Monday, December 22, 2008

Peak to Average Power Ratio II: An Introduction

A Signal Processing Perspective

The statistic properties of the PAPR of multi-carrier signals can be described by CCDF (Complementary Cumulative Distribution Function). If we assume the frequency-domain symbol is complex Gaussian distributed. When the number of subcarriers, L, become large, the instantaneous power of each multi-carrier signal chip can be modeled by a Chi-distributed signal with two degree of freedom.
CCDF( γ ) = Pr( PAPR > γ ) = 1- Pr( PAPR <= γ ) = 1 – [ Pr( p <= γ ) ]L >~ 1 – ( 1 – e )L 
image
Figure 1.  A statistic modeling of PAPR

A Coding Perspective

Given a code of length n, coding rate R, what is the achievable region of triplets (R, d, PMEPR) ?  What is the relationship between PMEPR and the minimum Euclid distance (mED) d* ? All these questions belong to a Sphere Packing Problem. Given a codeset of q-ary code c with length n, what is the relationship between the size of the codeset and minimum Hamming distance (mHD) D ? This is a Sphere Packing Problem again.
image
Figure 2.  A geometric modeling of PAPR

An Implementation Perspective

image
Figure 3. Rapp’s SSPA model
 
image
Figure 4. AM/AM characteristics of the Rapp SSPA model, P = 2. 

Popular PAPR Reduction Approaches

Clipping: In-band distortion mostly is negligible. But out-of-band distortion is more serious.
Filtering and Signal Processing
  • time-invariant linear filter results in less peak regrowth and lower PAPR than DFT filter in general, if there is no spectral masking.
  • Partial Transmit Signaling (PTS): divide/Group into clusters and each of them is done with a smaller IFFT. [Muller and Huber, 97]
  • Tone Reservation (TR): inserting anti-peak signals in unused or reserved subcarriers. The objective is to find the time-domain signal to be added into the original time-domain signal such that PAPR is reduced. [Tellado, 00]
Coding: The idea is to select a codeword with less PAPR. it still is an open problem to construct codes with both low PAPR and short Hamming distance.
Selected Mapping (SLM): it is based on selecting one of the transformed blocks for each data block, which has the lowest PAPR. [Bauml, Fisher and Huber, 96]
Constellation Optimization
  • Tone Injection (TI): the basic idea is to increase the constellation size so that each of the points in the original basic constellation can be mapped into several equivalent points in the expanded constellation.
  • Active constellation extension (ACE): similar to TI. [Krongold and Jones, 03]

Monday, December 8, 2008

Peak to Average Power Ratio I: OFDM PAPR Reduction


With the upcoming deployment of wideband wireless network with throughput greater than 100Mbps over high frequency bands such as 5-GHz band and the adopting of multicarrier modulations, more and more challenges are brought to system and hardware design. OFDM, frequently referred as multi-carrier modulation, is becoming the de facto standard for next-generation wideband wireless networks. However, one of the critical issues of OFDM as well as other multicarrier modulation scheme is its high peak-to-average power ratio (PAPR), which usually requires large backoff and highly efficient high power amplifier (HPA), large dynamic range analog-to-digital converter (ADC), high linearity up-converter, etc. These requirements lead to expensive hardware systems that are difficult to design. Hence it becomes more and more important to alleviate the burden of hardware design with employing advanced PAPR reduction technologies.

In this tutorial contribution, OFDM RF design challenges and PAPR reduction technologies are presented. They are discussed in terms of both theory and implementation with many examples. Especially a patent search is given too. There are five major parts in this tutorial. In the first part, an introduction to OFDM, the RF design challenges and the PAPR issue is presented. We outline the challenges, which include high power peak, linearity limitation, image rejection, phase noise and distortion, etc., brought to each component of OFDM RF design. Here we focus on high peak power and try to solve the PAPR issue, which is defined from both signal processing and coding perspectives. The problem of PAPR issue is outlined from implementation perspective with the discussion of the effectiveness of signal clipping, which is known as one of the simplest PAPR reduction technique. In the second part, an overview of most popular PAPR reduction approaches is given. It includes coding, signal processing and filtering, selection mapping, signal constellation optimization, etc. The pros and cons of these approaches are compared in terms of performance and implementation complexity. In the third part, PAPR reduction techniques adopted in existing standards are presented and discussed. We cover some of the most important standards including GSM, WCDMA, LTE and UMB. In the fourth part, many PAPR reduction patent examples are presented, followed by a presentation of our recent contributions to PAPR reduction. Our approaches are simple and efficient, with low implementation complexity on the receiver. The conclusions and further works are outlined in the last part.

In summary, this tutorial is intended to provide a comprehensive overview of PAPR reduction form OFDM for a wide array of audiences. It includes not only the background theory, implementation considerations and related standards but also our recent contributions.

