Access Point Selection Strategy Research Articles

Maximum Throughput First Access Point Selection Scheme for Multi-Rate Software-Defined Wireless Network

As the number of devices increases, many businesses deploy multiple access points (APs) in neighboring areas to provide Internet service. In such networks, a mobile station (MS) often has multiple APs within its coverage area, and deciding which AP to associate with becomes a significant issue that can influence network performance. Software Defined Network (SDN) has recently become a popular technology for making intelligent mobile station assignment decisions for APs. In a multi-rate Wi-Fi network, the actual bandwidth of high-rate links can be reduced to that of low-rate links. In this paper, we propose a Maximum Throughput First (MTF)AP selection scheme, which is a refinement of existing Mininet Wi-Fi mechanisms. MTF considers the parameters of actual throughput achieved by the access point and the number of associated stations as selection metrics when making the association decision. The simulation results show that MTF provides better performance, especially in multi-rate environments.

A Machine-Learning-Based Access Point Selection Strategy for Automated Guided Vehicles in Smart Factories.

Automated Guided Vehicles (AGVs) are becoming popular at many manufacturing facilities. To ensure mobility and flexibility, AGVs are often controlled by wireless communication, eliminating the constraints of physical cables. These AGVs require multiple Access Points (APs) to ensure uninterrupted coverage across the site. As AGVs move, they need to switch between these APs seamlessly. A primary challenge is that the communication downtime during this link-switching process must be minimal for effective AGV monitoring and control. Current AP selection strategies based on observed Received Signal Strength Indicator (RSSI) often fail in manufacturing environments due to RSSI's inherent instability. This paper introduces a new AP selection technique for AGVs navigating these sites. Our approach harnesses the distinct movement patterns of AGVs and uses machine learning techniques to learn location-, trajectory-, and orientation-specific RSSI from the APs. Real-world factory data from our unique dataset revealed that our method extends the potential communication duration per route by 1.34 times compared to the prevalent signal strength-based switching methods commonly implemented in current drivers provided by chipset vendors or open-source Wi-Fi drivers. These results indicate that the automatic evaluation and tuning of the wireless environment using the proposed method is beneficial in reducing the time and effort required to investigate the detailed propagation paths needed to adapt AGV to existing APs.

Spatial Sparsity-Based Pilot Attack Detection and Transmission Countermeasure for Cell-Free Massive MIMO System

In this article, we investigate the security performance of a cell-free (CF) massive multiple-input–multiple-output (MIMO) system that is composed of a large number of randomly distributed access points (APs) and multiple users. There may be an eavesdropper who launches an active pilot attack during uplink channel estimation stage to eavesdrop on information in data transmission stage, which threaten the security of the system. A spatial sparsity-based pilot attack detection (SS-based-PAD) method is proposed in this study to determine whether there is an active eavesdropper. Different data transmission schemes were developed according to the detection results. In the presence of an eavesdropper, rate maximization power allocation (SRM-PA) algorithm is proposed to maximize the security of the eavesdropped user. On the contrary, achievable rate equalization power allocation (ARE-PA) algorithm is proposed to provide fair services for all legitimate users. Moreover, an AP selection scheme and corresponding conjugate beamforming are used to enhance the security performance of the system and mitigate the impact of a pilot spoofing attack.

Energy-Efficient AP Selection Using Intelligent Access Point System to Increase the Lifespan of IoT Devices.

With the emergence of various Internet of Things (IoT) technologies, energy-saving schemes for IoT devices have been rapidly developed. To enhance the energy efficiency of IoT devices in crowded environments with multiple overlapping cells, the selection of access points (APs) for IoT devices should consider energy conservation by reducing unnecessary packet transmission activities caused by collisions. Therefore, in this paper, we present a novel energy-efficient AP selection scheme using reinforcement learning to address the problem of unbalanced load that arises from biased AP connections. Our proposed method utilizes the Energy and Latency Reinforcement Learning (EL-RL) model for energy-efficient AP selection that takes into account the average energy consumption and the average latency of IoT devices. In the EL-RL model, we analyze the collision probability in Wi-Fi networks to reduce the number of retransmissions that induces more energy consumption and higher latency. According to the simulation, the proposed method achieves a maximum improvement of 53% in energy efficiency, 50% in uplink latency, and a 2.1-times longer expected lifespan of IoT devices compared to the conventional AP selection scheme.

