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This paper proposes a two-dimensional resource allocation technique for vehicle-toinfrastructure (V2I) communications. Vehicular communications requires high data rates, low latency, and reliability simultaneously. The 3rd generation partnership project (3GPP) included various numerologies to support this, leading to diversification of transmit time interval (TTI). It enables the two-dimensional resource allocation that considers time and frequency simultaneously, which has yet to be studied much. To tackle this issue, we propose a reinforcement learning approach to solve the two-dimensional resource allocation problem for V2I communications. A reinforcement learning agent in a base station allocates a quality of service (QoS) guaranteed two-dimensional resource block to each vehicle to maximize the sum of achievable data quantity (ADQ). It exploits received power information and a resource occupancy status as input. It outputs vehicles' allocation information that consists of a time-frequency position, bandwidth, and TTI, which is a solution to the two-dimensional resource allocation. The simulation results show that the proposed method outperforms the fixed allocation method. Because of the ability to pursue ADQ maximization and QoS guarantee, the proposed method performs better than an optimization-based benchmark method if each vehicle has a QoS constraint. Also, we can see that the resource the agent selects according to the QoS constraint varies and maximizes the ADQ.INDEX TERMS Deep reinforcement learning, V2X communications, quality of service, resource allocation. I. INTRODUCTIONWith the advent of complex applications that combine high data rates, low latency, or high reliability, discussions on next-generation communication networks have been actively conducted to support them. The international telecommunication union (ITU) radiocommunication sector has defined three service types to meet the requirements of new applications: enhanced mobile broadband (eMBB) for applications requiring high data rates, massive machine-type communications (mMTC) for applications requiring high-The associate editor coordinating the review of this manuscript and approving it for publication was Hao Wang . density networks, and ultra-reliable low-latency communications (URLLC) for latency-sensitive applications. Vehicleto-everything (V2X) communications is a highly complex application requiring all three service types. It consists of several vehicle-related communications, such as vehicle-toinfrastructure (V2I) and vehicle-to-vehicle (V2V) communications. Vehicles utilize V2V communications for direct information exchange between themselves and V2I communications to convey information to the infrastructure such as base stations or roadside units (RSUs) and vice versa [1], [2], [3], [4].Early vehicular communications focused on collision avoidance to reduce car crashes. They need delay-sensitive

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Among various index modulation technology, adaptive orthogonal frequency division multiplexing with index modulation (A-OFDM-IM) improves reliability by solving the problem of deep fading. However, the maximal likelihood (ML) detector of A-OFDM-IM has high complexity by simultaneously performing active subcarrier detection and quadrature amplitude modulation (QAM) symbol demodulation. A simpler type of ML detector can be applied since the A-OFDM-IM has independent active states of each subcarrier and uses a single QAM constellation. In this paper, we propose two low-complexity detectors for the A-OFDM-IM. The proposed detectors have a two-stage receiving process, active subcarrier detection, and QAM demodulation. In active subcarrier detection, the presence or absence of the QAM symbol is more important than its information. Therefore we derive the thresholds using the absolute value of the in-phase and quadrature components of the received signal in the frequency domain. Using the absolute value, the distribution of noise added to inactive subcarrier is half normal. This is different from noise distribution of the ML detector, and the proposed detectors select the threshold considering the corresponding noise distribution. The first proposed detector uses a threshold derived by the ML estimation method. The second detector estimates the active subcarriers via a support vector machine. The proposed detectors have lower complexity than the ML detector because of the divided receiving process. Moreover, the theoretical analysis and simulation results show that the proposed detectors have better reliability than the ML detector due to different noise distribution and thresholds.INDEX TERMS index modulation, support vector machine, low complexity detector, OFDM