اشترك بالحزمة الذهبية واحصل على وصول غير محدود شمرا أكاديميا
تسجيل مستخدم جديدMarket-based Short-Term Allocations in Small Cell Wireless Networks
62
0
0.0
(
0
)
اسأل ChatGPT حول البحث
ﻻ يوجد ملخص باللغة العربية
Mobile users (or UEs, to use 3GPP terminology) served by small cells in dense urban settings may abruptly experience a significant deterioration in their channel to their serving base stations (BSs) in several scenarios, such as after turning a corner around a tall building, or a sudden knot of traffic blocking the direct path between the UE and its serving BS. In this work, we propose a scheme to temporarily increase the data rate to/from this UE with additional bandwidth from the nearest Coordinated Multi-Point (CoMP) cluster of BSs, while the slower process of handover of the UE to a new serving BS is ongoing. We emphasize that this additional bandwidth is additional to the data rates the UE is getting over its primary connection to the current serving BS and, after the handover, to the new serving BS. The key novelty of the present work is the proposal of a decentralized market-based resource allocation method to perform resource allocation to support Coordinated Beamforming (CB) CoMP. It is scalable to large numbers of UEs and BSs, and it is fast because resource allocations are made bilaterally, between BSs and UEs. Once the resource allocation to the UE has been made, the coordinated of transmissions occurs as per the usual CB methods. Thus the proposed method has the benefit of giving the UE access to its desired amount of resources fast, without waiting for handover to complete, or reporting channel state information before it knows the resources it will be allocated for receiving transmissions from the serving BS.
قيم البحث
اقرأ أيضاً
Explosive growth of mobile data demand may impose a heavy traffic burden on fronthaul links of cloud-based small cell networks (C-SCNs), which deteriorates users quality of service (QoS) and requires substantial power consumption. This paper proposes
an efficient maximum distance separable (MDS) coded caching framework for a cache-enabled C-SCNs, aiming at reducing long-term power consumption while satisfying users QoS requirements in short-term transmissions. To achieve this goal, the cache resource in small-cell base stations (SBSs) needs to be reasonably updated by taking into account users content preferences, SBS collaboration, and characteristics of wireless links. Specifically, without assuming any prior knowledge of content popularity, we formulate a mixed timescale problem to jointly optimize cache updating, multicast beamformers in fronthaul and edge links, and SBS clustering. Nevertheless, this problem is anti-causal because an optimal cache updating policy depends on future content requests and channel state information. To handle it, by properly leveraging historical observations, we propose a two-stage updating scheme by using Frobenius-Norm penalty and inexact block coordinate descent method. Furthermore, we derive a learning-based design, which can obtain effective tradeoff between accuracy and computational complexity. Simulation results demonstrate the effectiveness of the proposed two-stage framework.
Traditional macro-cell networks are experiencing an upsurge of data traffic, and small-cells are deployed to help offload the traffic from macro-cells. Given the massive deployment of small-cells in a macro-cell, the aggregate power consumption of sm
all-cells (though being low individually) can be larger than that of the macro-cell. Compared to the macro-cell base station (MBS) whose power consumption increases significantly with its traffic load, the power consumption of a small-cell base station (SBS) is relatively flat and independent of its load. To reduce the total power consumption of the heterogeneous networks (HetNets), we dynamically change the operating states (on and off) of the SBSs, while keeping the MBS on to avoid any service failure outside active small-cells. First, we consider that the wireless users are uniformly distributed in the network, and propose an optimal location-based operation scheme by gradually turning off the SBSs closer to the MBS. We then extend the operation problem to a more general case where users are non-uniformly distributed in the network. Although this problem is NP-hard, we propose a joint location and user density based operation scheme to achieve near-optimum (with less than 1% performance loss in our simulations) in polynomial time.
We study the problem of interference management in large-scale small cell networks, where each user equipment (UE) needs to determine in a distributed manner when and at what power level it should transmit to its serving small cell base station (SBS)
such that a given network performance criterion is maximized subject to minimum quality of service (QoS) requirements by the UEs. We first propose a distributed algorithm for the UE-SBS pairs to find a subset of weakly interfering UE-SBS pairs, namely the maximal independent sets (MISs) of the interference graph in logarithmic time (with respect to the number of UEs). Then we propose a novel problem formulation which enables UE-SBS pairs to determine the optimal fractions of time occupied by each MIS in a distributed manner. We analytically bound the performance of our distributed policy in terms of the competitive ratio with respect to the optimal network performance, which is obtained in a centralized manner with NP (non-deterministic polynomial time) complexity. Remarkably, the competitive ratio is independent of the network size, which guarantees scalability in terms of performance for arbitrarily large networks. Through simulations, we show that our proposed policies achieve significant performance improvements (from 150% to 700%) over the existing policies.
In this paper, a novel approach for optimizing the joint deployment of small cell base stations and wireless backhaul links is proposed. This joint deployment scenario is cast as a multi-objective optimization problem under the constraints of limited
backhaul capacity and outage probability. To address the problem,a novel adaptive algorithm that integrates $epsilon$-method, Lagrangian relaxation and tabu search is proposed to obtain the Pareto optimal solution set. Simulation results show that the proposed algorithm is quite effective in finding the optimal solutions. The proposed joint deployment model can be used for planning small cell networks.
Cooperative transmission can greatly improve communication system performance by taking advantage of the broadcast nature of wireless channels. Most previous work on resource allocation for cooperation transmission is based on centralized control. In
this paper, we propose two share auction mechanisms, the SNR auction and the power auction, to distributively coordinate the resource allocation among users. We prove the existence, uniqueness and effectiveness of the auction results. In particular, the SNR auction leads to a fair resource allocation among users, and the power auction achieves a solution that is close to the efficient allocation.
سجل دخول لتتمكن من نشر تعليقات
التعليقات
جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
