4,454 research outputs found
Joint Beamforming and Power Control in Coordinated Multicell: Max-Min Duality, Effective Network and Large System Transition
This paper studies joint beamforming and power control in a coordinated
multicell downlink system that serves multiple users per cell to maximize the
minimum weighted signal-to-interference-plus-noise ratio. The optimal solution
and distributed algorithm with geometrically fast convergence rate are derived
by employing the nonlinear Perron-Frobenius theory and the multicell network
duality. The iterative algorithm, though operating in a distributed manner,
still requires instantaneous power update within the coordinated cluster
through the backhaul. The backhaul information exchange and message passing may
become prohibitive with increasing number of transmit antennas and increasing
number of users. In order to derive asymptotically optimal solution, random
matrix theory is leveraged to design a distributed algorithm that only requires
statistical information. The advantage of our approach is that there is no
instantaneous power update through backhaul. Moreover, by using nonlinear
Perron-Frobenius theory and random matrix theory, an effective primal network
and an effective dual network are proposed to characterize and interpret the
asymptotic solution.Comment: Some typos in the version publised in the IEEE Transactions on
Wireless Communications are correcte
On the capacity of MIMO broadcast channels with partial side information
In multiple-antenna broadcast channels, unlike point-to-point multiple-antenna channels, the multiuser capacity depends heavily on whether the transmitter knows the channel coefficients to each user. For instance, in a Gaussian broadcast channel with M transmit antennas and n single-antenna users, the sum rate capacity scales like Mloglogn for large n if perfect channel state information (CSI) is available at the transmitter, yet only logarithmically with M if it is not. In systems with large n, obtaining full CSI from all users may not be feasible. Since lack of CSI does not lead to multiuser gains, it is therefore of interest to investigate transmission schemes that employ only partial CSI. We propose a scheme that constructs M random beams and that transmits information to the users with the highest signal-to-noise-plus-interference ratios (SINRs), which can be made available to the transmitter with very little feedback. For fixed M and n increasing, the throughput of our scheme scales as MloglognN, where N is the number of receive antennas of each user. This is precisely the same scaling obtained with perfect CSI using dirty paper coding. We furthermore show that a linear increase in throughput with M can be obtained provided that M does not not grow faster than logn. We also study the fairness of our scheduling in a heterogeneous network and show that, when M is large enough, the system becomes interference dominated and the probability of transmitting to any user converges to 1/n, irrespective of its path loss. In fact, using M=αlogn transmit antennas emerges as a desirable operating point, both in terms of providing linear scaling of the throughput with M as well as in guaranteeing fairness
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