Neighboring Access Points Research Articles

Lyapunov Drift-Plus-Penalty-Based Cooperative Uplink Scheduling in Dense Wi-Fi Networks.

In high-density network environments with multiple access points (APs) and stations, individual uplink scheduling by each AP can severely interfere with the uplink transmissions of neighboring APs and their associated stations. In congested areas where concurrent uplink transmissions may lead to significant interference, it would be beneficial to deploy a cooperative scheduler or a central coordinating entity responsible for orchestrating cooperative uplink scheduling by assigning several neighboring APs to support the uplink transmission of a single station within a proximate service area to alleviate the excessive interference. Cooperative uplink scheduling facilitated by cooperative information sharing and management is poised to improve the likelihood of successful uplink transmissions in areas with a high concentration of APs and stations. Nonetheless, it is crucial to account for the queue stability of the stations and the potential delays arising from information exchange and the decision-making process in uplink scheduling to maintain the overall effectiveness of the cooperative approach. In this paper, we propose a Lyapunov drift-plus-penalty framework-based cooperative uplink scheduling method for densely populated Wi-Fi networks. The cooperative scheduler aggregates information, such as signal-to-interference-plus-noise ratio (SINR) and queue status. During the aggregation procedure, propagation delays are also estimated and utilized as a value of expected cooperation delays in scheduling decisions. Upon aggregating the information, the cooperative scheduler calculates the Lyapunov drift-plus-penalty value, incorporating a predefined model parameter to adjust the system accordingly. Among the possible scheduling candidates, the proposed method proceeds to make uplink decisions that aim to reduce the upper bound of the Lyapunov drift-plus-penalty value, thereby improving the network performance and stability without a severe increase in cooperation delay in highly congested areas. Through comprehensive performance evaluations, the proposed method effectively enhances network performance with an appropriate model parameter. The performance improvement is particularly notable in highly congested areas and is achieved without a severe increase in cooperation delays.

Reinforcement Learning-Based Handover Scheme with Neighbor Beacon Frame Transmission

Mobility support to change the connection from one access point (AP) to the next (i.e., handover) becomes one of the important issues in IEEE 802.11 wireless local area networks (WLANs). During handover, the channel scanning procedure, which aims to collect neighbor AP (NAP) information on all available channels, accounts for most of the delay time. To reduce the channel scanning procedure, a neighbor beacon frame transmission scheme (N-BTS) was proposed for a seamless handover. N-BTS can provide a seamless handover by removing the channel scanning procedure. However, N-BTS always requires operating overhead even if there are few mobile stations (MSs) for the handover. Therefore, this paper proposes a reinforcement learning-based handover scheme with neighbor beacon frame transmission (MAN-BTS) to properly consider the use of N-BTS. The optimization equation is defined to maximize the expected reward to find the optimal policy and is solved using Q-learning. Simulation results show that the proposed scheme outperforms the comparison schemes in terms of the expected reward.

An efficient producer mobility management technique for real-time communication in NDN-based Remote Health Monitoring systems

The remote Health Monitoring (RHM) system is a remote healthcare platform that provides real-time monitoring services and brings convenience and safety for doctors as well as patients. Named Data Network (NDN) is a future network architecture, which is a kind of Information-Centric Network. NDN’s advantages in data security and performance can make the health system more reliable and efficient. However, in the existing NDN network architecture, there exist some producer mobility issues that can impede the proceeding towards real-time and seamless communication. To solve the problem, a Neighborhood Registration Scheme (NRS) is proposed here. When the mobile producer moves from one AP to another, it has not registered into the new AP and thus cannot receive the Interest Packet (IntP) from the consumer in time, which can cause data losses. Meanwhile, the updating process in the NDN map also takes a considerable time, which can also cause delay, referred to as updating delay. This is due to some consumers having got the new location of the mobile producer while others have not. The scheme proposes in this paper is to register the mobile producer not only into the nearest Access Point (AP) but also multiple neighbor APs. In this way, the delay caused by AP switching and package loss rate can be effectively reduced, which allows mobile producers to move across the cells with low latency and Packet Loss (PL) rate. NRS is a comprehensive routing algorithm that designs the modified packet transmission strategy. Compare to the Anchor-less strategy, the proposed NRS decreases about 80% PL rate and saves 16% delay time, whereas, NRS reduces more than 90% PL rate and saves 40% delay time than Hop-count strategy in high-speed producer mobility scenarios. These improvements are necessary for RHM because low delay and PL are the most important requirements of the RHM that can strengthen the real-time monitoring for patients, information interaction between doctors and patients, and safety assurance. By setting the proper parameters, NRS can promise to have no PL and acceptable network cost.

