Moon-Gun Song

Recursive Channel Estimation Method for OFDM-Based Cooperative Systems

In this letter, we propose a recursive channel estimation scheme for orthogonal frequency division multiplexing (OFDM) over amplify-and-forward (AF) relaying channels. Among pilot-symbol aided channel estimation techniques, linear interpolation is considered to be a simple and practical method. However, linear interpolation for the source-relay-destination channel may not work well due to the severe frequency selectivity of the cascaded channels. Specifically, extrapolation for edge subcarriers significantly distorts the channel estimation performance. To mitigate the detrimental effects of extrapolation, we partially null the channel estimates at the edge subcarriers. The proposed method effectively reduces the error propagation due to inaccurate channel estimates. In addition, we design a low complexity recursive linear interpolation scheme in which each update process utilizes both the reliability of feedback information and the characteristics of linear interpolation.

Soft Combining for Cooperative Spectrum Sensing over Fast-Fading Channels

We propose soft combining methods for cooperative spectrum sensing in cognitive radio networks over fast-fading channels. In fast-fading environment, it is difficult to obtain the exact probability distribution of the total received energy during an observation period. Therefore, we apply the mixture of gamma (MoG) approximation to obtain the distribution function of the total received energy. By exploiting the approximated distribution, we derive a soft combining method that reduces the probability of missed detection for a given probability of false alarm based on the Neyman-Pearson criterion. In addition, we propose a simplified soft combining method by using the dominant component of the MoG distribution. Simulation results show that the proposed combining methods outperform the conventional methods based on the central limit theorem, the equal gain combining, and the SNR weighted combining.

Decentralized Beamformer Design with Limited Multi-Cell Cooperation for Interference Channel of Cognitive Radio Networks

In spectrum-sharing-based cognitive radio networks, multiple secondary systems can access a licensed spectrum to better utilize scarce radio resources. When the multiple secondary transmitters are co-located, the weighted sum-rate of the secondary users (SUs) is mainly limited by the inter-cell interference (ICI). With limited cooperation among co-located secondary transmitters, we propose an algorithm for decentralized beamforming with power allocation via dual decomposition. To maximize the weighted sum-rate of the SUs, the proposed decentralized algorithm efficiently mitigates the ICI by the undesired leakage power limitation at each secondary transmitter. Because the channel information is not perfectly known at the transmitter in practical applications, we also develop a decentralized robust beamformer. To efficiently design the robust beamformer, a convex problem is formulated by semi-definite relaxation. Simulation results show that the proposed algorithm with perfect channel state information (P-CSI) efficiently maximizes the weighted sum-rate performance by the undesired leakage power limitation. For an imperfect CSI with a small error bound, the proposed robust beamformer approaches the performance of a P-CSI case, without causing harmful interference to the primary user.

Cooperation-Based Dynamic Spectrum Leasing via Multi-Winner Auction of Multiple Bands

In this paper, we study a cooperation-based dynamic spectrum leasing mechanism via multi-winner auction of multiple bands. Based on a second-price auction mechanism, the primary users independently conduct auctions to determine winners who are then granted access to leased bands and prices for those bands. Before auctions, each secondary user jointly chooses bands which they want to lease and generates bids for those bands with a limited transmit power budget. To this end, we determine the feasibility conditions for band selection and for power and time allocation. Further, we propose a low complexity bidding algorithm that iteratively selects a band and calculates the bid for that band. For the paying price among winners after auctions, a multi-objective optimization problem is considered. We derive Pareto optimal solutions and propose paying schemes that prioritize the objectives with pre-defined weighting rules. In addition, the payment and secondary utility are investigated with regard to the number of winners. Simulation results show that secondary users achieve significantly increased utility as more winners are chosen, while primary users are guaranteed a non-negative benefit from spectrum leasing. The power consumed by the secondary users for cooperative transmission is compared for the proposed paying schemes.

Power Efficient Transceiver Designs for Multi-Cell Coordination in MIMO Cognitive Radio Networks

In cognitive radio networks, multiple secondary systems can access a licensed spectrum when none of the secondary transmitters cause harmful interference to the Primary Users (PUs). For spectrum sharing with coordination among co-located secondary systems, we propose both centralized and distributed beamforming algorithms. The proposed algorithms minimize the total transmit power of secondary systems, while maintaining the interference to PUs below a certain threshold and satisfying the Quality-of-Service (QoS) constraint for each secondary system. The centralized algorithm achieves the optimal transmit power by exploiting the virtual uplink-downlink duality using the knowledge of the channel state information for all the secondary links. However, the assumption of global channel knowledge at each secondary system may not be allowed in practical applications for multi-cell coordination. To address this problem, we design a distributed transceiver beamformer that satisfies the interference constraint to protect PUs. On the basis of this distributed beamformer, we also propose power allocation algorithms that guarantee the QoS for secondary systems. Distributed beamforming and power allocations operate iteratively to minimize the total transmit power. Simulation results show that the distributed algorithms achieve a near-optimal transmit power while satisfying both the QoS and interference constraints.

