基于Q-Learning算法的毫微微小區(qū)功率控制算法
doi: 10.11999/JEIT181191
-
重慶郵電大學(xué)移動通信技術(shù)重慶市重點實驗室 ??重慶 ??400065
Power Control Algorithm Based on Q-Learning in Femtocell
-
Chongqing Key Laboratory of Mobile Communication Technology, The Chongqing University of Posts and Telecommunications, Chongqing 400065, China
-
摘要: 該文研究macro-femto異構(gòu)蜂窩網(wǎng)絡(luò)中移動用戶的功率控制問題,首先建立了以最小接收信號信干噪比為約束條件,最大化毫微微小區(qū)的總能效為目標(biāo)的優(yōu)化模型;然后提出了基于Q-Learning算法的毫微微小區(qū)集中式功率控制(PCQL)算法,該算法基于強(qiáng)化學(xué)習(xí),能在沒有準(zhǔn)確信道狀態(tài)信息的情況下,實現(xiàn)對小區(qū)內(nèi)所有用戶終端的發(fā)射功率統(tǒng)一調(diào)整。仿真結(jié)果表明該算法能實現(xiàn)對用戶終端的功率有效控制,提升系統(tǒng)能效。
-
關(guān)鍵詞:
- 集中式功率控制 /
- Q-Learning算法 /
- 能效優(yōu)化
Abstract: The power control problem of mobile users in macro-femto heterogeneous cellular networks is studied. Firstly, an optimization model that maximizes the total energy efficiency of femtocells with the minimum received signal-to-noise ratio as the constraint is established. Then, a femtocell centralized Power Control algorithm based on Q-Learning (PCQL) is proposed. Based on reinforcement learning, the algorithm can adjust the transmit power of the user terminal without accurate channel state information simultaneously. The simulation results show that the algorithm can effectively control the power of the user terminal and improve system energy efficient. -
表 1 基于Q-Learning算法的毫微微小區(qū)功率控制算法(PCQL)
輸入:W, ${n_0}$, $P_{b,\mu }^{\rm{c}} $, ${\rm{SINR}}_{b,\mu }^{\min }$, $p_{b,\mu }^{{\rm{max}}}$, $\gamma $, $\alpha $, $T\;$, $\varepsilon $,動作空間${A_b}$; 輸出:${{\text{π}}^ * }$, $p_{b,\mu }^*$($\mu \in {U_b}$); 定義:${\text{k}}$表示代理選取的動作;${\rm{SINR}}_{b,\mu }^{{\rm{real}}}$表示${u_{b,\mu }}$與基站$b$通信時 的實際信干噪比; $Q\left( {{{\text{s}}_b},{{\text{a}}_b}} \right) = 0$, ${\text{π}}\left( {{{\text{s}}_b},{{\text{a}}_b}} \right) = \frac{1}{{\left| {{A_b}\left( {{{\text{s}}_b}} \right)} \right|}}$, $\text{s}_b^t = \text{s}_b^0$; for $t = 0,1, ·\!·\!· ,T\;$ do 若rand()<$\varepsilon $,從${A_b}$中隨機(jī)選動作${\text{k}}$;否則${\text{k}} \!=\! \mathop {\arg \max }\limits_{{\text{a}}_b^t} \!Q\left( {{\text{s}}_b^t,{\text{a}}_b^t} \right)$; 根據(jù)式(1)確定${\rm{SINR}}_{b,\mu }^{{\rm{real}}}$; for $\mu = 1,2, ·\!·\!· ,{N_b}$ do 若${\rm{SINR}}_{b,\mu }^{{\mathop{\rm real}\nolimits} } \ge {\rm{SINR}}_{b,\mu }^{\min }$,那么${\lambda _{b,\mu }} = 1$;否則${\lambda _{b,\mu }} = 0$; end for; 根據(jù)式(7)計算采取動作${\text{a}}_b^t = {\text{k}}$所帶來的獎賞值${\Re _b}\left( {{\text{s}}_b^t,{\text{a}}_b^t} \right)$; ${\text{a}}_b^{t + 1} = {\text{π}}\left( {{\text{s}}_b^{t + 1}} \right)$;
