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IEEE 802.11 Wlan networks are constantly challenged for maximum overall service performance and new mobility applications : real-time, interactive communications and presence analytics. They require a solid design approach that considers but not limited to : highly varying characteristics of dense environments, technology advances, feasibility of s...
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... Our solution approach is built on a novel and realistic per-Beam coverage representation that is different from research models: per-Range and per-Zone. In theses works [1,2], we detail our coverage representation approach and demonstrate how it generalizes the other common and advanced literature approaches. In this study, we show that our optimization achieves a 92.58% time reduction by processing only 6.5% of available coverage points in average. ...
... The authors in these works [6][7][8], modeled the coverage area per-range: transmit, interference, and not-talk ranges, using a circular or disk pattern. The way this model represents the coverage is common but may not hint on some opportunities to transmit as discussed in this work [2]. The author in this work [9], focused instead on the interaction that an AP may have with its neighboring AP. ...
... The result is a per-zone, Voronoi zone, negotiated coverage pattern. This model is difficult to put into practice technologically and economically as it was discussed in [1,2]. Both models: per-zone and per-range, do not take upper layer constraints into account. ...
Dynamic radio resource management, RRM, is an essential design block in the functional architecture of any Wifi controller in IEEE 802.11 indoor dense enterprise Wlans. In a mono-channel condition, it helps tackle co-channel interference problem and enrich end-to-end Wifi clients experience. In this work, we present our dRRM solution: WLCx, and demonstrate its performance over related-work and vendor approaches. Our solution is built on a novel and realistic per-Beam coverage representation approach. Unlike the other RRM solutions, WLCx is dynamic: even the calculation system parameters are processed. This processing comes at price in terms of processing time. To overcome this limitation, we constructed and implemented a NURBS surface-based optimization to our RRM solution. Our NURBS optimized WLCx, N-WLCx, solution achieves almost 92.58% time reduction in comparison with basic WLCx. Furthermore, our optimization could easily be extended to enhance others, vendors and research, RRM solutions.
... Based on the presumption that the measured phenomenon is linear and on the enabled WDs' sporadic measures, we approximate the coverage of all the other points under the coverage area. In [7] and [8], two major modelization approaches families that may be referred to as "simplistic" and "idealistic" are discussed. In the first category, the coverage is approximated based on the range or distance of a WD from APs. ...
... In [7] and [8], a "realistic", beam-based, approach that is also, a generalization of the two previous ones is proposed. In this model, the covearge area of an AP is the area covered by the formed beams in each supported AP transmit direction. ...
... In Formula (5), T interference , is the necessary time for interference and local radio characteristics processing, L, the number of iterations to achieve the desired accuracy, T eirp , the necessary time for signal strength measurement, T proc , the necessary time for beamforming adaptation processing at WLC level, T feed , the necessary time to report the measurements to the WLC. The interference processing depends on the chosen model, as it was described in [7] work and its extension [8]. In general, an area coverage representation model is either of Range-Based, or Zone-based. ...
... The authors in these works [2,3,4], modeled the coverage area per-range: transmit, interference, and not-talk ranges, using a circular or disk pattern. The way this model represents the coverage is common but may not hint on some opportunities to transmit as discussed in this work [5]. ...
... The result is a per-zone, Voronoi zone, negotiated coverage pattern. This model is difficult to put into practice technologically and economically as it was discussed in [5] and [7]. Both models: per-zone and per-range, do not consider upper layer constraints. ...
... In work [5], we discuss our WLC2 dRRM solution. For further details about our solution, refer to [7] work that is an extension of the previous one. ...
... The implementation of dynamic RRM requires deep and feasible approaches to represent and model the network radio coverage. In work [1] and its extension [2], we discuss different coverage representations and evaluate the corresponding solution models' performance. ...
