While there exist photovoltaic (PV) forecasting methods, persistent challenges remain, such as dynamically changing working conditions, nonlinearities, limited interpretability, and dependency on quality datasets. To address these issues, this article proposes a multiscale-dynamic and neural-tuned physics-formulated PV forecasting method, and thus develops a physics-embedded automated modeling (PEAM) framework for engineering systems. Different from a physics-informed neural network (PINN), PEAM explicitly integrates first-principle derived physical equations directly into the model for interpretability, with neural networks exploiting their nonlinear fitting and adaptation capabilities. Here, Bayesian optimization is utilized systematically to address hyperparameter sensitivity and significance analysis, thus automatically identifying critical environmental impact factors, focusing on the most influential input variables, and providing effacacy for data usage. Comprehensive experimental validation conducted on three different prediction steps, ten testing datasets, and one extreme weather test set from two real-world solar stations shows that PEAM consistently outperforms existing methods such as PINN. Overall, the PEAM framework enhances the predictive accuracy, interpretability, and robustness for practical deployment of PV forecasting systems.

Numerical comparison serves as a major tool in evaluating the performance of optimization algorithms, especially nondeterministic algorithms, but existing methods may suffer from a ‘cycle ranking’ paradox and/or a ‘survival of the non-fittest’ paradox. This paper searches for paradox-free data analysis methods for numerical comparison. It is discovered that a class of sufficient conditions exist for designing paradox-free analysis. Rigorous modeling and deduction are applied to a class of profile methods employing a filter. It is thus further discovered and proven that algorithm-independent filter conditions can prevent cycle ranking and survival of non-fittest paradoxes from occurring. By adopting an algorithm-independent filter, popular profile methods such as the ‘modified data profile method’‘, the accuracy profile method’, and ‘the operational characteristics zones method’ can be paradox free in comparing or benchmarking the performance of optimization algorithms.

Numerical comparison is often key to verifying the performance of optimization algorithms, especially, global optimization algorithms. However, studies have so far neglected issues concerning comparison strategies necessary to rank optimization algorithms properly. To fill this gap for the first time, we combine voting theory and numerical comparison research areas, which have been disjoint so far, and thus extend the results of the former to the latter for optimization algorithms. In particular, we investigate compatibility issues arising from comparing two and more than two algorithms, termed “C2” and “C2+” in this article, respectively. Through defining and modeling “C2” and “C2+” mathematically, it is uncovered and illustrated that numerical comparison can be incompatible. Further, two possible paradoxes, namely, “cycle ranking” and “survival of the nonfittest,” are discovered and analyzed rigorously. The occurrence probabilities of these two paradoxes are also calculated under the no-free-lunch assumption, which shows the first justifiable use of the impartial culture assumption from voting theory, providing a point of reference to the frequency of the paradoxes occurring. It is also shown that significant influence on these probabilities comes from the number of algorithms and the number of optimization problems studied in the comparison. Further, various limiting probabilities when the number of optimization problems goes to infinity are also derived and characterized. The results would help guide benchmarking and developing optimization and machine learning algorithms.

An embedding is a function that maps entities from one algebraic structure into another while preserving certain characteristics. Embeddings are being used successfully for mapping relational data or text into vector spaces where they can be used for machine learning, similarity search, or similar tasks. We address the problem of finding vector space embeddings for theories in the Description Logic ??⁺⁺ that are also models of the TBox. To find such embeddings, we define an optimization problem that characterizes the model-theoretic semantics of the operators in ??⁺⁺ within ℝⁿ, thereby solving the problem of finding an interpretation function for an ??⁺⁺ theory given a particular domain Δ. Our approach is mainly relevant to large ??⁺⁺ theories and knowledge bases such as the ontologies and knowledge graphs used in the life sciences. We demonstrate that our method can be used for improved prediction of protein--protein interactions when compared to semantic similarity measures or knowledge graph embeddings.

Indoor coverage is essential to the mobile user experience, because the signal power from the remote macro base stations is seriously weakened after penetrating the walls. Traditional indoor coverage system is a good solution but it can not be deployed in all the buildings. In order to enhance the received signal power inside the building, a relay can be used in a long term evolution (LTE) system. The benefits of relay are: it can be deployed anywhere quite near to the building since it uses wireless backhaul with less position limitation, and thus it can amplify the transmitting power due to the nearby position to the building comparing to the remote macro base stations. Then even with penetration loss, the received signal power inside the building is still high enough to carry traffic. The field test results show that a TD-LTE relay can improve the indoor received signal power and traffic obviously, and thus it's a good solution to enhance outdoor to indoor coverage.

