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 PU's 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.

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.

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 system's data signal and transmitting the secondary system's 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.

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.

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.

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.

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.