text: The ability of robots to estimate their location is crucial for a wide variety of autonomous operations. In settings where GPS is unavailable, measurements of transmissions from fixed beacons provide an effective means of estimating a robot's location as it navigates. The accuracy of such a beacon-based localization system depends both on how beacons are distributed in the environment, and how the robot's location is inferred based on noisy and potentially ambiguous measurements. We propose an approach for making these design decisions automatically and without expert supervision, by explicitly searching for the placement and inference strategies that, together, are optimal for a given environment. Since this search is computationally expensive, our approach encodes beacon placement as a differential neural layer that interfaces with a neural network for inference. This formulation allows us to employ standard techniques for training neural networks to carry out the joint optimization. We evaluate this approach on a variety of environments and settings, and find that it is able to discover designs that enable high localization accuracy.
  @cite_16: To deploy a multi-cell 802.11 wireless local area network (WLAN), access point (AP) placement and channel assignment are two primary design issues. For a given pattern of traffic demands, we aim at maximizing not only the overall system throughput, but also the fairness in resource sharing among mobile terminals. A novel method for estimating the system throughput of multi-cell WLAN is proposed. An important feature of this method is that co-channel overlapping is allowed. Unlike conventional approaches that decouple AP placement and channel assignment into two phases, we propose to jointly solve the two problems for better performance. The optimal solution can be found using exhaustive searching. Due to the high computational complexity involved in exhaustive searching, an efficient local searching algorithm, called patching algorithm, is designed. Numerical results show that for a typical indoor environment, patching algorithm can provide a close-to-optimal performance with much lower time complexity than exhaustive searching
  @cite_10: The design of a large-scale wireless LAN poses a number of interesting questions. Building the Wireless Andrew network at Carnegie Mellon University has given us the opportunity to deal with these questions. This article describes the approaches we have developed for the design of similar networks. A large-scale wireless LAN must be designed so that all of the target space has radio coverage (i.e., there are no coverage gaps). It must also be designed so that its capacity is adequate to carry the expected load. These requirements generally can be met by using the proper combination of access point locations, frequency assignments, and receiver threshold settings.
summary: The above methods address spatial placement but not transmission channel assignments, and associated issues with interference. Automatic channel assignment methods have been considered previously, but only for optimizing communication throughput @cite_10 @cite_16 --- to minimize interference from two beacons in the same channel at any location. Note that this is a very different and simpler objective than one of enabling accurate localization, where the goal is to ensure that there is a unique mapping from every RSS signature (with or without interference) to location.