11,069 research outputs found
Distributed drone base station positioning for emergency cellular networks using reinforcement learning
Due to the unpredictability of natural disasters, whenever a catastrophe happens, it is vital that not only emergency rescue teams are prepared, but also that there is a functional communication network infrastructure. Hence, in order to prevent additional losses of human lives, it is crucial that network operators are able to deploy an emergency infrastructure as fast as possible. In this sense, the deployment of an intelligent, mobile, and adaptable network, through the usage of drones—unmanned aerial vehicles—is being considered as one possible alternative for emergency situations. In this paper, an intelligent solution based on reinforcement learning is proposed in order to find the best position of multiple drone small cells (DSCs) in an emergency scenario. The proposed solution’s main goal is to maximize the amount of users covered by the system, while drones are limited by both backhaul and radio access network constraints. Results show that the proposed Q-learning solution largely outperforms all other approaches with respect to all metrics considered. Hence, intelligent DSCs are considered a good alternative in order to enable the rapid and efficient deployment of an emergency communication network
A line-of-sight optimised MIMO architecture for outdoor environments
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Indoor Massive MIMO Deployments for Uniformly High Wireless Capacity
Providing consistently high wireless capacity is becoming increasingly
important to support the applications required by future digital enterprises.
In this paper, we propose Eigen-direction-aware ZF (EDA-ZF) with partial
coordination among base stations (BSs) and distributed interference suppression
as a practical approach to achieve this objective. We compare our solution with
Zero Forcing (ZF), entailing neither BS coordination or inter-cell interference
mitigation, and Network MIMO (NeMIMO), where full BS coordination enables
centralized inter-cell interference management. We also evaluate the
performance of said schemes for three sub-6 GHz deployments with varying BS
densities -- sparse, intermediate, and dense -- all with fixed total number of
antennas and radiated power. Extensive simulations show that: (i) indoor
massive MIMO implementing the proposed EDA-ZF provides uniformly good rates for
all users; (ii) indoor network densification is detrimental unless full
coordination is implemented; (iii) deploying NeMIMO pays off under strong
outdoor interference, especially for cell-edge users
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