Penalized and Decentralized Contextual Bandit Learning for WLAN Channel Allocation with Contention-Driven Feature Extraction

In this study, a contextual multi-armed bandit (CMAB)-based decentralized channel exploration framework disentangling a channel utility function (i.e., reward) with respect to contending neighboring access points (APs) is proposed. The proposed framework enables APs to evaluate observed rewards compositionally for contending APs, allowing both robustness against reward fluctuation due to neighboring APs' varying channels and assessment of even unexplored channels. To realize this framework, we propose contention-driven feature extraction (CDFE), which extracts the adjacency relation among APs under contention and forms the basis for expressing reward functions in disentangled form, that is, a linear combination of parameters associated with neighboring APs under contention). This allows the CMAB to be leveraged with a joint linear upper confidence bound (JLinUCB) exploration and to delve into the effectiveness of the proposed framework. Moreover, we address the problem of non-convergence — the channel exploration cycle — by proposing a penalized JLinUCB (P-JLinUCB) based on the key idea of introducing a discount parameter to the reward for exploiting a different channel before and after the learning round. Numerical evaluations confirm that the proposed method allows APs to assess the channel quality robustly against reward fluctuations by CDFE and achieves better convergence properties by P-JLinUCB.

Partial Cooperative Zero-Forcing Decoding for Uplink Cell-Free Massive MIMO

We propose a partial cooperative zero-forcing (PCZF) decoding scheme for the uplink cell-free massive MIMO system, wherein the neighboring access points (APs) around each user equipment (UE) share the channel state information (CSI) and jointly suppress the interference using the zero-forcing technique. Using asymptotic analysis, we derive a closed-form asymptotic expression for a lower bound on the achievable rates. Considering the unique and complex form of the achievable rates, we propose power control schemes according to two criteria. The first criterion is to maximize the minimum achievable rate. For this criterion, we propose a target-SINR-tracking (TST)-based bisection algorithm. Since the power control update functions are standard interference functions, the TST-based bisection method always converges to the optimal solution. The second criterion is to maximize the sum rate, for which we propose two power control algorithms: 1) randomization and scaling algorithm (RSA) and 2) fractional programming algorithm (FPA). In each iteration of the RAS algorithm, we first exploit the randomization technique to transform the sum-rate maximization problem into a series of power minimization problems, and then improve the sum rate by scaling. In the FP algorithm, we derive a lower bound on the sum rate, and then propose an iterative approach based on the Lagrangian dual transform and fractional programming to maximize the sum-rate lower bound. Numerical results validate the theoretical analysis and verify the efficiency of the proposed power control algorithms.

Two-Stage Channel Estimation Approach for Cell-Free IoT With Massive Random Access

We investigate the activity detection and channel estimation issues for cell-free Internet of Things (IoT) networks with massive random access. In each time slot, only partial devices are active and communicate with neighboring access points (APs) using non-orthogonal random pilot sequences. Different from the centralized processing in cellular networks, the activity detection and channel estimation in cell-free IoT is more challenging due to the distributed and user-centric architecture. We propose a two-stage approach to detect the random activities of devices and estimate their channel states. In the first stage, the activity of each device is jointly detected by its adjacent APs based on the vector approximate message passing (Vector AMP) algorithm. In the second stage, each AP re-estimates the channel using the linear minimum mean square error (LMMSE) method based on the detected activities to improve the channel estimation accuracy. We derive closed-form expressions for the activity detection error probability and the mean-squared channel estimation errors for a typical device. Finally, we analyze the performance of the entire cell-free IoT network in terms of coverage probability. Simulation results validate the derived closed-form expressions and show that the cell-free IoT significantly outperforms the collocated massive MIMO and small-cell schemes in terms of coverage probability.

