed a heuristic algorithm to solve this problem. Firstly we place minimal number of APs to ensure that connection requirements and per-client degrading traffic demand can be met. Secondly, we continue to add APs to meet per-client normal traffic demand. Finally, we place more APs to tolerate failure of the APs placed in the first step. Simulation are conducted to show the performance of our proposed method.

This paper proposes a cell-free massive multiple-input multiple-output (CF-mMIMO) architecture with joint list-based detection with soft interference cancellation (soft-IC) and access points (APs) selection. In particular, we derive a new closed-form expression for the minimum mean-square error receive filter while taking the uplink transmit powers and APs selection into account. This is achieved by optimizing the receive combining vector by minimizing the mean square error between the detected symbol estimate and transmitted symbol, after canceling the multi-user interference (MUI). By using low-density parity check (LDPC) codes, an iterative detection and decoding (IDD) scheme based on a message passing is devised. In order to perform joint detection at the central processing unit (CPU), the access points locally estimate the channel and send their received sample data to the CPU via the front haul links. In order to enhance the system's bit error rate performance, the detected symbols are iteratively exchanged between the joint detector and the LDPC decoder in log likelihood ratio form. Furthermore, we draw insights into the derived detector as the number of IDD iterations increase. Finally, the proposed list detector is compared with existing detection techniques.

In recent years, indoor fingerprint-based localization algorithm has been widely used by applications on smart phone. In these localization algorithms, it’s very popular to use WiFi signal characteristics to represent the location fingerprint. With the fast popularization of WiFi, the WiFi access points (APs) could be seen everywhere. However, as the number of APs increases, the dimension of the fingerprint and the complexity of fingerprint-based localization algorithm subsequently increase. Responding to the above challenges, this paper proposes a novel indoor localization algorithm MAPS (indoor localization algorithm based on multiple access point selection). MAPS could effectively reduce the complexity of localization computation, and improve the performance of localization through AP selection method. With the first round AP selection, MAPS can obtain a stable subset of AP, thus reducing the dimension of fingerprint, and obtaining better discrimination. And with the second round of AP selection, AP subset could be further condensed to construct a decision tree in each location cluster. This step can further improve the localization performance. The experimental results shown, as compared with classical indoor localization algorithm, MAPS has better positioning accuracy, and could achieve the accuracy of over 90% within 2m location error.