认知无线电下蜂窝与D2D多播联合资源分配

研究论文
0 下载量 137 浏览量 更新于2024-07-15 收藏 1.31MB PDF 举报
本文标题"Joint Resource Allocation for Cellular and D2D Multicast Based on Cognitive Radio"探讨了在认知无线网络环境下，如何有效地整合蜂窝网络和设备对设备(D2D)通信的资源，以支持多播服务。多播作为一种高效的传输模式，对于未来的移动社交接触服务具有重要意义，特别当它结合D2D技术时，能够显著提升系统容量和频谱效率。 文章首先强调了D2D通信在无线网络中的潜力，它通过共享蜂窝网络的频谱资源，能够在不增加额外基础设施的情况下，实现设备间的直接连接，从而提高了数据传输的速率和覆盖范围。在这个背景下，多播传输被赋予了新的价值，因为它可以同时向多个接收终端发送相同的数据，大大减少了重复的带宽消耗。 作者们关注的核心问题是联合资源分配策略，这是在认知无线网络中实现蜂窝网络和D2D多播服务的关键。他们提出了一种优化方法，旨在最大化系统整体性能，比如吞吐量、能量效率或服务质量(QoS)，同时考虑到不同用户的需求和频谱的动态可用性。这种策略可能包括频谱感知、协作调度、功率控制以及可能的多层网络架构优化。 具体的研究内容可能包括： 1. 认知无线电特性：文中可能介绍了认知无线电如何动态地识别和利用未被充分利用的频谱资源，这对于在D2D多播中平衡蜂窝网络与非授权用户的利益至关重要。 2. 协同资源管理：通过协调蜂窝基站和D2D设备的传输，可能设计了一种算法来优化频谱、时间和功率分配，以确保多播信号的高效传播。 3. QoS保障：文章可能会讨论如何在多用户环境中保持公平性和服务质量，防止某些用户的干扰导致整体性能下降。 4. 仿真与评估：通过理论分析和计算机仿真，可能展示了联合资源分配策略在实际网络环境中的效果，包括性能提升、频谱利用率等关键指标。 5. 挑战与未来方向：文中可能还提到了当前面临的挑战，如频谱监管、安全问题以及如何进一步提高多播D2D的普适性和效率。 这篇研究论文提供了在认知无线网络环境中，通过优化资源配置来实现蜂窝网络与D2D多播协同工作的理论框架和技术方法，对于推动未来移动通信系统的高效和可持续发展具有重要的参考价值。

Energy-Efficient Resource Allocation for Heterogeneous Cognitive Radio Networks

2012-12-07 上传
Energy-Efficient Resource Allocation for Heterogeneous Cognitive Radio Networks with Femtocells

Multi-Objective Resource Allocation in a NOMA Cognitive Radio Network

173 浏览量
同时无线信息传输与能量传输（Simultaneous Wireless Information and Power Transfer, SWIPT）的引入，允许设备在接收信息的同时收集能量，从而提高了系统的自维持能力。尽管SWIPT在NOMA系统中的应用已经得到了一些...

Efficient Resource Allocation for D2D Communication

143 浏览量
在本文中，我们将深入探讨基于D2D（Device-to-Device）通信的高效资源分配问题，以及如何通过MATLAB仿真来实现这一目标。D2D通信是一种新兴的技术，允许设备直接进行通信，而不通过基站，这可以显著提高网络效率、...

Abstract—In heterogeneous networks (HetNets), user association approaches should be able to achieve load balancing among base stations (BSs). This paper investigates the joint optimization of user association and resource allocation in Backhaul-constrained HetNets for capacity enhancements. We consider two major limitations in HetNets: the backhaul bottleneck of BSs and the capability of user equipment (UE). We establish a framework based on a multi-leader multi-follower Stackelberg game, in which resource allocation is formulated as a follower-level game and user association is cast as a leader-level game. Because of the backhaul bottleneck of small BSs, the given preference order of users renders the final association result unstable. Thus, the resident-oriented GaleShapley (GS) algorithm is included in the proposed framework to obtain a stable single-BS association. Furthermore, congestion factors are introduced to reflect the relative backhaul congestion degrees of BSs, which enables load balancing among the small BSs in the proposed algorithm. The study considers user association and resource allocation with and without limitations on the number of serving users for small BSs in HetNets. Extensive simulation results suggest that the proposed algorithm can adaptively respond to a wide variety of network situations.中文

112 浏览量
在异构网络（HetNets）中，用户关联方法应该能够实现基站（BS）之间的负载平衡。本文研究了在受限于回程链路的HetNets中，用户关联和资源分配的联合优化问题，以增强系统容量。我们考虑了HetNets中的两个主要限制：...

请帮我将下面的英文翻译为中文：where σ2 is the noise power, and Tj is the transmission power of BS j. hij , which is assumed to be independently and identically distributed based on an exponential distribution with a unit mean, is the channel gain between useri and BS j; the resource schedule-ing horizon τr should be equal to the channel coherence time. Thus the channel statements are regarded as static during the resource allocation process. The achievable rate cij is averaged over an association horizon τa, τa is assumed to be a much larger time scale than that for channel changes, where τa = Nr × τr, and Nr is the number of resource allocation horizons in an association horizon. Therefore, cij is assumed to be constant during an association horizon [5]. This model is suitable for dynamic scenarios.

214 浏览量
其中，σ2是噪声功率，Tj是基站j的传输功率。hij被假设为独立同分布的指数分布，均值为1，表示用户i和基站j之间的信道增益；资源调度时长τr应该等于信道相干时间。因此，在资源分配过程中，信道状态被认为是静态的...