Saturday, November 22, 2008

Interference Cancellation: IV A Blind Receiver Design Perspective

[ Interference Cancellation. I. A Short Overview of Multiusr Detection ]
[ Interference Cancellation: II. A Conventional Receiver Design Perspective ]
[ Interference Cancellation: III. A Signal Subspace Perspective ]


While the conventional signal model provides a foundation for both optimal and conventional multiuser receiver design and the subspace signal model aids understanding of the underlying signal structure, neither is simple enough for developing blind multiuser receivers for high-speed CDMA systems [Andrews 05]. In order to address the near-far problem with minimum prior knowledge and computational complexity, a blind multiuser signal model and blind multiuser receiver design framework are presented here. Within this framework, the blind receiver only requires several previously received symbols in addition to its own signal signature(s), amplitude(s) and timing(s). Different to the conventional multiuser model and subspace signal model [Verdu 98, Wang 98], there is no signal signature or signal subspace basis of interfering signals necessary and no signal signature estimation or signal subspace separation procedure required in the proposed detection framework. Based on this model and detection framework, several optimal blind linear multiuser detectors are individually developed and analyzed with maximum likelihood (ML), MMSE and least squares (LS) criteria. In order to further reduce the complexity, some implementation considerations are outlined. In addition, I compared the proposed multiuser receivers with existing ones from several practical implementation prospects. For each of these blind linear multiuser receiver, I not only evaluate its link-level performance but also discuss how it behaves in a large-scale system. It shows that there is an additional noise enhancement in the proposed detection framework due to the limited number of previous knowledge but its computation complexity and detection delay still is lower than most existing multiuser receivers. In a large-scale system with large spreading gain and high SINR, the asymptotic performance of the proposed blind multiuser receivers are close to the conventional ones.

In general, one of the major difficulties in developing blind multiuser receivers with either the conventional signal model or subspace signal model is that the signal signatures {sk : k ≠ 1} or the signal subspace matrix Us are unknown beforehand. In most blind multiuser receivers, either the signal signature matrix S and the subspace transform matrix Ф are required to be estimated along the detection of desired signal, which is b1 in this paper. Instead, I propose a known blind signature matrix S, which is constructed by simply concatenating available information known by user 1 into a L x M matrix, so that

S = [A1s1 r1 r2 ... rM−1 ]= SA[ e1 B] + N = SAB + N

where {rm : m = 1, 2, ... , M−1} are (M − 1) previously received symbols, el is a K × 1 identity vector with a 1 as the lth element and 0’s as the rest, the K × 1 vectors bm denotes the data sent by all K users with rm and the data matrix B is

B = [ b1 b2 ... bM−1 ] = [g FH ]H





The proposed blind receiver design framework


A comparison between the blind receiver design framework and other detection approaches


A performance comparison of various multiuser receivers

Thursday, November 20, 2008

Interference Cancellation: III A Signal Subspace Perspective

[ Interference Cancellation. I. A Short Overview of Multiusr Detection ]
[ Interference Cancellation: II. A Conventional Receiver Design Perspective ]
[ Interference Cancellation: IV. A Blind Receiver Design Perspective ]

In realities it is known to be difficult to directly and precisely estimate the signal signatures {sk : k ≠ 1} for taking advantage of well-developed optimum or conventional multiuser detection schemes. In Figure 1, the design of a linear MMSE interference cancellation receiver for CDMA systems is shown as an example. As we can see, there are at least two challenges in the implementation. The first one is you need know the signal signatures of all involved users. The second one is it requires the computation-intensive matrix inverse operation. Design challenges like these make the conventional interference cancellation methodology unattractive in practical applications.

Figure 1. The challenges in employing conventional interference cancellation design. An example of linear MMSE interference cancellation

Now it is known that interference cancellation is able to be designed with a signal subspace model and statistic signal processing techniques for reconstructing the conventional detectors. Signal subspace methods are empirical linear approaches for dimensionality reduction and noise reduction in signal processing. They have attracted significant interest and investigation in the context of antenna array signal processing and speech signal processing for a long time. In later 1970s and early 1980s, G. Bienvenu and L. Kopp (1980) and R. O. Schmidt published their pioneer work applying signal subspace approaches on array signal processing. It is worth mentioning the well-known multiple signal classification (MUSIC) scheme introduced by R. O. Schmidt has been widely studied for estimating direction of arrivals (DOA) or frequency of arrivals (FOA). In 1901, Karl Pearson suggested the principal component analysis (PCA) approach, which essentially is similar to signal subspace approaches and widely applied in audio and speech signal processing. It is notable that Xiaodong Wang and Vincent Poor suggested further applying this concept on blind multiuser receiver design in 1998. The basic idea behind signal subspace approaches is to transform a series of samples, e.g., time-domain correlated samples, into a set of usually uncorrelated or less correlated representations in a linear subspace.

In the subspace signal model, the received signal vector r is modelled by a combination of the signal subspace bases {usk : 1 ≤ k ≤ K} according to

r = Us φ + n

where Us = [ us1 us2 . . . usK ], φ is a vector defined by

φ = Φ A b

With Φ being a K × K matrix. The original signal signature matrix S can now be expressed as

S = Us Φ .

