Robust Joint Transmit Beamforming With QoS Guarantees in Time-Asynchronous DAS

Yan, Jiong; Li, Jiandong; Zhao, Liang; Chen, Rui

doi:10.1109/tvt.2014.2330957

Cited by 13 publications

(14 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Algorithm 4 WMMSE Algorithm 1: Initialize iterative number l = 1, maximum number of iterations l max , feasible V (0) , calculate U (0) and W (0) by using (15) with V (0) , tolerance ε, calculate the objective value of Problem (24), denoted as Obj(V (l−1) ).…”

Section: B Algorithm To Solve Problem (23)mentioning

confidence: 99%

“…Denote the converged solution of the WMMSE algorithm as V , U and W . With given U and W , the Lagrange function of Problem(24) can be written asL (V, λ, µ) = k∈UV H k G kVk + k∈U λ k (R k,min − h k (V, U k , W k )) − P i,max , (B.1)where λ = {λ k , ∀k ∈ U} and µ = {µ i , ∀i ∈ I} are the corresponding Lagrange multipliers.According to Theorem 3, the BCD algorithm can obtain the globally optimal solution of Problem (27) (also Problem (24)) with given U and W , there must exist λ and µ such that {V , λ , µ } satisfy the following KKT conditions∇V k L = ∇V k k∈UV ,H k G kV k − k∈U λ k ∇V k h k (V , U k , W k )) + i∈I µ i ∇V k k∈U i = 0, ∀k ∈ U, (B.2) λ k (h k (V , U k , W k ) − R k,min ) = 0, ∀k ∈ U, (B.3) µ i P i,max − k∈U i = 0, ∀i ∈ I, (B.4) h k (V , U k , W k ) ≥ R k,min , ∀k ∈ U, (B.5) k∈U i ≤ P i,max , ∀i ∈ I. (B.6)Since U and W are updated by using(15), we have h k (V , U k , W k ) = R k (V ) according to Lemma 1.…”

mentioning

confidence: 99%

“…W (l−1) . Hence, h k V (l) , U (l−1) k , W (l−1) k ≥ R k,min , ∀k hold since V (l) is feasible for Problem(24). According toLemma 1, h k V (l) , U (l−1) k , W (l−1) k is a lower-bound of R k (V (l) ), i.e., R k (V (l) ) ≥ h k V (l) , U (l−1) k , W (l−1) k .…”

mentioning

confidence: 99%

See 2 more Smart Citations

Joint Precoding and RRH Selection for User-Centric Green MIMO C-RAN

Pan

Zhu

Gomes

et al. 2017

IEEE Trans. Wireless Commun.

