A NEW CONCEPT
In recent years, DSL systems have confronted many new troubles, such as crosstalk. The standardized DMTs modulation techniques are deployed more densely than ever, making radio resources allocation a severe problem to handle. Insertion of additive safeguard bands like cyclic prefixes led to the addition of new deal. These insertions aren't enough to simply improve the capability of an individual traditional DMT transceivers to counteract the impact of the crosstalk. It is that network development takes a new way of thinking. Predicated on an optimization-based perspective, next era DMTs require coordinate network nodes, frequencies and bands, and uniformly set up network resources, and capable to provide optimal individual experience.
Therefore, Huawei proven a new strategy: Customer Centric Network (UCN). Customer Centric Network (UCN) is a concept of user-centric network structure. In traditional network structure, base stations were focused, and users were served with a certain base stop. As users may be located in various places, it is just a problem to ensure stable and reliable performance for users. Interference between adjacent platform stations also reduces the learning resource efficiency of the complete network. Along with the new idea of UCN, resources are coordinated, merged, and optimized in allocation, predicated on a user-centric beliefs so the individual experience will be enhanced. UCN is also a fresh user-centric theory in term of operation. In the traditional way, providers can just sell simple data deals to customers.
USER BENEFITS
UCN targets users - it can offer a whole lot of benefits for end users. First, UCN can eliminate cell limitations, providing noborder service experience and bettering the peak and average rates. Second, UCN enables multiple cells to get signs from terminals in a coordinated way, reducing requirements for transmit electric power of terminals and prolongs their standby time. Third, UCN uses versatile networks, providing custom-made services and tariff deals for users.
UCN AND 4. 5G, 5G
Here we must emphasize that UCN is a network engineering concept beyond the definition of cordless technology generations. UCN and 4. 5G or 5G aren't simply a one-to-one relationship. UCN can be executed phase by period in 4. 5G and 5G. For example, UCN systems can be used in the 4. 5G stage, such as distributed MIMO. Distributed MIMO uses distributed, multi-site, multiple antenna beamforming and multiuser multiplexing systems on the RAN part to reduce disturbance and increase capacity. Inside the recent field trials, distributed MIMO demonstrated 3- to 4- folds of cell capacity.
RECENT RESEARCH ON
UCN At the moment, the amount of base channels deployed on 4G networks has already reached several hundreds of thousands. The recent research on UCN focuses on how to apply the leading-edge UCN strategy to these basic stations early. We live pleased to notice that the entire industry has made successful progress in UCN research. CloudRAN-based know-how such as distributed MIMO can ultimately control intersite interference and enable extremely thick deployment of sites, without the need to upgrade terminals on live networks. 4. 5G sent out MIMO has been placed into trial use on live systems for advanced providers. For instance, the inter-site distance of lamp fixture pole sites on Shanghai's Bund is as brief as 50 m. With sent out MIMO, the data rate of cell edge users has increased from 8. 2 Mbps to 15 Mbps, an improvement of 80%, and the common cell throughput has increased from 45 Mbps to 65 Mbps, an increase of 45%.
Minimum Mean Square Problem (MMSE) Estimation for Disturbance Identification
We are interested in an estimation of the time-varying route gain matrix. It is obtained through a statistical estimation strategy that combines the measurements with (i) statistical understanding of measurement uncertainty, and (ii) prior knowledge of spatial relationship of the interference links. We suppose known positions of the transmitted and received vectors and known sound vectors from which the a priori distribution of the route gain matrix with a mean and a covariance matrix is derived.
Statistical understanding of the channel gain vector and way of measuring doubt is exploited. Given some physical-layer measurements, an excellent linear model in which the prior distribution of the interference matrix and the uncertainty distribution is Gaussian in linear range is derived. This model relates the measurements to the channel gain vector and for that reason can be used to derive an optimal linear MMSE (LMMSE) estimator for the channel gain vector. Since disturbance is often assumed to truly have a log-normal distribution, a more realistic model where the prior path-loss distribution is log-normal and the doubt distribution is Gaussian in dB level is used. In cases like this, the model becomes non-linear, and therefore a closed-form "linearized" MMSE estimator, called linearized log MMSE (LLMMSE), is derived to calculate the channel gain vector. The results provided here show the way the accuracy of disturbance estimation obtained from the proposed MMSE Estimator is influenced by two system parameters, namely the Reference point Signal Received Electricity (RSRP) doubt Ж and the route variance. The performance of the MMSE is set alongside the simple least squares (LS) estimator.
The simulation brings about Amount 2-2 show that the proposed MMSE estimator outperforms the LS estimator. Increases in size are large for high noise levels or when the route variance is small. The performance in low sound situations is similar to the LS performance as in such instances the solution of the MMSE estimator converges to the main one of the LS estimator. Same behaviour is observed when the channel variance is high.
