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It has become a strategic consensus of the international community for accelerating the deployment of 5G network. This paper presents an approach for the deployment of 5G base stations under the considerations of both the cost and the signal coverage. We formulate an optimization problem for the site selection and location of 5G macro and micro base stations. An implementation procedure is proposed in the paper for the cooperative operation and deployment scheme of optimizing the location of 5G heterogeneous base stations, which aims to optimally reduce the setup cost and strengthen the signal coverage while deploying 5G base stations. A series of numerical examples are solved in the paper to demonstrate the proposed approach, and a cost-benefit analysis is also conducted to determine the optimal deployment plan for the number of macro and micro base stations. In the conclusion, a balanced executable solution is presented to make the signal strength of all demand points in the studied 5G network reach the strongest under the budget constraint.

This paper analyzes the 2-D short-channel effect in ultrathin SOI MOSFETs. An empirical, channel length-dependent scale length is extracted from the lateral field slope of a series of numerically simulated devices. We show how this scale length is related to the short-channel threshold voltage roll-off and minimum channel length with and without a substrate bias. The benefit of a reverse substrate bias is investigated and understood in terms of the field and distribution of inversion charge in the silicon film. In particular, how a bulk-like short-channel effect is achieved when an accumulation layer is formed at the back surface. Furthermore, the effect of a high-κ gate insulator is studied and scaling implications discussed.

We study the task of deterministically extracting randomness from sources containing computational entropy. The sources we consider have the form of a conditional distribution (f(X)|X), for some function f and some distribution X, and we say that such a source has computational min-entropy k if any circuit of size 2k can only predict f(x) correctly with probability at most 2-k given input x sampled from X. We first show that it is impossible to have a seedless extractor to extract from one single source of this kind. Then we show that it becomes possible if we are allowed a seed which is weakly random (instead of perfectly random) but contains some statistical min-entropy, or even a seed which is not random at all but contains some computational min-entropy. This can be seen as a step toward extending the study of multisource extractors from the traditional, statistical setting to a computational setting. We reduce the task of constructing such extractors to a problem in computational learning theory: learning linear functions under arbitrary distribution with adversarial noise, and we provide a learning algorithm for this problem. In fact, this problem is a well-recognized one in computational learning theory and variants of this problem have been studied intensively before. Thus, in addition to its application to extractors, our learning algorithm also has independent interest of its own, and it can be considered as the main technical contribution of this paper.

The current-voltage (I- V) characteristics of the InGaN nanorod light-emitting diode (LED) are evaluated using nanoprobes installed on a field-emission scanning electron microscope. The saturated current of the InGaN nanorod LED (IO = 2 × 10-9A) is found to be orders of magnitude higher than those obtained by downscaling a conventional InGaN LED to a chip size of 300 × 300 μm2 (IO1 = 1 × 10-25 A; IO2 = 1 × 10-14 A). This observation is explained by the fact that the nanorod LED is associated with enhanced tunneling of injected carriers and, therefore, reduction of the defect-assisted leakage current and the diffusion-recombination process that normally occurs in InGaN LEDs.

This paper describes a novel structure of an AlGaInP light-emitting diode (LED) to extract the waveguided light for high-brightness applications. Four devices are considered and compared. They are AlGaInP-sapphire LEDs with: 1) a planar Ag reflector (LED-A); 2) a patterned Ag reflector (LED-B); 3) a planar Ag reflector and surface-roughened features (LED-C); and 4) a patterned Ag reflector and surface-roughened features (LED-D). The patterned Ag reflector can be used to direct some of the waveguided light that bounces within the AlGaInP LED and sapphire substrate to the top escape cone of the LED surface. Additionally, the roughened features, which are randomly distributed on the LED surface, enable the waveguided light that is trapped inside the LED chip to be coupled efficiently into the air. As a result, the external quantum efficiencies of LED-A, LED-B, LED-C, and LED-D measured at I = 350 mA are η = 9.20%, 10.46%, 15.22%, and 16.75%, respectively.

In this paper, we consider the task of deterministically extracting randomness from sources consisting of a sequence of n independent symbols from {0,1}d. The only randomness guarantee on such a source is that the whole source has min-entropy k . We give an explicit deterministic extractor which extract Ω(logk-loglog(1/ e)) bits with error e , for any n, d, k ∈ \BBN and e ∈ (0,1). For sources with a larger min-entropy, we can extract even more randomness. When k ≥ n1/2+γ, for any constant γ ∈ (0,1/2), we can extract m=k-O(d log(1/ e)) bits with any error e ≥ 2-Ω(nγ). When k ≥ logcn , for some constant c > 0, we can extract m=k-(1/ e)O(1) bits with any error e ≥ k-Ω(1). Our results generalize those of Kamp and Zuckerman and Gabizon which only work for bit-fixing sources (with d=1 and each bit of the source being either fixed or perfectly random). Moreover, we show the existence of a nonexplicit deterministic extractor which can extract m=k-O(log(1/ e)) bits whenever k=ω(d+log(n/ e)) . Finally, we show that even to extract from bit-fixing sources, any extractor, seeded or not, must suffer an entropy loss k-m=Ω(log(1/ e)). This generalizes a lower bound of Radhakrishnan and Ta-Shma on extracting from general sources.

The junction temperature of a light-emitting diode (LED) directly and greatly affects its performances. Therefore, the reliable measurement and accurate estimation of the junction temperature of an LED is extremely important. This paper proposes an approach for directly determining the dependence of junction temperature on injected currents in InGaN and AlGaInP LEDs. Various important physical parameters that affect the junction temperature of an LED are also considered.

We study the problem of deterministically extracting almost perfect random bits from multiple weakly random sources that are mutually independent. With two independent sources, we have an explicit extractor which can extract a number of random bits that matches the best construction currently known, via the generalized leftover hash lemma. We also extend our construction to extract randomness from more independent sources. One nice feature is that the extractor still works even with all but one source exposed. Finally, we apply our extractor for a cryptographic task in which a group of parties wants to agree on a secret key for group communication over an insecure channel, without using ideal local randomness.

The effect of gradual indium gallium nitride (InGaN) quantum wells (QWs) on the suppression of efficiency-droop in green light-emitting diodes (LEDs) is numerically investigated. The presented scheme increases the internal quantum efficiency by 45.5% at I=20nmA and 55.7% at I=100nmA, indicating a considerable reduction of efficiency-droop. This improvement is attributable mainly to the use of the gradual InGaN QW's structure that significantly alleviates band bending in the valence band, improving the transport efficiency of injected holes above that of conventional LEDs. The radiative recombination is thus enhanced as the overlap between electron and hole wave functions is increased. Most importantly, the leakage of injected electrons to p-type region is correspondingly reduced, in turn suppressing the efficiency-droop in the LED.

We present experimental results on the improved performance and high stable temperature characteristics of the InGaN green light-emitting diode (LED) with pre-trimethlyindium (pre-TMIn) flow treatment. By using pre-TMIn flow treatment, a relatively large radiative coefficient (B= 3.34×10-11 cm3·s-1) corresponding to a 9.2% enhancement in the internal quantum efficiency, as well as a significant reduction of leakage paths for injected carriers, was obtained. Most important, the pre-TMIn flow treatment evidently reduces the dependence of the external quantum efficiency on temperature and efficiency droop of green LEDs. The improvement is thought to be attributable to the preferential formation of In-rich dots upon pre-TMIn flow treatment, which effectively suppresses the trapping of excitons by threading dislocations and the overflowing of injected carriers outside the active regions at elevated temperatures.