One most attractive feature of the subspace signal model is that the signal subspace bases {usk : 1 ≤ k ≤ K} are much easier to be blindly estimated than the actual signal signature waveform so that the blind receiver design can be simplified. In theory, these signal bases can be estimated by applying subspace decomposition on the autocorrelation matrix R

R = E{ rrH } = [ Us Un ] diag{[Λs Λn]} [ Us Un ]H

where Un denotes the noise subspace bases.

Figure 2. Mathematical illustration of signal subspace linear MMSE interference cancellation

With the signal subspace approach, the linear MMSE interference cancellation shown in Figure 1 now can be redesigned in a different way. This is shown in Figure 2 and 3.  The estimation and separation of signal and noise subspaces essentially help identify the signal signature of the desired components from the received signals. On  the other conventional MMSE receiver can be blindly constructed with the signal and noise subspaces bases.  No explicit signal signature estimation is necessary.

Figure 3. The receiver structure of signal subspace linear MMSE interference cancellation

Saturday, October 25, 2008

Interference Cancellation: II A Conventional Receiver Design Perspective

[ Interference Cancellation. I. A Short Overview of Multiuser Detection ]
[ Interference Cancellation: III. A Signal Subspace Perspective ]
[ Interference Cancellation: IV. A Blind Receiver Design Perspective ]

Introduction

Interference cancellation provides a promising alternative to the conventional or optimum detectors in multiuser detection. Interference cancellation methods typically require less implementation complexity while practically o ering similar performance. The idea behind interference cancellation is to estimate the multiple access and/or multipath induced interference and then to subtract the interference estimate from the received signal. Hence, compared to other multiuser detection schemes, interference cancellation pays more attention on the estimation of the multiple access interference (MAI). Different schemes for the MAI estimation lead to different interference cancellation schemes. Actually, interference cancellation detector will cancel the interfering signal exactly provided that the decision was correct and channel information is known. Otherwise it will double the contribution of the interferers. The main alternatives for implementation of interference cancellation are parallel hard interference cancellation (PIC) and serial/successive hard interference cancellation (SIC), while many other variants on these basic principles have also been developed. With conventional PIC, all user are simultaneously demodulated and detected in a parallel behave. With conventional SIC, a decision for the symbol of the stronger user is made rst, the interference from this user is subsequently removed in the the next stronger user's receiver before the next user's receiver make its decision and so on.

On this blog, we consider a synchronous DS/CDMA system and review the principles of interference cancellation. several soft interference cancellation schemes, including direct interference cancellation detector, MAME interference cancellation detector and MMSE interference detector, are introduced with di fferent MAI estimation schemes. I show that, besides MAME interefence cancellation, the proposed direct interference cancellation detector has the same performance of the classic decorrelating detector while the MMSE multiuser detection and the MMSE interference cancellation actually are the same detectors.

System Model

Figure 1. Multiuser Detection System Model
We consider a single-cell synchronous DS/CDMA model and assume that there are K active users in the cell, the data { bk[n]: k = 1, 2, ..., K } of these K users are individually spread using the corresponding spreading sequences { ck = [c1k c2k ... cLck]T : k = 1, 2, ..., K; } with the spreading gain Lc and synchronously received from these users through multipath channel and corrupted additive white Gaussian noise (AWGN) with the variance ¾2 n [3]. The channel is a multipath channel with up to P resolvable paths and corrupted by AWGN. The baseband representation of the received signal due to user k is given by

rk(t) =  Σp=1PαpkAk[n]bk[n]ck(t-nT-τp) + nk(t)

where αpk is the gain of the pth path of user k’s signal and bk[n] is the nth bit sent by user k. We assume that the { bk[n] : k = 1, 2, . . . , K } are independent and identically distributed random variables with E{bk[i]} = 0 and E|bk[i]|2 = 1. The parameters {ck(t) : k = 1, 2, . . . , K} denote the normalized spreading signal waveform of K users during the interval [0, T] and {Ak[n] : k = 1, 2, . . . , K} are the signal amplitudes at time t = nT. Without loss of generality, the P propagation delays from the base station to user k are ordered such that 0 ≤ τ1 ≤ τ2 ≤ . . . ≤ τP

At the receiver side, the received signal passes through chip-matched filter (CMF) φ (t) and then RAKE combiner. The combined output r (t) is

r (t) = A1b1c1(t − nT − τ1) ⊗ φ (t − τ1) + mISI (t) + mMAI (t) + n (t)

Figure 2. A typical matrix representation of conventional multiuser detection system model

Interference Cancellation

Figure 3. The block structure of a basic interference cancellation detector.

As in Figure 3, it shows that there are usually two basic stages in interference cancellation realization. At the fi rst stage, the MAI from other users are reconstructed. At the second stage, the MAI is removed from the received signal and the nal decision is made from the rest signal through a matched lter. Thus, the key part in interference cancellation is how to estimate MAI as effciently as possible.

