Access Point Association Research Articles

Wireless Multi-Interface Connectivity with Deep Learning-Enabled User Devices: An Energy Efficiency Perspective

Undoubtedly, future Beyond 5G (B5G) and 6G networks will be empowered by AI and Machine Learning (ML) technologies. However, current Deep Learning methods consume tremendous power, making them unsuitable to be deployed at edge Access Points (AP) or user devices. As energy efficiency will become one of the major Key Performance Indicators in 6G, there is an urge for realizing lowpower yet highly efficient Deep Learning at user devices, the most severely battery-limited entities. We analyze this issue through the problem of distributed user-to-multiple APs association optimization in B5G networks that integrate diverse wireless interfaces such as Sub-6GHz and mmWave bands. In the proposed Deep Reinforcement Learning (DRL) framework, each device is equipped by a light-weight Deep Q-Network (DQN), which enables it to self-optimize its APs and interfaces association to fulfill the stringent Quality of Service (QoS) requirements of its various applications. We analyze the energy consumption of the device DQN, and investigate key performance trade-offs in terms of sum-rate, user outage and energy efficiency, while disclosing the major power consuming elements of user DQN. Finally, crucial open research issues are identified for realizing energy-efficient AI at all network levels.

Efficient Optimal and Suboptimal Joint Access Point Association and Radio Resource Allocation in WLANs

Efficient Optimal and Suboptimal Joint Access Point Association and Radio Resource Allocation in WLANs

AP Association Algorithm Based on VR User Behavior Awareness

With the rapid development of virtual reality (VR) technology, this paper proposes an access point (AP) correlation method based on VR user behavior awareness to address the problem of how current AP correlation methods only focus on the performance improvements of ordinary users and ignore the impact of VR user behavior on service quality. This paper analyzes the AP association method under the coverage scenario of a multi-access point (multi-AP) scenario environment and controls the performance improvement of VR user APs or APs under the access controller (AC) by association. Firstly, the VR network application scenario and system model were constructed, and secondly, the user behavior was sensed by analyzing the viewing habits of users. Then, the VR user association problem based on VR user behavior perception was transformed into a “many-to-many” matching problem between VR user devices and APs, and the generalized multidimensional multiple choice knapsack (GMMKP) model was established to solve the problem using the backpack problem theory; the suboptimal solution algorithm was selected to obtain the best VR user AP association strategy. The experimental results show by simulation that the proposed algorithm in this paper performed better in terms of the AP load balancing and average network download latency compared to the comparison algorithms.

Distributed user-to-multiple access points association through deep learning for beyond 5G

Future wireless networks will be facing unprecedented difficulties arising from mobile traffic growth, network densification, as well as diversification of applications and services. Indeed, future user devices are expected to integrate diverse radio interfaces such as 5G, WBAN or IoT, enabling each user to be served a wide range of applications at any time. This poses significant challenges in terms of wireless resource sharing and interference management, as more and more stringent Quality of Service (QoS) constraints should be jointly satisfied in dense interfering environments. Furthermore, future networks are expected to be highly autonomous and decentralized. To meet these challenges, this work proposes distributed user-to-multiple Access Points (AP) association methods, where the objective is to maximize the long-term sum-rate subject to application QoS constraints, as well as to AP load constraints. Our distributed methods enable each user to leverage their Deep Reinforcement Learning (DRL) capabilities, in particular Deep Q-Learning (DQL), to self-optimize their APs’ selection solely based on their local network state knowledge, so as to best satisfy their diverse requirements. Numerical results show that, compared to baseline schemes, the proposed methods enable global throughput enhancements while reducing user QoS outage probabilities, even in large and dense networks.

