150 research outputs found
A Study about Heterogeneous Network Issues Management based on Enhanced Inter-cell Interference Coordination and Machine Learning Algorithms
Under the circumstance of fast growing demands for mobile data, Heterogeneous Networks (HetNets) has been considered as one of the key technologies to solve 1000 times mobile data challenge in the coming decade. Although the unique multi-tier topology of HetNets has achieved high spectrum efficiency and enhanced Quality of Service (QoS), it also brings a series of critical issues. In this thesis, we present an investigation on understanding the cause of HetNets challenges and provide a research on state of arts techniques to solve three major issues: interference, offloading and handover.
The first issue addressed in the thesis is the cross-tier interference of HetNets. We introduce Almost Blank Subframes (ABS) to free small cell UEs from cross-tier interference, which is the key technique of enhanced Inter-Cell Interference Coordination (eICIC). Nash Bargain Solution (NBS) is applied to optimize ABS ratio and UE partition. Furthermore, we propose a power based multi-layer NBS Algorithm to obtain optimal parameters of Further enhanced Inter-cell Interference Coordination (FeICIC), which significantly improve macrocell efficiency compared to eICIC. This algorithm not only introduces dynamic power ratio but also defined opportunity cost for each layer instead of conventional zero-cost partial fairness. Simulation results show the performance of proposed algorithm may achieve up to 31.4% user throughput gain compared to eICIC and fixed power ratio FeICIC.
This thesis’ second focusing issue is offloading problem of HetNets. This includes (1) UE offloading from macro cell and (2) small cell backhaul offloading. For first aspect, we have discussed the capability of machine learning algorithms tackling this challenge and propose the User-Based K-means Algorithm (UBKCA). The proposed algorithm establishes a closed loop Self-Organization system on our HetNets scenario to maintain desired offloading factor of 50%, with cell edge user factor 17.5% and CRE bias of 8dB. For second part, we further apply machine learning clustering method to establish cache system, which may achieve up to 70.27% hit-ratio and reduce request latency by 60.21% for Youtube scenario. K-Nearest Neighbouring (KNN) is then applied to predict new users’ content preference and prove our cache system’s suitability. Besides that, we have also proposed a system to predict users’ content preference even if the collected data is not complete.
The third part focuses on offloading phase within HetNets. This part detailed discusses CRE’s positive effect on mitigating ping-pong handover during UE offloading, and CRE’s negative effect on increasing cross-tier interference. And then a modified Markov Chain Process is established to map the handover phases for UE to offload from macro cell to small cell and vice versa. The transition probability of MCP has considered both effects of CRE so that the optimal CRE value for HetNets can be achieved, and result for our scenario is 7dB. The combination of CRE and Handover Margin is also discussed
On the Convergence of Deep Learning with Differential Privacy
In deep learning with differential privacy (DP), the neural network achieves
the privacy usually at the cost of slower convergence (and thus lower
performance) than its non-private counterpart. This work gives the first
convergence analysis of the DP deep learning, through the lens of training
dynamics and the neural tangent kernel (NTK). Our convergence theory
successfully characterizes the effects of two key components in the DP
training: the per-sample clipping (flat or layerwise) and the noise addition.
Our analysis not only initiates a general principled framework to understand
the DP deep learning with any network architecture and loss function, but also
motivates a new clipping method -- the global clipping, that significantly
improves the convergence while preserving the same privacy guarantee as the
existing local clipping.
In terms of theoretical results, we establish the precise connection between
the per-sample clipping and NTK matrix. We show that in the gradient flow,
i.e., with infinitesimal learning rate, the noise level of DP optimizers does
not affect the convergence. We prove that DP gradient descent (GD) with global
clipping guarantees the monotone convergence to zero loss, which can be
violated by the existing DP-GD with local clipping. Notably, our analysis
framework easily extends to other optimizers, e.g., DP-Adam. Empirically
speaking, DP optimizers equipped with global clipping perform strongly on a
wide range of classification and regression tasks. In particular, our global
clipping is surprisingly effective at learning calibrated classifiers, in
contrast to the existing DP classifiers which are oftentimes over-confident and
unreliable. Implementation-wise, the new clipping can be realized by adding one
line of code into the Opacus library
Practice with Graph-based ANN Algorithms on Sparse Data: Chi-square Two-tower model, HNSW, Sign Cauchy Projections
Sparse data are common. The traditional ``handcrafted'' features are often
sparse. Embedding vectors from trained models can also be very sparse, for
example, embeddings trained via the ``ReLu'' activation function. In this
paper, we report our exploration of efficient search in sparse data with
graph-based ANN algorithms (e.g., HNSW, or SONG which is the GPU version of
HNSW), which are popular in industrial practice, e.g., search and ads
(advertising).