An Active Path-Associated Cache Scheme for Mobile Scenes

With the widespread growth of mass content, information-centric networks (ICN) have become one of the research hotspots of future network architecture. One of the important features of ICN is ubiquitous in-network caching. In recent years, the explosive growth of mobile devices has brought content dynamics, which poses a new challenge to the original ICN caching mechanism. This paper focuses on the WiFi mobile scenario of ICN. We design a new path-associated active caching scheme to shorten the time delay of users obtaining content to enhance the user experience. In this article, based on the WiFi scenario, we first propose a solution for neighbor access point selection from a theoretical perspective, considering the caching cost and transition probability. The cache content is then forwarded based on the selected neighbor set. For cached content, we propose content freshness according to mobile characteristics and consider content popularity at the same time. For cache nodes, we focus on the size of the remaining cache space and the number of hops from the cache to the user. We have implemented this strategy based on the value of caching on the forwarding path. The simulation results show that our caching strategy has a significant improvement in performance compared with active caching and other caching strategies.

Majority Voting-Based MAC Protocol for Exploiting Link-Layer Diversity in Wireless Networks.

In wireless local area networks (WLANs), the effect of interference signals between neighboring nodes increases as the number of wireless nodes using limited radio frequency resources in a limited space increases, which can significantly degrade the reliability of data transmission. In high-density WLANs, there can be several neighboring access points (APs) that can receive uplink transmission from a station. In conventional medium access control (MAC) protocols, uplink data frames containing errors or transmitted from a non-associated station are discarded at APs. Alternatively, we propose a MAC protocol using redundant wireless links between neighboring APs and the non-associated stations. In the proposed MAC protocol, we consider a centralized WLAN with a control node that performs error corrections of erroneous uplink data frames via a majority voting algorithm-based link-layer diversity scheme using uplink data received from multiple APs to increase the reliability of data transmission. In addition, we propose an adaptive carrier sensing ranging mechanism to improve the uplink network throughput in the proposed centralized WLAN system. Further, we conduct simulation studies and software-defined radio-based experiments to evaluate the performance of the proposed MAC protocol in various WLAN scenarios.

Partial Interference Suppression in Massive MIMO Systems: Taxonomy and Experimental Analysis

Densely deploying Massive MIMO access points results eventually in cell-free systems. To achieve scalability, users should be allocated only to neighbouring access points as it is not realistic to assume that all access points serve all users. At the same time interference from other nearby users should be suppressed, as it is not practical to suppose that channel state information from all users is available for full MMSE-based interference suppression. Motivating the use of partial MMSE combining vectors. This work shows the taxonomy of proposed MMSE combining vectors for different clustering techniques, and three approaches to partially suppress interference based on 1) interferers with common access points, 2) strongest channel gain and 3) strongest eigendirections. In order to analyse the pros and cons of the three interference suppression methods, a general parameterised MMSE combining vector is proposed, that is generic enough to represent any clustering methodology and any partial interference suppression technique. The results are presented in terms of spectral efficiency and computational cost, with the aid of a dense indoor massive MIMO experiment and contrasted with an indoor simulation using the WINNER II channel model. Experimental and simulation results show that partial suppression based on shared access points is not suitable for systems with a small probability of users sharing the serving access points, as dominant interference is likely to come also from users connected to access points not serving the harmed user. Channel gain based interference suppression provides the best controllable approach in terms of spectral efficiency and computational cost. However, eigendirections-based interference suppression achieves the highest spectral efficiency, when the number of antennas per access point is high enough.

