Energy Efficient Coordinated Beamforming for Multicell System: Duality-Based Algorithm Design and Massive MIMO Transition

In millimeter wave (mmWave) systems, antenna architecture limitations make it difficult to apply conventional fully digital precoding techniques but call for low cost analog radio-frequency (RF) and digital baseband hybrid precoding methods. This paper investigates joint RF-baseband hybrid precoding for the downlink of multiuser multi-antenna mmWave systems with a limited number of RF chains. Two performance measures, maximizing the spectral efficiency and the energy efficiency of the system, are considered. We propose a codebook based RF precoding design and obtain the channel state information via a beam sweep procedure. Via the codebook based design, the original system is transformed into a virtual multiuser downlink system with the RF chain constraint. Consequently, we are able to simplify the complicated hybrid precoding optimization problems to joint codeword selection and precoder design (JWSPD) problems. Then, we propose efficient methods to address the JWSPD problems and jointly optimize the RF and baseband precoders under the two performance measures. Finally, extensive numerical results are provided to validate the effectiveness of the proposed hybrid precoders. Index TermsManuscript received Jan. 06, 2017; revised Apr. 14, 2017; accepted Jun. 24, 2017.

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“…where P λ key L dyn = L λ key L (P RF C + M P P S + P DAC ) + P sta . Problem (45) can be formulated as:…”

mentioning

confidence: 99%

Codebook-Based Hybrid Precoding for Millimeter Wave Multiuser Systems

Wang

Huang

et al. 2017

IEEE Trans. Signal Process.

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164

153

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“…This problem is exacerbated in mmWave communications, as we discuss in the next subsection. Beamforming design for Multiple Input Multiple Output networks has a very rich literature with focus on spectral efficiency [124]- [126], energy efficiency [125], [127], [128], and interference cancellation [129]- [131], among others. However, surprisingly, designing beamforming for low-latency MIMO networks is a largely open problem.…”

Section: E Transmission Capacity Boosting and Sharing Techniquesmentioning

confidence: 99%

Low-Latency Networking: Where Latency Lurks and How to Tame It

et al. 2019

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While the current generation of mobile and fixed communication networks has been standardized for mobile broadband services, the next generation is driven by the vision of the Internet of Things and mission critical communication services requiring latency in the order of milliseconds or submilliseconds. However, these new stringent requirements have a large technical impact on the design of all layers of the communication protocol stack. The cross layer interactions are complex due to the multiple design principles and technologies that contribute to the layers' design and fundamental performance limitations. We will be able to develop low-latency networks only if we address the problem of these complex interactions from the new point of view of sub-milliseconds latency. In this article, we propose a holistic analysis and classification of the main design principles and enabling technologies that will make it possible to deploy low-latency wireless communication networks. We argue that these design principles and enabling technologies must be carefully orchestrated to meet the stringent requirements and to manage the inherent trade-offs between low latency and traditional performance metrics. We also review currently ongoing standardization activities in prominent standards associations, and discuss open problems for future research.

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“…where ν is a auxiliary variable. Moreover, based on the analysis in [19], [31], the slack variable ν ⋆ is obtained by solving a power minimization problem subject to the same peruser power constraints in (14c) and throughput requirements in (14b). Assuming a total transmit power as K k=1 q k , based on [19], [31], the power minimization problem (PMP) can be defined as follows:…”

Section: Energy Efficiency Maximization Schemementioning

confidence: 99%

“…After solving Problem P PMP and finding the optimal solution q + k , ∀k, the slack variable ν ⋆ is obtained as Hence, we can convert the original total energy efficiency maximization problem into a throughput maximization problem with the new total power constraint. Next, Problem P 3 is iteratively solved by performing a one-dimensional search over the variable ν ⋆ ≤ ν ≤ 1 [19]. Note that for a given ν, the denominator of the objective function of Problem P 3 is a constant.…”

Section: Energy Efficiency Maximization Schemementioning

confidence: 99%

On the Energy Efficiency of Limited-Backhaul Cell-Free Massive MIMO

Bashar

Cumanan

Burr

et al. 2019

ICC 2019 - 2019 IEEE International Conference on Communications (ICC)

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We investigate the energy efficiency performance of cell-free Massive multiple-input multiple-output (MIMO), where the access points (APs) are connected to a central processing unit (CPU) via limited-capacity links. Thanks to the distributed maximum ratio combining (MRC) weighting at the APs, we propose that only the quantized version of the weighted signals are sent back to the CPU. Considering the effects of channel estimation errors and using the Bussgang theorem to model the quantization errors, an energy efficiency maximization problem is formulated with per-user power and backhaul capacity constraints as well as with throughput requirement constraints. To handle this non-convex optimization problem, we decompose the original problem into two sub-problems and exploit a successive convex approximation (SCA) to solve original energy efficiency maximization problem. Numerical results confirm the superiority of the proposed optimization scheme.

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Energy Efficient Coordinated Beamforming for Multicell System: Duality-Based Algorithm Design and Massive MIMO Transition

Cited by 44 publications

References 59 publications

Codebook-Based Hybrid Precoding for Millimeter Wave Multiuser Systems

Codebook-Based Hybrid Precoding for Millimeter Wave Multiuser Systems

Low-Latency Networking: Where Latency Lurks and How to Tame It

On the Energy Efficiency of Limited-Backhaul Cell-Free Massive MIMO

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