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Register a new userOn the Asymptotic Behavior of Ultra-Densification under a Bounded Dual-Slope Path Loss Model
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Added by
Yanpeng Yang
Publication date
2017
fields
Informatics Engineering
and research's language is
English
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In this paper, we investigate the impact of network densification on the performance in terms of downlink signal-to-interference (SIR) coverage probability and network area spectral efficiency (ASE). A sophisticated bounded dual-slope path loss model and practical user equipment (UE) densities are incorporated in the analysis, which have never been jointly considered before. By using stochastic geometry, we derive an integral expression along with closed-form bounds of the coverage probability and ASE, validated by simulation results. Through these, we provide the asymptotic behavior of ultra-densification. The coverage probability and ASE have non-zero convergence in asymptotic regions unless UE density goes to infinity (full load). Meanwhile, the effect of UE density on the coverage probability is analyzed. The coverage probability will reveal an U-shape for large UE densities due to interference fall into the near-field, but it will keep increasing for low UE densites. Furthermore, our results indicate that the performance is overestimated without applying the bounded dual-slope path loss model. The derived expressions and results in this work pave the way for future network provisioning.
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In an Ultra-dense network (UDN) where there are more base stations (BSs) than active users, it is possible that many BSs are instantaneously left idle. Thus, how to utilize these dormant BSs by means of cooperative transmission is an interesting question. In this paper, we investigate the performance of a UDN with two types of cooperation schemes: non-coherent joint transmission (JT) without channel state information (CSI) and coherent JT with full CSI knowledge. We consider a bounded dual-slope path loss model to describe UDN environments where a user has several BSs in the near-field and the rest in the far-field. Numerical results show that non-coherent JT cannot improve the user spectral efficiency (SE) due to the simultaneous increment in signal and interference powers. For coherent JT, the achievable SE gain depends on the range of near-field, the relative densities of BSs and users, and the CSI accuracy. Finally, we assess the energy efficiency (EE) of cooperation in UDN. Despite costing extra energy consumption, cooperation can still improve EE under certain conditions.
Intelligent load balancing is essential to fully realize the benefits of dense heterogeneous networks. Current techniques have largely been studied with single slope path loss models, though multi-slope models are known to more closely match real deployments. This paper develops insight into the performance of biasing and uplink/downlink decoupling for user association in HetNets with dual slope path loss models. It is shown that dual slope path loss models change the tradeoffs inherent in biasing and reduce gains from both biasing and uplink/downlink decoupling. The results show that with the dual slope path loss models, the bias maximizing the median rate is not optimal for other users, e.g., edge users. Furthermore, optimal downlink biasing is shown to realize most of the gains from downlink-uplink decoupling. Moreover, the user association gains in dense networks are observed to be quite sensitive to the path loss exponent beyond the critical distance in a dual slope model.
The 5G system has finally begun commercialization, and now is the time to start discussing the road map for the 6G system. While the 5G system was designed with a focus on discovering new service types for high speed, low-latency, and massive connective services, the evolution of the network interface for 6G should be considered with an eye toward supporting these complicated communication environments. As machine-driven data traffic continues to increase exponentially, 6G must be able to support a series of connection methods that did not previously exist. In departure from base-station-oriented cell densification, network diversification is necessary if we are to satisfy the comprehensive requirements of end terminals for diverse applications. In this article, we predict what will drive 6G and look at what key requirements should be considered in 6G. We then diversify four types of network architectures according to link characteristics, communication ranges, and target services. The four types of networks play complementary roles while at the same time collaborating across the entire 6G network. Lastly, we call attention to key technologies and challenges in the air, network, and assistive technologies that will have to be addressed when designing the 6G system.
We consider multiple source nodes (consumers) communicating wirelessly their energy demands to the meter data-management system (MDMS) over the subarea gateway(s). We quantify the impacts of passive and active security attacks on the wireless communications systems reliability and security as well as the energy-demand estimation-error cost in dollars paid by the utility. We adopt a multiple-input multiple-output multi-antenna-eavesdropper (MIMOME) wiretap channel model. To secure the MIMO wireless communication system, the legitimate nodes generate artificial noise (AN) vectors to mitigate the effect of the passive eavesdropping attacks. In addition, we propose a redundant design where multiple gateways are assumed to coexist in each subarea to forward the consumers energy-demand messages. We quantify the redundant designs impact on the communication reliability between the consumers and the MDMS and on the energy-demand estimation-error cost.
Quantifying the impact of parametric and model-form uncertainty on the predictions of stochastic models is a key challenge in many applications. Previous work has shown that the relative entropy rate is an effective tool for deriving path-space uncertainty quantification (UQ) bounds on ergodic averages. In this work we identify appropriate information-theoretic objects for a wider range of quantities of interest on path-space, such as hitting times and exponentially discounted observables, and develop the corresponding UQ bounds. In addition, our method yields tighter UQ bounds, even in cases where previous relative-entropy-based methods also apply, e.g., for ergodic averages. We illustrate these results with examples from option pricing, non-reversible diffusion processes, stochastic control, semi-Markov queueing models, and expectations and distributions of hitting times.
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