Multi-stream receive-spatial modulation based cell-free MIMO downlink transmission method

Multi-stream receive-spatial modulation (MSR-SM) technology is applied to the cell-free multiple-input multiple-output (MIMO) downlink transmission, where both users and access points (APs) are multi-antenna, providing a new idea for green and energy-saving wireless communication. The power control coefficients of each AP are obtained using the sequential convex approximation (SCA) algorithm to optimize energy efficiency (EE) under the constraints of the highest transmitting power of the AP and the lowest signal interference-to-noise ratio of the user. This paper proposes an access point selection scheme based on a water-filling algorithm to reduce the number of unnecessary service APs and the total power consumption of the system. The simulation results show that the proposed power allocation algorithm outperforms the max-min power control algorithm in terms of energy efficiency. The MSR-SM-based downlink transmission mode is more energy efficient than the traditional space division multiplexing MIMO digital modulation mode. Furthermore, the SCA algorithm based on AP selection can improve energy efficiency performance compared with the power control algorithm without AP selection.

Time-Sequential Cooperative Localization for Moving Sensor in Millimeter-Wave Cell-Free Massive MIMO System

To address the moving-sensor localization problem in millimeter-wave (mm-wave) cell-free massive multiple-input multiple-output (MIMO) systems, a novel cooperative localization framework is proposed in this article. First, the angle of arrival (AoA) of the target sensor is extracted by each access point (AP) in the angle domain. Then, for the purpose of establishing the motion equation that is used to predict the location of the target sensor in the next period, a two-step velocity estimation algorithm is proposed to estimate the velocity and corresponding variance of the moving sensor simultaneously. Furthermore, based on horizontal dilution of precision (HDOP), an AP selection scheme is proposed to decide the subset of APs that participate in localization. On this basis, a time-sequential cooperative localization (TSCL) framework is constructed by jointly utilizing the estimated AoAs and the motion equation. The location estimation is converted to a joint posterior probability problem. The solution that maximizes the posterior probability is obtained through factor graph theory. The simulation results show that the proposed algorithm improves localization accuracy and robustness in time-varying environments.

Location-aided uplink transmission for user-centric cell-free massive MIMO systems: a fairness priority perspective

In this paper, we investigate the uplink transmission for user-centric cell-free massive multiple-input multiple-output (MIMO) systems. The largest-large-scale-fading-based access point (AP) selection method is adopted to achieve a user-centric operation. Under this user-centric framework, we propose a novel inter-cluster interference-based (IC-IB) pilot assignment scheme to alleviate pilot contamination. Considering the local characteristics of channel estimates and statistics, we propose a location-aided distributed uplink combining scheme to balance the relationship among the spectral efficiency (SE), user equipment (UE) fairness and complexity, in which local partial minimum mean-squared error (LP-MMSE) combining is adopted for some APs, while maximum-ratio (MR) combining is adopted for the remaining APs. A corresponding AP selection scheme based on a novel proposed metric representing inter-user interference is proposed. We also propose a new fairness coefficient taking SE performance into account to indicate the UE fairness. Moreover, the performance of the proposed scheme is investigated under fractional power control and max–min fairness (MMF) power control. Simulation results demonstrate that the channel estimation accuracy of our proposed IC-IB pilot assignment scheme outperforms that of the conventional pilot assignment schemes. It is also shown that compared with the benchmark LP-MMSE combining, the proposed location-aided combining trades 13.45% average SE loss for 26.61% UE fairness improvement and 28.58% complexity reduction when gamma = 0.6. And by adjusting the threshold gamma, a good trade-off between the average SE, UE fairness and computational complexity can be provided by the proposed scheme. Furthermore, the proposed scheme with fractional power control can better demonstrate the advantages of trade-off performance than MMF power control and full power transmission.

