This post is descriptive of our work on RRM or radio resource management in WLAN (Wireless LAN) and especially on the operation of beamforming.

Beamforming is a major building block of Radio Resource Management (RRM) in WLAN networks that helps mitigate the interference and maximize the transmission opportunity of Access Points (AP) toward Wireless Devices (WD). The idea of this technique is simple: maximize the radiated electromagnetic energy toward the target WD to maximize the gain and enhance the condition of the communication (enhance the SNR or the signal to noise ratio).

In contrast to the related-work In-Path approaches, we propose a novel Out-Of-Path approach to beamforming calibration that is based on concepts from Computer Aided Graphical Design (CAGD) field and now-a-day network design best practices. A full reference to this work is accessible via this link: Bézier Curves-Based Novel Out-of-Path Beamforming Calibration Technique in IEEE 802.11 WLAN Networks

In this article, the enhancement that the presented solution adds to the beamforming operation time and results’ accuracy is investigated. It is demonstrated that the processing of beamformer’s parameters: array elements’ signal weighting and phase shifting, to achieve the desired angle of radiation, direction of arrival and gain, is possible at Wireless Lan Controller (WLC) level in indoor controlled WLAN networks.

Further, we introduced in the same context, the concept of a pseudoAP, a virtual AP that represents a cluster of all possible real AP beamformers to a given WD and investigate the enhancement that it may add.

The results of both simulations show an important enchancement of the conventional beamformer processing time at AP level. But at WLC level, a trade-off exists between maximizing the transmission opportunity and reducing the required processing time.

In the next figure we illustrate the concept behind our solution:

The figure represents a coverage area of randomly distributed AP (access points); each point of this coverage map may corrspond to mobility device or user (WD, wireless device). For a particular WD, the initial task si to choose the AP of association (that is on reach and gives the best RSSI or similar). Now from the perspective of the WLC (that is the central intelligence of our network), the communication between the WD and it’s AP of association is affected by the neighboring AP.

The processing of our solution help quantify this impact by drawing the corresponding Bézier specific curves and hinting the AP of association on the best direction to use towards this WD (instead of running this beamforming calculation locally). Besides the advantage of alleviating the processing of beamforming at AP levle, another advantage of our solution, is that it adopts dynamically to quickly changing network condition (in terms of radio coverage). The changes that our solution supports are not limited to the measurement of the coverage area but can be expanded to measures from upper layer services and applications such QoS and SLA.