Figure 4. The structure of a direct interference cancellation, an equivalent to multiuser decorrelation detector 
Figure 5. The structure of a linear MAME interference cancellation, an equivalent to multiuser MAME detector 
Figure 7. The structure of a linear MMSE interference cancellation, an equivalent to multiuser MMSE detector 

Wednesday, October 8, 2008

Interference Cancellation: I. A Short Overview Multiuser Detection

[ Interference Cancellation: II. A Conventional Receiver Design Perspective ]
[ Interference Cancellation: III. A Signal Subspace Perspective ]
[ Interference Cancellation: IV. A Blind Receiver Design Perspective ]
[Toward Forward Link Interference Cancellation, CDMA Development Group (CDG) Technology Forum 2006]

CDMA cellular network capacity is known to be interference-limited since the same spectrum is shared by many users and there exists a near-far problem due to multiple access interference (MAI). Multiuser receiver is highly regarded as one of the promising interference management techniques improving spectrum efficiency and achieving high-data rates for wireless multimedia communication. It has been intensively investigated over the last two decades and received much attention for next-generation radio access network [Andrews 05, Wang 05]. Optimum multiuser receivers and conventional multiuser receivers are known to be able to solve the near-far problem at the knowledge of the signature information of all users [Verdu 98]. However this assumption isn’t always consistent with practical situations where the receiver may know only the signatures of the expected signals not interfering signals. Recent research work is mostly devoted to semiblind or blind multiuser receiver design and also signal signature estimation [Verdu 98, Madhow 98, Andrews 05]. It is known that most blind implementation of conventional multiuser receivers achieve the same performance as conventional multiuser receivers providing enough pilots or training signals available [Honig 95, Torlak 97, Wang 98, Zhang 02]. For example, with sufficiently training on receiver, blind minimum mean squared error (MMSE) receiver is known to achieve the performance of conventional MMSE detector [Verdu 98]. If enough signals are available for signal subspace separation or signal signature estimation, subspace-based blind multiuser receivers can achieve the performance of various conventional multiuser detectors too [Wang 98]. As long as interfering signal structure is pretty stable during the signal detection procedure, these blind multiuser receivers can work well with constant performance. They may not function well when the multiuser channel or system load experiences dynamic changes. However, this happens very frequently in reality, especially when the channel coherence time is not large enough. Therefore it is important to design multiuser receiver that requires a limited number of previous signals with better channel tracking capability and possible low implementation complexity. And it is also interesting to understand how the number of employed previous signals affect the performance of blind multiuser receivers and what are the tradeoffs between complexity and performance of blind multiuser receiver design in this case and in general too.

Figure 1. A List of Conventional Multiuser Detection Design Approaches

In the development of advanced multiuser receivers, it is known that a proper received signal model can give a lot of help with multiuser receiver design in addition to the understanding of received signals. There are two most popular multiuser signal models which have extensively been discussed and employed for receiver design and performance analysis. They are the conventional multiuser signal model and the subspace-based multiuser signal model [Verdu 98, Wang 98]. In the conventional signal model, each received symbol is expressed as a linear combination of actual signal signatures with their amplitude and timing [Verdu 98, Honig 95, Zhang 02]. The theory of optimal multiuser receivers and the conventional receivers are well developed with this model [Verdu 98]. Most related blind multiuser receivers are also developed either by explicitly estimating the signal signature [Torlak 97] or by removing interfering signal components using adaptive filtering techniques, e.g., blind receiver design with Wiener [Honig 95] and Kalman filter [Zhang 02] techniques. Though the conventional signal model provides a natural view of received signals, the involved signature waveforms information are unknown in reality and it requires considerable processing to obtain them before detection. To compensate for the weakness of the conventional signal model, the subspace signal model was developed with the statistic signal spectral analysis techniques termed subspace-based signal processing [Wang 98]. Subspace-based signal processing techniques are previously developed for array signal processing since 1970s [Schmidt 86, Hero 98]. In the multiuser subspace signal model, each received signal is taken as a linear combination of signal subspace bases, which are obtained by subspace analysis on the correlation matrices of the received signals. The subspace signal model can also be considered as the result of parametric signal modelling, which provides an in-depth description of the received signals. Though the subspace-based approach does not require explicit estimation of each user’s signature waveform and the adaptivity speed can be improved with some subspace tracking techniques [Yang 95], the signal subspace formation procedure itself is not trivial.