Machine Learning Enabled Preamble Collision Resolution in Distributed Massive MIMO

Preamble collision is a bottleneck that impairs the performance of random access (RA) user equipment (UE) in grant-free RA (GFRA). In this paper, by leveraging distributed massive multiple input multiple output (mMIMO) together with machine learning, a novel machine learning based framework solution is proposed to address the preamble collision problem in GFRA. The key idea is to identify and employ the neighboring access points (APs) of a collided RA UE for its data decoding rather than all the APs, so that the mutual interference among collided RA UEs can be effectively mitigated. To this end, we first design a tailored deep neural network (DNN) to enable the preamble multiplicity estimation in GFRA, where an energy detection (ED) method is also proposed for performance comparison. With the estimated preamble multiplicity, we then propose a K-means AP clustering algorithm to cluster the neighboring APs of collided RA UEs and organize each AP cluster to decode the received data individually. Simulation results show that a decent performance of preamble multiplicity estimation in terms of accuracy and reliability can be achieved by the proposed DNN, and confirm that the proposed schemes are effective in preamble collision resolution in GFRA, which are able to achieve a near-optimal performance in terms of uplink achievable rate per collided RA UE, and offer significant performance improvement over traditional schemes.

RF channel management scheme for seamless multimedia service in 802.11 wireless networks

The latest mobile devices such as smartphone, laptop, and tablet PC have supported IEEE 802.11 interfaces for broadband wireless data services. To extend the coverage in medium or large-scale areas such as office buildings, airports, campuses, or convention center, multiple access points (APs) need to be deployed. When a mobile station (MS) moves an AP to another, it typically experiences a long delay time for reconnection and communications to an AP due to the four sequential phases such as detection, channel scanning, authentication and reassociation. Consequently, the traditional WLAN is not adequate for mobile multimedia services such as real-time video conferences, augmented reality (AR), virtual reality (VR), etc., especially, in a mobile environment where requires to deploy several APs. This paper proposes a new handoff scheme where the channel scanning is completely omitted by having active MSs hold the channel consistently which is acquired at an initial connection stage to an AP even when it moves to a neighboring AP. Each AP changes its own channel periodically in a predetermined order while providing the orthogonality between the channels of the neighboring APs to mitigate interference. Therefore, seamless mobility can be supported for multimedia services by eliminating link layer handoff in the IEEE 802.11 WLAN. Moreover, any IEEE 802.11-based mobile devices can roam seamlessly without any modification. In other words, the proposed scheme can maintain backward compatibility with the legacy DCF and only requires the modification of APs. By adopting the periodic channel switching scheme, additionally, the proposed scheme can uniformly distribute all the MSs over the entire channels. This distribution can also reduce collision probability by decreasing the number of competing MSs over the same channel. Performance evaluation shows that the proposed scheme can provide low handoff latency and enhance throughput performance due to the decrease of collision probability.

Concurrent decentralized channel allocation and access point selection using multi-armed bandits in multi BSS WLANs

Enterprise Wireless Local Area Networks (WLANs) consist of multiple Access Points (APs) covering a given area. In these networks, interference is mitigated by allocating different channels to neighboring APs. Besides, stations are allowed to associate to any AP in the network, selecting by default the one from which receive higher power, even if it is not the best option in terms of the network performance.Finding a suitable network configuration able to maximize the performance of enterprise WLANs is a challenging task given the complex dependencies between APs and stations. Recently, in wireless networking, the use of reinforcement learning techniques has emerged as an effective solution to efficiently explore the impact of different network configurations in the system performance, identifying those that provide better performance.In this paper, we study if Multi-Armed Bandits (MABs) are able to offer a feasible solution to the decentralized channel allocation and AP selection problems in Enterprise WLAN scenarios. To do so, we empower APs and stations with agents that, by means of implementing the Thompson sampling algorithm, explore and learn which is the best channel to use, and which is the best AP to associate, respectively. Our evaluation is performed over randomly generated scenarios, which enclose different network topologies and traffic loads. The presented results show that the proposed adaptive framework using MABs outperform the static approach (i.e., using always the initial default configuration, usually random) regardless of the network density and the traffic requirements. Moreover, we show that the use of the proposed framework reduces the performance variability between different scenarios. Also, results show that we achieve the same performance (or better) than static strategies with less APs for the same number of stations. Finally, special attention is placed on how the agents interact. Even if the agents operate in a completely independent manner, their decisions have interrelated effects, as they take actions over the same set of channel resources.

