  Due to the publicly-known deterministic character- istic of pilot tones,
pilot-aware attack, by jamming, nulling and spoofing pilot tones, can
significantly paralyze the uplink channel training in large-scale MISO-OFDM
systems. To solve this, we in this paper develop an independence-checking
coding based (ICCB) uplink training architecture for one-ring scattering
scenarios allowing for uniform linear arrays (ULA) deployment. Here, we not
only insert randomized pilots on subcarriers for channel impulse response (CIR)
estimation, but also diversify and encode subcarrier activation patterns (SAPs)
to convey those pilots simultaneously. The coded SAPs, though interfered by
arbitrary unknown SAPs in wireless environment, are qualified to be reliably
identified and decoded into the original pilots by checking the hidden channel
independence existing in subcarri- ers. Specifically, an independence-checking
coding (ICC) theory is formulated to support the encoding/decoding process in
this architecture. The optimal ICC code is further developed for guaranteeing a
well-imposed estimation of CIR while maximizing the code rate. Based on this
code, the identification error probability (IEP) is characterized to evaluate
the reliability of this architecture. Interestingly, we discover the principle
of IEP reduction by exploiting the array spatial correlation, and prove that
zero-IEP, i.e., perfect reliability, can be guaranteed under
continuously-distributed mean angle of arrival (AoA). Besides this, a novel
closed form of IEP expression is derived in discretely- distributed case.
Simulation results finally verify the effectiveness of the proposed
architecture.