... It is necessary then, to model the radio coverage and predict the corresponding measures based only on the enabled WDs. In [1] and [2] works, we discuss two major modelization approaches families that may be referred to as "simplistic", Range-based, and "idealistic", Voronoi-based. ...
... It may be technologically possible but not economically! In [1] and [2] works, a "realistic", Beam-based approach is presented that is a generalization of the previous two approaches. In this scheme, the AP coverage is the region covered by the beams in the AP's supported transmit directions. ...
... In the end, we conclude and further our work. This paper is an extension of work originally presented in 2017 International Conference on Information Networking (ICOIN) [3]. ...
IEEE 802.11 WLAN indoor networks face major inherent and environmental issues such as interference, noise, and obstacles. At the same time, they must provide a maximal service performance in highly changing radio environments and conformance to various applications' requirements. For this purpose, they require a solid design approach that considers both inputs from the radio interface and the upper-layer services at every design step. The modelization of radio area coverage is a key component in this process and must build on feasible work hypotheses. It should be able also to interpret highly varying characteristics of dense indoor environments, technology advances, service design best practices, end-to-end integration with other network parts: Local Area Network (LAN), Wide Area Network (WAN) or Data Center Network (DCN). This work focuses on Radio Resource Management (RRM) as a key tool to achieve a solid design in WLAN indoor environments by planning frequency channel assignment, transmit directions and corresponding power levels. Its scope is limited to tackle co-channel interference but can be easily extended to address cross-channel ones. In this paper, we consider beamforming and costing techniques to augment conventional RRM's Transmit Power Control (TPC) procedures that market-leading vendors has implemented and related research has worked on. We present a novel approach of radio coverage modelization and prove its additions to the cited related-work's models. Our solution model runs three algorithms to evaluate transmission opportunities of Wireless Devices (WD) under the coverage area. It builds on realistic hypotheses and a thorough system operation's understanding to evaluate such an opportunity to transmit, overcomes limitations from compared related-work's models, and integrates a hierarchical costing system to match Service Level Agreement (SLA) expectations. The term "opportunity" in this context relates also to the new transmission's possibilities that related-work misses often or overestimates.
IEEE 802.11ax is the standard for the new generation WiFi networks. In this paper, we formulate the problem of joint access point (AP) placement and power-channel-resource unit assignment for 802.11ax-based dense WiFi. The objective is to minimize the number of APs. Two quality-of-service (QoS) requirements are to be fulfilled: (1) a two-tier throughput requirement which ensures that the throughput of each station is good enough, and (2) a fault tolerance requirement which ensures that the stations could still use WiFi even when some APs fail. We prove that this problem is NP-hard. To tackle this problem, we first develop an analytic model to derive the throughput of each station under the OFDMA mechanism and a widely used interference model. We then design a heuristic algorithm to find high-quality solutions with polynomial time complexity. Simulation results under both fixed-user and mobile-user cases show that: (1) when the area is small (50 × 50
$\rm m^2$
), our algorithm gives the optimal solutions; when the area is larger (80 × 60
$\rm m^2$
), our algorithm can reduce the number of APs by 34.9-87.7% as compared to the Random and Greedy algorithms. (2) Our algorithm can always get feasible solutions that fulfill the QoS requirements.
Dynamic Radio Resource Management helps overcome interferences in dense WLAN deployments. By processing data from upper-layers services, it could optimize and enrich end-to-end wireless client experience. This experience may be tight to a transmit opportunity function that hints on the radio interface condition, and effectiveness of the transmission itself from different points of view: application, service and underlying network infrastructure. Transmit opportunity calculations that are done at WLAN central intelligence level are resource consuming due to processing of huge amount of raw data from lower and upper system layers. One major part of this processing is the establishment of a coverage map that indicates in real-time and at any point the quality of a radio transmission. This work helps optimize transmit opportunity map calculations by exploring a novel approach based on NURBS Bézier surface technique. It demonstrates that map processing’s time and changes to radio environment could be enhanced.