Improved Spectrum-Sharing Protocol for Cognitive Radio Networks With Multiuser Cooperation

This paper proposes an improved spectrum-sharing protocol for multiuser cooperation in cognitive radio (CR) networks. In CR networks, a secondary user (SU) can access the licensed bands of a primary user (PU) as compensation for cooperative transmission. During cooperative transmission, the SU concurrently transmits its own signal and network-coded signal from the PUs. However, detection errors at the SU cause error propagation which degrades the performance of the PU and SU. To address this problem, we develop a cooperative maximal-ratio combining scheme that mitigates the error propagation and achieves diversity gain. To evaluate the combining scheme, we derive a diversity order and closed-form bit error rate (BER) expression for arbitrary M-QAM at high SNR. The analysis results show that the diversity order and BER depend on the fraction of the transmit power at the SU. Based on the dependency of the fraction factor, we propose an optimization problem to minimize the BER of the SU while guaranteeing the PUs BER. Further, we apply the BER-constrained optimization problem to the adaptive modulation system. Simulation results show that the proposed cooperation provides full diversity gain to the PU and thus improves its spectral efficiency by using the optimized fraction factor.

Rate Adaptation and Power Allocation for Cognitive Radio Networks with HARQ-Based Primary System

In this paper, we propose a protocol for the coexistence of primary and secondary systems over block-fading channels. In the protocol, the primary system employs a hybrid automatic repeat request (HARQ). When the primary system retransmits the data signal, the secondary system serves as a relay for the retransmission of the primary system and simultaneously transmits its data signal. To efficiently accomplish the protocol, we analyze the average throughput of the primary and secondary systems by using the long-term average throughput (LAT). We formulate an optimization problem to maximize the LAT of the secondary system. The constraint of the optimization problem is that the LAT of the primary system with secondary system is not less than that of the primary system alone. Through the optimization problem, we obtain the closed-form solutions of the transmission rate of the secondary system and the fraction of the transmit power for relaying the primary systems data signal and transmitting the secondary systems data signal. Numerical results show that the primary system does not lose the average throughput, and rather achieves an additional throughput gain by adjusting the fraction of the transmit power of the secondary system.

Maximization of Long-Term Average Throughput for Cooperative Secondary System With HARQ-Based Primary System in Cognitive Radio Network

We consider a cognitive radio network in which a secondary system (SS) coexists with a hybrid-automatic-repeat-request (HARQ)-based primary system (PS) over block-fading channels. We propose a coexistence scheme in which the SS acts as a relay for the PS and gains spectrum access opportunities as a reward for the cooperation. The proposed scheme aims at achieving nontrivial throughput gain for the SS without affecting the performance of the PS. To this end, we first analyze the long-term average throughputs (LATs) of the PS and SS by applying total probability theorem and a mixture-of-Gamma approximation. Then, we optimize the transmit power ratio and the target rate of the SS to maximize its LAT. In simulations, the proposed scheme provides significant LAT gain for the SS while maintaining the LAT of the PS.

MSE-Based Transceiver Design for Decentralized Transmit Power Minimization of Multi-Cell MIMO Cognitive Radio Systems

This paper considers multiple-input multiple-output (MIMO) cognitive radio (CR) networks in which multiple primary and secondary systems coexist. We propose a transceiver design algorithm to minimize the total transmit power of secondary systems subject to the signal to interference plus noise ratio (SINR) constraint for each secondary user and the interference constraint for each primary user. Due to the complexity of backhaul among secondary systems, perfect knowledge about the channel state information (CSI) may not be available in practice. To overcome this problem, we decompose the transmit power minimization problem into cascade problems of decentralized beamformer design with the interference constraint and power allocation with the SINR constraint. For beamformer design, we formulate the total mean squared error of data symbol minimization problem, to enhance the feasible rate and only use the local CSI via dual decomposition. The power allocation problem is established as linear programming. Simulation results show that, compared to the conventional centralized algorithm, the proposed decentralized algorithm achieves much higher feasible rate with slightly increased transmit power.

Performance analysis for decode-and-forward relay system with HARQ in Nakagami-m fading channels

In this paper, we analyze the outage probability and the long-term average throughput (LAT) of a decode-and-forward (DF) cooperative scheme with hybrid automatic repeat request (HARQ) over Nakagami-m fading channels. To derive the outage probability, the distribution of the weighted sum of two independent gamma random variables is needed. We approximately obtain the distribution by using gamma approximation and derive a closed-form expression of the outage probability of the DF cooperative scheme with high SNR assumption. By exploiting the outage probability, we obtain the LAT of the DF cooperative scheme. Numerical results show that the derived LAT of the DF cooperative scheme is able to predict the average throughput and the DF cooperative scheme achieves higher LAT than the direct transmission scheme because of higher diversity gains.