# Abstract:
The cloud radio access network (Cloud-RAN) has recently been proposed as one of the cost-effective and energy-efficient techniques for 5G wireless networks. By moving the signal processing functionality to a single baseband unit (BBU) pool, centralized signal processing and resource allocation are enabled in cloud-RAN, thereby providing the promise of improving the energy efficiency via effective network adaptation and interference management. In this paper, we propose a holistic sparse optimization framework to design green cloud-RAN by taking into consideration the power consumption of the fronthaul links, multicast services, as well as user admission control. Specifically, we first identify the sparsity structures in the solutions of both the network power minimization and user admission control problems, which call for adaptive remote radio head (RRH) selection and user admission. However, finding the optimal sparsity structures turns out to be NP-hard, with the coupled challenges of the ℓ0-norm-based objective functions and the nonconvex quadratic QoS constraints due to multicast beamforming. In contrast to the previous works on convex but nonsmooth sparsity inducing approaches, e.g., the group sparse beamforming algorithm based on the mixed ℓ1/ℓ2-norm relaxation, we adopt the nonconvex but smoothed ℓp-minimization (0 <; p ≤ 1) approach to promote sparsity in the multicast setting, thereby enabling efficient algorithm design based on the principle of the majorization-minimization (MM) algorithm and the semidefinite relaxation (SDR) technique. In particular, an iterative reweighted-ℓ2 algorithm is developed, which will converge to a Karush-Kuhn-Tucker (KKT) point of the relaxed smoothed ℓp-minimization problem from the SDR technique. We illustrate the effectiveness of the proposed algorithms with extensive simulations for network power minimization and user admission control in multicast cloud-RAN.
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matlab算法,工具源码,适合毕业设计、课程设计作业,所有源码均经过严格测试,可以直接运行,可以放心下载使用。有任何使用问题欢迎随时与博主沟通,第一时间进行解答! matlab算法,工具源码,适合毕业设计、课程设计作业,所有源码均经过严格测试,可以直接运行,可以放心下载使用。有任何使用问题欢迎随时与博主沟通,第一时间进行解答! matlab算法,工具源码,适合毕业设计、课程设计作业,所有源码均经过严格测试,可以直接运行,可以放心下载使用。有任何使用问题欢迎随时与博主沟通,第一时间进行解答! matlab算法,工具源码,适合毕业设计、课程设计作业,所有源码均经过严格测试,可以直接运行,可以放心下载使用。有任何使用问题欢迎随时与博主沟通,第一时间进行解答! matlab算法,工具源码,适合毕业设计、课程设计作业,所有源码均经过严格测试,可以直接运行,可以放心下载使用。有任何使用问题欢迎随时与博主沟通,第一时间进行解答! matlab算法,工具源码,适合毕业设计、课程设计作业,所有源码均经过严格测试,可以直接运行,可以放心下载使用。有任何使用问题欢迎随
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毕业设计&课设“带用户准入控制的绿色云RAN平滑Lp最小化”的Matlab仿真代码….zip (32个子文件)
matlab_codes
code_useadmission
Rankone.m 838B
powermin_cvx.m 3KB
新建文本文档.txt 1KB
smallfading.m 881B
powermin_uacl2.m 3KB
code_user_convergence
user_convergence.m 2KB
Rankone.m 838B
powermin_cvx.m 3KB
smallfading.m 881B
powermin_uac.m 3KB
largefading.m 1KB
admission_main.m 23KB
channel_realization.m 1KB
largefading.m 1KB
powermin_cvx_user.m 3KB
powermin_mdr.m 4KB
powermin_uacl1.m 3KB
code_network_power
Rankone.m 838B
powermin_cvx.m 3KB
smallfading.m 881B
main_original.m 26KB
savedata.m 1KB
channel_realization.m 1KB
largefading.m 1KB
baseline_cvx.m 4KB
code_rrh_convergence
convergence.m 3KB
Rankone.m 838B
powermin_cvx.m 3KB
smallfading.m 881B
channel_realization.m 1KB
largefading.m 1KB
README.md 2KB
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