For a better design of multiuser receivers, the evaluation and understanding of multiuser receiver are traditionally based on the link-level parameters like asymptotic multiuser efficiency (AME) and near-far resistance (NFR) in addition to signal-to-interference/noise ratio (SINR) and bit-error rate (BER) [Verdu 98]. These parameters show the capability of a multiuser receiver to reject various interference. Among them, One of the most important measurements is SINR and most other parameters can be derived from it. For example, the AME of a multiuser receiver is the ratio between the actual multiuser receiver output SINR and the corresponding single-user output signal-to-noise (SNR) in high SNR region. The AME shows how steep the slope of a multiuser is when its BER goes to zero in logarithmic scale. The best achievable AME value is 1, which means the receiver works exactly the same way as if there is no interference at all. The worst value is close to 0 when the outputs of a multiuser receiver are completely determined by interference. The NFR of a multiuser receiver is defined by the minimum AME over the received energies of all other users. It shows the receiver’s ability to reject the worst-case interference. However, link level evaluation usually depends on the actual employed receiver structure and the signature waveforms. Recently several large-system measurements are developed to evaluate multiuser receiver’s behaviors in a system with large processing gain and lots of users [Tse 99]. In a large-scale system, one concept termed effective interference describes how the total received interference can actually be taken as a sum of other individual user’s effective interference. Effective interference itself is independent to the particular realization of random signal signatures. Extended from the concept of effective interference, effective bandwidth and user capacity are the terms describing how the system resource, in terms of power and degree of freedom, are assigned to users for achieving their target SINRs. I will show that these parameters can also be derived from asymptotic SINR too. One interesting thing here is these large-system evaluations only depend on the power distribution, system load, target SINR and receiver design, not the actual employed signal signatures for each user. This make it easy for giving some insights of multiuser receiver behaviors in an actual wireless communication environment.

While the conventional signal model provides a foundation for both optimal and conventional multiuser receiver design and the subspace signal model aids understanding of the underlying signal structure, neither is simple enough for developing blind multiuser receivers for high-speed CDMA systems [Andrews 05]. In order to address the near-far problem with minimum prior knowledge and computational complexity, a blind multiuser signal model and blind multiuser receiver design framework are firstly presented here. With this framework, the blind receiver only requires several previously received symbols in addition to its own signal signature(s), amplitude(s) and timing(s). Different to the conventional multiuser model and subspace signal model [Verdu 98, Wang 98], there is no signal signature or signal subspace basis of interfering signals necessary and no signal signature estimation or signal subspace separation procedure required in the proposed detection framework. Based on this model and detection framework, several optimal blind linear multiuser detectors are individually developed and analyzed with maximum likelihood (ML), MMSE and least squares (LS) criteria. In order to further reduce the complexity, some implementation considerations are outlined. In addition, I compared the proposed multiuser receivers with existing ones from several practical implementation prospects. For each of these blind linear multiuser receiver, I not only evaluate its link-level performance but also discuss how it behaves in a large-scale system. It shows that there is an additional noise enhancement in the proposed detection framework due to the limited number of previous knowledge but its computation complexity and detection delay is lower than most existing multiuser receivers. In a large-scale system with large spreading gain and high SINR, the asymptotic performance of the proposed blind multiuser receivers are close to the conventional ones. Due to the limited space, many detailed proofs of my conclusions are omitted in this blog.

REFERENCE
[1] J. G. Andrews. Interference cancellation for cellular systems: A contemporary overview. IEEE Wireless communications, pages 19–29, April 2005.
[2] S. Wang et al. Towards forward-link interference cancellation. In CDMA Development Group (CDG) Tehnical Forum, San Francisco, CA, April 2006.
[3] S. Verdu. Multiuser Detection. Cambridge University Press, 1998.
[4] U. Madhow. Blind adaptive interference suppression for direct-sequence cdma. Proceedings of the IEEE, 86(10):2049–2069, October 1998.
[5] M. Honig, U. Madhow and S. Verdu. Blind adaptive multiuser detection. IEEE Trans. on Information Theory, 41:944–960, July 1995.
[6] M. Torlak and G. Xu. Blind multiuser channel estimation in asynchronous cdma systems. IEEE Trans. on Signal Processing, 45:137–147, January 1997.
[7] X. Wang and H. V. Poor. Blind multiuser detection: A subspace approach. IEEE Trans. on Information Theory, 44:677-691, March 1998.
[8] X. Zhang and W. Wei. Blind adaptive multiuser detection based on kalman filtering. IEEE Transactions on Signal Processing.
[9] Schmidt R. Multiple emitter location and signal parameter estimation. IEEE Trans. on AP, 34(3):267–280, 1986.
[10] et al. A Hero, H. Messer. Highlights of statistical signal and array processing. IEEE Signal Processing Magazine, 15(5):21–64, September 1998.
[11] B. Yang. Projection approximation subspace tracking. IEEE Trans. on Signal Processing, 43:95–107, January 1995.
[12] D. N. C. Tse and S. V. Hanly. Linear multiuser receivers: Effective interference, effective bandwidth and user capacity. IEEE Trans. on Information Theory, 45:641–657, March 1999.

Wednesday, September 17, 2008

H.264 Network Abstract Layer Header

RTP Packetization for H.264 NAL Units
How to Broadcast Multimedia Contents?

