[신간의 별자리x] 우리/미술, 그리고 ‘슬픔의 박물관’

Reconfigurable intelligent surfaces (RISs) are an emerging transmission technology for application to wireless communications. RISs can be realized in different ways, which include (i) large arrays of inexpensive antennas that are usually spaced half of the wavelength apart; and (ii) metamaterial-based planar or conformal large surfaces whose scattering elements have sizes and inter-distances much smaller than the wavelength. Compared with other transmission technologies, e.g., phased arrays, multi-antenna transmitters, and relays, RISs require the largest number of scattering elements, but each of them needs to be backed by the fewest and least costly components. Also, no power amplifiers are usually needed. For these reasons, RISs constitute a promising software-defined architecture that can be realized at reduced cost, size, weight, and power (C-SWaP design), and are regarded as an enabling technology for realizing the emerging concept of smart radio environments (SREs). In this paper, we (i) introduce the emerging research field of RIS-empowered SREs; (ii) overview the most suitable applications of RISs in wireless networks; (iii) present an electromagnetic-based communication-theoretic framework for analyzing and optimizing metamaterial-based RISs; (iv) provide a comprehensive overview of the current state of research; and (v) discuss the most important research issues to tackle. Owing to the interdisciplinary essence of RIS-empowered SREs, finally, we put forth the need of reconciling and reuniting C. E. Shannon’s mathematical theory of communication with G. Green’s and J. C. Maxwell’s mathematical theories of electromagnetism for appropriately modeling, analyzing, optimizing, and deploying future wireless networks empowered by RISs.

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Smart Radio Environments Empowered by Reconfigurable Intelligent Surfaces: How It Works, State of Research, and The Road Ahead

In this chapter we deal with the following nonlinear fractional programming problem: 

$$P:\mathop{{\max }}\limits_{{x \in s}} q(x) = (f(x) + \alpha )/((x) + \beta )$$

where f, g: R n → R, α, β ∈ R, S ⊆ R n . To simplify things, and without restricting the generality of the problem, it is usually assumed that, g(x) + β + 0 for all x ∈ S,S is non-empty and that the objective function has a finite optimal value.

Nonlinear Fractional Programming

What is a reconfigurable intelligent surface? What is a smart radio environment? What is a metasurface? How do metasurfaces work and how to model them? How to reconcile the mathematical theories of communication and electromagnetism? What are the most suitable uses and applications of reconfigurable intelligent surfaces in wireless networks? What are the most promising smart radio environments for wireless applications? What is the current state of research? What are the most important and challenging research issues to tackle? 
These are a few of the many questions that we investigate in this short opus, which has the threefold objective of introducing the emerging research field of smart radio environments empowered by reconfigurable intelligent surfaces, putting forth the need of reconciling and reuniting C. E. Shannon's mathematical theory of communication with G. Green's and J. C. Maxwell's mathematical theories of electromagnetism, and reporting pragmatic guidelines and recipes for employing appropriate physics-based models of metasurfaces in wireless communications.

Smart Radio Environments Empowered by Reconfigurable Intelligent Surfaces: How it Works, State of Research, and Road Ahead.

Radio access technologies for cellular mobile communications are typically characterized by multiple access schemes, e.g., frequency division multiple access (FDMA), time division multiple access (TDMA), code division multiple access (CDMA), and OFDMA. In the 4th generation (4G) mobile communication systems such as Long-Term Evolution (LTE) (Au et al., Uplink contention based SCMA for 5G radio access. Globecom Workshops (GC Wkshps), 2014. doi:10.1109/GLOCOMW.2014.7063547) and LTE-Advanced (Baracca et al., IEEE Trans. Commun., 2011. doi:10.1109/TCOMM.2011.121410.090252; Barry et al., Digital Communication, Kluwer, Dordrecht, 2004), standardized by the 3rd Generation Partnership Project (3GPP), orthogonal multiple access based on OFDMA or single carrier (SC)-FDMA is adopted. Orthogonal multiple access was a reasonable choice for achieving good system-level throughput performance with simple single-user detection. However, considering the trend in 5G, achieving significant gains in capacity and system throughput performance is a high priority requirement in view of the recent exponential increase in the volume of mobile traffic. In addition the proposed system should be able to support enhanced delay-sensitive high-volume services such as video streaming and cloud computing. Another high-level target of 5G is reduced cost, higher energy efficiency and robustness against emergencies.

