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77
Joint Base Station Clustering and Beamformer Design for Partial Coordinated Transmission in Heterogeneous Networks
, 2012
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Decomposition by partial linearization: Parallel optimization of multiuser systems
 IEEE Trans. on Signal Processing
, 2014
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Joint linear precoder optimization and base station selection for an uplink MIMO network: A game theoretic approach
 in the Proceedings of the IEEE ICASSP
, 2012
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The practical challenges of interference alignment
 IEEE Wireless Communications
, 2013
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Fast Converging Algorithm for Weighted Sum Rate Maximization
 in Multicell MISO Downlink,h IEEE Signal Process. Letters
, 2012
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Joint beamforming and power control in coordinated multicell: Maxmin duality, effective network and large system transition
 IEEE TRANS. WIRELESS COMMUN
, 2013
"... 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 signaltointerferenceplusnoise ratio. The optimal solution and distributed algorithm with geometrically fast convergence rate are ..."
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Cited by 8 (1 self)
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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 signaltointerferenceplusnoise ratio. The optimal solution and distributed algorithm with geometrically fast convergence rate are derived by employing the nonlinear PerronFrobenius 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 PerronFrobenius theory and random matrix theory, an effective primal network and an effective dual network are proposed to characterize and interpret the asymptotic solution.
Joint power and antenna selection optimization in large distributed MIMO networks
, 2013
"... Large multipleinput multipleoutput (MIMO) networks promise high energy efficiency, i.e., much less power is required to achieve the same capacity compared to the conventional MIMO networks if perfect channel state information (CSI) is available at the transmitter. However, in such networks, huge o ..."
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Cited by 7 (0 self)
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Large multipleinput multipleoutput (MIMO) networks promise high energy efficiency, i.e., much less power is required to achieve the same capacity compared to the conventional MIMO networks if perfect channel state information (CSI) is available at the transmitter. However, in such networks, huge overhead is required to obtain full CSI especially for FrequencyDivision Duplex (FDD) systems. To reduce overhead, we propose a downlink antenna selection scheme, which selects S antennas from M> S transmit antennas based on the large scale fading to serve K ≤ S users in large distributed MIMO networks employing regularized zeroforcing (RZF) precoding. In particular, we study the joint optimization of antenna selection, regularization factor, and power allocation to maximize the average weighted sumrate. This is a mixed combinatorial and nonconvex problem whose objective and constraints have no closedform expressions. We apply random matrix theory to derive asymptotically accurate expressions for the objective and constraints. As such, the joint optimization problem is decomposed into subproblems, each of which is solved by an efficient algorithm. In addition, we derive structural solutions for some special cases and show that the capacity of very large distributed MIMO networks scales as O (KlogM) when M → ∞ with K,S fixed. Simulations show that the proposed scheme achieves significant performance gain over various baselines.
Transmit Optimization with Improper Gaussian Signaling for Interference Channels
 IEEE TRANSACTIONS ON SIGNAL PROCESSING, ACCEPTED
, 2013
"... This paper studies the achievable rates of Gaussian interference channels with additive white Gaussian noise (AWGN), when improper or circularly asymmetric complex Gaussian signaling is applied. For the Gaussian multipleinput multipleoutput interference channel (MIMOIC) with the interference tre ..."
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Cited by 6 (2 self)
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This paper studies the achievable rates of Gaussian interference channels with additive white Gaussian noise (AWGN), when improper or circularly asymmetric complex Gaussian signaling is applied. For the Gaussian multipleinput multipleoutput interference channel (MIMOIC) with the interference treated as Gaussian noise, we show that the user’s achievable rate can be expressed as a summation of the rate achievable by the conventional proper or circularly symmetric complex Gaussian signaling in terms of the users ’ transmit covariance matrices, and an additional term, which is a function of both the users ’ transmit covariance and pseudocovariance matrices. The additional degrees of freedom in the pseudocovariance matrix, which is conventionally set to be zero for the case of proper Gaussian signaling, provide an opportunity to further improve the achievable rates of Gaussian MIMOICs by employing improper Gaussian signaling. To this end, this paper proposes widely linear precoding, which efficiently maps proper informationbearing signals to improper transmitted signals at each transmitter for any given pair of transmit covariance and pseudocovariance matrices. In particular, for the case of twouser Gaussian singleinput singleoutput interference channel (SISOIC), we propose a joint covariance and pseudocovariance optimization algorithm with improper Gaussian signaling to achieve the Paretooptimal rates. By utilizing the separable structure of the achievable rate expression, an alternative algorithm with separate covariance and pseudocovariance optimization is also proposed, which guarantees the rate improvement over conventional proper Gaussian signaling.
Decomposition by successive convex approximation: A unifying approach for linear transceiver design in heterogeneous networks
, 2014
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Optimized transmission with improper Gaussian signaling in the Kuser MISO interference channel
 IEEE Trans. Wireless Commun
, 2013
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