  Enlightening our understanding of the first galaxies responsible for driving
reionisation requires detecting the 21-cm signal from neutral hydrogen.
Interpreting the wealth of information embedded in this signal requires
Bayesian inference. Parameter inference from the 21-cm signal is primarily
restricted to the spherically averaged power spectrum (1D PS) owing to its
relatively straightforward derivation of an analytic likelihood function
enabling traditional Monte-Carlo Markov-Chain (MCMC) approaches. However, in
recent years, simulation-based inference (SBI) has become feasible which
removes the necessity of having an analytic likelihood, enabling more complex
summary statistics of the 21-cm signal to be used for Bayesian inference. In
this work, we use SBI, specifically marginal neural ratio estimation to learn
the likelihood-to-evidence ratio with Swyft, to explore parameter inference
using the cylindrically averaged 2D PS. Since the 21-cm signal is anisotropic,
the 2D PS should yield more constraining information compared to the 1D PS
which isotropically averages the signal. For this, we consider a mock 1000 hr
observation of the 21-cm signal using the SKA and compare the performance of
the 2D PS relative to the 1D PS. Additionally, we explore two separate
foreground mitigation strategies, perfect foreground removal and wedge
avoidance. We find the 2D PS outperforms the 1D PS by improving the
marginalised uncertainties on individual astrophysical parameters by up to
$\sim30-40$ per cent irrespective of the foreground mitigation strategy.
Primarily, these improvements stem from how the 2D PS distinguishes between the
transverse, $k_{\perp}$, and redshift dependent, $k_{\parallel}$ information
which enables greater sensitivity to the complex reionisation morphology.
