User Scheduling Based on Multi-Agent Deep Q-Learning for Robust Beamforming in Multicell MISO Systems

• Maximizing the rate in multiple input single output (MISO) systems using distributed algorithms is an important task that typically incurs high computational cost. In this work, we propose two deep Q-learning-based user scheduling schemes to solve the beamforming problem of sum-rate maximization with per base station power constraints in multicell MISO scenarios. The two key features of the proposed algorithms are that they are executed in a distributed fashion and are robust with respect to channel state information (CSI) errors. Simulation results show that in the presence of CSI errors the proposed schemes outperform state-of-the-art algorithms both in terms of average spectral efficiency and execution time.

Ämnesord

TEKNIK OCH TEKNOLOGIER  -- Elektroteknik och elektronik -- Telekommunikation (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Electrical Engineering, Electronic Engineering, Information Engineering -- Telecommunications (hsv//eng)

Nyckelord

Channel estimation

Array signal processing

MISO communication

Robustness

Signal to noise ratio

MIMO communication

Electronic mail

MISO

deep Q-learning

CSI errors

multicell

Publikations- och innehållstyp

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