Access Point Selection Algorithm Research Articles

Enhancing security offloading performance in NOMA heterogeneous MEC networks using access point selection and meta-heuristic algorithm

The research delves into the intricate domain of security offloading within the context of non-orthogonal multiple access (NOMA) heterogeneous mobile edge computing (het-MEC) networks operating over Rayleigh fading channels. The investigation centers on a system model comprising a single antenna-equipped edge user, denoted as U, which strategically offloads computational tasks to two distinct heterogeneous wireless access points (APs): the far AP (AP1) and the near one (AP2), employing NOMA techniques. Notably, the research accounts for a passive eavesdropper (E) intending to intercept the U−AP2 transmission. A four-phase protocol is proposed to ensure the security offloading process, namely SAPS, which leverages wireless access point selection (APS) and physical layer security (PLS) techniques. The focus extends to derive a closed-form expression for a novel critical system performance metric: the secrecy successful computation probability (SSCP). Furthermore, an algorithm based on Ant Colony Optimization (ACO) within the continuous domain is introduced, which aims to enhance the SSCP by intelligently determining system parameters. The impact of critical factors such as transmit power, power allocation coefficient, bandwidth, CPU frequency, and task division ratio under the SAPS scheme is explored and compared to the conventional approach using pure NOMA. Remarkably, the algorithm in the proposed scheme demonstrates up to a 3% performance improvement. The validity and accuracy of the study findings are verified through Monte-Carlo simulations. The work contributes significantly to advancing secure offloading strategies in NOMA-based MEC networks, offering valuable insights for practical deployment and optimization.

A region-wise indoor localization system based on unsupervised learning and ant colony optimization technique

WiFi-based indoor localization has gained widespread attention in the recent past with the ubiquitous deployment of WLAN. However, for sustainable performance, it is crucial to identify and maintain the important WiFi Access Points (APs). Interestingly, the localization capabilities of APs differ even within their area of coverage depending on the indoor ambience and building properties. Thus, the significance of an AP to the localization performance can be better assessed if the entire region is divided into optimal number of clusters/subregions. This type of problem is hardly investigated in the literature. Consequently, in this paper, the aforementioned challenges are addressed from the perspective of expert systems through applying machine learning and meta-heuristic techniques. Accordingly, our contribution is three-fold. First, we have designed a Region-wise Indoor Localization System (RwILS) in which a sub-regional division algorithm is proposed using DBSCAN approach. Second, a sub-region-wise important AP selection algorithm is designed based on Ant Colony Optimization technique. Third, a location estimation approach is proposed that first identifies the sub-region and then predicts the location point in that sub-region using supervised learning classifiers. A publicly available dataset, JUIndoorLoc is used for experimental evaluation. The localization accuracy of RwILS is improved to 95.68% while the state-of-the-art classifiers give 71% to 83% accuracy. RwILS also outperforms some recent works which further validates its effectiveness. Thus, this proposed technique can lead to an effective expert system in the domain of indoor localization.

Robust Resource Control Based on AP Selection in 6G-Enabled IoT Networks.

The diverse application vertices of internet-of-things (IoT) including internet of vehicles (IoV), industrial IoT (IIoT) and internet of drones things (IoDT) involve intelligent communication between the massive number of objects around us. This digital transformation strives for seamless data flow, uninterrupted communication capabilities, low latency and ultra-high reliability. The limited capabilities of fifth generation (5G) technology have given way to sixth generation (6G) wireless technology. This paper presents a dynamic cell-free framework for a 6G-enabled IoT network. A number of access points (APs) are distributed over a given geographical area to serve a large number of user nodes. A pilot-based AP selection (PBAS) algorithm is proposed, which offers robust resource control through AP selection based on pilots. Selecting a subset of APs against all APs for each user node results in improved performance. In this paper, the performance of the proposed transmission model is evaluated for the achieved data rate and spectral efficiency using the proposed algorithm. It is shown that the proposed PBAS algorithm improves the spectral efficiency by 22% at the cell-edge and 1.5% at the cell-center. A comparison of the different combining techniques used at different user locations is also provided, along with the mathematical formulations. Finally, the proposed model is compared with two other transmission models for performance evaluation. It is observed that the spectral efficiency achieved by an edge node with the proposed scheme is 5.3676 bits/s/Hz, compared to 0.756 bits/s/Hz and 1.0501 bits/s/Hz, attained with transmission schemes 1 and 2, respectively.

