MAPS: Indoor Localization Algorithm Based on Multiple AP Selection

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.

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.

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.

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.

It is usually possible to associate with more than one Access Point (AP) in IEEE 802.11 Wireless LANs. AP selection is a crucial issue because the performance achieved by a user heavily depends on the AP selected. A received signal strength (RSS)-based association mechanism is specified by the IEEE 802.11 standard. However, this does not consider the channel conditions and AP load, which leads to a low throughput and a low user transmission rate. An alternative to using the RSS is to exploit the airtime metric, which provides users with information on how busy the channel is, but the results of our simulations show that the airtime metric cannot indicate the exact status of the channel. In this paper, we present a new association framework using channel occupancy in order to provide users with exact channel status information of APs. Via the proposed scheme, users can compare the achievable throughput provided by each AP and select the best AP. We validate our proposed association method via simulation results, which show that the throughput improvement of the method is worthy of notice.

This paper focuses on addressing the Access Point (AP) selection problem by relying on a centralized controller that provides a global view of the network. This approach follows the Software-Defined Networking (SDN) concept, which has long been considered in the literature as an innovative method to control management functionalities for wired networks and that is also now becoming a hot topic in the context of Wi-Fi networks. The proposed AP selection approach is based on a novel algorithm which relies on the Fittingness Factor (FF) concept, to maximize a function that reflects the suitability of the available spectrum resources to the application requirements. Specifically, this paper describes the development of a framework that implements the FF-based algorithm for smart AP selection in a centralized controller. The simulated performance comparison of this algorithm against a strategy that maximizes the achievable data rate considered in many papers in the literature, illustrates the important achievements that have been obtained in terms of saved bandwidth and users' satisfaction.

This paper studies the secrecy performance of light-fidelity (LiFi) networks under the consideration of random device orientation and partial knowledge of the eavesdroppers’ channel state information. Particularly, the secrecy capacity and secrecy outage probability are analysed for the case of a single eavesdropper as well as for the case of multiple eavesdroppers. Moreover, a machine learning based access point (AP) selection algorithm is presented with the objective of maximising the secrecy capacity of legitimate users. Our results show that optimising the AP selection while taking into account the random behaviour of the optical channel results in a significant enhancement in the achievable secrecy performance. In fact, using the derived realistic secrecy expressions as the basis for AP selection results in up to 30% secrecy capacity enhancement compared to the limited assumption of fixed orientation.

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.

Many indoor positioning algorithms have been proposed in the last decade, most of which are based on WiFi RSS fingerprints. However, the environment has changed dramatically since the original algorithms using only a few Access Points (APs). A typical building with densely deployed APs might contain hundreds of APs. The explosive growth of the number of APs introduces new challenges to these WiFi-based localization algorithms. This paper presents an empirical study of WiFi fingerprint-based indoor localization algorithms in a real-world environment with hundreds of APs. Our study aims to answer several important research questions regarding the influence of the number of APs, time variance and device variance. The study implements four existing algorithms and also proposes a new algorithm called LCS that is designed specifically for an AP-intensive environment. We compare the localization accuracy of different algorithms with different variances in the experimental results, which shows that the proposed LCS algorithm is able to efficiently resist diverse variances in an AP-intensive setup.

Wireless stations (WSs) in an IEEE 802.11 access network compete with each other for collective bandwidth offered by access points (APs). The competition involves selecting an AP with the consideration of potential link rate and workload status. From the perspective of system, a good AP selection policy should be stable, increase overall system throughput, and maintain bandwidth fairness among WSs. This paper models AP selections under the framework of game theory, where each WS's sole goal is to maximize its achievable throughput. The achievable throughput depends on not only the number of WSs that associate with the same AP but the set of link rates these WSs use as well. It is not a monotonically decreasing function of WS population when considering the effect of performance anomaly. We have proven the stability of this game (Nash equilibrium) and shown that selfish behavior of individual WSs in fact improves overall bandwidth fairness among WSs. Thorough simulations were conducted to demonstrate the validity of the analytical results and compare the performance of the proposed game with that of counterparts.

Background: Non-orthogonal multiple access (NOMA) is viewed as the key multiple access technology for 5G and beyond networks, attracting the attention of academics and industries. NOMA and the multiple input multiple output (MIMO-NOMA) technology can improve a system’s throughput, latency, and energy efficiency (EE) in future-generation communication networks. Objective: The objective of this paper is to achieve maximum EE by applying the Max-min Power Control Algorithm (MMPCA) through sub-channel optimization, resource allocation (RA) optimization, access point selection (APS), and user association. The EE results obtained with and without using MMPCA are compared to the RA optimization from a conventional water-filling algorithm (WFA). Method: This paper formulates a framework for user-centric (UC) joint resource allocation, such as backhaul connection via beam-forming and Access point (AP) to user connection via MIMO-NOMA. The user without interference is decoded using the NOMA principle. The MMPCA was also used to optimize cooperative power allocation, sub-channel allocation, and efficient user association. The RA for EE is framed as a mixed non-convex and non-linear function using successive convex approximation and sum ratio decoupling converted into convex and linear. A bisection method was used to achieve optimal RA, user association, and sub-channel assignment. Results and Conclusion: The simulation shows energy efficiency (EE) improvement. Similarly, it is observed that MMPCA outperforms the WFA.

In this letter, we investigate user-centric access point (AP) selection for cell-free massive multiple-input multiple-output (MIMO) systems. In contrast to existing works, we study this problem from the perspective of game theory and model user-centric AP service cluster formation as a local altruistic game, which is proven to be an exact potential game (EPG) and to have at least one pure Nash equilibrium (NE). Then, an improved maximum non-neighbor-set-based concurrent spatial adaptive play (MNSB-C-SAP) algorithm is proposed to achieve the NE. Simulation results show that the proposed algorithm outperforms existing algorithms in terms of convergence speed and verify the superiority of the game-theoretic AP selection method compared with some existing approaches.

This paper studies a new concept so-called green cell-less radio frequency (RF) wireless power transfer (WPT) networks. We consider a scenario in which multiple indoor access points (APs) equipped with outdoor energy harvesters are connected with a central control unit via backhaul links. Further, such APs exploit the harnessed green energy to recharge wirelessly indoor devices under the coordination of the control unit. Considering the network, we focus on AP selection and beamforming optimization to maximize the total energy harvesting (EH) rate. The resulting mathematical problem has the form of mixed-integer optimization that is intractable to solve. Thus, we propose an algorithm to tackle this difficulty. Through numerical results, we show the advantages of the cell-less design over the conventional small-cell one to validate our ideas. In particular, the issue on safety requirements of human exposure to RF radiation is discussed. Finally, potential future research is provided.

A dynamic access point allocation algorithm for dense wireless LANs using potential game