Yangyang Zhang

Optimizing Polyphase Sequences for Orthogonal Netted Radar

Orthogonal netted radar transmitter signals require a very low aperiodic autocorrelation peak sidelobe level (PSL), low aperiodic cross-correlation, and a good resilience to small Doppler shifts. A new set of polyphase sequences is presented with good correlation properties as well as resilience to Doppler shifts. These sequences are built using numerical optimization based on correlation properties. A structural constraint is imposed on the optimized polyphase sequences, which maintains Doppler tolerance. Cross entropy (CE) technique is used to optimize the sequences. Correlation and Doppler results are compared with best-known sequences on various merit factors and shown to be superior

A low complexity user scheduling algorithm for uplink multiuser MIMO systems

A low complexity user scheduling algorithm based on a novel adaptive Markov chain Monte Carlo (AMCMC) method is proposed to achieve the maximal sum capacity in an uplink multiple-input multiple-output (MIMO) multiuser system. Compared with the existing scheduling algorithms, our algorithm is not only more efficient but also converges to within 99% of the optimal capacity obtained by exhaustive search. We demonstrate the convergence of the proposed scheduling algorithm and study the tradeoff between its complexity and performance.

Receive antenna selection for MIMO systems over correlated fading channels

In this letter, we propose a novel receive antenna selection algorithm based on cross entropy optimization to maximize the capacity over spatially correlated channels in multiple-input multiple-output (MIMO) wireless systems. The performance of the proposed algorithm is investigated and compared with the existing schemes. Simulation results show that our low complexity algorithm can achieve near-optimal results that converge to within 99% of the optimal results obtained by exhaustive search. In addition, the proposed algorithm achieves near-optimal results irrespective of the mutual relationship between the number of transmit and receive antennas, the statistical properties of the channel and the operating signal-to-noise ratio.

A Low-Complexity Receive-Antenna-Selection Algorithm for MIMO–OFDM Wireless Systems

In this paper, a novel low-complexity antenna-selection algorithm based on a constrained adaptive Markov chain Monte Carlo (CAMCMC) optimization method is proposed to approach the maximum capacity or minimum bit error rate (BER) of receive-antenna-selection multiple-input multiple-output (MIMO)-orthogonal frequency division multiplexing (OFDM) systems. We analyze the performance of the proposed system as the control parameters are varied and show that both the channel capacity and the system BER achieved by the proposed CAMCMC selection algorithm are close to the optimal results obtained by the exhaustive search (ES) method. We further demonstrate that this performance can be achieved with less than 1% of the computational complexity of the ES rule and is independent of the antenna-selection criteria, outage rate requirements, antenna array configuration, and channel frequency selectivity. Similar to the existing antenna-selection algorithms, both channel capacity and system BER improvements achieved by the proposed CAMCMC method are reduced as the channel frequency selectivity increases. Therefore, we conclude that, whether it is designed to maximize the channel capacity or minimize the system BER, the CAMCMC-optimization-method-based antenna-selection technique is appropriate for a MIMO-OFDM system with low frequency selectivity.

On Hybrid Overlay–Underlay Dynamic Spectrum Access: Double-Threshold Energy Detection and Markov Model

In this correspondence, we propose a hybrid strategy that combines overlay and underlay dynamic spectrum access (DSA) schemes. Utilizing a double-threshold energy detection method, unlicensed or secondary users (SUs) can switch between full- and partial-access modes dynamically. A Markov chain model is developed to derive performance metrics for evaluating the proposed strategy. Numerical results show that the proposed strategy can greatly improve the system interfering probability performance. In addition, SUs can adjust the access probability of partial-access mode to tradeoff between throughput and interfering probability performance.

