# Presentation: 112224

## THE WIRELESS REVOLUTION:

*THE WIRELESS REVOLUTION:*
*A Signal Processing Perspective*
* *
*Vince Poor*
*(poor@princeton.edu)*

*Federal Communications Commission*
*May 29, 2001*

**May 29, 2001 - The Wireless Revolution**

## OUTLINE

- The Role of Signal Processing in Wireless
- Some Recent Signal Processing Advances
- Space-time Multiuser Detection
- Turbo Multiuser Detection
- Quantum Multiuser Detection
- Conclusion

**May 29, 2001 - The Wireless Revolution**

## THE ROLE OF SIGNAL PROCESSING IN WIRELESS

**May 29, 2001 - The Wireless Revolution**

## Motivating Factors

- Telecommunications is a $1012/yr. business
- c. 2005:  wireless > wireline
- > 109 subscribers worldwide
- Explosive growth in wireless services
- Use of a public resource (the radio spectrum)
- Convergence with the Internet

**The Role of Signal Processing in Wireless**

## Wireless Applications

- Mobile telephony/data/multimedia (3G)
- Nomadic computing
- Wireless LANs
- Bluetooth (piconets)
- Wireless local loop
- Wireless Internet/m-commerce

**The Role of Signal Processing in Wireless**

## Wireless is Rapidly Overtaking Wireline

**The Role of Signal Processing in Wireless**

- _Source_:
*The Economist*
- Sept. 18-24, 1999

## Traffic Increasingly Consists of  Data

- _Source_: http://www.qualcomm.com

**The Role of Signal Processing in Wireless**

## Demand Growing Exponentially

**The Role of Signal Processing in Wireless**

- _Source_: CTIA

-  - As of 05/01/01, there were 114,546,113, in U.S., according to www.wow-com.com
-  - Every 2.25 secs., a new subscriber signs up for cellular in U.S.

## There’s Plenty of Room to Grow - I

- Mobile Phones
- Subscribers per 100 inhabitants, 1998

**The Role of Signal Processing in Wireless**

## There’s Plenty of Room to Grow - II

- Market Penetration, 2000

**The Role of Signal Processing in Wireless**

- _Courtesy of_: Tom Sugrue (FCC)

## Wireless Challenges

- High data rate (multimedia traffic)
- Networking (seamless connectivity)
- Resource allocation (quality of service - QoS)
- Manifold physical impairments
- Mobility (rapidly changing physical channel)
- Portability (battery life)
- Privacy/security (encryption)

**The Role of Signal Processing in Wireless**

## Wireless Channels

- _Fading_: Wireless channels behave like linear systems whose gain depends on _time_, _frequency_ and _space_.
- _Limited Bandwidth_ (data rate, dispersion)
- _Dynamism_ (random access, mobility)
- _Limited Power_ (on at least one end)
- _Interference_ (multiple-access, co-channel)

**The Role of Signal Processing in Wireless**

## Not Growing Exponentially

- Spectrum - 3G auctions!
- Battery power
- Terminal size

**The Role of Signal Processing in Wireless**

## Moore’s and “Eveready”’s Laws

- _Courtesy of_: Ravi Subramanian (MorphICs)

**The Role of Signal Processing in Wireless**

## Signal Processing to the Rescue

- _Source Compression_
- _Transmitter Diversity (Fading Countermeasures)_:
- Spread-spectrum: CDMA, OFDM (frequency selectivity)
- Temporal error-control coding (time selectivity)
- Space-time coding (angle selectivity)
- _Advanced Receiver Techniques_:
- Interference suppression (multiuser detection - MUD)
- Multipath combining & space-time processing
- Equalization
- Channel estimation
- _Improved Micro-lithography_ (phase-shifting masks)

**The Role of Signal Processing in Wireless**

## Advances in ASIC Technology

- _Courtesy of_: Andy Viterbi

**Microns**

**.8**

**.5**

**.35**

**.25**

**.18**

**Time**

**1991**

**Future**

**1998**

**1997**

**1995**

**The Role of Signal Processing in Wireless**

- _5/30/00_: 25 nm gate announced with optical lithography using phase-shifting masks (T. Kailath, et al.).