We experiment with the proprietary ads targeting application, as well as
benchmark public datasets. For ads targeting, we train embeddings with the
standard ``cosine two-tower'' model and we also develop the ``chi-square
two-tower'' model. Both models produce (highly) sparse embeddings when they are
integrated with the ``ReLu'' activation function. In EBR (embedding-based
retrieval) applications, after we the embeddings are trained, the next crucial
task is the approximate near neighbor (ANN) search for serving. While there are
many ANN algorithms we can choose from, in this study, we focus on the
graph-based ANN algorithm (e.g., HNSW-type).
Sparse embeddings should help improve the efficiency of EBR. One benefit is
the reduced memory cost for the embeddings. The other obvious benefit is the
reduced computational time for evaluating similarities, because, for
graph-based ANN algorithms such as HNSW, computing similarities is often the
dominating cost. In addition to the effort on leveraging data sparsity for
storage and computation, we also integrate ``sign cauchy random projections''
(SignCRP) to hash vectors to bits, to further reduce the memory cost and speed
up the ANN search. In NIPS'13, SignCRP was proposed to hash the chi-square
similarity, which is a well-adopted nonlinear kernel in NLP and computer
vision. Therefore, the chi-square two-tower model, SignCRP, and HNSW are now
tightly integrated
Spatial Scattering Modulation with Multipath Component Aggregation Based on Antenna Arrays
In this paper, a multipath component aggregation (MCA) mechanism is
introduced for spatial scattering modulation (SSM) to overcome the limitation
in conventional SSM that the transmit antenna array steers the beam to a single
multipath (MP) component at each instance. In the proposed MCA-SSM system,
information bits are divided into two streams. One is mapped to an
amplitude-phase-modulation (APM) constellation symbol, and the other is mapped
to a beam vector symbol which steers multiple beams to selected strongest MP
components via an MCA matrix. In comparison with the conventional SSM system,
the proposed MCA-SSM enhances the bit error performance by avoiding both low
receiving power due to steering the beam to a single weak MP component and
inter-MP interference due to MP components with close values of angle of
arrival (AoA) or angle of departure (AoD). For the proposed MCA-SSM, a union
upper bound (UUB) on the average bit error probability (ABEP) with any MCA
matrix is analytically derived and validated via Monte Carlo simulations. Based
on the UUB, the MCA matrix is analytically optimized to minimize the ABEP of
the MCA-SSM. Finally, numerical experiments are carried out, which show that
the proposed MCA-SSM system remarkably outperforms the state-of-the-art SSM
system in terms of ABEP under a typical indoor environment
K-BERT: Enabling Language Representation with Knowledge Graph
Pre-trained language representation models, such as BERT, capture a general
language representation from large-scale corpora, but lack domain-specific
knowledge. When reading a domain text, experts make inferences with relevant
knowledge. For machines to achieve this capability, we propose a
knowledge-enabled language representation model (K-BERT) with knowledge graphs
(KGs), in which triples are injected into the sentences as domain knowledge.
However, too much knowledge incorporation may divert the sentence from its
correct meaning, which is called knowledge noise (KN) issue. To overcome KN,
K-BERT introduces soft-position and visible matrix to limit the impact of
knowledge. K-BERT can easily inject domain knowledge into the models by
equipped with a KG without pre-training by-self because it is capable of
loading model parameters from the pre-trained BERT. Our investigation reveals
promising results in twelve NLP tasks. Especially in domain-specific tasks
(including finance, law, and medicine), K-BERT significantly outperforms BERT,
which demonstrates that K-BERT is an excellent choice for solving the
knowledge-driven problems that require experts.Comment: 8 pages, 2019091
Missile-Borne SAR Raw Signal Simulation for Maneuvering Target
SAR raw signal simulation under the case of maneuver and high-speed has been a challenging and urgent work recently. In this paper, a new method based on one-dimensional fast Fourier transform (1DFFT) algorithm is presented for raw signal simulation of maneuvering target for missile-borne SAR. Firstly, SAR time-domain raw signal model is given and an effective Range Frequency Azimuth Time (RFAT) algorithm based on 1DFFT is derived. In this algorithm, the “Stop and Go” (SaG) model is adopted and the wide radar scattering characteristic of target is taken into account. Furthermore, the “Inner Pulse Motion” (IPM) model is employed to deal with high-speed case. This new RFAT method can handle the maneuvering cases, high-speed cases, and bistatic radar cases, which are all possible in the missile-borne SAR. Besides, this raw signal simulation adopts the electromagnetic scattering calculation so that we do not need a scattering rate distribution map as the simulation input. Thus, the multiple electromagnetic reflections can be considered. Simulation examples prove the effectiveness of our method
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