On the Capacity of Wireless Multicast Networks

The problem of maximizing the average rate in a multicast network subject to a coverage constraint (minimum quality of service) is studied. Assuming the channel state information is available only at the receiver side and single antenna nodes, the highest expected rate achievable by a random user in the network, called expected typical rate, is derived in two scenarios: hard coverage constraint and soft coverage constraint. In the first case, the coverage is expressed in terms of the outage probability, while in the second case, the expected rate should satisfy certain minimum requirement. It is shown that the optimum solution in both cases (achieving the highest expected typical rate for given coverage requirements) is achieved by an infinite layer superposition code for which the optimum power allocation among the different layers is derived. For the MISO case, a suboptimal coding scheme is proposed, which is shown to be asymptotically optimal, when the number of transmit antennas grows at least logarithmically with the number of users in the network.

[1]  Roy D. Yates,et al.  Service outage based power and rate allocation for parallel fading channels , 2003, IEEE Transactions on Information Theory.

[2]  Zhi-Quan Luo,et al.  Capacity Limits of Multiple Antenna Multicast , 2006, 2006 IEEE International Symposium on Information Theory.

[3]  E. Erkip,et al.  Source and Channel Coding for Quasi-Static Fading Channels , 2005, Conference Record of the Thirty-Ninth Asilomar Conference onSignals, Systems and Computers, 2005..

[4]  Bruno O. Shubert,et al.  Random variables and stochastic processes , 1979 .

[5]  Shlomo Shamai,et al.  Fading Channels: Information-Theoretic and Communication Aspects , 1998, IEEE Trans. Inf. Theory.

[6]  Giuseppe Caire,et al.  Lossy transmission over slow-fading AWGN channels: a comparison of progressive and superposition and hybrid approaches , 2005, Proceedings. International Symposium on Information Theory, 2005. ISIT 2005..

[7]  Aarnout Brombacher,et al.  Probability... , 2009, Qual. Reliab. Eng. Int..

[8]  Hamid Jafarkhani,et al.  Transmission Over Slowly Fading Channels Using Unreliable Quantized Feedback , 2007, 2007 Data Compression Conference (DCC'07).

[9]  A. Bayesteh,et al.  Multilevel coding strategy for two-hop single-user networks , 2008, 2008 24th Biennial Symposium on Communications.

[10]  Babak Hassibi,et al.  On the capacity of MIMO broadcast channel with partial side information , 2003, The Thrity-Seventh Asilomar Conference on Signals, Systems & Computers, 2003.

[11]  Stefania Sesia.  Broadcasting of a Common Source: Information-Theoretic Results and System Challenges , 2003 .

[12]  Shlomo Shamai,et al.  A broadcast strategy for the Gaussian slowly fading channel , 1997, Proceedings of IEEE International Symposium on Information Theory.

[13]  Andrea J. Goldsmith,et al.  Capacity of Fading Broadcast Channels with Rate Constraints , 2004 .

[14]  Youjian Liu,et al.  Optimal rate allocation for superposition coding in quasi-static fading channels , 2002, Proceedings IEEE International Symposium on Information Theory,.

[15]  Babak Hassibi,et al.  On the capacity of MIMO broadcast channels with partial side information , 2005, IEEE Transactions on Information Theory.

[16]  Shlomo Shamai,et al.  Multi-Layer Broadcasting over a Block Fading MIMO Channel , 2007, IEEE Transactions on Wireless Communications.

[17]  Shlomo Shamai,et al.  The capacity region of the Gaussian MIMO broadcast channel , 2004, International Symposium onInformation Theory, 2004. ISIT 2004. Proceedings..

[18]  John G. Proakis,et al.  Probability, random variables and stochastic processes , 1985, IEEE Trans. Acoust. Speech Signal Process..

[19]  William Equitz,et al.  Successive refinement of information , 1991, IEEE Trans. Inf. Theory.

[20]  Shlomo Shamai,et al.  A broadcast approach for a single-user slowly fading MIMO channel , 2003, IEEE Trans. Inf. Theory.

[21]  M. Feder,et al.  Static broadcasting , 2000, 2000 IEEE International Symposium on Information Theory (Cat. No.00CH37060).

[22]  Thomas M. Cover,et al.  Elements of Information Theory , 2005 .

[23]  Thomas M. Cover,et al.  Cooperative broadcasting , 1974, IEEE Trans. Inf. Theory.

[24]  Andrea J. Goldsmith,et al.  Capacity and optimal power allocation for fading broadcast channels with minimum rates , 2003, IEEE Trans. Inf. Theory.

[25]  Andrea J. Goldsmith,et al.  Minimum Expected Distortion in Gaussian Layered Broadcast Coding with Successive Refinement , 2007, 2007 IEEE International Symposium on Information Theory.

[26]  Mikael Skoglund,et al.  On the Expected Rate of Slowly Fading Channels With Quantized Side Information , 2007, IEEE Transactions on Communications.

[27]  S. Brendle,et al.  Calculus of Variations , 1927, Nature.

[28]  Andrea J. Goldsmith,et al.  Capacity and optimal power allocation for fading broadcast channels with minimum rates , 2001, GLOBECOM'01. IEEE Global Telecommunications Conference (Cat. No.01CH37270).

[29]  J. Nicholas Laneman,et al.  Characterizing Source-Channel Diversity Approaches Beyond the Distortion Exponent ∗ , 2005 .

[30]  S. Shamai,et al.  Multi-Layer Broadcast Hybrid-ARQ Strategies , 2008, 2008 IEEE International Zurich Seminar on Communications.

[31]  Shlomo Shamai,et al.  Achievable Rates with Imperfect Transmitter Side Information Using a Broadcast Transmission Strategy , 2008, IEEE Transactions on Wireless Communications.