Access Point Selection Algorithm Based on Coevolution Particle Swarm in Cell-Free Massive MIMO Systems

Access Point Selection Algorithm Based on Coevolution Particle Swarm in Cell-Free Massive MIMO Systems

Power Minimization in Cell-Free Massive MIMO with AP Selection Algorithm

Background: The cell-free massive multiple input multiple outputs (CF M-MIMO) is the key and emerging technology for 5G and beyond, which gains more attention from many researchers and academicians. The cell-free M-MIMO enhances the system throughput, latency, spectral efficiency (SE), and energy efficiency (EE) of the communication network. Objective: In this paper, we formulate a framework for joint access point selection (APS) and power control algorithm for improving the EE and at the same time maintaining the system capacity. Methods: The max-min power control algorithm is used for efficient power allocation among the access points (APs) by using the minimum mean square error (MMSE), zero-forcing (ZF), and conjugate beam-forming (CB). Results: We were successfully able to enhance and maintain the optimal system capacity of the cellfree M-MIMO systems. Conclusion: The simulation result shows that the system capacity is improved when efficiently allocating the power with max-min power control algorithm.

Resource Allocation for TDD Cell-Free Massive MIMO Systems

In this paper, we investigate a joint resource allocation algorithm in a time-division duplex (TDD)-based cell-free massive MIMO (CFMM) system, which has great potential to improve spectrum efficiency and throughput. Because the throughput of the system is a bottleneck due to the sharing of the pilot, we attempted to alleviate pilot contamination. We propose a pilot assignment approach called user-distance-ordering-based pilot assignment (UDOPA) based on the distance between users and the center, which can be calculated by the K-means method. Then, using an access point (AP) selection algorithm, only the APs having a major impact on the macro diversity gain of a user are selected as the serving APs. In contrast to the existing AP selection algorithms, users with the same pilot are not allowed to share the same serving AP in the proposed AP selection algorithm, which also significantly reduces the complexity of data processing. Finally, a modified max–min power control scheme with teaching–learning-based optimization (TLBO) is proposed to further improve the performance of the systems and guarantee the minimum user rate. Simulation results show that the proposed joint resource allocation scheme can effectively enhance CFMM systems’ performance.

An Effective Fingerprint-Based Indoor Positioning Algorithm Based on Extreme Values

Wi-Fi-based fingerprint indoor positioning technology has gained special attention, but the development of this technology has been full of challenges such as positioning time cost and positioning accuracy. Therefore, selecting reasonable Wireless Access Points (APs) for positioning is essential, as the more APs used for positioning, the higher the online computation, energy and time cost. Furthermore, the received signal strength (RSS) is easily affected by diverse interference (obstacles, multipath effects, etc.), decreasing the positioning accuracy. AP selection and positioning algorithms are proposed in this paper to solve these issues. The proposed AP selection algorithm fuses RSS distribution and interval overlap degree to select a small number of APs with high importance for positioning. The proposed positioning algorithm uses the location distance between reference points (RPs) to construct a circle and leverages extreme values (maximum and minimum values) of circles to determine the possibility that the test point (TP) appears in each circle, then it finds useful APs to determine the weight of RPs. Extensive experiments are conducted in two different areas, and the results show the effectiveness of the proposed algorithm.

Deep Learning-Based Decoding and AP Selection for Radio Stripe Network

Cell-Free massive MIMO (mMIMO) offers promising features such as higher spectral efficiency, higher energy efficiency and superior spatial diversity, which makes it suitable to be adopted in beyond 5G (B5G) networks. However, the original form of Cell-Free massive MIMO requires each AP to be connected to CPU via front haul (front-haul constraints) resulting in huge economic costs and network synchronization issues. Radio Stripe architecture of cell-free mMIMO is one such architecture of cell-free mMIMO which is suitable for practical deployment. In this paper, we propose DNN Based Parallel Decoding in Radio Stripe (DNNBPDRS) to decode the symbols of User Equipments (UEs) in the uplink in a parallel fashion to reduce computational complexity by reducing delay in processing. Moreover, to solve the issue of Access Point (AP) selection in radio stripe networks, we propose a Channel link-based AP selection (CLBAPS) algorithm to choose the best APs in terms of channel link quality. The proposed DNNBPDRS framework not only improves Symbol Error Rate (SER) performance when compared to counterparts but is also proved to be comparatively far lesser computational complex. Moreover, the numerical result indicates the proposed AP selection algorithm CLBAPS performs better than random selection of AP in radio stripe networks.

Realistic Secrecy Performance Analysis for LiFi Systems

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.

