One of the leading enabling technologies of 5G wireless networks is to use the millimeter wave spectrum band. Despite its large and wide frequency bandwidth, the obtained data rate can be diminished due to link blockage in this frequency band. In this paper, we formulate a bi-objective optimization problem to optimize user association in cellular networks with millimeter wave enabled base stations. The two objectives to minimize are maximum base station utility and blockage score (to indicate the chance of a link getting blocked). We simulate three different scalarization methods to turn a bi-objective vector into a scalar. Since the combinatorial bi-objective problem is NP-Hard, we conduct Lagrangian dual analysis on all of the scalarization methods. Solving the dual problem decreases the time complexity of the solver algorithm, but the solution has a distance from the optimal point created by solving the primal. We also solve the primal optimization problem with a single objective optimization tool. Compared to the time complexity of the primal problem of scalarization methods, the time complexities of solutions to the dual problems are lower. The results show that our solution to the bi-objective optimization problem has a better outcome in terms of the number of link blockage and the maximum base station utility compared to optimizing each objective alone.

Millimeter-wave technology has proved to be one of the enabling technologies of the fifth generation of wireless networks. Despite the merits of the millimeter-wave spectrum, its directionality imposes more overhead on communication systems than the non-millimeter wave spectrum. In this paper, we propose using signaling in another frequency band to discover the paths surrounding a receiver working in the millimeter-wave spectrum. In this way, the receiver does not have to do an expensive beam scan in the millimeter wave band and repair the interrupted ongoing millimeter communication much faster by choosing the beam covering the best path discovered by another frequency band. Our experiments in various environments by millimeterwave hardware platforms show that a non-millimeter wave frequency band's paths can be used to align the beams in millimeterwave frequency. Existing out-of-band path discovery methods typically need a specially designed hardware for scanning and can only find the single line-of-sight path. Our proposed method uses commercial off-the-shelf products to perform out-of-band scanning and discover both line-of-sight path and non-line-of-sight paths for millimeter wave communication.

To employ millimeter wave technology in the 5G standard, two inherent challenges need to be addressed in dynamic outdoor environments. Firstly, different types of obstacles can easily block the links. Secondly, the link quality can drop significantly in a mobile environment. It is critical to discriminate between the two different situations to take appropriate actions. The existing work to make the distinction is based on RSSI variation measured in a time window, which is very time-consuming leading to large volume of data loss in order to achieve high accuracy. In this paper, we propose a learning-based prediction framework to classify link blockage and link movement efficiently and quickly. A classifier is trained with data blockage instances using different learning methods and is used to make a prediction based on diffraction values on different multipath components formed around a receiver. The simulations show that the prediction framework can predict blockage with close to 90% accuracy. The prediction framework will eliminate the need for having time-consuming methods to discriminate between link movement and link blockage. Experiments show that our framework does not need large amount of training data to get to the desired prediction accuracy.

mm-Wave is a promising technology to meet the enormous bandwidth demands of the future generation cellular networks. This technology has vast amount of unused bandwidth, but has problem of human blockage. Blockage mitigation methods for indoor environments cannot be applied to outdoor scenarios effectively. In this paper, we mitigate human blockage of the mm-Wave technology by proposing an algorithm that provides intelligent user association in mm-Wave networks. The proposed algorithm collects the history blockage incidents throughout the network and exploits the history incidents to associate user equipment to the base station with lower blockage possibility. The blockage incidents happened at different locations in the network. When user equipment attempts to find a base station to associate to, the algorithm examines the history blockage incidents near the location of the user equipment. In this way, the user equipment is associated to a base station that has smaller chance of being blocked. The simulation results show that our proposed algorithm is performing better in terms of improving SINR, rate of the links and blockage rate in the network compared to another state-of-the-art user association algorithm designed for mm-Wave networks and common user association algorithms of associating user to closest base station and base station with maximum SINR.