: Multi-Dimensional Spatial Reuse Enhancement for Directional Millimeter-Wave Wireless Networks

Millimeter wave (mmWave) wireless networks are envisioned to bring a very high degree of spatial reuse, i.e., multiple links can operate simultaneously without interference. The vision, however, is becoming doubtful, as recent studies found that non-negligible interference exists due to imperfect beam patterns. In this paper, we extensively measure the spatial reuse issue in a dense 60 GHz mmWave network consisting of multiple access points (AP) and users. Our measurement quantifies the impact of interference on network performance and finds that the existing prediction based on interference-resolving approaches are insufficient. Motivated by the findings, we propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MDSR</i> , which enhances the spatial reuse in 60 GHz mmWave networks. Instead of relying on interference prediction, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MDSR</i> takes a new measurement principle of building a conflict graph that implicitly takes into account the impact of both beam imperfection and reflections. Using the conflict graph, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MDSR</i> improves the spatial reuse from three dimensions: AP association, user scheduling, and beam selection, which can determine the optimal AP-user-beam combination and minimize interference in each scheduling cycle. We prototype and evaluate <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MDSR</i> on the testbed using commodity mmWave radios. The evaluation results demonstrate that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MDSR</i> improves network throughput by multi-folds compared with the state-of-the-art one.

Throughput Optimization Based Joint Access Point Association and Transmission Time Allocation in WLANs

In this paper, the joint access point (AP) association and transmission time allocation problem in densely deployed multirate WLANs based on time sharing medium access control (MAC) is considered. In our study, the centralized coordinated joint AP association and transmission time allocation problem with heterogeneous station (STA) throughput demands is first formulated as an NP-hard single non-zero programming (SNZP) optimization problem with proportionally fair (PF) throughput based objective function. To solve the NP-hard SNZP problem, two novel algorithms, the SNZP relaxation (SNZPR) algorithm and the iterative SNZPR (iSNZPR) algorithm, are proposed. The property and complexity of the proposed algorithms are analyzed. In addition, for dynamic network scenarios where there are new STAs arriving at and trying to join the network, a distributed joint admission, AP association and transmission time allocation (DAAA) algorithm is proposed to determine whether to admit a new STA arriving at the network or not. The performance of the proposed algorithms is then investigated and compared with that of some existing algorithms based on numerical results.

Interference and Coverage Analysis for Terahertz Networks With Indoor Blockage Effects and Line-of-Sight Access Point Association

Providing high-bandwidth and fast-speed links, wireless local area networks (WLANs) in the Terahertz (THz) band have huge potential for various bandwidth-intensive indoor applications. However, due to the specific phenomena in the THz band, including severe reflection loss, indoor blockage effects, multi-path fading, the analysis on the interference and coverage probability at a downlink is challenging. In this paper, indoor blockage effects caused by the walls and human bodies are analyzed. Next, a statistical THz channel model is proposed to characterize the THz indoor propagation. In light of these, the moment generating functions of the aggregated interference and theoretical expressions for the mean interference power are derived. As a result, the approximated coverage probability and average network throughput are derived. Extensive numerical results show that for the nearest access point (nearest-AP) user association scheme, the optimal AP density is 0.15/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , which results in the coverage probability reaches 93% and the average network throughput is 30 Gbps/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> . In addition, by adopting a novel line-of-sight access point (LoS-AP) user association mechanism, the coverage probability and the average network throughput can be further improved by 3 percent and 2 Gbps/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , respectively.

Optimizing Networked Flying Platform Deployment and Access Point Association in FSO-Based Fronthaul Networks

Applying interconnected networked flying platforms (NFPs) as fronthauling is a promising solution to flexibly reconfigure the fronthaul network upon requests. Meanwhile, free space optical (FSO) communications has been demonstrated to achieve a higher data rate, longer transmission range, and less interference between two endpoints as compared to traditional radio frequency communications. This letter applies FSO communications in NFP relay networks, which can significantly enhance the network throughput. We investigate and formulate the joint NFP deployment and access point (AP) association problem in the context of an FSO-based NFP relay network, and design an efficient algorithm to solve it. Simulation results demonstrate the performance of the proposed algorithm.

