Robust Transceiver Optimization in Downlink Multiuser MIMO Systems

Vučić, Nikola; Boche, Holger; Shi, Shuhui

doi:10.1109/tsp.2009.2020030

Cited by 132 publications

(88 citation statements)

References 39 publications

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“…There are statistical approaches which assume the channel to be random but according to a certain statistic that is known. For example heuristics are developed for the multi-antenna downlink scenario from a signal processing point of view in [64,65]. These approaches are developed for conventional cellular systems, and it would be interesting for future work to analyze if these approaches can be extended to the case with unknown interference from other coexisting wireless networks.…”

Section: Discussionmentioning

confidence: 99%

Impact of Interference in Coexisting Wireless Networks with Applications to Arbitrarily Varying Bidirectional Broadcast Channels

Wyrembelski

Boche

2012

Entropy

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The paradigm shift from an exclusive allocation of frequency bands, one for each system, to a shared use of frequencies comes along with the need of new concepts since interference will be an ubiquitous phenomenon. In this paper, we use the concept of arbitrarily varying channels to model the impact of unknown interference caused by coexisting wireless systems which operate on the same frequencies. Within this framework, capacity can be zero if pre-specified encoders and decoders are used. This necessitates the use of more sophisticated coordination schemes where the choice of encoders and decoders is additionally coordinated based on common randomness. As an application we study the arbitrarily varying bidirectional broadcast channel and derive the capacity regions for different coordination strategies. This problem is motivated by decode-and-forward bidirectional or two-way relaying, where a relay establishes a bidirectional communication between two other nodes while sharing the resources with other coexisting wireless networks.Keywords: unknown interference; arbitrarily varying channel; bidirectional relaying; bidirectional broadcast channel; input and state constraints; capacity region; coexisting networks Entropy 2012, 14 NotationIn this paper we denote discrete random variables by non-italic capital letters and their corresponding realizations and ranges by lower case italic letters and script letters, e.g., X, x, and X , respectively; the notation X n stands for the sequence X 1 , X 2 , ..., X n of length n; N and R + denote the set of positive integers and non-negative real numbers; all logarithms, exponentials, and information quantities are taken to the basis 2; I(·; ·), H(·), and D(·∥·) are the mutual information, entropy, and Kullback-Leibler (information) divergence; E[·] and P{·} denote the expectation and probability; ⟨·, ·⟩ is the inner product and | · | + = max{·, 0}; P(·) is the set of all probability distributions and (·) c is the complement of a set; W ⊗n is the n-th memoryless extension of the stochastic matrix W ; lhs := rhs means the value of the right hand side (rhs) is assigned to the left hand side (lhs); lhs =: rhs is defined accordingly.

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Section: Discussionmentioning

confidence: 99%

Impact of Interference in Coexisting Wireless Networks with Applications to Arbitrarily Varying Bidirectional Broadcast Channels

Wyrembelski

Boche

2012

Entropy

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“…Extending our proposed techniques to design such a module is an interesting open problem. Finally, developing robust versions of the results developed in this paper, by adopting a bounded CSI error model (as in [8], [9]) is also an interesting problem. While such an extension is not difficult for the continuous codebook case, its discrete counterpart seems challenging since the submodularity property may no longer hold for the worst-case (over all error realizations) per-user rate.…”

Section: Discussionmentioning

confidence: 99%

“…For example, one approach is to mimic the naive zero-forcing based precoding design for multiuser MIMO and let the BS design the precoders after assuming the channel estimates available to it to be perfect. Another more sophisticated approach is also possible by explicitly modeling the CSI errors; see for instance [8], [9].…”

Section: System Model and Problem Statementmentioning

confidence: 99%

Precoder Design for Physical Layer Multicasting

Zhu

Prasad²,

Rangarajan³

2012

IEEE Trans. Signal Process.

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This paper studies the instantaneous rate maximization and the weighted sum delay minimization problems over a K-user multicast channel, where multiple antennas are available at the transmitter as well as at all the receivers. Motivated by the degree of freedom optimality and the simplicity offered by linear precoding schemes, we consider the design of linear precoders using the aforementioned two criteria.We first consider the scenario wherein the linear precoder can be any complex-valued matrix subject to rank and power constraints. We propose cyclic alternating ascent based precoder design algorithms and establish their convergence to respective stationary points. Simulation results reveal that our proposed algorithms considerably outperform known competing solutions. We then consider a scenario in which the linear precoder can be formed by selecting and concatenating precoders from a given finite codebook of precoding matrices, subject to rank and power constraints. We show that under this scenario, the instantaneous rate maximization problem is equivalent to a robust submodular maximization problem which is strongly NP hard. We propose a deterministic approximation algorithm and show that it yields a bicriteria approximation. For the weighted sum delay minimization problem we propose a simple deterministic greedy algorithm, which at each step entails approximately maximizing a submodular set function subject to multiple knapsack constraints, and establish its performance guarantee.

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“…However, perfect CSI is usually unavailable in practical systems due to many practical factors, such as inaccurate channel estimation, quantization of CSI, erroneous or outdated feedback, and time delays or frequency offsets between the reciprocal channels. For these reasons, robust linear and nonlinear beamforming (precoding) designs to combat against channel uncertainty have received intensive research interests for different multi-user multiple-input multiple-output (MIMO) (single-/multi-cell downlink or relay) systems [8][9][10][11][12][13][14][15][16][17][18][19]. Generally, robust beamforming is addressed by either a stochastic or a worst-case approach.…”

Section: Introductionmentioning

confidence: 99%

“…In this case, the robustness is achieved by optimizing the system under the worst-case channel condition and it usually leads to a min-max or max-min problem formulation. With this approach, robust linear beamforming designs were developed for max-min SINR [11][12][13][14][15][16] or WSR maximization [17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Robust beamforming designs for multiuser MISO downlink with per-antenna power constraints

Wang

Huang

Wang

et al. 2015

J Wireless Com Network

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This paper studies the robust beamforming designs for a multiuser multiple-input single-output (MISO) downlink system. Different from the conventional sum-power constraint across all transmit antennas, we consider individual power constraints per antenna at the base station. Assuming that the channel uncertainty is bounded by a spherical region, we develop the optimal robust designs to maximize the minimum worst-case signal-to-interference-plus-noise ratio (SINR) among all users. Specifically, we show that the optimal max-min SINR beamformers can be obtained by solving a sequence of "dual" min-max power problems. Relying on the S-procedure and the linear matrix inequality representation for the cone of Lorentz-positive maps (LPMs), respectively, two designs, which are referred to as Robust-SP and Robust-LPM, are proposed to efficiently solve such min-max power problems. Building on either Robust-SP or Robust LPM, a bisection search algorithm is then developed to find the robust max-min SINR beamformers with guaranteed global optimality and geometrically fast convergence speed. Using the max-min SINR solutions as a cornerstone, we further put forth the optimal robust design for the worst-case weighted sum-rate (WSR) maximization. By formulating the worst-case WSR maximization problem into a monotonic program (MP), we develop a polyblock outer approximation algorithm to obtain the globally optimal solution. Numerical results are presented to demonstrate the merits of the proposed robust beamforming designs.

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Robust Transceiver Optimization in Downlink Multiuser MIMO Systems

Cited by 132 publications

References 39 publications

Impact of Interference in Coexisting Wireless Networks with Applications to Arbitrarily Varying Bidirectional Broadcast Channels

Impact of Interference in Coexisting Wireless Networks with Applications to Arbitrarily Varying Bidirectional Broadcast Channels

Precoder Design for Physical Layer Multicasting

Robust beamforming designs for multiuser MISO downlink with per-antenna power constraints

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