Transmit MIMO Radar Beampattern Design via Optimization on the Complex Circle Manifold

Alhujaili, Khaled; Monga, Vishal; Rangaswamy, Muralidhar

doi:10.1109/tsp.2019.2914884

Cited by 116 publications

(80 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this subsection, we adopt the CCM method proposed in [35] for directly solving Problem (35). We first transform Problem (35) into the following equivalent problem…”

Section: ) Complex Circle Manifold (Ccm) Methodmentioning

confidence: 99%

“…then the sequence of the solutions obtained in each iteration will result in a monotonically decreasing OF {f (ϕ t ), t = 1, 2, • • • } and finally converge. The converged solution satisfies the Karush-Kuhn-Tucker (KKT) optimality conditions of Problem (35) [44]. The first two conditions represent that the OF g(ϕ|ϕ t ) introduced and its first-order gradient should be the same as the original OF and its first-order gradient at point ϕ t .…”

Section: ) Majorization-minimization (Mm) Algorithmmentioning

confidence: 99%

“…We adopt the MM algorithm [34] to solve Problem (35), which was originally introduced in [40]. Then, this method has been widely in resource allocation for wireless communication networks [41]- [43].…”

Section: ) Majorization-minimization (Mm) Algorithmmentioning

confidence: 99%

“…The main idea is to solve a difficult problem by constructing a series of more tractable approximate subproblems. Specifically, let us denote the solution of the subproblem at the tth iteration by ϕ t , and the OF value of Problem (35) at the tth iteration by f (ϕ t ). Then, at the (t+1)st iteration, we have to introduce an upper bound 2 of the OF function based on the previous solution, which is denoted as g(ϕ|ϕ t ).…”

Section: ) Majorization-minimization (Mm) Algorithmmentioning

confidence: 99%

“…The first one is the Majorization-Minimization (MM) Algorithm [34], where a closed-form solution can be obtained in each iteration. The second is based on the Complex Circle Manifold (CCM) Method [35], where we show that the unit modulus constraints of all phase shifters constitute a complex circle manifold. Both the MM algorithm and the CCM algorithm are guaranteed to obtain at least a locally optimal solution.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Multicell MIMO Communications Relying on Intelligent Reflecting Surfaces

Pan

Ren

Wang

et al. 2020

IEEE Trans. Wireless Commun.

945

604

View full text Add to dashboard Cite

Intelligent reflecting surfaces (IRSs) constitute a disruptive wireless communication technique capable of creating a controllable propagation environment. In this paper, we propose to invoke an IRS at the cell boundary of multiple cells to assist the downlink transmission to cell-edge users, whilst mitigating the inter-cell interference, which is a crucial issue in multicell communication systems. We aim for maximizing the weighted sum rate (WSR) of all users through jointly optimizing the active precoding matrices at the base stations (BSs) and the phase shifts at the IRS subject to each BS's power constraint and unit modulus constraint. Both the BSs and the users are equipped with multiple antennas, which enhances the spectral efficiency by exploiting the spatial multiplexing gain. Due to the nonconvexity of the problem, we first reformulate it into an equivalent one, which is solved by using the block coordinate descent (BCD) algorithm, where the precoding matrices and phase shifts are alternately optimized. The optimal precoding matrices can be obtained in closed form, when fixing the phase shifts. A pair of efficient algorithms are proposed for solving the phase shift optimization problem, namely the Majorization-Minimization (MM) Algorithm and the Complex Circle Manifold (CCM) Method. Both algorithms are guaranteed to converge to at least locally optimal solutions. We also extend the proposed algorithms to the more general multiple-IRS and network MIMO scenarios. Finally, our simulation results confirm the advantages of introducing IRSs in enhancing the cell-edge user performance.

show abstract

“…In this subsection, we adopt the CCM method proposed in [35] for directly solving Problem (35). We first transform Problem (35) into the following equivalent problem…”

Section: ) Complex Circle Manifold (Ccm) Methodmentioning

confidence: 99%

Section: ) Majorization-minimization (Mm) Algorithmmentioning

confidence: 99%