Elements of information theory
Elements of information theory
Big Omicron and big Omega and big Theta
ACM SIGACT News
Optimal throughput-delay scaling in wireless networks: part I: the fluid model
IEEE/ACM Transactions on Networking (TON) - Special issue on networking and information theory
Communication over fading channels with delay constraints
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
Writing on dirty paper (Corresp.)
IEEE Transactions on Information Theory
On the achievable throughput of a multiantenna Gaussian broadcast channel
IEEE Transactions on Information Theory
Duality, achievable rates, and sum-rate capacity of Gaussian MIMO broadcast channels
IEEE Transactions on Information Theory
On the capacity of MIMO broadcast channels with partial side information
IEEE Transactions on Information Theory
The Capacity Region of the Gaussian Multiple-Input Multiple-Output Broadcast Channel
IEEE Transactions on Information Theory
On the Optimal Number of Active Receivers in Fading Broadcast Channels
IEEE Transactions on Information Theory
Providing quality of service over a shared wireless link
IEEE Communications Magazine
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A Multi-Input Multi-Output (MIMO) broadcast channel with large number (n) of users is considered. It is assumed each user either receives the minimum rate constraint of R"m"i"n or remains silent. Accordingly, for the case of random beamforming, an user selection strategy together with a proper power allocation method is proposed, showing the maximum number of active users scales as Mlog(log(n))R"m"i"n-@q(1) in the asymptotic case of n-~, where M represents the number of transmit antennas. Noting the asymptotic sum-rate capacity of such channel is Mlog(log(n)), the proposed method is able to approach the asymptotic sum-rate capacity within a constant gap. Moreover, it is shown the expected delay of this fair power allocation strategy behaves like R"m"i"nMnlog(n)log(log(n))-@q(1)+@wnlog(log(n)), where the expected delay is defined as the minimum number of channel uses to make sure each user receives at least one packet. Accordingly, it is proved that for sufficiently large (k) number of channel uses, the average number of services received by a randomly selected user scales as kMlog(log(n))nR"m"i"n1+Olog(k)k.