## Signal Processing for Wireless (v 1.0)

**Fleming Valve**
**        ****1910**

-     **Helical Transformer**
**                    ****1919**

**Marconi Crystal Receiver**
**                   ****1919**

**DeForest Tubular Audion**
**                   ****1916**

**The Role of Signal Processing in Wireless**

## SOME RECENT SIGNAL PROCESSING ADVANCES

- Introduction
- Space-time Multiuser Detection (3G)
- Turbo Multiuser Detection (2.5G)
- Quantum Multiuser Detection (?G)

**May 29, 2001 - The Wireless Revolution**

## INTRODUCTION  

**Some Recent Signal Processing Advances**

## First, A Few Words About MUD 

- Multiuser detection (MUD) refers to data detection in a _non-orthogonal_ multiplex; it’s of interest, e.g., in
- CDMA channels
- TDMA channels with channel imperfections
- DSL with crosstalk
- MUD can potentially _increase the capacity_ (e.g., bits-per-chip) of _interference-limited systems_ significantly
- MUD comes in various flavors
- _Optimal_ (max-likelihood, MAP)
- _Linear_ (decorrelator, MMSE)
- Nonlinear _interference cancellation_

**Some Recent Signal Processing Advances**

## Some Recent Developments 

- The basic idea of MUD is to exploit (rather than ignore) cross-correlations among signals to improve data detection.  Recent developments in this area:_ _
- _Space-Time MUD_
- Joint exploitation of spatial and temporal structure._ _
- _Turbo MUD_
- Joint exploitation of temporal structure induced by channel coding, and the multi-access channel.
- _Quantum MUD_
- Joint exploitation of quantum measurements and the multi-access channel.

**Some Recent Signal Processing Advances**

## SPACE-TIME MUD 

**Some Recent Signal Processing Advances**

## Multi-{Access, Antenna, Path} Channel

**Space-Time MUD**

## Single-Antenna Reception

**Space-Time MUD**

## Space-Time MA Signal Model

- [*b**k*(*i*): data symbol;  *s**k*(*t*): signaling waveform]

-   Vector channel (impulse response) of the *k*-th user:

- [*kl*:  path delay;    *g**kl*:  path gain;    _*a*_*kl*:  array response]

-   Received signal:

**Space-Time MUD**

## A Sufficient Statistic: Space-Time Matched Filter Bank

-   **H** is a matrix of cross-correlations among the received signals
-   Sufficient statistic {*y**k*(*i)*}: space-time matched filter output

- A Sufficient Statistic: Space-Time Matched Filter Bank

- [*kl*:  path delay;    *g**kl*:  path gain;    _*a*_*kl*:  array response]

**Space-Time MUD**

## Space-Time Multiuser Receiver

**Maximum Likelihood**
**Sequence Detection**
**OR**
**Iterative Interference**
**Cancellation**

**Space-Time MUD**

## Optimal Space-Time MUD

- [: multipath delay spread]

-   Computational complexity: *O*(2*K*)

**Space-Time MUD**

## Linear S-T Interference Cancellers

-   _Gauss-Seidel Iteration_: (Serial IC)

- _Problem_:

- with

-   _Jacobi Iteration_: (Parallel IC)

-   Computational complexity: *O(K ****m**max**)*

- Solve

**Space-Time MUD**

## Simulation  [K = 8; L = 3; P = 3] 

- Simulation  [*K *= 8;* L *= 3;* P *= 3]

- Direct-sequence
- spread-spectrum
- (16 chips/bit).

**Space-Time MUD**

## Nonlinear S-T Interference Cancellers

- Cholesky Decomposition:

-   _Successive Cancellation_:

-   _EM/SAGE-Based IC_:  (Interfering symbols are “hidden” data)

-   _Turbo MUD_:  - Coded channels  (**b** has constraints).

**Space-Time MUD**

## TURBO MUD 

**Some Recent Signal Processing Advances**

## MUD & The Decoding of Error-Control Codes 

- Recall: the basic idea of MUD is to exploit cross-correlations among signals to improve data detection.
- Similarly, error-control coding exploits dependencies among channel symbols to improve data detection.
- Turbo MUD is a technique for _jointly_ exploiting these two types of dependencies.

**Turbo MUD**

## Coded Multiple-Access Channel 

- Like other concatenated codes, this code can be efficiently decoded via a turbo-style receiver.

- Coded Multiple-Access Channel

_**Basic Idea of Turbo MUD**_**:**

**Turbo MUD**

## Rate-R-Coded Multiaccess Signal Model

- _Received Signal_:

* **K *= # active users.
-  *B*  = # _channe_l symbols per frame
** ****d***k *= set of *RB* data symbols transmitted by user *k*
-  **b***k*(**d***k*)** **= vector of channel symbols obtained by encoding **d***k*
* **p**k *= _rec’d_ waveform of user *k *; *1/T* = per-user signaling rate.
-  {*n(t)*} = unit AWGN;  = noise intensity

**Turbo MUD**

## Sufficient Statistic

- is sufficient for inferring **b***1*(**d***1*) **b***2*(**d***2*) ... **b***K*(**d***K*) and **d***1 ***d***2 *...* ***d***K*.

