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Introduction Signal Retrieval and Communication Theory of systems for the conveyance of information Characteristics of communication systems Uncertainty Keep in mind: Signal retrieval problem Noise and “information” (deterministic vs probabilistic) Communication (only particular type of signal retrieval problem) Usually two resources to consider Bandwidth vs Power Innovation in microelectronics and signal processing have led to the proliferation of communication systems Block Diagram of a Communication System Keep in mind that this is only a model! Can we make it simpler? More complicated? Consequences? Components of Block Diagram Input transducer Messages Message conversion Analog or digital E.g speech voltage variations Transmitter Couple the message to the channel Modulation, filtering, amplification, and coupling Modulation For the ease of radiation To reduce noise and interference For channel assignment For multiplexing or transmission of several messages over a single channel To overcome equipment limitations Channel Characteristics Channel Signal degradation (convolutive noise) Additive “noise” Receiver Sensor noise, thermal noise, interference (e.g MUI, jammer, …) Demodulation, amplification Output transducer Loudspeaker, tape recorder, PCs, CRT, LCD, etc Channel Characteristics Additive noise sources (usu less troublesome) Internal noise Noise generated by components within a communication system, such as resistors and solid-state active devices Thermal noise Shot noise Random motion of free electrons in conductor or semiconductor excited by thermal excitation Random arrival of discrete charge carriers in thermionic tubes or semiconductor junction devices Flicker noise Produced in semiconductors by a mechanism not well understood and is more severe in lower frequency Channel Characteristics External noise Noise generated from sources outside a communication system, including atmospheric, man-made, and extraterrestrial sources Atmospheric noise Impulsive in nature, i.e large amplitude, short-duration bursts (how should we model it? Why model it?) Man-made noise Impulsive Automobile and aircraft ignition noise, radio-frequency interference (RFI), e.g MUI Extraterrestrial noise Solar and cosmic noise Channel Characteristics Convolutive noise (usu very troublesome) Multiple transmission paths Diffuse type Specular type One or two strong reflected rays Fading Numerous reflected components Random changes in attenuation within the transmission medium How we model it? Why we care? 10 DM: Slope Overload If message signal m(t) has a slope greater than can be followed by the stair-step approximation ms(t) Assume step-size = δ0 max slope = δ0/Ts Example (right) 53 Mathematical Analysis of Slope Overload Problem Assuming m ( t ) = A sin ( 2π f1t ) Max slope that ms ( t ) can follow is S m = δ0 Ts d m ( t ) = 2π Af1 cos ( 2π f1t ) dt ∴ ms ( t ) can follow m ( t ) without slope overload if δ0 Ts ≥ 2π Af So there is a BW constraint on m ( t ) in order to avoid this problem 54 Adaptive DM: Solution to Slope Overload Adjust the step-size δ0 based on xc(t) Idea: If m(t) ≈ constant, xc(t) alternates in sign leads to small DC (close to zero) at output of LPF – this controls gain at variable gain amplifier δ0↓ at the integrator input If m(t)↑ (or ↓) rapidly, xc(t) has the same polarity leads to big value of the magnitude of the output of LPF δ0 ↑ at the integrator input reducing time-span of slope overload 55 Adaptive Delta Demodulator Notice the demodulator is part of the modulator The receiver is required to match changes in δ0 that was made at the modulator This is often used in waveform coders (speech and video) Known as analysis-bysynthesis coding Determine what parameters the coder should used by duplicating what the decoder does 56 Pulse-Code Modulation (PCM) m(t) samples (analog amplitude) quantized samples binary representation binary modulated waveform (ASK (AM), PSK (PM), FSK (FM)) Pros More reliable communication Cons Wide BW ( reduced by “compression”) Complicated circuits ( cost reduced by VLSI) 57 BW of PCM Assume the number of quantization levels = q= 2n log q binary pulses must be transmitted for each sample of the message signal ⇒n= Let: Message BW= W Sampling rate = 2W ⇒ 2nW binary pulse/second Thus, max width of each binary pulse is 2nW ⇒ transmission BW ≈ knW , k is a proportionality constant (See Sec 2.7) ( ∆τ )max = Hence, B ≈ 2Wk log q Recovered message error is due mainly to quantization error Thus, q ↑ → error ↓ → B ↑ 58 PCM Modulating RF Carrier PCM waveform can be transmitted on an RF carrier using amplitude, phase, or frequency modulation Figures shows data bits are represented by an non-return to zero (NRZ) waveform bits are shown (101001) ASK PSK Carrier amplitude determined by data bit for that interval Phase of carrier is established by the data bit FSK Carrier freq is established by the data bit 59 Multiuser Communication Systems Multiplexing A number of data sources share the same communication Used mainly to multiplex signals from different users onto the same channel for transmission Can also be used for stereophonic FM transceiver to multiplex sum and differences of signals Different multiplexing techniques FDM QM TDM 60 Frequency- Division