Search

Multiple-input Multiple-output (MIMO) technologies play an important role in modern and future wireless communication systems as they can improve the capacity without increasing the bandwidth. MIMO detection is one of the key technologies for MIMO system designs. MIMO detection schemes based on Message Passing (MP) algorithms have attracted extensive attention in recent years. The MIMO detection scheme based on Expectation Propagation (EP), which is also a kind of MP algorithms, has been proved to achieve the Bayes-optimal performance under the conditions of large system limit and compression rate threshold. However, there is improvement space for the detection performance of the EP algorithm when the conditions are not satisfied, which are the common cases in the practical applications. In this paper, when the conditions of the large system limit and compression rate threshold are not satisfied, we analyze the influences of two factors, the initial parameters selection and the moment matching strategy, on the performance of EP based detection scheme. As a result, we propose an improved EP detection scheme based on optimizing the two factors. Simulation results show that the proposed scheme outperforms the original EP detection scheme in different MIMO scenarios.

Background: The intraoperative visualization of tumor cells is a powerful modality for surgical treatment of solid tumors. Since the completeness of tumor excision is closely correlated with the survival of patients, probes that can assist in distinguishing tumor cells are highly demanded. Purpose: In the present study, a fluorescent probe JF1 was synthesized for imaging of tumor cells by conjugating a substrate of cathepsin B (quenching moiety) to Oregon Green derivative JF2 using a self-immolative linker. Methods: JF1 was then loaded into the folate-PEG modified CaCO3 nanoparticles. The folate receptor-targeted, pH-dependent, and cathepsin B activable CaCO3 nanoprobe was test in vitro and in vivo for tumor imaging. Results: CaCO3 nanoprobe demonstrated good stability and fast lighting ability in tumors under low pH conditions. It also showed lower fluorescence background than the single cathepsin B dependent fluorescent probe. The pH-dependent and cathepsin B controlled "turn-on" property enables precise and fast indication of tumor in vitro and in vivo. Conclusion: This strategy of controlled drug delivery enables in vivo imaging of tumor nodules with a high signal-to-noise ratio, which has great potential in surgical tumor treatment.

Multiple-input Multiple-output (MIMO) technologies play an important role in modern and future wireless communication systems. The low-complexity and high-performance MIMO detection schemes are desired but challenging. The iterative MIMO detection scheme based on the Expectation Propagation (EP) algorithm is regarded as state-of-the-art due to its outstanding performance. In this paper, we analyze the influence of the initial parameters on the performance of the EP based MIMO detection and propose a scheme to optimize the initial parameters. Numerical results show the proposed optimization scheme for EP detection scheme outperforms the original scheme about 1.5 dB in different scenarios. Thus, the proposed scheme can improve the performance for EP based MIMO detection.

With the expansion of modern power systems, the stability issues become more and more prominent. Transient stability emergency control is usually designed in open-loop schemes and applies proper actions to avoid system collapse when transient stability cannot be guaranteed in serious contingencies. Taking transient stability and economic efficiency of power system into consideration, the emergency control problem can be modeled as an optimal control problem, which is computational expensive. In this paper, an optimal control method with constraint aggregation is proposed to reduce computational complexity. The yield nonlinear problem is a fairly small-scale optimization problem which can be efficiently solved by predictor–corrector interior point method. The adjoint sensitivity analysis (ASA) is employed to evaluate the first-order derivative while Broyden–Fletcher–Goldfarb–Shanno (BFGS) algorithm is used to obtain the second-order derivative. Besides, very dishonest Newton (VDHN) method and reusage of LU factorization results are explored to accelerate the forward and backward integration phase of ASA, respectively. The proposed approach is tested on four cases with different scales, and shows its potential in computational efficiency.

This paper proposes a low-complexity expectation propagation (EP) algorithm for massive multiple- input multiple-output (MIMO) detections. The original EP detection algorithm shows a near-optimal performance but suffers from the unaffordable computational complexity. In this paper, we use an iterative successive updating scheme to reduce the complexity caused by the exact matrix inversion in each iteration and ameliorate the efficiency and accuracy of messages updating to accelerate the convergence, which leads to a low-complexity high- performance massive MIMO detector. Numerical analysis shows the proposed algorithm can outperform 0.2 dB in bit error rate (BER) with a huge of computational complexity saved compared with the previous EP detector.

High performance and high efficiency detection scheme is one of the key issues for MIMO-LDPC system designs. In this paper, we proposed a hybrid jointed detection scheme for MIMO-LDPC detection, in which the expectation propagation (EP) algorithm is used in MIMO detection and the belief propagation (BP) algorithm is adopted for the low density parity check code (LDPC) decoding. In the proposed scheme, we provide a jointed detection algorithm (EP-BP JDD) with the integrated factor graph for MIMO-LDPC systems. Compared with conventional MMSE-PIC IDD and BP-BP JDD, the proposed method can achieve low computational complexity with high performance. The complexity and performance gains become more significant with the number of antennas increasing.