Access Point Association in Uplink Two-Hop Cellular IoT Networks With Data Aggregators

Node clustering and data aggregation help extend the coverage of cellular networks and increase the number of supported devices, while meeting the various service quality requirements and reducing energy consumption, making them suitable for enabling the future massive cellular Internet-of-Things (IoT) applications. Consequently, we propose to overlay the cellular network with a layer of data aggregators (DAs) to act as relays. DAs use cellular backhauling, and thus, the network provides both single- and two-hop routes; however, DAs share radio resources with single-hop devices, creating a dependency between the two routes. Thus, the proper design of the DA-enabled network becomes critical for cost effectiveness and efficient radio resource utilization. In this article, we formulate a joint access point association, resources utilization, and energy-efficient communication optimization problem that takes into account various networking factors, such as the number of devices, the number of DAs, the number of available resource units, interference, the transmission power limitation of the devices, DA transmission performance, and channel conditions. The objective is to show the usefulness of data aggregation and shed light on the importance of network design when the number of devices is massive. We propose a coalition game theory-based algorithm PAUSE to transform the optimization problem into a simpler form that can be successfully solved in polynomial time. Different network scenarios are simulated to showcase the effectiveness of PAUSE and to draw observations on cost-effective network design with DAs.

Novel AP association and fair channel access in high throughput WLAN for energy efficiency

Distribution of wireless stations (STAs) becomes skewed towards a few groups of wireless access points (APs) due to the dense deployment of APs in a single hotspot and the signal strength based AP association mechanism in IEEE 802.11. Such a skewed distribution of wireless STAs may result in severe energy wastage, where a few APs could go to the sleep state if AP association is maintained optimally. This wastage increases in high throughput wireless extensions over IEEE 802.11, such as IEEE 802.11n/ac, where wider channels are used for communication (by channel bonding). Wider channels consume more power but do not necessarily provide high throughput under all different channel conditions. In this paper, we develop a joint management framework for energy-efficient AP association along with channel-width selection for active APs. Exploring the interdependency between these two objectives, we develop an optimization framework leading to GreenAP for AP association and channel width selection in high throughput wireless local area networks. We evaluate the performance of GreenAP using both simulation experiment and testbed implementations. We show that it is capable of saving significant energy without any loss in network performance parameters, compared to other competing schemes available in the literature.

Distributed algorithm for AP association with random arrivals and departures of users

Here, the authors study the novel problem of optimising access point (AP) association by maximising the network throughput, subject to the degree bound of AP. The formulated problem is a combinatorial optimisation. They resort to the Markov Chain approximation technique to design a distributed algorithm. They first approximate their optimal objective via Log-Sum-Exp function. Thereafter, they construct a special class of Markov Chain with steady-state distribution specify to their problem to yield a distributed solution. Furthermore, they extend the static problem setting to a dynamic environment where the users can randomly leave or join the system. Their proposed algorithm has provable performance, achieving an approximation gap of ( 1 / η ) log ⁡ | F | . It is simple and can be implemented in a distributed manner. Their extensive simulation results show that the proposed algorithm can converge very fast, and achieve a close-to-optimal performance with a guaranteed loss bound.

Optimisation of indoor hybrid PLC/VLC/RF communication systems

In this study, the authors propose a hybrid power line communication (PLC)/visible light communication (VLC)/radio frequency (RF) fronthaul with a fibre-based wired backhaul system to support massive number of smart devices (SDs). Since a signal-to-noise ratio-based access point (AP) association and bandwidth (BW) allocation for each SD do not necessarily improve the system capacity, they propose novel and efficient AP association and BW allocation strategies to maximise the sum rate capacity (SRC) of the hybrid system under consideration. An optimisation problem is formulated for the SRC with the AP association and BW allocation as the optimisation parameters and a hierarchical decomposition method is used to convert the non-linear optimisation problem into a set of convex optimisation problems. Then, the proposed strategies are used to solve the optimisation problem in an iterative manner until the SRC converges to an optimal value. Further, an analytical approximation for the BW allocated to each SD for a given AP association is derived using the Lagrangian multiplier method. The performance of the proposed system is evaluated through extensive numerical results. Moreover, the effect of the increased number of SDs on the optimal SRC is analysed.

