Sponsored by the
IEEE Hong Kong Information Theory Chapter
| Session 1 | HKUST 7th Floor Council Chamber | |
| 10:30AM -- 11:00AM |
Complex Lattice Reduction Algorithm for Low-Complexity MIMO Detection |
GAN Ying Hung |
| 11:00AM -- 11:30AM |
Identity Based Group Signatures |
Yuen Tsz Hon |
| 11:30AM -- 12:00PM | Superposition Coding with Peak-Power Limitation |
Tong Jun |
| Lunch | HKUST G/F Chinese restaurant | |
| 12:00 PM - 1:30 PM | ||
| Session 2 | HKUST 7th Floor Council Chamber | |
| 1:30PM -- 2:00PM | Turbo Equalization Based on Vector Factor Graphs |
Qinghua Guo |
| 2:00PM -- 2:30PM | The Quadrangle-Inequality Dynamic-Programming Speedup is a Consequence of Totally Monotonicity |
ZHANG Yan |
| 2:30PM -- 3:00PM | Ho Siu Wai |
All talks will be given in the 7th floor council chamber at the Hong Kong University of Science and Technology. For information on how to get to HKUST please see this. Once at HKUST take lifts 13-15 to the 7th floor. Upon exiting the lifts make a right and you will be at the council chamber.
| Name | University | Status | |
| GAN Ying Hung | HKUST (EEE) | PhD | eegyh@ust.hk |
| CHAN Ho Yin, Herbert | HKUST(EE) | PhD | eechan@ust.hk |
| AU Kwok Shum, Edward | HKUST(EE) | PhD | eeedward@ust.hk |
| CHENG Sin Ying | HKUST(CS) | MPhil | vivying@cs.ust.hk |
| ZHANG Yan | HKUST(CS) | PhD | cszy@cs.ust.hk |
| ZHEN Zhou | HKUST(CS) | PhD | cszz@cs.ust.hk |
| WANG Yajun | HKUST(CS) | PhD | yalding@cs.ust.hk |
| ZHU Wenqi | HKUST(CS) | PhD | wqzhu@cs.ust.hk |
| HUANG Qiong | HKUST(CS) | PhD | bestivy@cs.ust.hk |
| XIA Jian | HKUST(CS) | PhD | piper@cs.ust.hk |
| Gerhard Trippen | HKUST(CS) | PhD | trippen@cs.ust.hk |
| Mordecai GOLIN | HKUST(CS) | Staff | golin@cs.ust.hk |
| MOW Wai Ho | HKUST(EE) | Staff | eewhmow@ust.hk |
| DING Cunsheng | HKUST(CS) | Staff | csding@cs.ust.hk |
| TONG Jun | CityU(EE) | PhD | jun.tong@student.cityu.edu.hk |
| Qinghua Guo | CityU(EE) | PhD | qh.guo@student.cityu.edu.hk |
| Peng Wang | CityU(EE) | PhD | pengwang@cityu.edu.hk |
| Yuan Xiaojun | CityU(EE) | PhD | xjyuan@cityu.edu.hk |
| LI Yueqian | CityU(EE) | MPhil | yueqian.li@student.cityu.edu.hk |
| Shuling Che | CityU(EE) | Staff | shlche@cityu.edu.hk |
| Wu Hao | CityU(EE) | MPhil | Jason.Wu@student.cityu.edu.hk |
| Ho Siu Wai | CUHK(IE) | PhD | swho4@ie.cuhk.edu.hk |
| YANG Shenghao | CUHK(IE) | PhD | shyang5@ie.cuhk.edu.hk |
| Tsz Hon YUEN | CUHK(IE) | MPhil | thyuen4@ie.cuhk.edu.hk |
| KWOK, Pui Wing | CUHK(IE) | MPhil | pwkwok4@ie.cuhk.edu.hk |
| Ngai Chi Kin | CUHK(IE) | PhD | ckngai2@ie.cuhk.edu.hk |
| Fong Lik Hang Silas | CUHK(IE) | MPhil | lhfong5@ie.cuhk.edu.hk |
| Ho Siu Ting | CUHK(IE) | PhD | stho3@ie.cuhk.edu.hk |
| Shen Yuxiu | CUHK(IE) | MPhil | yxshen5@ie.cuhk.edu.hk |
| Sun Qifu | CUHK(IE) | MPhil | qfsun5@ie.cuhk.edu.hk |
| Yuen Pak Ho | CUHK(IE) | MPhil | phyuen@cuhk.edu.hk |
| Raymond Yeung | CUHK(IE) | Staff | whyeung@ie.cuhk.edu.hk |
Registration will be $70HKper student and $100HK per non-student to be paid on site. The registration fee includes lunch
Title:
Complex Lattice Reduction Algorithm for Low-Complexity MIMO Detection
Speaker: GAN Ying Hung
HKUST
Abstract:
Recently, lattice-reduction-aided detectors have been proposed for
multiple-input multiple-output (MIMO) communication systems to give performance
with full diversity like maximum likelihood optimal receiver yet with complexity
similar to linear receiver. However, these lattice-reduction-aided detectors are
based on the traditional LLL reduction algorithm that was originally introduced
for reducing real lattice basis, even though the channel matrices are inherently
complex-valued.