Improving Sum-Rate of Cell-Free Massive MIMO With Expanded Compute-and-Forward

Cell-free massive multiple-input multiple-output (MIMO) employs a large number of distributed access points (APs) to serve a small number of user equipments (UEs) via the same time/frequency resource. Due to the strong macro diversity gain, cell-free massive MIMO can considerably improve the achievable sum-rate compared to conventional cellular massive MIMO. However, the performance of cell-free massive MIMO is upper limited by inter-user interference (IUI) when employing simple maximum ratio combining (MRC) at receivers. To harness IUI, the expanded compute-and-forward (ECF) framework is adopted. In particular, we propose power control algorithms for the parallel computation and successive computation in the ECF framework, respectively, to exploit the performance gain and then improve the system performance. Furthermore, we propose an AP selection scheme and the application of different decoding orders for the successive computation. Finally, numerical results demonstrate that ECF frameworks outperform the conventional CF and MRC frameworks in terms of achievable sum-rate.

On the optimal energy efficiency and spectral efficiency trade-off of CF massive MIMO SWIPT system

Energy efficiency is a key requirement for future network design, and user-centric (UC) cell-free (CF) massive multi-input multi-output (MIMO) networks can achieve over ten times the energy efficiency. Based on this, this paper studies a CF MIMO simultaneous wireless information and power transmission system and proposes a UC access point (AP) selection method and a trade-off performance optimization scheme for spectral efficiency and energy efficiency. In this system, users have both energy recovery and information transmission functions. According to the difference between the interference in the energy harvesting and information transmission process, a flexible AP selection scheme is designed. Blindly pursuing high spectral efficiency will result in waste of resources. This paper proposes an evaluation index that takes into account both energy efficiency and spectral efficiency, analyses the trade-off between energy efficiency and spectral efficiency, and jointly optimizes the AP selection scheme and the uplink (UL) and downlink (DL) time switching ratio to maximize the trade-off performance. Then, the non-convex problem is converted to a geometric planning problem to solve. The simulation results show that by implementing a suitable AP selection scheme and UL and DL time allocation, the information processing scheme on the AP side has a slight loss in spectral efficiency, but the energy efficiency is close to the performance of global processing on the central processing unit.

An SDN‐based technique for reducing handoff times in WiFi networks

SummaryThe handoff process in WiFi networks has a very important role to provide continuity of the service to wireless users where the access points (APs) are in numerous numbers. In traditional WiFi networks, the handoff process takes a few seconds and is initiated by the wireless devices, but these few seconds could be a reason for information loss when dealing with delay sensitive applications. In this paper, we propose a reduced detection and discovery time approach (DeRy), which generates a solution based on software defined WiFi networks (SD‐WiFi). A simple network management protocol (SNMP) manager and received signal strength indicator (RSSI) manager are employed to report the AP traffic conditions and RSSI values to the SDN controller and instead of the wireless devices, the handoff decisions are taken at the controller end. When to initiate the handoff (detection phase) and which destination AP to associate to (discovery phase) are handled by the centralized SDN controller. To implement DeRy, extensive simulation runs are carried out on Mininet‐NS3‐WiFi network simulator. The simulation results show that the DeRy significantly reduces the handoff times by 60–70% and reduces the average number of retransmission by 4–49%, hence maintaining the throughput for delay sensitive applications such as VoIP, when compared to the standard RSSI‐based handoff scheme, channel measurement based access selection scheme (CMAS) and (DL‐SINR) downlink‐signal to interference plus noise ratio AP selection scheme (DASA).

Hidden terminal-aware access point selection for IEEE 802.11ah networks

Connectivity provisioning in large-scale Internet-of-Things (IoT) networks raises several challenges, one of which is the high collision rate among the transmission attempts of IoT devices. Transmission collisions result in prolonged data delivery delay and increased energy consumption of IoT devices. Contention-based wireless communication technologies such as WiFi is particularly vulnerable to this issue. As a remedy, a new standard IEEE 802.11ah is designed and such methods as RAW (Restricted Access Window) grouping is incorporated in order to curb the high collision rate. Meanwhile, in IEEE 802.11ah the transmission range and the number of devices that can be accommodated are also drastically increased. As a result, the impact of the hidden terminal problem is significantly amplified, which causes transmission collisions. In this paper, we propose an approach to avoid the high collision rate at the phase of the AP(Access Point) selection for IEEE 802.11ah networks. The proposed scheme considers the traffic load balancing and the hidden terminal issue simultaneously, without incurring any burden to the IoT devices. No modification of the IEEE 802.11ah standard is needed. Via extensive NS-3 simulations, we have verified that the proposed AP selection scheme outperforms the existing schemes in terms of data delivery delay and energy consumption by reducing transmission collisions.

