Abstract
In wireless communication systems, reliability, low latency and power are essential in large scale multi-hop environment. Multi-hop based cooperative communication is an efficient way to achieve goals of wireless networks. This paper proposes a relay selection scheme for reliable transmission by selecting an optimal relay. The proposed scheme uses a signal-to-noise ratio (SNR) based Q-learning relay selection scheme to select an optimal relay in multi-hop transmission. Q-learning consists of an agent, environment, state, action and reward. When the learning is converged, the agent learns the optimal policy which is a rule of the actions that maximize the reward. In other words, the base station (BS) knows the optimal relay to select and transmit the signal. At this time, the cooperative communication scheme used in this paper is a decode-and-forward (DF) scheme in orthogonal frequency division multiplexing (OFDM) system. The Q-learning in the proposed scheme defines an environment to maximize a reward which is defined as SNR. After the learning process, the proposed scheme finds an optimal policy. Furthermore, this paper defines a reward which is based on the SNR. The simulation results show that the proposed scheme has the same bit error rate (BER) performance as the conventional relay selection scheme. However, this paper proposes an advantage of selecting fewer relays than conventional scheme when the target BER is satisfied. This can reduce the latency and the waste of resources. Therefore, the performance of the multi-hop transmission in wireless networks is enhanced.
Highlights
Wireless communication systems have achieved a high data rate and high bandwidth efficiency by using multiple antennas at the transmitter and receiver
In the threshold-based relay selection scheme, the performance of the cooperative communication is degraded since the threshold only considers the source-relay channel, not all channel state information (CSI)
The greatest benefit of the proposed signal-to-noise ratio (SNR) based Q-learning scheme is less exchange of CSI and the optimal relay can be selected through the self-learning. It reduces the latency and the waste of resources when the target bit error rate (BER) is satisfied. It can mitigate the overhead occurred in a cooperative communication without exchanging the information and can reduce the latency compared to the conventional Best harmonic mean (BHM) scheme
Summary
Wireless communication systems have achieved a high data rate and high bandwidth efficiency by using multiple antennas at the transmitter and receiver. This paper proposes a method that achieves the same performance and throughput using fewer relays, and the relay selection scheme is very important. The BHM scheme causes latency and waste of resources to find an optimal relay and the number of relays is increased in this process to earn an optimal performance. The existing study in [8] did not consider the number of selected hops, and it causes serious decrease in maximum data rate which is one of very important performance indicators in mobile communication systems. For solving the drawbacks of the existing study efficiently, the proposed scheme selects the number of minimum relays for obtaining target error performance. This paper uses a Q-learning based relay selection scheme to select an optimal relay and reduce the latency in multi-hop wireless networks.
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