In this talk, we will introduce the complex LLL algorithm for direct application
to the channel matrix which naturally defines the basis of a complex lattice.
Simulation results reveal that the new complex LLL algorithm can achieve a
saving in complexity of nearly 50% over the traditional LLL algorithm, when
applied to MIMO detection. In addition, we shall present a novel technique
called the "joint basis labeling and reduction" which, by exploiting the degree
of freedom of labeling the basis vectors, can further accelerate the LLL
reduction algorithm. It is noteworthy that the complex LLL algorithms
aforementioned incur negligible bit-error-rate performance loss relative to the
traditional LLL algorithm
Title: Identity Based Group
Signatures
Speaker: Yuen Tsz Hon
CUHK
Abstract:
We present the first group signature scheme with provable security and short
signature size where the group manager, the group members, and the Open
Authority are all identity-based. We use the security model of Bellare, Shi, and
Zhang, except to add three identity managers for manager, members, and OA
respectively, and we discard the Open Oracle. Our construction uses
identity-based signatures summarized in Bellare, Namprempre, and Neven for
manager, Boneh and Franklin's IBE for OA, and we extend Bellare et al.'s group
signature construction by verifiably encrypt an image of the member public key,
instead of the public key itself.
Title:
Superposition Coding with Peak-Power Limitation
Speaker: Tong Jun
CityU
Abstract:
In this work, we apply clipping to superposition coding systems to reduce the
peak-to-average power ratio (PAPR) of the transmitted signal. The performance
limit is investigated through evaluating the mutual information driven by the
induced peak-power-limited input signals. It is shown that the channel capacity
can be approached by clipped superposition coding systems. To alleviate the
performance degradation due to clipping noises, we develop a soft compensation
algorithm that is combined with soft-input-soft-output (SISO) decoding
algorithms in an iterative manner. Simulation results show that with the
proposed algorithm, most performance loss can be recovered.
Title: Turbo
Equalization Based on Vector Factor Graphs
Speaker: Qinghua Guo
CityU
Abstract:
A factor graph approach to turbo equalization is proposed. Unlike the existing
linear MMSE turbo equalization methods, which operate with truncated windows
(sliding or extending window), the proposed is a full-window approach with low
complexity. This approach supports a high-speed parallel implementation
technique, which makes it an attractive option in practice.
Title:
The Quadrangle-Inequality Dynamic-Programming Speedup is a Consequence of
Totally Monotonicity
Speaker: ZHANG Yan
HKUST
Abstract:
There exist several general techniques in the literature for speeding up naive
implementations of dynamic programming. Two of the best known are the Knuth-Yao
quadrangle inequality speedup and the SMAWK algorithm for finding the row-minima
of totally monotone matrices. Although both of these techniques use a quadrangle
inequality and seem similar they are actually quite different and have been used
differently in the literature.
In this talk we show that the Knuth-Yao technique is actually a direct
consequence of total monotonicity. As well as providing new derivations of the
Knuth-Yao result, this also permits showing how to solve the Knuth-Yao problem
directly using the SMAWK algorithm. Another consequence of this approach is a
method for solving online versions of problems with the Knuth-Yao property. The
online algorithms given here are symptotically as fast as the best previously
known static ones.
This is joint work with Wolf Bein, Mordecai Golin and Larry Larmore
Title:
On the Discontinuity of the Shannon Information Measures
Speaker: Ho Siu Wai
CUHK
Abstract:
It is well known that the Shannon information measures are continuous functions
of the probability distribution when the support is finite. This, however, does
not hold when the support is countably infinite. In this work, we investigate
the continuity of the Shannon information measures for countably infinite
support. With respect to some commonly used divergence measures including the
Kullback-Liebler divergence and the variational distance, we use two different
approaches to show that all the Shannon information measures are in fact
discontinuous at all probability distributions with countably infinite support.
For probability distributions with finite alphabet, some bounds are given to
relate their support size and the difference of their entropy. These bounds show
the limitation of certain algorithms of entropy estimation.
Page maintained by Mordecai
Golin.
Last updated
11/25/2005 02:41 PM +0800