173

108

View full text Add to dashboard Cite

This paper jointly optimizes the precoding matrices and the set of active remote radio heads (RRHs) to minimize the network power consumption (NPC) for a user-centric cloud radio access network (C-RAN), where both the RRHs and users have multiple antennas and each user is served by its nearby RRHs. Both users' rate requirements and per-RRH power constraints are considered. Due to these conflicting constraints, this optimization problem may be infeasible. In this paper, we propose to solve this problem in two stages. In Stage I, a low-complexity user selection algorithm is proposed to find the largest subset of feasible users. In Stage II, a low-complexity algorithm is proposed to solve the optimization problem with the users selected from Stage I. Specifically, the re-weighted l 1 -norm minimization method is used to transform the original problem with non-smooth objective function into a series of weighted power minimization (WPM) problems, each of which can be solved by the weighted minimum mean square error (WMMSE) method. The solution obtained by the WMMSE method is proved to satisfy the Karush-Kuhn-Tucker (KKT) conditions of the WPM problem. Moreover, a lowcomplexity algorithm based on Newton's method and the gradient descent method is developed to update the precoder matrices in each iteration of the WMMSE method. Simulation results demonstrate the rapid convergence of the proposed algorithms and the benefits of equipping multiple antennas at the user side.Moreover, the proposed algorithm is shown to achieve near-optimal performance in terms of NPC.find the optimal solution. Although these two approaches yield the optimal solution, they have an exponential complexity. The third approach is the smooth function method, where the l 0norm was approximated as Gaussian-like function in [19], the exponential function in [20], and arctangent function in [21]. However, the smooth function cannot produce sparse solutions in general. The last approach was inspired by the compression sensing, named re-weighted l 1 -norm minimization method [27]. This method has been widely adopted in the literature [22]-[26], [28] due to its low computational complexity and sparsity guarantee, which will also be applied in this paper.All of the above papers only considered the single-antenna user (SAU) case. With the increasing development in antenna technology [29], [30], it is possible to equip the wireless devices with multiple antennas. When both the transmitter and the receiver are equipped with multiple antennas, multiple streams can be transmitted simultaneously, rather than only one stream in the SAU case. Simulation results show that with the increasing number of receive antennas, more users can be admitted. Therefore, in this paper, we consider the multiple-antenna user (MAU) case and jointly optimize the precoding matrices and the set of active RRHs to minimize the NPC subject to users' rate requirements and per-RRH power constraints.Unfortunately, the techniques in [16]-[26] dealing with the SAU case cannot be extended d...

show abstract

Section: B Algorithm To Solve Problem (23)mentioning

confidence: 99%

mentioning

confidence: 99%

See 1 more Smart Citation

Joint Precoding and RRH Selection for User-Centric Green MIMO C-RAN

Pan

Zhu

Gomes

et al. 2017

IEEE Trans. Wireless Commun.

173

108

View full text Add to dashboard Cite

show abstract

“…According to half of the round-trip drift, the forward phase time difference should be increased by 1850 ps, which is 150 ps less than 2 ns, so the phase time should be reduced by 150 ps, slightly differing from the measured 142.75 ps due to the system asymmetry. The optical compensation is turned on at 2155 s, and it can be known that m = 0 according to (13). Thus, trt is compensated to near tr0+2mT = 290505151 ps first, and then φrt is accurately compensated to φr0.…”

Section: Resultsmentioning

confidence: 99%

“…The phase difference is constant during each independent running state, but when the system experiences operations from shutdown to restart, or changing the length of fiber link, the phase difference value after entering the steady state again will change. This situation can no longer meet the needs of some coherence applications [13]- [16], which not only require the phase difference to remain stable (that is, frequency synchronization), but also need absolutely consistent repeatable phase difference. This can provide the same-phase frequency reference between remote users, maintaining the coherence of the frequency reference, and realizing coherent processing more effectively.…”

Section: Introductionmentioning

confidence: 99%

Absolutely Consistent Fiber-Optic Phase Synchronization Based On Fixed-Phase-Reference Optical Active Compensation

Yang

Sun

et al. 2021

IEEE Photonics J.

View full text Add to dashboard Cite

An absolutely consistent fiber-optic phase synchronization scheme based on fixed-phase-reference optical active compensation is proposed. By measuring the time interval of time pulse and the phase difference of frequency signal, and then using the optical delay line to compensate the phase change in real time according to a fixed phase reference, the phase period ambiguity and the phase-lock control initial randomness are eliminated, realizing a stable and repeatable fixed phase difference between different sites. Then the phase synchronization performances of stability and absolute consistence have been demonstrated through experiments in different scenarios. The phase difference fluctuation within 10000 s is only ±0.0006 rad, showing an ultra-high stability. Under the total of 5 different operations, including the signal source restart, system restart, adding 20 m additional fiber, adding 1 km additional fiber, and adding 2 km additional fiber, the maximum inconsistency of the mean phase difference is about 1% of one full cycle. The proposed and verified absolutely consistent phase synchronization method can well maintain the coherence in transferring frequency signal, having important application prospects in radar network and other time-frequency-phase synchronization-based coherent detection.

show abstract