**Turbo MUD**

## Optimal MUD/Decoding

_**ML Detection (b)/Decoding (d)**_**:**

_**MAP Detection/Decoding**_**:**

*O*(2**) - convolutionally encoded symbols, constraint length 
- orthogonal signaling 			           [BCJR, Viterbi algo, etc.]

*O*(2*K***) - uncoded symbols, delay spread [MLSD; MAP MUD]

_**Complexity per Symbol (Assume Binary Symbols)**_**:**

**Turbo MUD**

## Turbo MUD: The Main Idea 

- For constraint-length-convolutionally coded transmission on an asynchronous *K*-user multiaccess channel, optimal detection/decoding has complexity *O*(2*K***) [Giallorenzi & Wilson].
- This complexity can be reduced to *O*(2*K*) + *O*(2**) via the _turbo principle_ [Moher].
- I.e., _iterate_ between MUD and channel decoding, exchanging soft (channel) symbol information at each iteration.

**Turbo MUD**

## Multiaccess Channel & Turbo Receiver

- SISO
- MUD

- SISO
- Decoders

- De-Int.

- Int.

- Channel Output

- Output Decision

-  Soft-input/soft-output (SISO)
-  Iterative
-  Interleaving removes correlations

- vs.

**Turbo MUD**

## SISO MUD

- To get posterior probabilities from the multiuser detector, we should use MAP MUD.
- _MAP MUD is prohibitively complex_  *O*(2*K*)  [*K* = # users]
- This differs from standard turbo decoding, in which the constituent decoders are of similar complexity.
- _Many lower complexity approaches_: [Alexander et al.; Honig et al., Lu & Wang, Müller & Huber, Naguib & Sheshadri, Reed et al., Schlegel, Tarköy, Wang & Chen, Wang & Poor (COM’99), & others]

**Turbo MUD**

## Recall: Low Complexity MUD

- _Recall the Model__:_

- MUD fits this model to the observations.
- As noted before, in addition to ML/MAP, there are many low-complexity techniques for doing this; e.g.,
- _Linear MUD_: decorrelator, MMSE, bootstrap (v. efficient iterative implementation as linear interference cancellers (IC’s))
- _Nonlinear IC’s_: successive cancellation, multistage, EM/SAGE
- Generally, these don’t allow the computation of the posterior probabilities needed for turbo MUD.

**Turbo MUD**

## Low Complexity SISO MUD

- _Conventional MMSE MUD:_
- MMSE output     desired symbol + Gaussian error [Poor & Verdú, IT’97]
- From this, posterior probabilities can be estimated from the MMSE detector output.
- This yields an effective low-complexity SISO MUD.
- _MMSE w/ Priors:_

**Turbo MUD**

## Simulation Example [K = 4; 

- Simulation Example [*K *= 4; **

- Rate-1/2 convolutional code; constraint length 5; 128-long random interleavers

**Turbo MUD**

## QUANTUM MUD 

**Some Recent Signal Processing Advances**

## Quantum MUD

- With quantum measurements, observation is restricted (uncertainty principles apply).
- In this case, the observation instrument must be designed jointly with the detector.
- Photon counting is usually not optimal.

**Quantum MUD**

## Quantum MUD Design Problem

**Quantum MUD**

## A Two-User Quantum Channel

**Quantum MUD**

## Two-User Example: Error Probabilities

**Quantum MUD**

## Conclusion

- The transformation from wireless voice to wireless data is causing exponentially increasing demand for wireless capacity.
- Signal processing is the great enabler:
- Source compression
- Fading countermeasures/transmitter diversity
- Interference suppression/space-time processing
- Micro-lithography
- Recent advances:

**May 29, 2001 - The Wireless Revolution**

## Conclusion - Cont’d

- MUD exploits signal cross-correlations to substantially improve data detection.
- Space-time MUD
- Combines exploitation of temporal & spatial cross-correlations.
- Turbo MUD
- Combines exploitation of cross-correlations introduced by the channel with exploitation of dependence introduced by coding.
- Quantum MUD
- Combines exploitation of cross-correlations with the instrument design for the quantum channels.
- Some Open Issues
- _Space-time MUD_:  Hardware implementation
- _Turbo MUD_: Adaptivity, convergence behavior
- _Quantum MUD_: Relevance in applications

**May 29, 2001 - The Wireless Revolution**

##   THANK YOU!

**May 29, 2001 - The Wireless Revolution**