Analysis of EMC problems with the purpose of developing an effective method of WLAN design on the basis of information systems considering electromagnetic

The article analyzes the scope, technologies and principles of wireless local area networks, which allowed to mark the “bottlenecks” of these technologies, which need special attention when designing: the problem of hidden node; mutual interference between neighboring cells (intrasystem EMC); intersystem interference; providing QoS for responsible applications; expansion of wireless local area networks; the impact of network deployment features. The methods used to solve EMC problems in wireless local area networks are shown and their efficiency is analyzed: the mechanism of access to the transmission medium; IEEE 802.11e (QoS) standard; additional standards designed to reduce the impact of interference (802.11 h and k); coding and modulation. It is shown that the accounting of intrasystem and intersystem interference should be carried out in the early stages of design of wireless local area networks. It is shown that the existing mechanisms of access to the transmission medium are designed to organize the conflict-free operation of transceivers within a single access point and are vulnerable to interference created by transmission stations of neighboring access points. The dynamic sensitivity control algorithm is aimed at partially solving this problem, however, the main disadvantage of the DCC algorithm proposed for use in Wi-Fi 6 is that stations located near access points have a higher probability of access to the environment due to a higher signal level. Relative to electromagnetic interference, which leads to a shorter detection range of the carrier. That requires the development of advanced mechanisms to control the sensitivity of the service area of access points. Based on the analysis, a conclusion is made about the need to develop a methodology for designing wireless local area networks based on information systems, taking into account electromagnetic interference. Which will reduce the time of their design and increase the efficiency of their further operation.

Fixing Target Access Point with low load during Handoff in WLAN

It is obvious that providing continuous service for a Mobile Node (MN) which is in frequent movement is a major challenge nowadays. Finding and fixing the best neighboring Access Point (AP) for Mobile Node which is in movement is a tedious task. In the existing system time period of Mobile Nodes waiting for all neighboring APs to respond is too long. AP which are busy may not be able to respond to the request. For this reason, novel idea to fix target access point during handoff in WLAN is proposed. A Neighboring Graph Model (NGM) is used which is built to store and retrieve the neighbors of the current AP. The lists of neighboring APs are provided by the NG and target AP is selected based on the low load. The simulation results showed that the proposed handoff considerably decreases the time to fix the target Access Point.

Neighbor access point identification using station information in High-Density Wi-Fi system

Neighbor access point identification using station information in High-Density Wi-Fi system

Energy and connectivity aware resource optimization of nodes traffic distribution in smart home networks

Smart home network is a network of connected devices and sensors in a house or a group of apartments in a building. It is expected that a huge number of sensors and access points (APs) are utilized in a smart home network in an integrated way to fulfill the necessary functionalities in homes and buildings. In this regard, energy optimization and providing network connectivity have an extremely important role in enabling smart home network in its mission. In this paper, we investigate the optimal measures that can be used to more optimally manage the sensor nodes traffic distribution between the available APs in a smart home network while considering joint constraints on the average energy consumption of the nodes and also the full coverage and connectivity across the network. In particular, we consider a typical smart home network scenario with multiple number of sensor nodes and APs distributed within the network area where the sensors have the freedom to choose between any neighboring APs for data communication that can better improve the energy efficiency and connectivity of the network. Accordingly, we consider constraints on the average battery consumption of the sensor nodes and also the energy consumption at the APs to improve energy efficiency and life-time of the smart home network. Moreover, an additional constraint is considered to guarantee the necessary full network connectivity and coverage across the proposed smart home network. In this regard, we consider a cost model for a typical smart home network and form an optimization problem that aims at minimizing the cost of the network while taking into account aforementioned constraints on the consumed energy by the sensor nodes and APs, and also addressing the coverage and connectivity requirements in the network. Finally, the benefits of using the proposed optimization problem in smart home networks are discussed and numerical results and comparisons regarding various operating scenarios are provided.

