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This paper addresses cooperative link scheduling problems for base station(BS) aided device-to-device (D2D) communications using limited channel stateinformation (CSI) at BS. We first derive the analytical form of ergodicsum-spectral efficiency as a function of network parameters, assumingstatistical CSI at the BS. However, the optimal link scheduling, whichmaximizes the ergodic sum-spectral efficiency, becomes computationallyinfeasible when network density increases. To overcome this challenge, wepresent a low-complexity link scheduling algorithm that divides the D2D networkinto sub-networks and identifies the optimal link scheduling strategy persub-network. Furthermore, we consider the scenario when the statistical CSI isnot available to the BS. In such cases, we propose a quasi-optimal scalablelink scheduling algorithm that utilizes one-bit feedback information from D2Dreceivers. The algorithm clusters the links and applies the UCB algorithm percluster using the collected one-bit feedback information. We highlight thateven with reduced scheduling complexity, the proposed algorithm identifies alink scheduling action that ensures optimality within a constant throughputgap. We also demonstrate through simulations that the proposed algorithmachieves higher sum-spectral efficiency than the existing link schedulingalgorithms, even without explicit CSI or network parameters knowledge.

BanditLinQ: A Scalable Link Scheduling for Dense D2D Networks with One-Bit Feedback