Wireless Communication Systems: Implementation perspective

Transcription

1 Wireless Communication Systems: Implementation perspective

2 Course aims To provide an introduction to wireless communications models with an emphasis on real-life systems To investigate a major wireless communication standard and understand how to read it To introduce architectures and implementation issues of wireless systems

3 Course contents The wireless channel Implementation of DSP functions Baseband modulation, interleaving, and channel coding Fundamentals of RTL OFDM Timing and frequency synchronization & channel estimation Diversity, MIMO, and MIMO decoding techniques and circuits Front-end

4 Evaluation 20% coursework: 4-6 assignments 80% Final examination Need contact info for class representative My

5 References OFDM Wireless LANs: A Theoretical and Practical Guide, Juha Heiskala, John Terry n standard, physical layer

6 Wireless communications Wireless communication systems were historically analog, today most systems are digital (what does this mean?) Examples of digital wireless systems include cellular phones, WLANs, Wireless Internet last mile, Satellite Communications, Digital Video Broadcasting

7 Frequency spectrum The spectrum is very busy, prize goes to whoever pushes more bits reliably in a smaller bandwidth Throughput= Bits/sec Goodput= Correct bits/sec

8 Components of a digital wireless system Tx = Transmitter Rx = Receiver RF = Radio Frequency DAC = Digital to Analog Conversion ADC = Analog to Digital Conversion

9 What is Tx RF? At the transmitter the RF takes the baseband signal and raises it to the frequency where it will be transmitted DAC: Transform signal to analog domain Pulse shaping filter; limits the signal power outside its band to limit its interference Mixer: raises the frequency of the signal Power amplifier: Raises the power of the signal so it can travel a long distance

10 RF at the receiver Frontend filter: Limits the out-of-band noise LNA: Low Noise Amplifier is used to raise the value of the signal without adding much noise, reduces the effect of noise in following stages Mixer: Lowers the frequency of the signal typically to baseband ADC: Transform signal to digital domain (1 s and 0 s)

11 Baseband/passband Data in its original form is in the baseband, i.e. its spectrum is centered at zero frequency Once the RF section mixes it to a higher frequency it is said to be in passband

12 What happens before/after the RF? The baseband accepts a payload of raw 1 s and 0 s and transforms it into a digital signal that has more desirable properties: Better Inter-symbol interference Better error immunity Higher transmission rate in the same bandwidth These are all functions of the Digital Baseband Radio, the topic of most of the course

13 Baseband Tx The baseband transmitter consists of the following subsystems (each covered in more detail later): Channel coding: Adding redundancy to the message so it can be reconstructed at the receiver without error Baseband modulation: Increases the spectral efficiency of the system by packing more bits into the same bandwidth as long as SNR allows it Pilots and headers: Additional data used by the baseband receiver to recover the channel OFDM: Used to allow communication over broader channels

14 Baseband Rx Packet and symbol timing; Detect when a packet is being received, lock on the frequency of the transmitter, and the start time Reversing OFDM Channel estimation; Try to estimate the conditions the signal went through Channel inversion; Invert the effects of these conditions Demodulation; Expand the symbols into bits again Forward Error Correction (FEC); Use the redundant bits to try and detect an error and fix it if possible

15 Questions about baseband What are some of the algorithms used to perform these tasks? How are these algorithms implemented? If they are implemented in hardware, what architectural issues arise?

16 Layers Layers are levels of abstraction in the processing of data In our treatment we will deal with three basic layers: Application, MAC, and PHY Application: The layer that generates the payload, it is aware of the nature of content and can process it knowing its properties MAC: Application layer wraps data and hands it to MAC (Medium Access Control) which regulates who gets to access the channel when, and if the data received is useful or not PHY: Mac wraps data and sends it to PHY which performs everything necessary till transmission, PHY is not aware of anything MAC or APP know

17 Packet-based communication As each layer packs data into packets and hands it to lower layers, eventually PHY does the same The PHY packet is thus the basic unit of communication PHY sends a packet, the receiver PHY accepts it, and decodes it If the packet is correct, fine If the packet is incorrect, upper layers will must handle it. If only one bit is irretrievable, the whole packet is considered lost

18 SNR SNR: Signal to noise ratio, the ratio of signal power to noise power In a wireless system SNR is always measured at the receiver SNR is normally reported in db SNR Meaning SNR in absolute units: 2 2 S R= NP signal P noise = V signal V noise SNR in db: S / / 0 Signal and noise are roughly equal 10 Signal is roughly 10 times bigger than noise 20 Signal is roughly 100 times bigger than noise R= N10log ( P / P ) = 20log ( V / V ) 10 signal noise 10 signal noise

19 BER BER is bit error rate and is defined as: (#error bits)/(total bits received)

20 PER PER is packet error rate and is more useful in real systems. If a single bit is wrong in a packet, the packet is useless. So PER is normally much higher than BER

21 Mathematical representation of signals Real wireless signals are sinusoids whose phase, frequency, and or amplitude are varied The sinusoids are high frequency and carry no information This high frequency sine is the carrier To avoid writing trigonometric expressions we write complex phasor expressions (amplitude and phase only) Since the frequency of the carrier is no longer included, this complex number (phasor) is a baseband signal This allows us to analyze the system much more easily

22 Course contents in more detail Random processes and the wireless channel Correlation, covariance, autocorrelation, autocovariance, PSD, AWGN, flat and dispersive fading, fast and slow fading, simulating the wireless channel, packet-based communication Implementation of DSP functions Complex binary arithmetic, relative complexity of arithmetic functions, comparison of implementation platforms Baseband modulation, interleaving, and channel coding memory contention in interleavers, standard hardware partitions of Viterbi decoders Fundamentals of RTL OFDM OFDM in frequency and time domains, FFT using Cooley-Tukey, implementing FFT for n (resource estimation) Timing and frequency synchronization & channel estimation Packet edge detection, symbol timing recovery, frequency and phase offset recovery and impact on constellation, using CORDIC processors to rotate vectors Diversity, MIMO, and MIMO decoding techniques and circuits Maximal ratio combining and selection receiver diversity, spatial multiplexing, the zero forcing, minimum mean square error, singular value decomposition, maximum likelihood, and sphere decoder algorithms. Implementation of MMSE and SVD using systolic arrays Front-end RF impact on constellation, AGC, CCA, and PAPR

23 Next We will discuss the wireless channel What happens to a signal as it travels through air? Does it only lose its power? What happens as it reflects and deflects through barriers? Why do you get bad reception in some places but not others? Why is it harder to communicate over a large bandwidth?