Energy Efficiency of mmWave MIMO Systems With Spatial Modulation and Hybrid Beamforming

Sefunç, Merve; Zappone, Alessio; Jorswieck, Eduard A.

doi:10.1109/tgcn.2019.2949224

Cited by 14 publications

(10 citation statements)

References 43 publications

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“…where • is Hadamard product for element-wise multiplication. In (1), ρ is the distance-based loss and H ∈ C Nant×N T is the small-scale fading channel which is defined based on Saleh Valenzuela model [17], [21] as…”

Section: A System Modelmentioning

confidence: 99%

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MARS: Message Passing for Antenna and RF Chain Selection for Hybrid Beamforming in MIMO Communication Systems

Shen¹,

Lo²,

Feng³

et al. 2022

Preprint

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In this paper, we consider a prospective receiving hybrid beamforming structure consisting of several radio frequency (RF) chains and abundant antenna elements in multi-input multi-output (MIMO) systems. Due to conventional costly full connections, we design an enhanced partially-connected beamformer employing low-density parity-check (LDPC) based structure. As a benefit of LDPC-based structure, information can be exchanged among clustered RF/antenna groups, which results in a low com-Hybrid beamforming, MIMO, LDPC-based connection, antenna selection, RF chain selection, message passing, power consumption, energy efficiency.

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Section: A System Modelmentioning

confidence: 99%

“…The operating power amplifiers and transmit power are considered in [20], [21] but without considerations of RF/antenna effects which are taken into account in [22], [23]. However, they all focus on fully-connected HBF optimizing EE which requires high complexity.…”

Section: Introductionmentioning

confidence: 99%

MARS: Message Passing for Antenna and RF Chain Selection for Hybrid Beamforming in MIMO Communication Systems

Shen¹,

Lo²,

Feng³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…The problem is transformed into a convex optimization problem by Lagrangian dual decomposition and an iterative gradient algorithm is proposed to solve the problem. Sefunç et al 21 investigate the problem of energy-efficient radio resource allocation in mmWaves, large-scale MIMO systems with spatial modulation and hybrid beamforming. The authors present a optimization framework to optimize the transmit power and the number of active RF chains subject to maximum power constraints, maximum and minimum number of active RF chains, and minimum achievable rate guarantee.…”

Section: Related Workmentioning

confidence: 99%

Energy‐efficient Link Scheduling in Time‐variant Dual‐Hop 60GHz Wireless Networks

Chen

et al. 2020

Concurrency and Computation

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Dual-hop 60 GHz wireless networks which support relay-assisted dual-hop transmission have been widely adopted in the recent years, aiming to prolong communication distance and bypass obstacles in 60 GHz band. However, it is very challenging to perform link scheduling in such dual-hop architecture while considering several factors, i.e., reducing network power consumption, avoiding overloaded APs/relays and adapting to network dynamics. To this end, we investigate the problem of energy-efficient link scheduling with load constraints (ELL), and propose solutions to deal with network dynamics. First, we present a fine-grained energy model for dual-hop 60 GHz networks, and formulate the ELL problem as an integer linear programming model that aims to minimize the network power consumption, while satisfying AP/relay load constraints. Then, we propose a polynomial-time global scheduling algorithm that obtains a near-optimal link scheduling solution via iterative relaxation, and the load constraint at each AP/relay can only be violated by at most an additive constant of two. Moreover, we present a local adjustment algorithm to adjust link-scheduling solutions efficiently, such as client arrival/departure and link blockage, and design a hybrid algorithm that combines global scheduling and local adjustment. Finally, we conduct simulation experiments that validate our algorithms' effectiveness and efficiency.

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“…If the increased degree of non-real-time service is higher than that of real-time service, the fairness of real-time service will be compromised. Therefore, we introduce a compensation factor including the Guaranteed Bit Rate (GBR) [27] to the fairness index C F to ensure the fairness of real-time service as follows:…”

Section: ) Calculation Of the Fairness Indexmentioning

confidence: 99%

Dynamic Resource Scheduling Optimization With Network Coding for Multi-User Services in the Internet of Vehicles

et al. 2020

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For Internet of Vehicles (IoV) systems with multiple users, network coding can be introduced to provide efficient error control and throughput improvement services. However, if the heterogeneity characteristics and requirements of the end users (vehicles) are neglected, it will be difficult for an IoV system to provide each end user with fair system services, without which the advantages of network coding cannot be fully achieved and the performance of the multiuser diversity system will be degraded. In this paper, we propose a Dynamic Resource Scheduling Optimization (DRSO) algorithm, a dynamic fair scheduling algorithm combined with network coding for system resource allocation in a multiuser IoV system. We construct a general solution framework for service scheduling: first, we estimate the fairness index for each end user (vehicle) with the key information on Quality of Service (QoS). Second, we construct a service scheduling control model based on the service capability of control entities (multiaccess edge computing servers), and propose a new utility evaluation function. Third, based on the fairness index, we select end users into multiple network coding sets. Network coding sets are the basic units of service scheduling. The optimization objective of the scheduling service is to maximize the total utility of all the network coding sets (the utility of the control entity). Finally, we establish a coding cache queue in the control entity based on the scheduling decision. To obtain the global optimal solution for active queue control, we combine a Quantum Particle Swarm Optimization (QPSO) algorithm with a Proportional Integral (PI) model. Then, the optimal scheduling decision can be made. Extensive simulation results show that DRSO outperforms related scheduling algorithms in varying traffic loads, demonstrating that DRSO can effectively guide service resource allocation. INDEX TERMS multiuser , fairness control, network coding set, cache queue, Internet of Vehicles

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Energy Efficiency of mmWave MIMO Systems With Spatial Modulation and Hybrid Beamforming

Cited by 14 publications

References 43 publications

MARS: Message Passing for Antenna and RF Chain Selection for Hybrid Beamforming in MIMO Communication Systems

MARS: Message Passing for Antenna and RF Chain Selection for Hybrid Beamforming in MIMO Communication Systems

Energy‐efficient Link Scheduling in Time‐variant Dual‐Hop 60GHz Wireless Networks

Dynamic Resource Scheduling Optimization With Network Coding for Multi-User Services in the Internet of Vehicles

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