Performance of Cell-Free Massive MIMO With Joint User Clustering and Access Point Selection

We consider an uplink cell-free massive multiple-input multiple-output (MIMO) system, in which the access points are connected to the central processing unit (CPU) through a fronthaul network. This system has the advantages of wide coverage and flexible deployment. However, the performance of this system depends on a capacity-limited fronthaul, and when the fronthaul is saturated, the quality of service will be reduced. To address this issue, we propose a joint user clustering and AP selection scheme, which can reduce the pressure on the fronthaul link while taking into account the system performance and computational complexity. We first derive a closed-form expression for the uplink spectral efficiency over Rician fading channels. Based on the derived expression, we formulate the problem of maximizing the minimum uplink spectral efficiency across all users by jointly optimizing the large-scale fading decoding (LSFD) coefficient and power control coefficient. Then, combined with the optimization results and channel estimation error, a suboptimal access point selection scheme is proposed. In addition, we propose a user clustering scheme to further reduce the complexity of the AP selection scheme. The simulation results show that the joint user clustering and access point selection scheme can reduce the system fronthaul link pressure, while the performance degrades only slightly.

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.

Efficient Angle-Domain Processing for FDD-Based Cell-Free Massive MIMO Systems

Cell-free massive MIMO communications is an emerging network technology for 5G wireless communications wherein distributed multi-antenna access points (APs) serve many users simultaneously. Most prior work on cell-free massive MIMO systems assume time-division duplexing mode, although frequency-division duplexing (FDD) systems dominate current wireless standards. The key challenges in FDD massive MIMO systems are channel-state information (CSI) acquisition and feedback overhead. To address these challenges, we exploit the so-called angle reciprocity of multipath components in the uplink and downlink, so that the required CSI acquisition overhead scales only with the number of served users, and not the number of AP antennas nor APs. We propose a low complexity multipath component estimation technique and present linear angle-of-arrival (AoA)-based beamforming/combining schemes for FDD-based cell-free massive MIMO systems. We analyze the performance of these schemes by deriving closed-form expressions for the mean-square-error of the estimated multipath components, as well as expressions for the uplink and downlink spectral efficiency. Using semi-definite programming, we solve a max-min power allocation problem that maximizes the minimum user rate under per-user power constraints. Furthermore, we present a user-centric (UC) AP selection scheme in which each user chooses a subset of APs to improve the overall energy efficiency of the system. Simulation results demonstrate that the proposed multipath component estimation technique outperforms conventional subspace-based and gradient-descent based techniques. We also show that the proposed beamforming and combining techniques along with the proposed power control scheme substantially enhance the spectral and energy efficiencies with an adequate number of antennas at the APs.

Towards QoS-Aware Load Balancing for High Density Software Defined Wi-Fi Networks

High density Wi-Fi networks require load balancing in order to ensure quality of service (QoS). In the traditional Wi-Fi networks, the wireless stations learn the access points (APs) load and make the association decisions themselves leading to uneven load distribution among the APs. The new paradigm, software defined networking (SDN), has a centralized architecture, which allows to manage, measure and control high density Wi-Fi networks easily. In this paper we propose a QoS-aware load balancing strategy (QALB) for software defined Wi-Fi networks (SD-Wi-Fi), as a solution to address the problem of Wi-Fi congestion among the OpenFlow enabled APs (OAPs). The SDN controller selects a load level up to which the association decisions are made by the OAPs without consulting the controller. The wireless stations from an overloaded OAP are handed to an underloaded OAP by considering multi-metrics such as the packet loss rate, received signal strength indicator (RSSI) and throughput. An emulation platform and a large-scale-low-cost testbed with the same settings are constructed to evaluate the performance of our load balancing strategy. The results show that in comparison to four non-static schemes such as, channel measurement based access selection scheme (CMAS), (DL-SINR) downlink-signal to interference plus noise ratio AP selection scheme (DASA), mean probe delay scheme (MPD) and RSSI scheme, the proposed QALB, optimizes the throughput up to 16%, reduces the average frame delay up to 19%, minimizes the number of re-transmissions by 49%, reduces the number of handoffs by 15%, improves the degree of load balancing by 22% and minimizes the re-association times by 38%, in high density SD-Wi-Fi.

