Access Point Selection Scheme for LiFi Cellular Networks using Angle Diversity Receivers

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.

The downlink of cell‐free massive multiple‐input multiple‐output network is considered. It is assumed that both access points (APs) and users are equipped with multiple antennas. A heuristic threshold‐based‐beamforming (TBF) protocol is proposed, which performs an AP selection algorithm to significantly improve the total energy efficiency (EE) of the network. The beamforming method that fits the model is the maximum eigenmode beamforming (MEB). The threshold level is determined in such a way that the strongest channel eigenmodes contribute in the downlink transmissions. Indeed, power control coefficients are chosen in such a way that the number of users served by each AP is limited according to the TBF‐based AP selection scheme. The spectral efficiency of the proposed method is investigated considering both perfect and imperfect knowledge of the channel state information at transmitters. Simulation results show that the proposed TBF protocol greatly enhances the total EE of the network compared to the MEB, conjugate beamforming, and zero‐force precoding techniques. Furthermore, a modification algorithm is proposed to obtain the minimum per‐user quality of service requirement, which prevents the dramatic reduction in the EE caused by high inter‐user interference levels.

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.

we study the uplink performance of a cell-free massive MIMO (Multiple Input Multiple Output) system. The channel is modeled as a rician fading channel that includes line-of-sight and scattering components, and the phase of the line-of-sight is uniformly distributed. In order to relieve the fronthaul burden and the computational complexity of the system, an access point selection scheme is researched. In addition, we derive the uplink spectral efficiency expression of the system with rician fading propagation taking into account the proposed access point selection. Numerical results show that the access point selection scheme reduces the fronthaul pressure in a cell-free massive MIMO system, and the spectral efficiency is only slightly reduced.

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.

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.

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.

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.

We propose a novel optical wireless (OW) system based on a power adaptive multibeam spot-diffusing transmitter serving multiple seven-segment maximum ratio combining (MRC) angle diversity receivers. A feedback link is assumed between the transceivers so that each receiver conveys to the multibeam transmitter the new beams transmit power weights to be used to achieve the best signal quality at a given receiver location. Two cases involving three and five receivers are considered. Four different configurations for the placement of the three-receiver case in the room are also examined. The system's performance in each case is evaluated in terms of signal-to-noise ratio (SNR) and is compared with the single receiver scenario with and without power adaptation. In the presence of one receiver, the transmit spot powers can be adjusted for optimum performance at that receiver location. For multiple receivers, there is conflict, and we propose spot power adaptation based on the average requirements (power distribution in spots), i.e., transmit equal gain combining (EGC) of spot power or MRC of transmit spot powers. The results show that the three receivers benefit most from an adaptive transmitter when each is placed at a corner of the room. In this case, an SNR increase of as much as 2.6 dB is achieved for all three receivers at the corners by both MRC and EGC. Moreover, when all receivers are placed away from the line of diffusing spots, our proposed MRC collaborative approach is 1 dB better than the noncollaborative system. This gain reduces the difference from the upper bound set by the single receiver adaptation, which is 3 dB. For a mobile transmitter, MRC also significantly improved the SNR for the farthest receivers at the opposite end from the transmitter located near one room corner.

Optimizing the Access Point (AP) deployment has a great role in wireless applications due to the need for providing an efficient communication with low deployment costs. Quality of Service (QoS), is a major significant parameter and objective to be considered along with AP placement as well the overall deployment cost. This study proposes and investigates a multi-level optimization algorithm called Wireless Optimization Algorithm for Indoor Placement (WOAIP) based on Binary Particle Swarm Optimization (BPSO). WOAIP aims to obtain the optimum AP multi-floor placement with effective coverage that makes it more capable of supporting QoS and cost-effectiveness. Five pairs (coverage, AP deployment) of weights, signal thresholds and received signal strength (RSS) measurements simulated using Wireless InSite (WI) software were considered in the test case study by comparing the results collected from WI with the present wireless simulated physical AP deployment of the targeted building - Computer Science Department at University of Baghdad. The performance evaluation of WOAIP shows an increase in terms of AP placement and optimization distinguished in order to increase the wireless coverage ratio to 92.93% compared to 58.5% of present AP coverage (or 24.5% coverage enhancement on average).

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.