Physical-Layer Security in Multiuser Visible Light Communication Networks

In this paper, we study the physical-layer security in a 3-D multiuser visible light communication (VLC) network. The locations of access points (APs) and mobile users are modeled as two 2-D, independent and homogeneous Poisson point processes at distinct heights. Using mathematical tools from stochastic geometry, we provide a new analytical framework to characterize the secrecy performance in multiuser VLC networks. Closed-form results for the outage probability and the ergodic secrecy rate are derived for networks without AP cooperation. Considering the cooperation among APs, we give tight lower and upper bounds on the secrecy outage probability and the ergodic secrecy rate. To further enhance the secrecy performance at the legitimate user, a disk-shaped secrecy protected zone is implemented in the vicinity of the transmit AP. Based on the obtained results, it is shown that cooperating neighboring APs in a multiuser VLC network can bring performance gains on the secrecy rate, but only to a limited extent. We also show that building an eavesdropper-free protected zone around the AP significantly improves the secrecy performance of legitimate users, which appears to be a promising solution for the design of multiuser VLC networks with high security requirements.

Improving fairness between close Wi-Fi access points

We consider some Wi-Fi access points (AP) communicating with some stations while being in the carrier sense area of neighbor AP. This corresponds to usual scenarios in local area networks, set-top boxes in houses, intelligent transportation systems, etc. First, using the network simulator ns-3, we observe interesting phenomena, depending on the number of pairs and its parity, confirming previous experiments and models of the literature. Short chains give high disparity in throughput. Moreover, adding a pair (i.e., an AP) can drastically change the throughput of a far ones. We also point out a notable asymptotic behavior. We show that the same phenomena appear while varying the number of stations per AP, the sending rate or the chain geometry. Second, we provide a new analytic model by considering the probability for a station to send data while its neighbors are waiting. This powerful model leads to a non-linear system of equations matching very well the N's-3 simulations. It is scalable and permits to study accurately both short and very large chains. In particular, we show interesting properties related to the fairness. Third, we apply our results to some practical cases. Thanks to our model, we are able to compute the optimal settings of the stations for ensuring the fairness in the scenarios listed above.

Dynamic Channel Allocation in IEEE 802.11 Networks

Wireless channel allocation plays as an important role in thedesign of wireless network, as it greatly influences the throughput and performance of the network. It is proposed a technique to improve the usage of wireless spectrum in the context of IEEE 802.11 wireless network using new channel assignment methods among interfering Access Points (APs). Reliable channel selection for each Access Point (AP) is essential in setting up and operating densely deployed 802.11 WLANs. The aim of the channel selection is to provide efficient reuse of the spectrum and therefore minimize interference and improve the performance. The focus is mainly on to design an algorithm to solve the problem of channel allocation using Simulated Annealing. The algorithm minimizes the interference among the Access Points (APs). All the access points in the network operate on this algorithm simultaneously and determine the best channel it should use to minimize the interference from the neighboring access points.

A New Ticket-Based Authentication Mechanism for Fast Handover in Mesh Network.

Due to the ever-growing popularity mobile devices of various kinds have received worldwide, the demands on large-scale wireless network infrastructure development and enhancement have been rapidly swelling in recent years. A mobile device holder can get online at a wireless network access point, which covers a limited area. When the client leaves the access point, there will be a temporary disconnection until he/she enters the coverage of another access point. Even when the coverages of two neighboring access points overlap, there is still work to do to make the wireless connection smoothly continue. The action of one wireless network access point passing a client to another access point is referred to as the handover. During handover, for security concerns, the client and the new access point should perform mutual authentication before any Internet access service is practically gained/provided. If the handover protocol is inefficient, in some cases discontinued Internet service will happen. In 2013, Li et al. proposed a fast handover authentication mechanism for wireless mesh network (WMN) based on tickets. Unfortunately, Li et al.’s work came with some weaknesses. For one thing, some sensitive information such as the time and date of expiration is sent in plaintext, which increases security risks. For another, Li et al.’s protocol includes the use of high-quality tamper-proof devices (TPDs), and this unreasonably high equipment requirement limits its applicability. In this paper, we shall propose a new efficient handover authentication mechanism. The new mechanism offers a higher level of security on a more scalable ground with the client’s privacy better preserved. The results of our performance analysis suggest that our new mechanism is superior to some similar mechanisms in terms of authentication delay.