Multiplexing (FDM) Signals from different sources can used different modulation BPF used at receiver to retrieve signal from different sources Source uses DSB Source uses SSB Source uses FM Guard bands are injected between each source signal before transmission to realize non-ideal BP filtering at Rx BW is lower bounded by the sum of the BWs of the message signals: N B = ∑ Wi i =1 61 Example of FDM: Stereophonic FM Broadcasting Stereo signal is perceived by having speakers outputting sum and differences of the monotonically recorded signal Backward compatibility is required Necessary for stereophonic FM receiver to demodulate monophonic FM signal 0-15 kHz carries L+R (for monophonic receiver) 24-53 kHz carries L-R (stereophonic receiver uses L+R and L-R) Information about the carrier is inserted by the Tx for coherent demodulation at the Rx 62 QM QM is not a FDM technique as spectra of m1(t) and m2(t) overlap in frequency SSB is a QM signal with m1(t) = m(t) and m2(t) = ± mˆ ( t ) = Modulation: x ( t ) c Ac m1 ( t ) cos ( 2π f c t ) + m2 ( t ) sin ( 2π f c t ) Demodulation: If carrier phase is unknown, i.e xr ( t )2 cos ( = 2π f c t + θ ) Ac m1 ( t ) cos θ − m2 ( t ) sin θ + Ac m1 ( t ) cos ( 4π f c t + θ ) + m2 ( t ) sin ( 4π f c t + θ ) After LPF: output becomes yDD ( t ) = Ac m1 ( t ) cos θ − m2 ( t ) sin θ (ideal: θ → 0) 63 Time-Division Multiplexing (TDM) Tx Data sources are assumed to have been sampled at Nyquist rate or higher Commutator interlaces the samples to form the baseband signal Rx Baseband signal is demultiplexed by using a second commutator 64 BW of TDM A "rough" estimate of BW Let: BW of i th channel = Wi Sampling period of baseband signal = T ⇒ Samples per T second = 2WiT N ⇒ Total samples per T second = ns = ∑ 2WiT i =1 or Assuming baseband is lowpass signal with BW B, required sampling rate is B Total samples: BT ⇒ ns= BT= N ∑ 2W T i =1 i N so B = ∑ Wi i =1 This is same as FDM 65 Example of TDM: Digital Telephone System Voice signal sampling: 8,000 ksamp/s Each sample is quantized to + bit 1-bit for signaling call establishment and synchronization) Bit rate: bit/samp * 8,000 ksamp/s= 64 kbps T1 line Group of 24 8-bit voice channels 24 voice ch * bit/samp + = 193 bit Extra 1-bit for frame synchronization Frame rate 193 bit/frame * 8,000 frame/sec – 1.544 Mbps 66 Comparison Between Different Mux Techniques FDM Pros Cons Intermodulation distortion (crosstalk) due to nonlinear channel TDM Pros Less crosstalk (assuming memoryless channel) Cons Simple to implement Difficult to keep synchronization (frame structure, header) QM Pros Efficient use of channel Cons Crosstalk between I and Q channels (needs coherent demodulation) 67 [...]... domain analyses to analyze and design systems for modulating and demodulating of information-bearing signals Analysis of interfering signals on system performance, and design of systems to counteract their effects are part of communication theory, which makes use of modulation theory 15 Probabilistic Approaches to System Optimization As seen earlier, proper modeling of (additive and convolutive) noise... discovery • Offloading traffic D2D backhaul Relay MBS Femto-BS Pico-BS Macrocells: 20-40 watts (large footprint) Pico-BS Characteristics • Wired backhaul • Operator-deployed • Open access Major Issues • Offloading traffic from macro to picocells • Mitigate interference 14 Systems Analysis Techniques Time and frequency domain analyses Looking at things from different perspective Modulation and communication. .. × = Network Density Required capacity 2 (bps/km = bps/Hz/cell × Hz × cell/km2) 1000x Capacity Traffic offloading (alternative means for communications) Non-orthogonal multiple access controller Massive MIMO, advanced receiver Spectrum efficiency WiFi offload, D2D, etc Dense urban Shopping mall Home/office Current capacity Cellular network assists local area radio access Spectrum extension Multiple access... its power Noise is often persistent and is often a power signal A realizable LTI system can be represented by a signal and mostly is an energy signal Power measure is useful for signal and noise analysis The energy and power classifications of signals are mutually exclusive, i.e cannot be both at the same time But a signal can be neither energy nor power signal 14 Signals and Linear Systems y(t) H x(t)... property: Defines a precise sample point of x ( t ) at time t (or t0 if δ ( t - t0 )) = x ( t0 ) ∫ x ( t ) δ ( t − t ) dt t 0 3 Basic function for linearly constructing a time signal = x (t ) 4 Some properties 1 δ ( at= δ (t ) ; ) a ∫τ x (τ ) δ ( t − τ ) dτ δ ( t= ) δ ( −t ) : even function 7 Signal Model and Classifications 5 What is δ ( t ) precisely? Some intuitive ways of realizing it: E.g 1 1 , t ...Signal Retrieval and Communication Theory of systems for the conveyance of information Characteristics of communication systems Uncertainty Keep in mind: Signal... proliferation of communication systems Block Diagram of a Communication System Keep in mind that this is only a model! Can we make it simpler? More complicated? Consequences? Components of Block Diagram... interfering signals on system performance, and design of systems to counteract their effects are part of communication theory, which makes use of modulation theory 15 Probabilistic Approaches to