  Millimeter-wave (mmWave) is a key enabler for next-generation transportation
systems. However, in an urban city scenario, mmWave is highly susceptible to
blockages and shadowing. Therefore, base station (BS) placement is a crucial
task in the infrastructure design where coverage requirements need to be met
while simultaneously supporting localisation. This work assumes a pre-deployed
BS and another BS is required to be added to support both localisation accuracy
and coverage rate in an urban city scenario. To solve this complex
multi-objective optimisation problem, we utilise deep reinforcement learning
(DRL). Concretely, this work proposes: 1) a three-layered grid for state
representation as the input of the DRL, which enables it to adapt to the
changes in the wireless environment represented by changing the position of the
pre-deployed BS, and 2) the design of a suitable reward function for the DRL
agent to solve the multi-objective problem. Numerical analysis shows that the
proposed deep Q-network (DQN) model can learn/adapt from the complex radio
environment represented by the terrain map and provides the same/similar
solution to the exhaustive search, which is used as a benchmark. In addition,
we show that an exclusive optimisation of coverage rate does not result in
improved localisation accuracy, and thus there is a trade-off between the two
solutions.