H.264 encoder is composed of two layers, video coding layer (VCL) and network abstraction layer (NAL). VCL translates the video information into bits streams. Since the underlying transportation layers are diversified, NAL maps VCL bitstreams into byte-oriented transportation-layer-friendly and HDLC-like NAL units prior to delivery.

During the mapping of VCL bitstreams to NAL units, at least three operations are done, which are byte alignment, emulation prevention, framing with an additional one-byte NAL unit header. The first byte after a NAL unit start code prefix is the NAL unit header. A NAL unit header consists of  three fields.
  1. forbidden_bit (1 bit): may be used to indicate a NAL unit is corrupted or not.
  2. nal_storage_idc (2 bit): signals relative importance, and if the picture is stored in the reference picture buffer. 
  3. nal_unit_type (5 bit): signals 1 of 10 different NAL unit types. H.264 defines two additional ranges of NAL unit type values as “Reserved” for future definition of compatible extensions and  “Unspecified” for application specific decoders
The field after NAL unit header is NAL unit payload or RBSP payload, which contains either coded slice or additional header or control information, such as sequence or picture parameter set, supplemental enhancement information (SEI), coded data partition, picture delimiter, filter data, etc.

Monday, August 25, 2008

How to Broadcast Multimedia Contents? II Lessons from The Channel

What Is The Next for Mobile System Design? I A Single-Cell Model Perspective on Downlinks
[How to Broadcast Multimedia Contents? I Introduction]
[How to Broadcast Multimedia Contents? IV Hierarchical Modulation]
[How to Broadcast Multimedia Contents? V Overloaded Transmission and IC]
[How to Broadcast Multimedia Contents? VI Open-Loop MIMO for BCMCS]
[How to Broadcast Multimedia Contents? VII Network Layer or Stream Layer Design]

COST 231 model, which was developed by European COST Action 231, and its variations are the most popular radio propagation model adopted in various standardization bodies, such as 3GPP, 3GPP2 and IEEE. Its modifications include COST 231-Hata Model and COST 231-Walfisch-Ikegami Model. The mathematical formulation of the COST 231-Hata model path loss in dB is

PL = 46.3 + 33.9 logf - 13.82log hBS - a( hMS ) + [ 44.9-6.55loghBS] log d + C

which features a carrier frequency f between 800MHz and 2GHz, an above-neighborhood base station antenna with a height hBS of 30~300m and a mobile station with an antenna height hMS of 1~10m. One instance of this path loss model can be shown in Figure 1.

Figure 1. An example of COST 231-Hata urban propagation model.

With Figure 1, we can observe that there are at least two different regions along the channel. The small area close to the base station usually has a high achievable throughput, while the large area on the cell-edge has a low achievable throughput. There two kind of areas are separated in space domain. On the other hand, we alway expect to achieve higher achievable throughput with a good coverage for broadcasting multimedia contents.

Lesson I. Coverage and Throughput Dilemma

Figure 2. The tradeoffs inside the channel

The COST 231 channel model confirms us that there is a well-known trade-off between reception and coverage. In Figure 2, with a 300-meter transmitter antenna, it shows the path-loss changes 0.6560 dB at every 90% coverage change, 1.3894 dB at every 80% coverage change, 2.2209 dB at every 70% coverage change and 3.1807 dB at every 60% coverage change. In general, if you want more coverage, then you may lose some capacity on the cell-edge.  Otherwise, you have to shrink your coverage.

Figure 3. Spectral Efficiency and Coverage Tradeoff

Lesson 2. Gaussian Broadcast Channel and Superimposed Transmission

From Figure 2 and 3, it shows you can't get both coverage and cell-edge throughput at the same time. Though we can't break the trade-offs, is there any way to move the trade-off curves in Figure 3 upwards a little bit? Fortunately, information theory has told us there is another option which is worth thinking about. The idea is called superposition precoding or superimposed transmission. The existing of superposition precoding is mostly due to the nonlinearity of Shannon capacity curve. Superimposed transmission suggests splitting one data stream and simultaneously transmitting together instead of orthogonally transmitting the multiple streams, such as in a time-division multiplexing or frequency-division multiplexing fashion. With this way, a higher achievable capacity is possible. This means, if Signals can be sent from two layers, a base layer and a enhancement layer, in which the base layer has the best coverage and the enhancement layer provides additional throughput, the user capacity can increase about 60% with a 3.2 dB path-loss difference between the two layers. If the difference is 1.4dB, the user capacity can increase about 80%.

Figure 4. Capacity Improvement through SPC


Figure 5. User Capacity and Coverage Tradoff

Lesson 3. Fading Channel and Overloaded Transmission

Figure 6. Fading Channel Capacity

It is well-known that homogeneous fading has no effect on CDMA spectral efficiency. However, higher forward-link spectral efficiency is achievable with taking advantage of multiuser diversity. In fact, if an optimum receiver is used and the system loading β=K/N is sufficiently high, even randomly spread CDMA incurs negligible spectral efficiency loss relative to no-spreading. And when β=K/N increases to the infinity, the achievable capacity with and without fading grows to the same ultimate limit. This means, the fading effect vanishes even for the linear receivers, such as matched filter and the MMSE receiver.