${\rm Q}\left( { { {\text{s} } }_b^t,{ {\text{a} } }_b^t} \right) \leftarrow {\rm Q}\left( { { {\text{s} } }_b^t,{ {\text{a} } }_b^t} \right) + \alpha ( {\Re _b}\left( { { {\text{s} } }_b^t,{ {\text{a} } }_b^t} \right) \!+\! \gamma \mathop {\max}\limits_{ {\rm{a} }_b^{t + 1} } \left( { {\rm Q}\left( { { {\text{s} } }_b^{t + 1},{ {\text{a} } }_b^{t + 1} } \right)} \right)$ $\left.- {{\rm Q}\left( {{{\text{s}}}_b^t,{{\text{a}}}_b^t} \right)} \right)$;${\text{s}}_b^t \leftarrow {\text{s}}_b^{t + 1}$; end for; ${{\text{π}}^ * }\left( {{{\text{s}}_b}} \right) = \mathop {\arg \max }\limits_{{{\text{a}}_b}} Q\left( {{{\text{s}}_b},{{\text{a}}_b}} \right),\forall {{\text{s}}_b} \in S$. 下載: 導(dǎo)出CSV
表 2 主要的仿真參數(shù)
參數(shù)名稱 參數(shù)值 MBS/FBS 1個/4個 MUE/FUE最大的發(fā)射功率 37 dBm/30 dBm MBS/FBS覆蓋范圍半徑 250 m/50 m ${{\rm{SINR}} _{b,\mu }}^{\min }$ –9 dB 固定的電路功耗 100 mW 信道帶寬 10 MHz 高斯白噪聲的功率譜密度 ${10^{ - 11}}$ W/Hz 下載: 導(dǎo)出CSV
-
LóPEZ-PéREZ D, DING M, CLAUSSEN H, et al. Towards 1 Gbps/UE in cellular systems: understanding ultra-dense small cell deployments[J]. IEEE Communications Surveys & Tutorials, 2015, 17(4): 2078–2101. doi: 10.1109/COMST.2015.2439636 YUNAS S F, VALKAMA M, and NIEMEL? J. Spectral and energy efficiency of ultra-dense networks under different deployment strategies[J]. IEEE Communications Magazine, 2015, 53(1): 90–100. doi: 10.1109/MCOM.2015.7010521 MARTOLIA D, SATHYA V, RANGISETTI A K, et al. Enhancing performance of victim macro users via joint ABSF and dynamic power control in LTE HetNets[C]. The Twenty-third National Conference on Communications, Chennai, India, 2017: 1–6. SHIN D and CHOI S. Dynamic power control for balanced data traffic with coverage in femtocell networks[C]. The 8th International Wireless Communications and Mobile Computing Conference, Limassol, Cyprus, 2012: 648–653. ZHANG Jinzhu, HONG Peilin, XUE Kaiping, et al. A novel power control scheme for femtocell in heterogeneous networks[C]. 2012 IEEE Consumer Communications and Networking Conference, Las Vegas, USA, 2012: 802–806. PAN Zhenni, MEGUMI, SAITOU, et al. Neuron control-based power adjustment scheme for sleep two-tier cellular networks[C]. 2014 IEEE Wireless Communications and Networking Conference, Istanbul, Turkey, 2014: 3201–3206. ZHOU Tianqing, LIU Zunxiong, ZHAO Junhui, et al. Joint user association and power control for load balancing in downlink heterogeneous cellular networks[J]. IEEE Transactions on Vehicular Technology, 2018, 67(3): 2582–2593. doi: 10.1109/TVT.2017.2768574 MARTIN-VEGA F J, GOMEZ G, AGUAYO-TORRES M C, et al. Analytical modeling of interference aware power control for the uplink of heterogeneous cellular networks[J]. IEEE Transactions on Wireless Communications, 2016, 15(10): 6742–6757. doi: 10.1109/TWC.2016.2588469 ZHANG Jing, LIAO Yan, and XIN Yili. Uplink power control for heterogeneous small cell networks[C]. 