Optimized signaling for MIMO interference systems with feedback

By Sigen Ye, Rick S. Blum. IEEE Transactions on Signal Processing, 2003

338 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/4633e045b5fff7b9b1c18f375332461843cc6fac

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In Computer Networks and Communications

In Electrical and Electronic Engineering

Citations in the corpus

Full-duplex bidirectional MIMO: Achievable rates under limited dynamic range (2011, methodology): “We solve5 this non-convex optimization problem via Gradient Projection (GP), taking inspiration from [24].”
An Iteratively Weighted MMSE Approach to Distributed Sum-Utility Maximization for a MIMO Interfering Broadcast Channel (2011, methodology): “Reference [2] proposed an iterative algorithm based on the gradient projection method for the transmit covariance matrix design problem.”
Communication Over MIMO X Channels: Interference Alignment, Decomposition, and Performance Analysis (2008, methodology): “In [9], several numerical schemes are proposed to compute suboptimal transmit covariance matrices for the MIMO interference channels.”
Dynamic Resource Allocation in Cognitive Radio Networks (2010, methodology): “This decentralized approach has been first proposed in [31], [32] to obtain some local optimal points for (P5) with the PTPCs only, where they differ in that the one in [31] maximizes the user individual rate at each iteration, while the one in [32] maximizes the user weighted sum-rate.”
Decomposition by Partial Linearization: Parallel Optimization of Multi-Agent Systems (2013, neutral): “VIII draws some conclusions.”
On the degrees-of-freedom of the MIMO interference channel (2008, neutral): “For the MIMO interference channel, several papers discuss techniques for finding the optimal transmit covariance matrix assuming the interference is treated as noise [10]– [13].”
Convex Optimization in Signal Processing and Communications (2009, background): “Figure 1.3 provides some intuition for the role of convexity and the Slater condition in establishing no duality gap and the boundedness of the dual optimal solution set.”
Optimal Resource Allocation for MIMO Ad Hoc Cognitive Radio Networks (2008, background): “The second derivative of is (see also [30])”