  In this paper, we propose a distributed collaborative beamforming (DCB)-based
uplink communication paradigm for enabling ground-space direct communications.
Specifically, DCB treats the terminals that are unable to establish efficient
direct connections with the low Earth orbit (LEO) satellites as distributed
antennas, forming a virtual antenna array to enhance the terminal-to-satellite
uplink achievable rates and durations. However, such systems need multiple
trade-off policies that variously balance the terminal-satellite uplink
achievable rate, energy consumption of terminals, and satellite switching
frequency to satisfy the scenario requirement changes. Thus, we perform a
multi-objective optimization analysis and formulate a long-term optimization
problem. To address availability in different terminal cluster scales, we
reformulate this problem into an action space-reduced and universal
multi-objective Markov decision process. Then, we propose an evolutionary
multi-objective deep reinforcement learning algorithm to obtain the desirable
policies, in which the low-value actions are masked to speed up the training
process. As such, the applicability of a one-time trained model can cover more
changing terminal-satellite uplink scenarios. Simulation results show that the
proposed algorithm outmatches various baselines, and draw some useful insights.
Specifically, it is found that DCB enables terminals that cannot reach the
uplink achievable threshold to achieve efficient direct uplink transmission,
which thus reveals that DCB is an effective solution for enabling direct
ground-space communications. Moreover, it reveals that the proposed algorithm
achieves multiple policies favoring different objectives and achieving
near-optimal uplink achievable rates with low switching frequency.
