Since chirp spread spectrum (CSS) system has robustness to reduce multipath fading effect and to extend spectrum bandwidth with low energy consumption, the CSS system has attracted much attention not only for radio detection and ranging systems but also for commercial wireless communication systems. In the spread-spectrum systems, low probability of detection (LPD) and anti-jamming (AJ) abilities are two key issues. In this paper, a multiple linear chirp (MLC)-based binary orthogonal keying (BOK)-CSS system considering cross-correlation coefficient for improving the LPD and the AJ performances is proposed. In order to analyze the performance of the MLC-based BOK-CSS system, the cross-correlation coefficient between two different chirps is analytically derived according to the time-bandwidth separation product, and it is numerically compared to a Monte Carlo simulation. From the results, the proposed MLC-based BOK-CSS system guarantees higher survivability than the conventional schemes by considering the cross-correlation coefficient according to the bandwidth separation.

A conventional relaying system is based on an orthogonal two‐phase transmission protocol where time slots for the source (the first phase) and relays (the second phase) are exclusively allocated to avoid interference. For this reason, the conventional relaying schemes suffer from the reduction in spectral efficiency. In order to overcome this drawback, a parallelised relaying protocol with simultaneous transmission has been proposed. However, simultaneous transmission may not be a good strategy in such a strong cross‐relay interference. In this study, the authors propose a parallelised relaying protocol with an opportunistic priority assignment. In the proposed scheme, they consider the cognitive radio technique as a way to improve the spectral efficiency of the conventional parallelised relaying protocol in the cross‐relay interference. They evaluate the instantaneous capacity of the proposed scheme through computer simulations and conclude that their scheme can improve the spectral efficiency of the conventional relaying systems significantly.

As a scheme to efficiently reduce the effects of multipath fading in next generation wireless communication systems, cooperative communication systems have recently come into the spotlight. Since these cooperative communication systems use cooperative relays with diverse fading coefficients to transmit information, having all relays participate in cooperative communication may result in unnecessary waste of resources, and thus relay selection schemes are required to efficiently use wireless resources. In this paper, we propose an efficient relay selection scheme through self-learning in cooperative wireless networks using Q-learning algorithm. In this scheme, we define states, actions and a reward to achieve good SER (Symbol Error Rate) performance, while selecting a small number of cooperative relays. When these parameters are well-defined, we can obtain good performance. The simulation results show that, compared to a scheme that obtains optimum numbers of relays through a mathematical analysis, the proposed scheme uses resources efficiently by using smaller numbers of relays with comparable SER performance. In particular, whereas the number of cooperative relays linearly increases as the number of relays that can participate increases in the case of the scheme modeled by a mathematical analysis, the proposed scheme selects 2.5 relays on average for cooperative communication. According to these simulation results, the proposed scheme can be considered as a good attempt for future communication.

In order to overcome fading in wireless channels and increase the performance of wireless systems, cooperation is needed between the source node and relay node. In a cooperative wireless communication system, PA (Power Allocation) and RS (Relay Selection) are important issues for improving performance of the system. In particular, PA is an effective way to save transmit power and improve the coverage and capacity of the system. Some papers analyze full CSI (Channel State Information) for obtaining optimal power allocation. However, this makes the system increasingly complex and presents a practical problem. Thus, there is a need for partial CSI to reduce the complexity. With PSO (Particle Swarm Optimization), individuals referred to as particles are “flown” through hyper-dimensional search space. Changes to the position of a particle within a swarm are influenced by the knowledge of its neighbors. The PSO algorithm maintains a swarm of particles, where each particle represents a potential solution. Therefore, we propose an improved scheme for power allocation in relay-based cooperative wireless communications. For this purpose, we employ partial CSI and PSO algorithms to solve the optimization problem of power allocation with reduced system complexity as well as optimized performance.

Cyclostationary empirical orthogonal functions, derived from satellite altimetry, are combined with historical sea level measurements from tide gauges to estimate nearly-global monthly sea level fields from 1900-2010. The reconstructed global mean sea level is shown to compare favorably with the satellite altimetry over the period from 1993-2010. The trend in global mean sea level for the fully reconstructed data record is 1.4 mm/yr. This reconstruction shows improvement over previously published reconstructions in its robust ability to capture sea level variability associated with the modulated annual cycle and El Nino-Southern Oscillation signals.