  We investigate the fundamental multiple access (MA) scheme in an active
intelligent reflecting surface (IRS) aided energy-constrained
Internet-of-Things (IoT) system, where an active IRS is deployed to assist the
uplink transmission from multiple IoT devices to an access point (AP). Our goal
is to maximize the sum throughput by optimizing the IRS beamforming vectors
across time and resource allocation. To this end, we first study two typical
active IRS aided MA schemes, namely time division multiple access (TDMA) and
non-orthogonal multiple access (NOMA), by analytically comparing their
achievable sum throughput and proposing corresponding algorithms.
Interestingly, we prove that given only one available IRS beamforming vector,
the NOMA-based scheme generally achieves a larger throughput than the
TDMA-based scheme, whereas the latter can potentially outperform the former if
multiple IRS beamforming vectors are available to harness the favorable time
selectivity of the IRS. To strike a flexible balance between the system
performance and the associated signaling overhead incurred by more IRS
beamforming vectors, we then propose a general hybrid TDMA-NOMA scheme with
user grouping, where the devices in the same group transmit simultaneously via
NOMA while devices in different groups occupy orthogonal time slots. By
controlling the number of groups, the hybrid TDMA-NOMA scheme is applicable for
any given number of IRS beamforming vectors available. Despite of the
non-convexity of the considered optimization problem, we propose an efficient
algorithm based on alternating optimization. Simulation results illustrate the
practical superiorities of the active IRS over the passive IRS in terms of the
coverage extension and supporting multiple energy-limited devices, and
demonstrate the effectiveness of our proposed hybrid MA scheme for flexibly
balancing the performance-cost tradeoff.