Eight-Diagram Based Access Point Selection Algorithm for Indoor Localization

A new method is proposed for selecting Access Points (APs) with propagation direction combination based on Eight-Diagram for indoor localization in indoor environments, which can not only improve positioning accuracy, but also reduce computational complexity by using fewer APs. With propagation direction combination based on eight basic directions, for instance, east, south, west, north, southeast, northeast, southwest and northwest, provided by the Eight-Diagram, the proposed AP selection algorithm can be applied to indoor scenarios where Wi-Fi RSSI is corrupted by multipath interference. Experiments were conducted and the results demonstrate that the proposed AP selection algorithm achieves an accuracy considerably better than the WKNN, MaxMean, InfoGain and PCA methods. Due to the use of fewer APs, the proposed algorithm has lower computational complexity than the MaxMean and InfoGain algorithms, and equivalent with the PCA algorithm.

Graph Coloring Based Pilot Assignment for Cell-Free Massive MIMO Systems

An efficient pilot assignment scheme based on graph coloring is proposed to mitigate the severe pilot contamination effect in cell-free massive multiple-input multiple-output systems. By exploiting the large-scale fading coefficients between the access points (APs) and the user equipments, we first utilize an AP selection algorithm to construct an interference graph. Then, the optimal pilot assignment can be achieved by updating the interference graph. In addition, we consider a fair policy for pilot reuse to improve the system performance. Numerical results verify that, compared with conventional pilot assignment schemes, the proposed graph coloring based pilot assignment scheme can significantly increase the 95 $\%$ -likely per-user throughput and reduce the complexity of assigning pilot sequences.

A Self-Adaptive AP Selection Algorithm Based on Multiobjective Optimization for Indoor WiFi Positioning

With the widely deployed wireless access points (APs) and the worldwide popularization of smartphones, WiFi-based indoor positioning has attracted great attention to both industry and academia. Locating and tracking objects within an indoor environment plays an important role in Internet of Things application and service. However, it is a challenging problem to achieve high accuracy using WiFi positioning technique due to the high instability in received signal strength from AP. Thus, it is desirable to select APs by considering both signal strength and connection quality. In this article, an AP selection algorithm based on multiobjective optimization is proposed to improve indoor WiFi positioning accuracy. The self-adaptive AP selection algorithm can be easily applied to various real scenarios and the performance of the new method is considerably better than classical algorithms. Learning algorithm is exploited to obtain the optimal solution for the self-adaptive AP selection algorithm. Experiments are conducted and the proposed algorithm is compared with classical algorithms. The experimental results demonstrate that the performance of the self-adaptive AP selection algorithm is at least a few decimeters better than classical algorithms in terms of RMSE of position estimation. Meanwhile, the new method is robust to the random generation of initial particles and normalizing factor as their effect on the positional accuracy is less than 1 decimeter.

Energy efficiency enhancement of cell‐free massive multiple‐input multiple‐output network employing threshold‐based beamforming

AbstractThe 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.

A Hybrid Fingerprint Quality Evaluation Model for WiFi Localization

The main drawback for large-scale applications of WiFi-based localization is the varying characteristics of received signal strength (RSS), which degenerates the localization performance seriously. To mitigate the variation problem, we propose a hybrid fingerprint quality evaluation model (HFQuM) for accurate WiFi localization. HFQuM can intelligently determine the location of a user by evaluating the hybrid fingerprint quality in different subareas, that is a high fingerprint quality indicates that the frequently occurred location label is more likely to be true. To achieve this, in the offline phase, instead of only collecting RSS fingerprints, we construct a WiFi-based group of fingerprints (GOOFs) consisting of RSS, signal strength difference (SSD), and hyperbolic location fingerprint (HLF). Given an RSS testing sample of a user at an unknown location in the online phase, we first construct the multiple supporting sets (MSSs), including a sample space and a label space, selected by the similarity between the online sample and the GOOF. Based on the MSS, HFQuM is able to estimate the user’s location as well as subareas and their hybrid fingerprint quality simultaneously by jointly modeling the process of generating the sample space and label space. To further reduce the computational complexity, HFQuM employs an access point (AP) selection algorithm to exclude redundancy APs. Experimental results in a typical library environment verify the superiority of HFQuM in terms of localization accuracy as compared with other existing fingerprint-based methods.

Energy harvesting-based data uploading for Internet of Things

Uploading data from tremendous devices is one of the most challenging tasks for Internet of Things (IoT) due to the heterogeneous data features and the energy constraints of IoT devices. In this paper, we study the data uploading problem from two tiers. In the first tier, we focus on the scheduling of data uploading from different IoT devices to a specific access point (AP) by considering the heterogeneous data features such as the freshness of the data, the data length, the data uploading period, and the energy state of the IoT device. In the second tier, we deal with the selection among different APs for one IoT device. An AP selection algorithm is proposed and we prove that the AP selection process would eventually reach a stable state. Simulation study shows that our proposed algorithm can improve the successful data uploading rate and the time slot utilization.