Single- and Multiple-Access Point Indoor Localization for Millimeter-Wave Networks

Millimeter-wave (mm-wave) location systems not only provide accurate positioning for location-based services but can also help optimize network operations, for example, through location-driven beam steering and access point association. In this paper, we design and evaluate localization schemes that exploit the characteristics of mm-wave communication systems. We propose two range-free algorithms belonging to the broad classes of triangulation and angle difference of arrival. The schemes work both with multiple anchors and with as few as a single anchor, under the only assumption that the floor plan and the positions of the mm-wave access points are known. Moreover, they are designed to be lightweight so that even computationally-constrained devices can run them. We evaluate our proposed algorithms against two benchmark approaches based on fingerprinting and angles of arrival, respectively. Our results, obtained both by means of simulations and through measurements involving commercial 60-GHz mm-wave devices, show that sub-meter accuracy is achieved in most of the cases, even in the presence of only a single access point. The availability of multiple access points substantially improves the localization accuracy, especially for large indoor spaces.

New Algorithms for Energy-Efficient VLC Networks With User-Centric Cell Formation

The ever-increasing demand in high data rate has pushed the attention of the researchers to utilize the unregulated visible light spectrum for communication. This paper proposes a joint access-point (AP) association and power allocation algorithms for energy-efficiency (EE) maximization in visible light communication networks. Based on the user-centric design, we first show that the cell formation and power allocation are interlinked problems and should be treated jointly. We start by proposing a new algorithm for users’ clustering and then associating all the APs to the clustered users based on a proposed metric. We then propose two algorithms that jointly allocate the power, under quality-of-service constraints, and decide which APs must be prevented from participating in communication. The first algorithm is designed to maximize the EE, while the other algorithm is designed to reduce the complexity of the first algorithm with acceptable degradation in the EE. Different from the related literature that allocated the power with the worst case interference information, we propose an iterative algorithm that allocates the power based on exact interference information, which significantly improves the EE. The numerical results demonstrate that the proposed algorithms significantly improve the EE compared to the existing work.

Kinematic Information Aided User-Centric 5G Vehicular Networks in Support of Cooperative Perception for Automated Driving

The cooperative perception of the driving environment via the sharing of locally sensed information among automated vehicles plays a fundamental role in ensuring the basic safety of automated driving in complicated public traffic. However, demanding requirements ranging from high data rate and large user density to ultra-high reliability and low latency, are imposed on the 5G network, which is considered the key enabler of cooperative automated driving. In this paper, we propose a novel ultra-dense 5G vehicular network architecture, which features the kinematic information aided user-centric access, to address these requirements. In particular, distributed local access and application centers (LAACs) are designed to perform application implementation and access control collectively, such that the kinematic information of the vehicles extracted at the application layer can be exploited in the dynamic management of network resources to sustain consistently high-performance wireless communications between vehicles and their serving LAACs. Focusing on the uplink transmission of the periodic cooperative sensing messages (CSMs), the possible design of key elements in the kinematic information aided user-centric access, including access point association, radio resource allocation, and mobility support, are discussed. Issues brought about by the practical network deployment and constraints are also considered. In addition, a practical benchmarking access strategy set, which addresses both the reliability and the latency requirements of CSMs, is proposed and evaluated by simulation under the freeway and intersection scenarios.

Cross-Mode Interference Characterization in Cellular Networks With Voronoi Guard Regions

Advances in cellular networks such as device-to-device communications and full-duplex radios, as well as the inherent elimination of intra-cell interference achieved by network-controlled multiple access schemes, motivate the investigation of the cross-mode interference properties under a guard region corresponding to the Voronoi cell of an access point (AP). By modeling the positions of interfering APs and user equipments (UEs) as Poisson distributed, analytical expressions for the statistics of the cross-mode interference generated by either APs or UEs are obtained based on appropriately defined density functions. The considered system model and analysis are general enough to capture many operational scenarios of practical interest, including conventional downlink/uplink transmissions with nearest AP association, as well as direct communication between UEs that do not necessarily lie in the same cell. Analysis provides insights on the level of protection offered by a Voronoi guard region and its dependence on type of interference and receiver position. Numerical examples demonstrate the validity/accuracy of the analysis in obtaining the system coverage probability for operational scenarios of practical interest.

