Energy Efficient Pilot and Link Adaptation for Mobile Users in TDD Multi-User MIMO Systems

In this paper, the scaling laws of scheduling gain and the feasibility of user scheduling for uplink massive multiple input–multiple output (MIMO) systems are investigated by analyzing the second moment of mutual information. We consider two well-known linear receivers of matched filter (MF) and zero-forcing (ZF). The exact distribution of the signal-to-interference-plus-noise ratio (SINR) and its moment-generating function are first obtained, and the approximated variance of the mutual information for a user is derived as a closed form with a function of the number of antennas. The achievable scheduling gain under the optimal user scheduler is then derived using the Gaussianity of the sum rate. From the analyses and simulation results, it is found that the scheduling gain for the MF receiver increases with the number of base station (BS) antennas, while that for the ZF receiver decreases as the number of BS antennas increases, for most cases (except some impractical scenarios). Therefore, it is verified that user scheduling is still beneficial for the MF receiver while random user selection is sufficient for the ZF receiver in massive MIMO systems.

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“…. Moreover, ν k and ∑ j ε j are independent of each other [33]. Therefore, we obtain Corollary 3 from Lemma 1.…”

Section: Proof Of Corollary 3 Let ν Kmentioning

confidence: 52%

“…As presented in the proof of Corollary 1, ν k ∼ X 2 2M and ∑ j =k ς j ∼ X 2 2(K−1) . ε k ∼ Exp (1) becausê w k is a unit-norm random vector, independent of the Gaussian random vector e k [33]. In addition, ν k , ∑ j =k ς j and ε k are independent of each other.…”

Section: Proof Of Corollary 2 Let ν Kmentioning

confidence: 99%

Scaling Laws of Scheduling Gain for Uplink Massive MIMO Systems: Is User Scheduling Still Beneficial for Massive MIMO?

Kim

Park

2020

Electronics

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“…[111], where the receiver side can obtain extra information bits by successfully detecting the transit antenna indices. Other energy efficient schemes including modulation diversity, cognitive and polarization have been discussed in [112]- [117], as summarized in Table II. In summary, the approaches in [96]- [102] can be applied to large scale and EE oriented wireless networks with user quality of service (QoS) requirements. While the insights on realizing fairness among users can be resorted to [104], [105].…”

Section: B Non-orthogonal Aggregationmentioning

confidence: 99%

Fundamental Green Tradeoffs: Progresses, Challenges, and Impacts on 5G Networks

Zhang

et al. 2017

IEEE Commun. Surv. Tutorials

294

174

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With years of tremendous traffic and energy consumption growth, green radio has been valued not only for theoretical research interests but also for the operational expenditure reduction and the sustainable development of wireless communications. Fundamental green tradeoffs, served as an important framework for analysis, include four basic relationships: spectrum efficiency (SE) versus energy efficiency (EE), deployment efficiency (DE) versus energy efficiency (EE), delay (DL) versus power (PW), and bandwidth (BW) versus power (PW). In this paper, we first provide a comprehensive overview on the extensive on-going research efforts and categorize them based on the fundamental green tradeoffs. We will then focus on research progresses of 4G and 5G communications, such as orthogonal frequency division multiplexing (OFDM) and non-orthogonal aggregation (NOA), multiple input multiple output (MIMO), and heterogeneous networks (HetNets).We will also discuss potential challenges and impacts of fundamental green tradeoffs, to shed some light on the energy efficient research and design for future wireless networks.Along with the dramatic traffic explosion, it is also promising to see that the next generation (5G) wireless communications shall include people-to-machine and machine-to-machine communications [23] in order to facilitate more flexible networked social information sharing. As a result, numerous sensors, accessories, or even tools may become the communication entities and the associated running applications over wireless networks will diverge. As reported in [24], wireless communications shall support up to millions of applications and billions of subscribers by year 2020, which is nearly 100 times of today's network. Consequently, with the surprisingly expanding demands for wireless transmission and supporting equipment, the network power consumption is no longer sustainable and the green radio technology becomes essential [25]. A flagship 5G research project from European Union, named Mobile April 28, 2016 DRAFT denote SE, EE and DE, respectively 2 .1 Since we mainly focus on the physical layer transmission, we only adopt the transmission delay here to characterize the delay-power tradeoff which can be regarded as a theoretical limit. 2 We note that area energy efficiency is also another important performance metric for cellular networks. Since it is more related with the cell size, we will emphasize it in heterogeneous networks in Section V. April 28, 2016 DRAFT In the practical systems, however, the tradeoff curves may behave differently. For example, if the circuit power consumption, P c , is considered in the EE evaluation, then η EE = W P +Pc log 2 1 + P W N 0 and the corresponding SE-EE/DE-EE tradeoff curves will be with a bell shape [16] as shown in Fig. 1. BW-PW/DL-PW tradeoffs under the circuit power assumptions have been also discussed in [16]. In particular, several open issues have been raised in [16], including tradeoff analysis for multi-cell systems and HetNet architectures.Four fundamental gr...

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“…Kim et. al [14] proposed an uplink and downlink pilot power and rate adaptation approach to improve energy efficiency. When adapting pilot spacing, Ksairi et.…”

Section: B Related Workmentioning

confidence: 99%

Adaptive Pilot Patterns for CA-OFDM Systems in Nonstationary Wireless Channels

Rao

Marojevic

Reed

2018

IEEE Trans. Veh. Technol.

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Abstract-In this paper, we investigate the performance gains of adapting pilot spacing and power for Carrier Aggregation (CA)-OFDM systems in nonstationary wireless channels. In current multi-band CA-OFDM wireless networks, all component carriers use the same pilot density, which is designed for poor channel environments. This leads to unnecessary pilot overhead in good channel conditions and performance degradation in the worst channel conditions. We propose adaptation of pilot spacing and power using a codebook-based approach, where the transmitter and receiver exchange information about the fading characteristics of the channel over a short period of time, which are stored as entries in a channel profile codebook. We present a heuristic algorithm that maximizes the achievable rate by finding the optimal pilot spacing and power, from a set of candidate pilot configurations. We also analyze the computational complexity of our proposed algorithm and the feedback overhead. We describe methods to minimize the computation and feedback requirements for our algorithm in multi-band CA scenarios and present simulation results in typical terrestrial and air-toground/air-to-air nonstationary channels. Our results show that significant performance gains can be achieved when adopting adaptive pilot spacing and power allocation in nonstationary channels. We also discuss important practical considerations and provide guidelines to implement adaptive pilot spacing in CA-OFDM systems.

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Energy Efficient Pilot and Link Adaptation for Mobile Users in TDD Multi-User MIMO Systems

Cited by 37 publications

References 30 publications

Scaling Laws of Scheduling Gain for Uplink Massive MIMO Systems: Is User Scheduling Still Beneficial for Massive MIMO?

Scaling Laws of Scheduling Gain for Uplink Massive MIMO Systems: Is User Scheduling Still Beneficial for Massive MIMO?

Fundamental Green Tradeoffs: Progresses, Challenges, and Impacts on 5G Networks

Adaptive Pilot Patterns for CA-OFDM Systems in Nonstationary Wireless Channels

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