Joint Design of Access Point Selection and Path Planning for UAV-Assisted Cellular Networks

Unmanned aerial vehicle (UAV)-assisted communication is envisioned as a potential solution to the data traffic explosion in the massive machine-type communications (mMTC) scenario. In this article, we investigate the UAV-assisted cellular networks, where a UAV acts as a flying relay to offload part of the data traffic from the overloaded cell to another. We utilize the practical spatial distribution of data traffic and a convincing air-to-ground channel model. The quality of service (QoS) is defined as a UAV utility function which is designed based on a packet loss ratio (PLR)-related users’ cost function to represent the performance improvements brought by the UAV. We formulate a joint optimization problem to maximize the UAV utility function and then decompose it into the subproblems about the access point selection and the UAV path planning, which influence the PLR by influencing the packet collision rate and channel state. Since the access point selection subproblem is NP-hard, a game-theory-based distributed algorithm is proposed, instructing the users to select the base station (BS) or the UAV as the access point autonomously. To achieve the most superior channel state, we solve the UAV path planning subproblem by a deep reinforcement learning (DRL)-based approach, instructing the UAV to take the optimal action in each position. The simulation results show that the proposed access point selection scheme can significantly reduce the average cost of users and the proposed UAV path planning method can achieve a path with smaller average channel pathloss compared with other approaches.

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.

AP Selection Scheme Based on Achievable Throughputs in SDN-Enabled WLANs

To ensure an efficient coverage and allow transparent mobility, it is common to find a number of access points (APs) deployed in the neighborhood of each other in current WLANs. A mobile terminal (MT) often has multiple potential APs that it can connect to, and the selection of an appropriate AP becomes an important factor affecting both the network performance and user experience. This paper proposes a new AP selection scheme, which is based on the analysis of achievable normalized throughputs (ANTs) that the MT could obtain in the network. Specifically, we first compute the normalized throughput that characterizes the channel competition among multiple basic service sets (BSSs) as well as MTs within the same BSS. By evaluating ANTs in situations where the MT associates with different APs, the most appropriate AP for the MT can then be selected. Then, we present the implementation of the proposed AP selection scheme under the SDN-WiFi framework, in which the AP selection process can be performed in a software manner. The experimental results show that our computations on ANTs are of high accuracy and the proposed AP selection scheme can achieve a better performance than the existing schemes in terms of both the quality of service of MTs and the fairness among MTs.

A Collaborative Filtering Recommendation-Based Scheme for WLANs With Differentiated Access Service

As the deployment of WLANs becomes extensive, users commonly have to select one access point (AP) for association among multiple available APs. However, the existing AP selection policies are not sufficient enough due to their drawbacks (e.g., imbalanced load and time consuming for association). In this paper, we present a collaborative filtering (CF) recommendation-based AP selection strategy in wireless local area networks (WLANs) with differentiated access service. At first, a differentiated AP selection strategy by utilizing analytic hierarchy process (AHP) is leveraged to optimize AP selection metric with multiple objectives for different users. Furthermore, an improved differentiated AP selection approach by CF recommendation is investigated. Based on our method, the new user can associate with the recommended AP promptly instead of channel scanning and selection metric computation. Meanwhile, load prebalancing module periodically excludes the AP considered to be overloaded from the recommendation list, while the pioneer user (PU) updates the corresponding module periodically with an adaptive method to replace the AP with low recommendation efficiency. Simulation results demonstrate that our presented strategies can provide differentiated quality-of-service (QoS) assurance to users, improve overall network throughput, balance network traffic, and degrade association time cost.

A novel AP selection scheme in software defined networking enabled WLAN

In traditional IEEE 802.11 protocols, the RSSI is usually taken as the criterion for AP selection, by which the AP with the greatest signal strength may be heavily burdened. In this paper, A SDN-based AP selection scheme called TPR is proposed. Through centralized control from SDN controller which has a full view of the network, our TPR enables AP selection more reasonable and efficient. In addition, the OpenFlow protocol embedded in the AP firmware is revised to efficiently collect the measurement data for AP selection. Besides, since the data processing task is shifted to the SDN controller, the burden of each STA is greatly alleviated thus resulting in fewer energy consumption and overheads at STAs. Experimental results demonstrate the ability of our TPR to increase the throughput of the WLAN system in multi-AP scenario and to reduce the number of handoffs among different APs.