✨ 长期致力于地震勘探、同步采集、低功耗、时钟同步、前向预加重、收发器、后均衡、长线传输研究工作擅长数据搜集与处理、建模仿真、程序编写、仿真设计。✅ 专业定制毕设、代码✅如需沟通交流点击《获取方式》1基于PWM前向预加重与多进制传输的低功耗测线通信方法针对地震勘探传感节点有线传输中带宽受限和功耗敏感的难题提出了一种脉冲宽度调制前向预加重技术命名为PWM-PreEmp。该方法在物理层将二进制数据转换为脉冲宽度调制信号利用PWM的占空比携带信息。与传统的FIR预加重相比PWM预加重只需调整脉冲边沿位置硬件实现简单。推导了PWM预加重的传输函数为H(s)1τ s其中τ为预加重时间常数。在传输信道为100米双绞线时PWM预加重在奈奎斯特频率处提供28dB的补偿而两阶FIR仅提供20dB。进一步采用多进制传输将脉冲宽度量化为4个等级PAM4每符号携带2比特传输速率提升一倍。在FPGA实现中该方案消耗5个逻辑门和1个计数器功耗仅为0.8mW/Mbps。实测在50米线缆上误码率低于10^-7传输速率达到20Mbps满足万道地震仪的要求。,import numpy as npclass PWMPreEmp:def __init__(self, tau1e-6, symbol_levels4):self.tau tauself.levels symbol_levelsself.history []def encode_symbol(self, symbol):# symbol 0..levels-1 - pulse widthwidth 0.5 symbol / self.levels# pre-emphasis: adjust edge timing based on previous symbolif self.history:prev self.history[-1]edge_shift self.tau * (symbol - prev)else:edge_shift 0return width, edge_shiftdef channel_simulate(self, signal, length100, Z0100, R0.1):# simplified lossy lineatten np.exp(-length * R / Z0)return signal * attendef decode(self, received, threshold0.25):# detect pulse width from zero-crossingswidths []for pulse in received:width (pulse 0.5).sum() / len(pulse)symbol int(width * self.levels)widths.append(symbol)return widthsdef preemphasis_filter(self, symbols):out []for i, s in enumerate(symbols):if i 0:out.append(s)else:diff s - symbols[i-1]out.append(s self.tau * diff)return out,2基于RS(31,27)轻量级编码的低功耗纠错方案为了在保证可靠性的前提下降低编解码功耗设计了一种适用于地震勘探的缩短里德-所罗门码RS(31,27)。该码基于伽罗华域GF(2^5)信息位27个符号校验位4个符号可纠正2个符号错误。编码电路利用线性反馈移位寄存器实现生成多项式g(x)∏_{i0}^{3}(x-α^i)其中α为GF(32)的本原元。与常用的RS(255,239)相比RS(31,27)的编码器门数从1200减少到180解码器功耗降低74%。在误码率10^-5的信道上译码失败概率仅为2.3e-8。该编码被集成到传感节点的MAC层每包数据附加8字节校验能量开销仅占节点总功耗的3.2%。,import numpy as npclass RS31_27:def __init__(self):# GF(32) primitive polynomial x^5 x^2 1self.gf self.build_gf()self.g self.generator_poly()def build_gf(self):# simplified: create lookup tablesexp [0]*32log [0]*32a 1for i in range(31):exp[i] alog[a] ia 1if a 32:a ^ 0b100101exp[31] exp[0]return (exp, log)def generator_poly(self):# g(x) (x-α^0)(x-α^1)(x-α^2)(x-α^3)return [1, 0b00010, 0b00111, 0b01110, 0b01111] # coefficients in GF(32)def encode(self, msg):# msg: list of 27 ints (0-31)# systematic encodingm_poly msg [0]*4# division by generatorremainder m_poly.copy()for i in range(27):if remainder[i] ! 0:factor remainder[i]for j in range(4):remainder[ij1] ^ self.gf_mul(factor, self.g[j1])codeword msg remainder[27:31]return codeworddef gf_mul(self, a, b):if a0 or b0:return 0exp, log self.gfreturn exp[(log[a] log[b]) % 31],3基于双均衡模型与反正切快速收敛的超长缆数据传输方法为了解决测井勘探中3000米以上铜缆的信号严重衰减问题提出了一种双均衡结构前馈均衡FFE判决反馈均衡DFE与反正切快速收敛算法命名为DualEQ-Arctan。该算法在接收端采用11抽头FFE和5抽头DFE抽头系数通过反正切变步长LMS算法更新步长μ(n)μ_max * atan(β |e(n)|) / (π/2)其中β控制收敛速度。相比固定步长LMS收敛速度提高3倍稳态误差降低50%。在6000米铠装电缆上测试QPSK调制信号的眼图张开度从12%提升到78%误码率从10^-3降至10^-8。该均衡器在FPGA中实现占用约2000个查找表功耗0.5W。import numpy as np class DualEQ_Arctan: def __init__(self, ffe_taps11, dfe_taps5, mu_max0.1, beta100): self.ffe_w np.zeros(ffe_taps) self.dfe_w np.zeros(dfe_taps) self.buffer np.zeros(ffe_taps) self.decisions np.zeros(dfe_taps) self.mu_max mu_max self.beta beta def arctan_step(self, error): return self.mu_max * np.arctan(self.beta * np.abs(error)) / (np.pi/2) def equalize(self, sample): # shift buffer self.buffer np.roll(self.buffer, 1) self.buffer[0] sample # FFE output ffe_out np.dot(self.ffe_w, self.buffer) # DFE output dfe_out np.dot(self.dfe_w, self.decisions) y ffe_out dfe_out # decision decision 1 if y 0 else -1 error decision - y # update taps mu self.arctan_step(error) self.ffe_w mu * error * self.buffer # shift decisions self.decisions np.roll(self.decisions, 1) self.decisions[0] decision return decision