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.

Selecting Critical WiFi APs for Indoor Localization Based on a Theoretical Error Analysis

The radio map built during the offline phase of any WiFi fingerprint-based localization system usually scales with the number of WiFi access points (APs) that can be detected at a single location by any mobile device, but this number in practice can be as large as 100, only a few of which essentially contribute to localization. Simply involving all the APs in location fingerprints and thus in the radio map not only wastes excessive storage but also leads to a large computational overhead. In this paper, a theoretical analysis of WiFi-based location determination is conducted to investigate the resulting localization errors and reveals that some critical APs contribute significantly more to localization than the others, implying that it is unnecessary to include every AP in localization. Consequently, a heuristic AP selection algorithm based on the error analysis is proposed to efficiently select a subset of APs for use in localization. Finally, extensive experiments are carried out by using both the UJI dataset available online and the dataset collected in our laboratory, and it is shown that the proposed algorithm not only significantly reduces the redundancy of APs in WiFi fingerprint-based localization but also substantially improves the localization accuracy of the $k$ nearest neighbor (KNN) method.

Coalition Formation Game Based Access Point Selection for LTE-U and Wi-Fi Coexistence

As a promising solution for the next generation mobile networks (5G) deployment, long term evolution-unlicensed (LTE-U) is expected to improve spectrum utilization and channel capacity, which is beneficial for the industrial Internet of Things integrating many heterogeneous networks. However, such technology migration can potentially induce severe interference to the devices and networks originally operating on unlicensed bands, especially to the Wi-Fi network. In this paper, first of all, we emulate the coexistence of LTE-U networks and Wi-Fi networks, and experimentally evaluate their mutual interference relying on deploying time division duplex based OpenWrt wireless routers. Moreover, an LTE-U and Wi-Fi coexistence mechanism is proposed, where users with diverse traffic demands are capable of accessing either the public LTE-U network or its nearby Wi-Fi network. Finally, an access point selection algorithm with the aid of coalition formation game is developed for improving the system's throughput. Sufficient simulations based on Network Simulator 3 demonstrate that the overall throughput of the LTE-U and Wi-Fi hybrid cosystem outperforms that of the single LTE-U network as well as of the single Wi-Fi network.

Green Wi-Fi Implementation and Management in Dense Autonomous Environments for Smart Cities

Advanced informatics technologies facilitate the construction of green smart cities, especially the Wi-Fi implementation and management, for rapidly increasing personal Wi-Fi devices in autonomous environments residing in nonoverlapped channels often result in low energy efficiency and severe cochannel interference. In this paper, a green Wi-Fi management framework is constructed in order to reduce the overall energy consumption through turning off a portion of access points (APs) and aggregating their users to the other active APs. A Tabu-search-assisted active AP selection algorithm is proposed to minimize the power consumption with a seamless wireless converge. For the active APs, based on our defined metric airtime cost that is integrated by the in-range interference and the hidden terminal interference, a reinforcement-learning-aided AP self-management algorithm is proposed to dynamically adjust APs’ channels in the partially overlapped channel space. Extensive simulations and field experiments demonstrate that the power consumption can be reduced by about 65%, and the airtime cost of APs can be reduced by 50% compared with the typical least congestion channel search algorithm.

Wi-Fi Seeker: A link and Load Aware AP Selection Algorithm

The demand of mobile traffic increases tremendously as various Internet services are provided to mobile phones and computing devices, making mobile carriers increase the capacity of their cellular network. Since current cell towers and auxiliary devices cannot carry an excessive amount of traffic, several mobile carriers try to install low cost wireless equipment such as a Wi-Fi access point (AP) in order to offload overloaded traffic on cell towers. However, APs are installed randomly unlike cell towers because they can be installed by anyone and anywhere. The randomized allocation of APs incurs interference that causes serious degradation of wireless communication quality. Therefore, it is necessary to design mobile devices to use the environment or channel that contains the least interference for proper Wi-Fi communication. In order to make a mobile device experience the best wireless download quality, this paper proposes a novel AP selection mechanism that selects an AP that contains the least interference level compared to others with different number of communicating devices and amount of traffic load. The proposed selection mechanism does not require any additional feedback from nearby devices or APs. Performance evaluation was done by using real mobile devices with open source platform called Android.