Lesson 4. Open-Loop MIMO Channel Capacity

Consider a M-transmit-antenna and N-receive-antenna MIMO link. When both transmitter and receiver know the channel response, it is well-known that the achievable channel capacity C is proportional to min( M, N )log(SNR) + O( 1 ). If only receiver knows the channel response, the achievable channel capacity C is proportional to min( M, N )log(SNR) + O( 1 ). If neither receiver nor transmitter knows the channel response, the achievable channel capacity C is proportional to min( M, N )( 1 - min( M, N )/T ) log(SNR) + O( 1 ). Therefore, in higher SNR region, that transmitter knows the channel may not help much in terms of achievable channel capacity, even though with proper CSI feedback and MIMO precoding at Tx, the required Rx design can be simplified.

Lesson 5. Break The Dilemma with Relay
Figure 8. Extend coverag with relay

Friday, August 8, 2008

How to Broadcast Multimedia Contents? I Introduction

What Is The Next for Mobile System Design? I A Single-Cell Model Perspective on Downlinks
[How to Broadcast Multimedia Contents? II Lessons from The Channel]
[How to Broadcast Multimedia Contents? IV Hierarchical Modulation]
[How to Broadcast Multimedia Contents? V Overloaded Transmission and IC]
[How to Broadcast Multimedia Contents? VI Open-Loop MIMO for BCMCS]
[How to Broadcast Multimedia Contents? VII Network Layer or Steam Layer Design]

Broadcast multicast service (BCMCS) has increasingly been popular for delivering multimedia content to mobile users. Traditional digital broadcast air interfaces are designed with the tradeoff between maximum achievable rate and intended coverage in mind. The actual rates are usually limited by the maximum transmit power and the worst channel condition so that every user in coverage can reliably receive the services as well as contents of same quality. The users under good reception condition may have no advantage, even if their potential throughputs can be much higher. This happens often, especially on the mobile users whose reception conditions change all the time. And there are rising interests in upgrading existing digital broadcast systems with more services for new users and delivering more quality of service (QoS) options to users with advanced receivers while still guaranteeing existing users' services. Furthermore, recent advances in wideband speech coding, e.g., EVRC-WB, and scalable video coding, e.g., H.264/MPEG-4 AVC, suggest unequal error protection on content delieveries with providing graceful degradation of quality in the presence of increasing packet loss. It is possible for the users in good reception condition have more opportunities to enjoy high quality services while the user with low throughput can still decode the content of basic quality. Many technologies are under investigation for these goals, e.g., rateless coding, hierarchical modulation, multiple-input multiple-output (MIMO), selective retransmission and superposition precoding (SPC). Backward compatibility, implementation complexity and upgrading cost are among the major concerns in upgrading existing systems with additional services. Among those candidates, hierarchical modulation, also called layered modulation, is the most popular one, in which multiple data streams are multiplexed and modulated into one single symbol consisting of base-layer subsymbols and enhancement-layer subsymbols. It has been widely proven and included in various standards, such as DVB-T, MediaFLO, UMB (Ultra Mobile Broadband, a new 3.5th generation mobile network standard developed by 3GPP2), etc., and is under study for DVB-H.

Table 1. Mobile TV standards as of year 2008

Friday, June 20, 2008

Location Based Services for Mobiles II: GPS, Assisted GPS and Network Assisted GPS

[IEEE C802.16m-08/1105 Network Assisted GPS (N-GPS) Positioning in WiMAX/16m]
[Location Based Service for Mobiles I: Technologies and Standards]

GPS - Global Positioning System

GPS System
Figure 1. GPS System Archiecture: Space Segment, Control Segment and User Segment


GPS is a Global Navigation Satellite System for determining the positions of receivers using signals broadcast by satellites.  It was developed and operated by US government to enhance the effectiveness of allied and US military forces.  The first experimental Block-I GPS satellite was launched in 1978. Since 1983, GPS has become an aid to civilian navigation worldwide, and a useful tool for survey, commerce, and scientific uses.  As of September 2007, there are 31 actively broadcasting satellites in the GPS constellation.  Satellites orbit 20,163 kilometers above the earth at 3.87 km/s.  6 orbital planes, each with 4+ satellites. Typically 6 to 12 satellites are visible from any place on the earth.  GPS based positioning is playing a critical role in modern location based services.              
GPS
Figure 2. The Block Digrame of GPS Positioning


A GPS receiver measures approximate distance to 3 or more satellites. It measures the time required for signal to get from the satellite to the receiver. It then calculates the distance and obtains satellite positions from satellite broadcasts. Finally it calculates the position using trilateration. During the positioning, it corrects for errors to improve accuracy with  calibrating the clock bias or applying differential correction.  It also corrects deliberate noise, such as selective availability and caliberates variable ionospheric and tropospheric propagation delays. 
  1. All GPS satellites transmit on L1 and L2 frequencies.
    • Each satellite uses different ranging codes: C/A code and P-code
    • L1 band is for civilian use. 
  2. The C/A code (coarse/acquisition code) is modulated onto the L1 carrier only, while the P-code (precise code) is modulated onto both the L1 and L2 carriers. 
  3. The C/A code is less precise and less complex than the P-code and available to all users.
    • The P-code is intended for military uses and is added to both L1 and L2. 

