Optimization of MIMO Device-to-Device Networks via Matrix Fractional Programming: A Minorization–Maximization Approach

Abstract

Interference management is a fundamental issue in device-to-device (D2D) communications whenever the transmitter-and-receiver pairs are located in close proximity and frequencies are fully reused, so active links may severely interfere with each other. This paper devises an optimization strategy named FPLinQ to coordinate the link scheduling decisions among the interfering links, along with power control and beamforming.The key enabler is a novel optimization method called matrix fractional programming (FP) that generalizes previous scalar and vector forms of FP in allowing multiple data streams per link. From a theoretical perspective, this paper provides a deeper understanding of FP by showing a connection to the minorizationmaximization (MM) algorithm. From an application perspective, this paper shows that as compared to the existing methods for coordinating scheduling in the D2D network, such as FlashLinQ, ITLinQ, and ITLinQ+, the proposed FPLinQ approach is more general in allowing multiple antennas at both the transmitters and the receivers, and further in allowing arbitrary and multiple possible associations between the devices via matching. Numerical results show that FPLinQ significantly outperforms the previous state-of-the-art in a typical D2D communication environment.Index Terms-Device-to-device (D2D) network, link scheduling, power control, beamforming, matrix fractional programming (FP), minorization-maximization (MM) algorithm.

Inter-Tenant Cooperative Reception for C-RAN Systems With Spectrum Pooling

“…it can be seen that the inequalities above are in fact equivalent to (13b), (13e), and (13f) [9], [10].…”

“…The problem (13) is non-convex due to the constraints (13b)-(13f). To find an efficient solution, we adopt the matrix FP approach proposed in [9]. Specifically, by using the results of [9, Prop.…”

“…In the optimization, we impose constraints on transmit power, fronthaul and backhaul capacity, as well as on the amount of information leaked on backhaul links, since exchange of information on backhaul links may cause privacy loss among operators. To find an effective, but suboptimal, solution, we propose an iterative algorithm based on the matrix fractional programming (FP) approach [9]. We observe that the tools used here are hence different from [8], which relied on successive convex approximation.…”

Mixed-Timescale Beamforming and Power Splitting for Massive MIMO Aided SWIPT IoT Network

“…Proof: Specifically, the proposed FP-BCD algorithm falls in the MM framework and similar proof is provided in [13]. From Theorem 4.4 in [11], every limiting point F N of sequence generated by the short-term FP-BCD algorithm is a stationary point of problem P 3 ρ, v,Ĥ , where problem P 3 ρ, v,Ĥ is the sample average approximation of problem P 2 ρ, v,Ĥ with N samples.…”

“…According to equation (15) in Theorem 3, it implies that the short-term solution F N (i) found by Algorithm 2 must satisfy the stationary condition approximately with certain error e (N ) that converges to zero exponentially as N → ∞. Moreover, the limiting point (v * , ρ * ) generated by Algorithm 1 also satisfies the stationary conditions in (13) and (14), respectively. Thus, Algorithm 1 converges to stationary solutions of the mixedtimescale optimization problem P. Note that since e (N ) converges to zero exponentially, Algorithm 2 with a small N can already achieve a good performance and avoids excessive computational complexity.…”