Introduction to the Issue on Hybrid Analog-Digital Signal Processing for Hardware-Efficient Large-Scale Antenna Arrays (Part I)

The papers in this special section focus on hybrid analog-digital signal processing for hardware efficient large scale antenna arrays. Hybrid analog-digital (HAD) processing provides a key technology for the coming generations of wireless networks, as a means of obtaining hardware-efficient transceivers. The principle behind HAD is that the transceiver processing is divided into the analog and digital domain, where networks of analog components implement large-dimensional processing at the transceiver front end, allowing for a low-dimensional digital processing which necessitates only a few RF chains. This technology has recently been brought at the forefront of research motivated by the proliferation of millimeter-wave (mmWave) communications, as a solution to circumvent the use of large numbers of expensive mmWave RF components. Its scope however is not limited solely tommWave, as hardwareefficient transmission is key for small cell deployments in the microwave frequencies and also in emerging applications such as the internet of things (IoT) involving massive connectivity. All these applications still rely on transceivers capable of beamforming, using cheap, low-power, and physically small devices. Accordingly, the aim of this Special Issue (SI) has been to gather the relevant contributions focusing on the practical challenges of hybrid analog-digital transmission

Tree-based Partition Querying: A Methodology for Computing Medoids in Large Spatial Datasets

Besides traditional domains (e.g., resource allocation, data mining applications), algorithms for medoid computation and related problems will play an important role in numerous emerging fields, such as location based services and sensor networks. Since the k-medoid problem is NP-hard, all existing work deals with approximate solutions on relatively small datasets. This paper aims at efficient methods for very large spatial databases, motivated by: (1) the high and ever increasing availability of spatial data, and (2) the need for novel query types and improved services. The proposed solutions exploit the intrinsic grouping properties of a data partition index in order to read only a small part of the dataset. Compared to previous approaches, we achieve results of comparable or better quality at a small fraction of the CPU and I/O costs (seconds as opposed to hours, and tens of node accesses instead of thousands). In addition, we study medoid-aggregate queries, where k is not known in advance, but we are asked to compute a medoid set that leads to an average distance close to a user-specified value. Similarly, medoid-optimization queries aim at minimizing both the number of medoids k and the average distance. We also consider the max version for the aforementioned problems, where the goal is to minimize the maximum (instead of the average) distance between any object and its closest medoid. Finally, we investigate bichromatic and weighted medoid versions for all query types, as well as, maximum capacity and dynamic medoids

Preface

[No abstract available

Authenticated multistep nearest neighbor search

10.1109/TKDE.2010.157IEEE Transactions on Knowledge and Data Engineering235641-654ITKE