2 research outputs found
Semi-Blind Post-Equalizer SINR Estimation and Dual CSI Feedback for Radar-Cellular Coexistence
Current cellular systems use pilot-aided statistical-channel state
information (S-CSI) estimation and limited feedback schemes to aid in link
adaptation and scheduling decisions. However, in the presence of pulsed radar
signals, pilot-aided S-CSI is inaccurate since interference statistics on pilot
and non-pilot resources can be different. Moreover, the channel will be bimodal
as a result of the periodic interference. In this paper, we propose a max-min
heuristic to estimate the post-equalizer SINR in the case of non-pilot pulsed
radar interference, and characterize its distribution as a function of noise
variance and interference power. We observe that the proposed heuristic incurs
low computational complexity, and is robust beyond a certain SINR threshold for
different modulation schemes, especially for QPSK. This enables us to develop a
comprehensive semi-blind framework to estimate the wideband SINR metric that is
commonly used for S-CSI quantization in 3GPP Long-Term Evolution (LTE) and New
Radio (NR) networks. Finally, we propose dual CSI feedback for practical
radar-cellular spectrum sharing, to enable accurate CSI acquisition in the
bimodal channel. We demonstrate significant improvements in throughput, block
error rate and retransmission-induced latency for LTE-Advanced Pro when
compared to conventional pilot-aided S-CSI estimation and limited feedback
schemes.Comment: 33 pages, 26 figure
Constrained Contextual Bandit Learning for Adaptive Radar Waveform Selection
A sequential decision process in which an adaptive radar system repeatedly
interacts with a finite-state target channel is studied. The radar is capable
of passively sensing the spectrum at regular intervals, which provides side
information for the waveform selection process. The radar transmitter uses the
sequence of spectrum observations as well as feedback from a collocated
receiver to select waveforms which accurately estimate target parameters. It is
shown that the waveform selection problem can be effectively addressed using a
linear contextual bandit formulation in a manner that is both computationally
feasible and sample efficient. Stochastic and adversarial linear contextual
bandit models are introduced, allowing the radar to achieve effective
performance in broad classes of physical environments. Simulations in a
radar-communication coexistence scenario, as well as in an adversarial
radar-jammer scenario, demonstrate that the proposed formulation provides a
substantial improvement in target detection performance when Thompson Sampling
and EXP3 algorithms are used to drive the waveform selection process. Further,
it is shown that the harmful impacts of pulse-agile behavior on coherently
processed radar data can be mitigated by adopting a time-varying constraint on
the radar's waveform catalog.Comment: 16 pages, 9 figures. arXiv admin note: text overlap with
arXiv:2010.1569