We consider the scenario where a robot is tasked with sending a fixed number of given bits of information to a remote station, in a limited operation time, as it travels along a pre-defined trajectory, and while minimizing its motion and communication energy costs. We propose a co-optimization framework that allows the robot to plan its motion speed, transmission rate and stop time, based on its probabilistic prediction of the channel quality along the trajectory. We show that in order to save energy, the robot should move faster (slower) and send less (more) bits at the locations that have worse (better) predicted channel qualities. We furthermore prove that if the robot must stop, it should then stop only once and at the location with the best predicted channel quality. We also prove some properties for two special scenarios: the heavy-task load and the light-task load cases. We also propose an additional stop-time online adaptation strategy to further fine tune the stop location as the robot moves along its trajectory and measures the true value of the channel. Finally, our simulation results show that our proposed framework results in a considerable performance improvement.

In this paper, we consider the problem of robotic router formation, where two nodes need to maintain their connectivity over a large area by using a number of mobile routers. We are interested in the robust operation of such networks in realistic communication environments that naturally experience path loss, shadowing, and multipath fading. We propose a probabilistic router formation and motion-planning approach by integrating our previously proposed stochastic channel learning framework with robotic router optimization. We furthermore consider power constraints of the network, including both communication and motion costs, and characterize the underlying tradeoffs. Instead of taking the common approach of formation optimization through maximization of the Fiedler eigenvalue, we take a different approach and use the end-to-end bit error rate (BER) as our performance metric. We show that the proposed framework results in a different robotic configuration, with a considerably better performance, as compared with only considering disk models for communication and/or maximizing the Fielder eigenvalue. Finally, we show the performance with a simple preliminary experiment, with an emphasis on the impact of localization errors. Along this line, we show interesting interplays between the localization quality and the channel correlation/learning quality.

CA (Carrier aggregation) is one of the most important techniques for LTE-Advanced system because of its capability to support wide bandwidth while achieving backward compatibility. In order to improve performance of control information transmission, cross-carrier scheduling is to be supported, i.e. control information on one CC (Component Carrier) can assign radio resource on another CC. Design of search space, in which control information is mapped, for different CCs is one of the most essential issues of cross-carrier scheduling. This paper presents two novel methods of search space design. In the first method, UE (User Equipment)-specific offset is introduced among search spaces of different CCs. Due to the UE-specific offsets, search spaces of different UEs are staggered with each other and probability that search space of one UE is wholly overlapped with that of another UE can be greatly reduced. In the second method, a novel randomization scheme is proposed to generate independent hashing functions for search spaces of different CCs. Because of ideal randomization of the proposed method, search space overlapping of different UEs is reduced. Simulation results show the proposed methods can effectively reduce blocking probability compared with existing methods.

This paper investigates the downlink system level performance for Layer-3 (L3) and Layer-1 (L1) relays with orthogonal frequency division multiple access (OFDMA) used in LTE-Advanced. Various practical factors are taken into account in the evaluations such as the processing delay and upper-bound of the amplifier gain of the L1 relay, capacity limitation of the backhaul channels, and empty buffer status at the L3 relay. Based on system-level simulations, we clarify the system impact of several conditions for the relay such as a relay location, the number of relay nodes, the number of backhaul (radio link between eNode B and L3 relay) subframes, and investigate the throughput performance gain of the Release 10 LTE L3 relay compared to the L1 relay.

Dynamic frequency management is considered to be beneficial for the uncoordinated self-deployment of Home eNB (HeNB) in LTE-A heterogeneous networks. In this paper, a new autonomous component carrier (CC) selection scheme is proposed that dynamically selects the CC with efficient inter-cell signaling design. The CC selection criterion is to minimize the interference contribution composed of the incoming and outgoing interference. Additionally, power control and weighted proportional fair (PF) scheduling are used to further improve the cell-edge performance. The proposed inter-cell signaling between HeNBs is independent of CC number, user number and CC allocation results in HeNB network, thus the inter-cell signaling overhead can be greatly reduced. Simulation results show that the proposed CC selection scheme can autonomously achieve high cell-average and cell-edge throughput. This distributed and adaptive scheme performed locally at each HeNB is suitable for the uncoordinated HeNB deployment without centralized control unit.

