Cognitive radio (CR) has been studied as a useful solution for efficient utilization of scarce radio spectrums. For it to succeed, two conflicting challenges are imposed on the secondary users: one is to ensure the quality of service (QoS) of the primary link, and the other is to maximize their own transmit throughput. To balance this tradeoff, beamforming and power control employing the multi-antenna in the base station (BS) of the CR network have been introduced. In the perfect beamforming situation, the power control algorithms suitable in the CR network with a multi-antenna BS (MBS) have been proposed in previous works. However, those algorithms are meaningless for realizing a practical CR network with the MBS since perfect beamforming is impossible. Therefore, unlike previous works, this paper proposes a joint beamforming and power control algorithm as a more practical strategy for realizing a CR network with the MBS. The algorithm is proposed so as to maximize the sum-rate of secondary users, while not degrading QoS for the primary link. Numerical results verify its effectiveness in a CR network with the MBS.
[Show abstract][Hide abstract] ABSTRACT: The downlink beamforiming technology plays a key role in a cognitive radio network (CR-Net). It can be used to reduce transmission power and interference to other users, etc. This paper presents a robust downlink beamforming method with power control for a multiuser multiple-input-single-output (MISO) CR-Net. In this proposed approach, the beamforming optimization problem is formulated as the second-order cone programming (SOCP). The presented method can not only minimize the transmitted power but also guarantee that the received signal-to-interference-plus-noise ratio (SINR) is strictly above the prescribed quality-of-service (Qos)-constrained threshold at each secondary user (SU) and the the interference power (IP) is strictly below the prescribed threshold at the primary user (PU). Simulation results are presented to verify the efficiency of the proposed method.
Progress In Electromagnetics Research M 01/2011; 18. DOI:10.2528/PIERM11051103
[Show abstract][Hide abstract] ABSTRACT: The main target of a 60 GHz transceiver system is to obtain data rates close to gigabit per second over short distances. The 60 GHz band suffers from severe path-loss, inter-symbol interference (ISI) and a limited link budget. To improve the link budget, we need to utilize beam-forming (BF) techniques. Antenna BF, i.e., combining signals from multiple receive antennas is one of the crucial aspects of the 60- GHz transceiver system. We consider uniform linear and circular antenna arrays for the proposed BF scheme. We use single carrier with frequency domain equalization (SC-FDE) modulation scheme in our system model. To suppress the ISI, we considered a cyclic prefix in the SC-FDE. The effects of physical parameters of antenna arrays on the bit error rate (BER) were investigated assuming both line of sight (LOS) and non-line of sight scenarios. We developed an efficient and computationally less complex beam-forming algorithm (BFA). We investigated the effects of perfect channel and non-perfect channel on the BER performance using the proposed BFA. We presented that the BER and sever ISI of 60 GHz band also improves using the proposed BFA.
ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 09/2013; 38(9). DOI:10.1007/s13369-013-0555-8 · 0.37 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: The use of beamforming and power control, combined or separately, has advantages and disadvantages, depending on the application. The combined use of beamforming and power control has been shown to be highly effective in applications involving the suppression of interference signals from different sources. However, it is necessary to identify efficient methodologies for the combined operation of these two techniques. The most appropriate technique may be obtained by means of the implementation of an intelligent agent capable of making the best selection between beamforming and power control. The present paper proposes an algorithm using reinforcement learning (RL) to determine the optimal combination of beamforming and power control in sensor arrays. The RL algorithm used was Q-learning, employing an ε-greedy policy, and training was performed using the offline method. The simulations showed that RL was effective for implementation of a switching policy involving the different techniques, taking advantage of the positive characteristics of each technique in terms of signal reception.
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