Near-Optimal Joint Antenna Selection for Amplify-and-Forward Relay Networks

This paper considers a joint antenna selection method in amplify-and-forward (AF) relay networks where the source, relay and destination terminals are all equipped with multiple antennas. The fact that the systems full diversity can be maintained by antenna selection at each terminal makes it a promising solution to reduce the hardware complexity of multiple-input multiple-output (MIMO) terminals while realizing the diversity benefits of MIMO in relay networks. Since the exhaustive search for antenna subset selection is computationally prohibitive, we devise a low-complexity near-optimal joint antenna selection algorithm based on a constrained cross entropy optimization (CCEO) method to maximize the achievable rate and the convergence is guaranteed. Simulation results reveal both the effectiveness and the efficiency of the proposed algorithm and the significant performance improvement over other benchmark selection techniques. Finally, it is illustrated that the proposed CCEO algorithm can always achieve near-optimal results regardless of the number of selected antennas, outage probabilities and the signal-to-noise ratios (SNRs) at the terminals.

Joint Antenna and User Selection Algorithm for Uplink of Multiuser MIMO Systems using Sequential Monte Carlo Optimization

A cross-layer optimization design is developed for the uplink of multiuser multiple-input multiple-output (MIMO) systems, in which the user-based selection scheduling is executed at the medium access control (MAC) layer, while the antenna selection is performed at the physical (PHY) layer. In order to obtain the optimal cross-layer design, a framework based on sequential Monte Carlo (SMC) optimization is presented to jointly consider the user and antenna selection. With the proposed joint user and antenna selection algorithm, the sum capacity of the multiuser MIMO uplink converges to within 99% of that obtained by exhaustive search method, while the complexity is substantial low.

Quality of service‐aware coordinated dynamic spectrum access: prioritized Markov model and call admission control

In this paper, we propose a heterogeneous-prioritized spectrum sharing policy for coordinated dynamic spectrum access networks, where a centralized spectrum manager coordinates the access of primary users (PUs) and secondary users (SUs) to the spectrum. Through modeling the access of PUs and multiple classes of SUs as continuous-time Markov chains, we analyze the overall system performance with consideration of a grade-of-service guarantee for both the PUs and the SUs. In addition, two new call admission control (CAC) strategies are devised in our models to enhance the maximum admitted traffic of SUs for the system. Numerical results show that the proposed heterogeneous-prioritized policy achieves higher maximum admitted traffic for SUs. The trade-off between the system’s serving capability and the fairness among multiple classes of SUs is also studied. Moreover, the proposed CAC strategies can achieve better performance under max-sum, proportional, and max-min fairness criteria than the conventional CAC strategies. Copyright

Optimizing relay selection and power allocation for orthogonal multiuser downlink systems

This paper studies the relay assignment and power allocation for orthogonal multiuser systems using amplify-and-forward (AF) relays in the downlink. Our objective is to maximize the sum-rate of all users, subject to individual and total power constraints on the relays and a relay assignment constraint. For fixed relay selection, the power allocation problem is convex and can be optimally solved, whereas the joint optimization of relay selection and power allocation appears to be non-convex and is not known to be tractable. To tackle this, we propose an algorithm using Markov chain Monte-Carlo (MCMC) in combination with cross-entropy optimization (CEO). Results show that the scheme outperforms significantly a greedy approach and achieves near-optimal performance at very low complexity.

A Stochastic Optimization Approach for Joint Relay Assignment and Power Allocation in Orthogonal Amplify-and-Forward Cooperative Wireless Networks

This paper addresses the joint relay assignment and power allocation problem for orthogonal multiuser systems using amplify-and-forward (AF) relaying nodes in the downlink. Our aim is to maximize the sum-rate subject to individual and total power constraints on the relays and a relay assignment constraint. In the case of fixed relay selection, the power allocation optimization is convex and an efficient recursive algorithm is proposed to achieve the optimum. The joint optimization of relay selection and power allocation, however, appears to be non-convex and is not known to be tractable. To tackle this, we propose a novel algorithm using Markov chain Monte-Carlo with Kullback-Leibler divergence minimization (MCMC-KLDM), which is proved to converge to the global optimum almost surely. Results show that the proposed scheme significantly outperforms a greedy approach and achieves near-optimal performance at very low complexity.