Non-Orthogonal Multiple Access (NOMA) for cellular future radio access

Conventional downlink non-orthogonal multiple access (NOMA) with spatial modulation (SM) limits the number of chosen transmitting antennas. Therefore, a multiuser NOMA-based generalized spatial modulation (GSM) system is proposed for the downlink scenario, where more than one transmitting antenna are selected. Different channel gains are considered in the NOMA-GSM system to reduce the impact of intra-group interference. Moreover, to make good use of power domain and spatial domain resources, a dynamic user grouping is proposed, where different users are selected from different sets according to their effective channel gains. Moreover, a dynamic power allocation scheme is proposed to maximize the achievable sum rate of users, which is the suboptimal solution of the product of two-user signal-to-interference-plus-noise ratio. To extend to three-user systems, another power allocation scheme is proposed. The achievable sum rate maximization problem is transformed into a convex optimization problem by introducing the auxiliary variable. Simulation results show that proposed user grouping and power allocation schemes have advantages compared with existing ones.

Power Allocation for Downlink Multiuser NOMA-Based Generalized Spatial Modulation

The invention discloses a robust resource allocation algorithm of a cognitive heterogeneous wireless network and belongs to the technical field of communication cognitive heterogeneous wireless networks. The robust resource allocation algorithm is characterized by considering a robust resource allocation algorithm in a cognitive heterogeneous multi-network environment influenced by channel parameter perturbation, considering cross-layer interference constraint and bounded channel uncertainty and interference power uncertainty in an objective function, converting the original infinite dimensional multivariate optimization problem into the convex optimization problem of certainty by using a robust optimization theory, obtaining a robust power control algorithm by using a lagrangian duality method, and achieving sharing of the robust resource of the cognitive heterogeneous wireless network. Simulation results show that the algorithm can effectively protect the performance of users of a macro-cellular network and has stronger robustness.

Robust resource allocation algorithm of cognitive heterogeneous wireless network

In this paper, we studied the joint subchannel and power allocation problem to maximize energy efficiency (EE) for the multi-carrier non-orthogonal multiple access (MC-NOMA) systems. First, a matching algorithm is proposed to allocate the user in the subchannel. Then, the EE maximization problem is converted into a series of subproblems, which can be solved by the penalty function method. Next, the joint channel allocation and power allocation is proposed based on the Dinkelbach algorithm. Simulation results show that the proposed algorithm is superior to the traditional fractional power allocation (FTPA) scheme and the OFDMA scheme in terms of the EE.

Energy efficient resource allocation algorithm in multi-carrier NOMA systems

Spatial modulation (SM) is a promising multiple-input multiple-output (MIMO) transmission technology, which exploits the indices of transmit antennas to encode information into the spatial dimension and needs less radio frequency chains. To detect the transmitted information, the maximum likelihood (ML) algorithm is often adopted which has high computational complexity. Some low-complexity detection algorithms only consider hard-decision cases with no channel coding. In this paper, a low-complexity soft output detection algorithm is proposed for coded spatial modulation systems. Its computational complexity doesn't increase with the modulation order and hence much lower than the soft output ML detection algorithm. Simulation results demonstrate that the bit error rate (BER) performance is near to the ML detection with obvious complexity reduction. By sorting the detection metrics and selecting less transmit antenna indices as search candidates to compute soft bit information, further reduction of computational complexity could be obtained.

A low-complexity soft output detection algorithm for spatial modulation systems

With the increasing requirements of high data rates and large connectivity, the application of the non-orthogonal multiple access (NOMA) technique in heterogeneous networks (HetNets) has become an inevitable trend for 5G communication systems in order to achieve good spectrum efficiency and system throughput. Moreover, the energy-constrained low-power nodes (e.g., small cell users) are the bottlenecks for restricting the overall network performance. To improve the system throughput and prolong the operation life of the energy-limited networks, a robust energy efficiency (EE) maximization problem is addressed for simultaneous wireless information and power transfer (SWIPT)-enabled heterogeneous NOMA networks. Considering the co-channel interference and the cross-tier interference, the resource allocation problem is formulated as a non-convex multivariable fractional programming problem under the time-switching energy harvesting mode. It is challenging to obtain the optimal solution. By using the min-max probability machine approach, the probabilistic interference constraint and the outage rate constraint are transformed into the convex ones. Based on the ellipsoidal uncertainty sets, the worst-case harvested energy is converted into a deterministic one. The obtained convex optimization problem is solved by CVX tools. Simulation results show the superiority of the proposed algorithm by comparing with the non-robust algorithm.

Yongjun Xu

Papers

Power Allocation for Downlink Multiuser NOMA-Based Generalized Spatial Modulation

Robust resource allocation algorithm of cognitive heterogeneous wireless network

Energy efficient resource allocation algorithm in multi-carrier NOMA systems

A low-complexity soft output detection algorithm for spatial modulation systems

Robust Energy Efficiency Optimization for SWIPT-enabled Heterogeneous NOMA Networks