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.

This letter proposes a distributed beamforming scheme based on block diagonalization (BD) for multicell multiuser multiple-input multiple-output (MU-MIMO) downlink systems. Although conventional BD can be directly extended to these systems in a distributed manner, it suffers from sum-rate degradation due to strict zero-forcing (ZF) constraints. To overcome this problem, we introduce a sum-rate maximization problem for BD, to find the optimal selection of ZF constraints. Then we propose a search algorithm to solve the problem in a distributed and heuristic manner. Simulation results show that the proposed scheme effectively finds a near-optimal solution and significantly improves simple extended BD such that it can provide higher sum rate than the conventional schemes.

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 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 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.

This paper proposes a space-time block coded (STBC) transmission with phase feedback for two-user multi-input multi-output multiple access channels (MIMO-MAC). In the proposed system, the phase feedback is transmitted to only one user, which reduces the overhead of the reverse link. We derive a closed-form solution for the proposed phase feedback, in order to maximize the overall received signal-to-interference-plus-noise power ratio (SINR). This allows the receiver to separate the two users with a linear detection. We also propose an efficient search method for the phase feedback, which reduces the complexity of computation. For practical applications, the quantized feedback of the proposed system is designed. Simulation results show that the proposed system with only 1-bit feedback considerably improves the performance of the MIMO-MAC system.

This paper proposes a bit-interleaved coded multilevel modulation for non-orthogonal cooperative transmissions. The proposed system encodes and interleaves multiple layers together, using one encoder and one interleaver, in order to average the performance of multiple layers with unequal error probabilities. The multiple layers are mapped by hierarchical symbol mapping. To improve the performance of the proposed system, the relay transmits the layers with a different hierarchical mapping pattern from the source, which is optimally derived for minimizing mean square error. To mitigate error propagation from the relay, we propose a power scaling scheme based on the error performance of the layers. The detected signals at the relay are scaled before being transmitted to the destination. A corresponding destination structure is also presented, where multiple layers can be decoupled by an iterative equalization with improved soft information. To estimate the performance of the proposed system, we develop a density evolution technique, and show that the predicted performances are consistent with simulated performances. Simulation results indicate that by using the optimal mapping pattern, power scaling, and iterative equalization with improved soft information, the proposed technique achieves both cooperative diversity and coding gains, and thus outperforms conventional cooperative schemes.

This paper proposes a spectral efficient cooperative diversity technique with multi-layered modulation for high data rate transmissions. The proposed system transmits with different cyclic delay and phase rotation patterns, in order to achieve cooperative diversity gain for all transmit signals from the source. Since these patterns increase the interference between the layers, we derive optimal cyclic delay and phase rotation patterns to minimize mean square error. With the optimal patterns, the proposed system still suffers from inter-layer interference, especially in high-order modulations. To overcome this problem, multiple layers are separately interleaved by layer-specific interleavers and are mapped by hierarchical symbol mapping for the high data rate transmission with cooperative diversity. A destination structure with iterative equalization is also presented to separate each layer and extract diversity gain for high data rate transmissions. The proposed system can use reliable feedback from the layers and effectively remove the inter-layer interference. To estimate the performance of the proposed system, we develop a density evolution technique. It is shown that the predicted performances agree with simulated performances. Simulation results indicate that the proposed system achieves cooperative diversity and has better performance than conventional cooperative systems.

Peak-to-average power ratio (PAPR) regrowth after clipping is one disadvantage of space-frequency block coded orthogonal frequency-division multiplexing (SFBC-OFDM). In this letter, we propose an effective PAPR reduction technique of SFBC-OFDM for multinode cooperative transmission. To reduce PAPR at the source (mobile equipment), the relay applies SFBC encoding, which enables the source to transmit clipped single-input single-output (SISO)-OFDM signals without any increase of PAPR. Simulation results show that the clipped signals of proposed scheme are effectively recovered, and the proposed scheme achieves the diversity of SFBC without the complexity of multiple antennas at the source.