  Future wireless networks may operate at millimeter-wave (mmW) and
sub-terahertz (sub-THz) frequencies to enable high data rate requirements.
While large antenna arrays are critical for reliable communications at mmW and
sub-THz bands, these antenna arrays would also mandate efficient and scalable
initial beam alignment and link maintenance algorithms for mobile devices.
Low-power phased-array architectures and phase-less power measurements due to
high frequency oscillator phase noise pose additional challenges for practical
beam tracking algorithms. Traditional beam tracking protocols require
exhaustive sweeps of all possible beam directions and scale poorly with high
mobility and large arrays. Compressive sensing and machine learning designs
have been proposed to improve measurement scaling with array size but commonly
degrade under hardware impairments or require raw samples respectively. In this
work, we introduce a novel long short-term memory (LSTM) network assisted beam
tracking and prediction algorithm utilizing only phase-less measurements from
fixed compressive codebooks. We demonstrate comparable beam alignment accuracy
to state-of-the-art phase-less beam alignment algorithms, while reducing the
average number of required measurements over time.