GPS Message 
 Figure 3. GPS Frame Structure and Navigation Data

  • TLM – Telemetry: 30 bits, sent at the beginning of each frame.
    • It is used for data synchronization and satellite maintenance.
    • They are usually constant for any one satellite for a long period of time.
  • HOW – Handover Word: 30 bits, sent after TLM.
    • It indicates the time at the beginning of the next subframe.
    • It also contains a sub-frame ID, some flags and parity bits.
  • Ephemeris: It is sent in each frame by each satellite.
    • It may take the GPS receiver up to 30 seconds to acquire Ephemeris.
  • Almanac: It is spread out over all 25 frames of the message.
    • For receiving the complete Almanac, the GPS receiver may need about 12.5 minutes.

 GPS Error Sources
Figure 4. GPS Positioning Error Sources 

Assisted Global Positioning System

Assisted GPS (A-GPS) with assistance server were first come out by Bell Labs and later developed to enhance the positioning performance of a GPS receiver and satisfy US FCC's E911 mandate. 

AGPS
Figure 5. The Block Diagram of A-GPS System

GPS assistance server can increase the capability of a stand-alone GPS receiver.  It can roughly locate mobiles along by itself. It can supply more GPS orbital data to the mobile. It has better knowledge of atmosphere conditions and other errors as well as better augmentation capability. With the GPS assistance server, A-GPS helps improve positioning in terms of

  • Location accuracy: the positioning error.
  • Yield: the positioning success rate.
  • Time to fix: the time for positioning.
  • Battery consumption: power consumption for positioning.
  • Mobile cost
Network Assisted Global Positioning System






NGPS
Figure 6. The Concept of N-GPS
 
With N-GPS, key GPS assistance is provided through control plan instead of user plan. No additional data channel setup overhead required. No additional layer-3 authentication or access control required. No roaming issue. It is more reliable than layer-3 A-GPS approaches and more efficient since the assistance data is periodically broadcasted. It is fully compatiable with most existing A-GPS receivers.

NGPS2
Figure 7. An Application Scenario of N-GPS






Comparison
Table 1:  A Comparison fo GPS, N-GPS and A-GPS

Sunday, June 8, 2008

Location Based Services for Mobiles I: Technologies and Standards

[Tutorial in IEEE International Conference on Communications (ICC) 2008]
[Location Based Services for Mobiles II: GPS, Assisted GPS and Network Assisted GPS]
[How to Improve Forward Link Positioning ... ? I. Introduction]
[How to Improve Forward Link Positioning ... ? II. Hearability and Accuracy]
[1x HDP Enhancements]
[Enhanced Location Based Services Support in cdma2000]
[IEEE C802.16m-08/1106 Enhance Downlink Positioning in WiMAX/16m]
[IEEE C802.16m-08/1105 Network Assisted GPS (N-GPS) Positioning in WiMAX/16m]


Location based services (LBS) for mobile are the services supported by cellular networks for providing mobile users with various location sensitive applications such as E911, Friendfinder, personalized advertisement, etc. LBS accelerate the convergence of 3C (computer, communication and consumer electronics). One aspect of LBS market is the rapid growth of GPS market, which is predicted to reach $28.9 billion by 2010 by GPS World. It is believed that LBS is bringing huge revenue opportunities for wireless network operators and service providers. The driving force behind of the growth of LBS market includes regulator’s mandates, the development of more efficient location technologies and the expanding of LBS from network operator to third service provider.

In this tutorial, the state of art of mobile location based services (LBS) will be explored in terms of technologies, standards and implementations. There are five major parts in this proposed tutorial. Within the first part, an introduction to LBS is presented along with an overview of the growing LBS market. Two examples of LBS, E911 and telematics, are emphasized. In the second part, LBS from a network operator perspective is discussed with a survey of wireless location technologies, the exploration of location management in cellular network, and LBS standards activities. The architecture and operation of the network-dependent LBS control plane of cdma2000 and UMTS networks are reviewed, respectively. In the third part, the IP-based LBS user plane is discussed from a service provide perspective. An overview of the related standards by OMA and 3GPP2 is given and the principles of LBS user plane are illustrated from multiple application scenarios. Finally, the further works and standard activities for LBS are presented.

In summary, this tutorial is intended to provide a comprehensive overview of mobile LBS for a wide array of audiences, including LBS services providers, application developers, marketing managers and system researchers, etc. It includes not only the background information but also standards activities.