CA (Carrier aggregation) is one of the most important techniques for LTE-Advanced system because of its capability to support wide bandwidth while achieving backward compatibility. This paper focuses on enhancements of downlink control channel resource allocation for CA system in order to improve the performance of control information transmission in cross-carrier scheduling. A novel metric, named impact factor, is proposed to represent the impact of control information location selection in the search space of one UE (User Equipment) to other UEs. On the basis of the proposed metric, an enhanced control information resource allocation algorithm is proposed to assign control region resource to multiple CCs (Component Carrier) and multiple UEs with low mutual impacts among each other. Furthermore, a novel control information placement method is proposed to solve the problem of active CC set mismatch between eNodeB and UE. Through this novel control information placement after CC activation, data transmission interruptions in cross carrier scheduling can be effectively reduced. Simulation results show the proposed method can effectively improve the control information transmission performance compared with existing methods.

Abstract-This paper concerns the coordinated transmission among multiple remote antenna units (RAUs) and user equipments (UEs) in distributed antenna system (DAS), where instantaneous channel state information (CSI) feedback of multiple links is a big challenge. By exploring the inherent relationship among coordinated UEs, the dominant UEs grouping algorithm is proposed only using long-term CSI in each proportional fairness (PF) scheduling decision. The so-called "dominant" refers to a subgroup of UEs as potential partners with higher probability to coordinate. In the proposed algorithm, each UE only needs to report a single value of large-scale fading (denoted as PL). At the central unit, use weighted PL instead of the resultant throughput to prioritize UEs, set the threshold according to the variance of all UEs' PL, and employ a simple scalar-wise metric for grouping. Performance evaluation shows that in contrast to the conventional methods, the proposed algorithm with twopart scheduling strategy can reduce about 20% of the feedback overhead and improve the UEs fairness. This proposal is more efficient for high-density hotspot areas where a large number of UEs exist to join the coordinated transmission.

This paper concerns the enhanced downlink transmit diversity scheme for EUTRA (Evolved Universal Terrestrial Radio Access) system. The performance of EUTRA downlink transmit diversity scheme, i.e. the combination of SFBC (Space-Frequency Block Codes) and FSTD (Frequency Switching Transmit Diversity), is tightly related to the subcarrier mapping method. In order to improve the performance of EUTRA downlink transmit diversity scheme, a dynamic antenna port pairing method based on slot index and a novel ordering method of adjusting symbol-order in SFBC and SFBC-order in FSTD are proposed. Simulation results show the proposed scheme is very effective and can achieve much better FER (Frame Error Rate) performance than conventional schemes.

We propose a magnetic and electric level spin-transfer torque random access memory (STT-RAM) cell model to simulate the write operation of an STT-RAM. The model of a magnetic tunneling junction (MTJ) is modified to take into account the electrical response of the MOS transistor that is connected to the MTJ. A dynamic design flow is also proposed to minimize any unnecessary design margin in an STT-RAM cell design by leveraging from the new STT-RAM cell model. The design of an STT-RAM cell with a one-transistor-one-MTJ (1T1J) structure shows that our technique can reduce more than 22% of the STT-RAM cell area, compared with a conventional STT-RAM cell model at a TSMC 90-nm technology node. The performance and the reliability of the memory cell were unaffected. By using our model, we analyzed the scalability of STT-RAM technology down to a 22-nm Bulk-CMOS technology node. The tradeoffs among the MTJ switching current, the thermal stability of the MTJ and the MOS transistor driving strength are discussed. Some magnetic- and circuit-level solutions to achieve 9F2STT-RAM cell area at 22-nm technology node are also discussed.

In this paper, soft decision based cooperative sensing for cognitive radio network is investigated. Cooperative sensing is generally regarded as a promising technique to improve the reliability of spectrum sensing in cognitive radio network. Two soft decision based cooperative sensing schemes are proposed to improve the detection performance by exploring SNR fluctuation and sophisticated data combination based on SNR, respectively. Simulation results show that the detection performance can be improved without extra signaling overhead, compared with existing soft decision based cooperative sensing methods.

Two-way relaying can significantly improve spectrum efficiency of relay network and thus is very promising for future wireless communication systems. Among all the two-way relaying schemes, denoise-and-forward (DNF) is very attractive for its high spectrum efficiency and good performance because noise can be eliminated at relay station by the special denoising signal processing. However, currently DNF can only be applied to low modulation order in fading channels. In this letter, an enhanced DNF scheme is proposed, which extends DNF to high modulation order for fading channels with low computational complexity. Simulation results show the proposed scheme can achieve higher spectrum efficiency than existing schemes.