To solve the high Bit Error Rate (BER) caused by rate oscillation in traditional Data Rate Change (DRC) algorithm, an improved DRC algorithm based on Adaptive Frame Length (AFL) was proposed for short-wave communication. Firstly, in the initialization phase, the frame length and transmission rate of the initial transmission were determined by the parameters of the current channel and the information of previous empirical values, and the data transmission was started. Then, if two frames with the same length were successively sent in the transmission process, the frame length would be accordingly increased. If the retransmission failed twice in a row, the frame length would be halved in the next transmission. Finally, the frame error rate was calculated based on the current frame length. The data rate would be increased if the value was less than the preset threshold. Compared with RapidM DRC, the average link BER of the proposed algorithm was decreased by 1.8 percentage points, and the link availability was increased by 11 percentage points. Experimental results show that the proposed algorithm can eliminate the rate oscillation and improve the communication capability of the short-wave communication system.

Since the limitation of inefficient use and unintelligent frequency selection of the HF (High Frequency) band, a method of HF cognitive frequency selection using Hidden Markov Model (HMM) was proposed. Applying cognitive radio principles to HF communications, HF legacy users were considered as primary users, and the HF radio using cognitive technologies were seen as the secondary user. Firstly, the HMM was established to predict channel states of HF legacy users based on the history data of spectrum sensing; secondly, channel parameters were estimated if the predicted state was idle; finally, the optimal frequency was selected among the channels whose predicted states were idle according to the estimated channel parameters. Simulation results show that the proposed method can be used to actually predict HF legacy users' channel states and quickly estimate channel parameters. Under the given simulation conditions, the successful transmission ratio of the proposed method is 5.54% and 10.56% higher than the methods of random channel selection using HMM prediction and energy detection, therefore the proposed method can select the optimal channel.