2016 IEEE 83rd Vehicular Technology Conference, Nanjing, China, 2016: 1–5. WANG Min, GAO Hui, and LV Tiejun. Energy-efficient user association and power control in the heterogeneous network[J]. IEEE Access, 2017, 5: 5059–5068. doi: 10.1109/ACCESS.2017.2690305 ZHANG Jing, XIANG Lin, NG D W K, et al. Energy efficiency evaluation of multi-tier cellular uplink transmission under maximum power constraint[J]. IEEE Transactions on Wireless Communications, 2017, 16(11): 7092–7107. doi: 10.1109/TWC.2017.2739142 PAN Zhenni and SHIMAMOTO S. Cell sizing based energy optimization in joint macro-femto deployments via sleep activation[C]. 2013 IEEE Wireless Communications and Networking Conference, Shanghai, China, 2013: 4765–4770. SHIFAT A S M Z, CHOWDHURY M Z, and JANG Y M. Game-based approach for QoS provisioning and interference management in heterogeneous networks[J]. IEEE Access, 2018, 6: 10208–10220. doi: 10.1109/ACCESS.2017.2704094 MISHRA S and MURTHY C S R. Increasing energy efficiency via transmit power spreading in dense femto cell networks[J]. IEEE Systems Journal, 2018, 12(1): 971–980. doi: 10.1109/JSYST.2016.2573845 GURUACHARYA S, NIYATO D, KIM D I, et al. Hierarchical competition for downlink power allocation in OFDMA femtocell networks[J]. IEEE Transactions on Wireless Communications, 2013, 12(4): 1543–1553. doi: 10.1109/TWC.2013.022213.120016 WANG Haining, WANG Jiaheng, and DING Zhi. Distributed power control in a two-tier heterogeneous network[J]. IEEE Transactions on Wireless Communications, 2015, 14(12): 6509–6523. doi: 10.1109/TWC.2015.2456055 MAO Tingli, FENG Gang, LIANG Liang, et al. Energy-efficient power control for macro-femto networks[C]. The 22nd Wireless and Optical Communication Conference, Chongqing, China, 2013: 122–125. MAO Tingli, FENG Gang, LIANG Liang, et al. Distributed energy-efficient power control for macro-femto networks[J]. IEEE Transactions on Vehicular Technology, 2016, 65(2): 718–731. doi: 10.1109/TVT.2015.2402618 LAI Weisheng, CHANG T H, and LEE T S. Joint power and admission control for spectral and energy efficiency maximization in heterogeneous OFDMA networks[J]. IEEE Transactions on Wireless Communications, 2016, 15(5): 3531–3547. doi: 10.1109/TWC.2016.2522958 LOODARICHEH R A, MALLICK S, BHARGAVA V K. Energy-efficient resource allocation for OFDMA cellular networks with user cooperation and QoS provisioning[J]. IEEE Transactions on Wireless Communications, 2014, 13(11): 6132–6146. doi: 10.1109/TWC.2014.2329877 GHADIMI E, CALABRESE F D, PETERS G, et al. A reinforcement learning approach to power control and rate adaptation in cellular networks[C]. 2017 IEEE International Conference on Communications, Paris, France, 2017: 1–7. 周志華. 機(jī)器學(xué)習(xí)[M]. 北京: 清華大學(xué)出版社, 2016: 372–390.ZHOU Zhihua. Machine Learning[M]. Beijing: Tsinghua University Press, 2016: 372–390. -