Mathematical modeling for optimal design of in-building distributed antenna systems

Abstract

In-building Distributed Antenna System (IB-DAS) has proven to be one of the most promising In-Building Solutions (IBS) to provide coverage and capacity for indoor users. We consider optimal deployment of the passive IB-DAS, focusing on mathematical optimization models based on integer programming, for the topology design and optimal equipment selection of IB-DAS. The models minimize the cable cost and keep the transmit power at each antenna within a given interval defined for coverage and interference control. The models can deliver optimal solutions to systems of which the size is of practical relevance. To improve the time efficiency, we develop preprocessing techniques that integrate the building layout data into the system modeling. Application of the models to realistic IB-DAS deployment demonstrates the effectiveness of the models.

Design of in-building distributed antenna systems: Mathematical models and efficient algorithms

“…The model is able to deliver optimal solution, but the application is limited to small scenarios. In [3], a further study has been conducted. The complexity of the same problem has been analyzed.…”

Exact and Approximation Algorithms for Optimal Equipment Selection in Deploying In-Building Distributed Antenna Systems

“…The number of children of each inner node follows a uniform and random distribution within [2,5]. Following the description of P-DAS in [9,15], the root power is set to 50 dBm, and the antenna target power is an integer in range [0, 10] dBm. As was mentioned earlier, the power of the root in the algorithmic computations can be set to be zero, with the target powers shifted accordingly.…”

“…For mathematical optimization of IB-DAS deployment, an integer programming formulation for determining the cabling and equipment is presented in [9], with the primary objective of reducing cable cost. Due to the nature of integer programming, the approach admits solutions for relatively small scenarios only.…”

“…Today, up to 80% of the mobile network traffic is generated by in-building users [9]. The trend is expected to escalate with the introduction of Long Term Evolution (LTE).…”

“…The approach is justified by the high complexity of the joint problem, for which scalability is prohibiting for obtaining reasonable solutions (by, e.g., integer programming [9]) for large-scale scenarios. Indeed, current DAS planning tools, such as [15], do follow the decomposition approach.…”