Joint AP Association and Bandwidth Allocation Optimization Algorithm in High-Dense WLANs

Regarding access point (AP) overload and performance anomaly which is caused by mobile terminals with different bitrates, a joint AP association and bandwidth allocation optimization algorithm is presented in this paper. Meanwhile, load balancing and proportional fairness are analyzed and formulated as an optimization model. Then, we present a Fair Bandwidth Allocation algorithm based on clients’ Business Priority (FBA-BP), which allocates bandwidth based on the bandwidth demand of clients and their business priority. Furthermore, we propose a Categorized AP Association algorithm based on clients’ demands (CAA-BD), which classifies APs by different types of clients and chooses an optimal associating AP for a new client according to AP categories and the aggregated demand transmission time that are calculated by the FBA-BP algorithm. The CAA-BD can achieve load balance and solve the performance anomaly caused by multi-rate clients coexisting. The simulation results show that our proposed algorithm obtains significant performance in terms of AP utilization, throughput, transmission delay and channel fairness in different client density levels compared with the categorized and Strong Signal First (SSF) algorithms.

Fine-Grained Traffic Engineering on SDN-Aware Wi-Fi Mesh Networks

Software-defined networking (SDN) has introduced noticeable progresses in the design and development of computer networks. In fact, SDN empowers us to run more advanced algorithms for resource management through the separation of control and data planes using a centralized approach. By applying SDN to different communication platforms, e.g., Wi-Fi mesh networks (WMNs), we gain an unprecedented opportunity to design more efficient and cost-effective solutions. In WMNs, the process of service provisioning incorporates tightly correlated steps, including access point association, gateway selection, and backhaul routing. In most of the prior studies, these steps were investigated as independent NP-hard problems. To the best of our knowledge, there is no thorough formulation that considers all the necessary steps at different tiers of WMNs to construct a unified and fine-grained solution. In this paper, we present a structured scheme to address the demands of end-users over SDN-aware WMNs. In contrast to most of the former works, our proposed formulation takes the key characteristics of wireless networks into account, especially for multichannel multiradio WMNs. In addition, for applying our solution to the large-scale scenarios, we introduce a heuristic algorithm that can find a suboptimal solution in polynomial time. The functionality of the presented work is evaluated through extensive numerical results and real testbed implementation.

Towards sustainable heterogeneous wireless networks: A decision strategy for AP selection with dynamic graphs

This paper considers a problem of user and access point associations in heterogeneous wireless networks consisting of multiple radio access technologies. The current association policies are mostly based on a selfish user behavior optimizing their own decision criteria. Such simple strategies, however, might fail especially in a congested place with a large number of users. From this perspective, this study propounds a strategy for optimizing the problem that aims at sustainably and fairly maximizing the minimal utility of users. The minimal utilities can be obtained from bare essentials of the quality of experience and defined by considering different requirements of each user. In order to analyze characteristics of the proposed selection strategy, we conduct simulations comparing it with the other selection method under each evaluation criterion. Numerical results suggest that the proposed selection i) enhances the total minimal utility in sustainable and fair manners, ii) indicates a distinctive trend in load balancing and iii) requires a comparable computational time and handover frequency in contrast with the other selection.

Survey of outdoor and indoor architecture design in TVWS networks

To understand the utilizability of TV white spaces (TVWS), a comprehensive overview of the outdoor and indoor network design over TVWS is given. The related challenges are analyzed. The potential approaches to overcoming these challenges are discussed. The open research issues are investigated. The result shows that: in the indoor scenario, the white space ratio is on average 18.4% higher than that in the outdoor scenario, which corresponds to 7.7 vacant TV channels. Both network design includes 7 key components: TV spectrum identification, access point (AP) discovery, AP association, spectrum allocation, band width adaptation, interface control and disruption handling. Due to building penetration loss, the indoor TVWS identification and AP placement should be carefully considered in the indoor scenario.