  Terahertz (THz) communication is envisioned as one of the candidate
technologies for future wireless communications to enable achievable data rates
of up to several terabits per second (Tbps). However, the high pathloss and
molecular absorption in THz band communications often limit the transmission
range. To overcome these limitations, this paper proposes intelligent
reconfigurable surface (IRS)-aided THz networks with imperfect channel state
information (CSI). Specifically, we present an angle-based trigonometric
channel model to facilitate the performance evaluation of IRS-aided THz
networks. In addition, to maximize the sum rate, we formulate the transmitter
(Tx)-IRS-receiver (Rx) matching problem, which is a mixed-integer nonlinear
programming (MINLP) problem. To address this non-deterministic polynomial-time
hard (NP-hard) problem, we propose a Gale-Shapley algorithm-based solutions to
obtain stable matching between transmitters and IRSs, and receivers and IRSs,
in the first and second sub-problems, respectively. The impact of the
transmission power, the number of IRS elements, and the network area on the sum
rate are investigated. Furthermore, the proposed algorithm is compared to an
exhaustive search, nearest association, greedy search, and random allocation to
validate the proposed solution. The complexity and convergence analysis
demonstrate that the computational complexity of our algorithm is lower than
that of the ES method.