翻译：Notably, the utility functions here do not consider the resource sharing fraction applied in Ye et al. [5]. Instead, we directly use the achievable rate in the utility function. The reasons for this approach are two-fold. First, a UE device can accurately estimate the channel state information (CSI) between itself and a certain BS, but it may not effectively determine the number of devices that are currently being serviced by the BS. Second, the resource allocation strategies adopted by BSs are almost unknown to each UE. Therefore, we remove the resource sharing fraction from the utility function and achieve load balancing based on the access price and the mutual game among BSs.

122 浏览量
值得注意的是，这里的效用函数并没有考虑Ye等人在[5]中使用的资源共享比例。相反，我们直接在效用函数中使用可达速率。采取这种方法的原因有两个。首先，用户设备可以准确估计自身与某个基站之间的信道状态信息(CSI)...

查询以下文献的GB/T 7713.1-2006的标准格式，包含期、卷和起止页码： MA Z, CHEN X, MA T, et al. Deep Deterministic Policy Gradient Based Resource Allocation in Internet of Vehicles[C].PAAP 2020: Parallel Architectures, Algorithms and Programming ,2020,pp. 295–306.

216 浏览量
Deep Deterministic Policy Gradient Based Resource Allocation in Internet of Vehicles. In Proceedings of PAAP 2020: Parallel Architectures, Algorithms and Programming, (pp. 295-306). (GB/T 7713.1-2006...

spark structured streaming

118 浏览量
Spark Structured Streaming is a high-level API built on top of ... It automatically manages the parallelism and resource allocation for processing data streams, optimizing performance and scalability.

Algorithm 1: The online LyDROO algorithm for solving (P1). input : Parameters V , {γi, ci}Ni=1, K, training interval δT , Mt update interval δM ; output: Control actions 􏰕xt,yt􏰖Kt=1; 1 Initialize the DNN with random parameters θ1 and empty replay memory, M1 ← 2N; 2 Empty initial data queue Qi(1) = 0 and energy queue Yi(1) = 0, for i = 1,··· ,N; 3 fort=1,2,...,Kdo 4 Observe the input ξt = 􏰕ht, Qi(t), Yi(t)􏰖Ni=1 and update Mt using (8) if mod (t, δM ) = 0; 5 Generate a relaxed offloading action xˆt = Πθt 􏰅ξt􏰆 with the DNN; 6 Quantize xˆt into Mt binary actions 􏰕xti|i = 1, · · · , Mt􏰖 using the NOP method; 7 Compute G􏰅xti,ξt􏰆 by optimizing resource allocation yit in (P2) for each xti; 8 Select the best solution xt = arg max G 􏰅xti , ξt 􏰆 and execute the joint action 􏰅xt , yt 􏰆; { x ti } 9 Update the replay memory by adding (ξt,xt); 10 if mod (t, δT ) = 0 then 11 Uniformly sample a batch of data set {(ξτ , xτ ) | τ ∈ St } from the memory; 12 Train the DNN with {(ξτ , xτ ) | τ ∈ St} and update θt using the Adam algorithm; 13 end 14 t ← t + 1; 15 Update {Qi(t),Yi(t)}N based on 􏰅xt−1,yt−1􏰆 and data arrival observation 􏰙At−1􏰚N using (5) and (7). i=1 i i=1 16 end With the above actor-critic-update loop, the DNN consistently learns from the best and most recent state-action pairs, leading to a better policy πθt that gradually approximates the optimal mapping to solve (P3). We summarize the pseudo-code of LyDROO in Algorithm 1, where the major computational complexity is in line 7 that computes G􏰅xti,ξt􏰆 by solving the optimal resource allocation problems. This in fact indicates that the proposed LyDROO algorithm can be extended to solve (P1) when considering a general non-decreasing concave utility U (rit) in the objective, because the per-frame resource allocation problem to compute G􏰅xti,ξt􏰆 is a convex problem that can be efficiently solved, where the detailed analysis is omitted. In the next subsection, we propose a low-complexity algorithm to obtain G 􏰅xti, ξt􏰆. B. Low-complexity Algorithm for Optimal Resource Allocation Given the value of xt in (P2), we denote the index set of users with xti = 1 as Mt1, and the complementary user set as Mt0. For simplicity of exposition, we drop the superscript t and express the optimal resource allocation problem that computes G 􏰅xt, ξt􏰆 as following (P4) : maximize 􏰀j∈M0 􏰕ajfj/φ − Yj(t)κfj3􏰖 + 􏰀i∈M1 {airi,O − Yi(t)ei,O} (28a) τ,f,eO,rO 17 ，这个算法中基于模型的DRL算法和无优化的DRL算法和DNN深度学习都各体现在哪

137 浏览量
然后在第6行，算法使用了一个无优化的离散化方法，即NOP（Number of On Positions）方法来将连续的决策xˆt离散化成为Mt个二进制决策xti。而DNN深度学习则体现在第12行，算法使用Adam算法来更新DNN的参数θt，以逐渐...

d2d资源分配matlab代码

307 浏览量
title('D2D Resource Allocation'); 以上代码实现了针对最大权重匹配算法的D2D资源分配，并通过图片展示出不同用户间的传输功率分布情况。当然，还可以针对不同情况进行优化算法的调整，实现更优化的D2D资源分配...
weixin_38693589
  • 粉丝: 5
  • 资源: 928
上传资源 快速赚钱

最新资源