Composite Technology Challenge System for Optimization in 5G Communications


The significance of approaches for improvement of systems/products has been increased. In the article, a modular technology challenge system is proposed as a basis for the system improvement process. The combinatorial framework for designing a modular technology challenge system is described: (1) collection of information items (literature sources on technology challenges/key technologies); (2) designing a hierarchy over the set of information items; (3) selection of the sub-hierarchy while taking into account the specified topic(s); (4) composition of a required information item configuration as technology challenge system. The composition stage is based on morphological design. An applied realistic numerical example illustrates the usage of the proposed framework to design a 5G technology challenge system as a set of the selected related optimization actions.



In recent decades, the significance of the following two processes has been increased: (1) improvement of existing systems/products and (2) movement from an existing system/product generation to the next system/product generation (i.e., paradigm shift). Many research efforts are targeted to descriptions and studies of the processes above: technological challenges, possible prospective system/product changes. Evidently, the above-mentioned efforts are based on information analysis of the corresponding applied domains. Fig. 1 illustrates the system/product improvement stage and its information support. Thus, dynamic analysis of research topics is widely used in many domains, for example: (a) information sciences [141], (b) software product lines [88], (c) knowledge-based systems [213], (d) operations research [112], (e) applied intelligence [211].

Fig. 1

Illustration for system improvement

In this paper, a combinatorial framework of structuring a set of technological challenges for a certain research domain is examined. The framework consists of four basic stages: (1) collection of information items on the basis of literature sources (e.g., papers and patents which correspond to key technologies or technological challenges); (2) designing a hierarchy over the set of information items; (3) selection of the sub-hierarchy while taking into account the specified topic(s); (4) designing a technology challenge system as a configuration of the selected and related information items (i.e., technology challenges). The suggested combinatorial framework is illustrated by consideration of a 5G technology challenge system (while taking into account optimization methods). Note, combinatorial evolution of communication technology generations (i.e., 1G, 2G, 3G, 4G, 5G, 6G) is described in [119]. Scheme of the research is shown in Fig. 2. This paper is based on a preliminary technical report [120].

Generalized Combinatorial Framework

The examined combinatorial framework was proposed in [120] (Fig. 3):

  1. 1.

    Generation of an initial set of information items (information sources as papers, patents, etc.):  \(A = \{ a_{1}, \ldots ,a_{i} \ldots , a_{n}\}\)  (Fig. 4).

  2. 2.

    Design of a hierarchy over the set of information items:  \(H = \{ H_{1}, \ldots ,H_{j} \ldots ,H_{m}\}\), \( \bigcup _{j=1}^{m} \quad H_{j} = A\)  (Fig. 5).

  3. 3.

    Selection of the sub-hierarchy: the item subsets by the specified topic(s):  \({\widetilde{H}} = \{ {\widetilde{H}}_{1}, \ldots ,{\widetilde{H}}_{j} \ldots ,{\widetilde{H}}_{m}\}\)  where  \( {\widetilde{H}}_{j} \subseteq H_{j} \)   \(\forall j = \overline{1,m}\)  (Fig. 6).

  4. 4.

    Composition of a required information item configuration:  \(S = < h_{11} \star \cdots \star h_{mk_{m}}>\)  (Fig. 6).

Fig. 2

Scheme of the research

Fig. 3

Framework for designing a technology challenge system

Fig. 4

Retrieval and collection of initial information items

Fig. 5

Basic hierarchy of information items

Fig. 6

Selected information items (by specified topic), item configuration

Basic Auxiliary Optimization Approaches

Recently, optimization approaches (mainly, combinatorial optimization [42, 49, 63, 78, 117]) are widely studied and used in communications systems (e.g., [42, 80, 81, 113, 120, 133, 134, 140, 159, 166, 195]). The set of the corresponding basic auxiliary optimization approaches (i.e., problems, frameworks) is listed in Table 1. The optimization approaches are often related (e.g., by problem formulations, by applications, by solving frameworks). Note, composite optimization frameworks based of several optimization problems (e.g., selection-allocation, location-routing, scheduling and allocation, placement, and topology optimization) are used as well (e.g., [23, 66, 93, 183, 208]).

Table 1 Optimization approaches in communications (i.e., problems, models, frameworks)

Morphological Design

Morphological analysis is a power tool for composition of multi-part (composite, modular) systems. The approach is widely used for many application domains (e.g., system design, technological forecasting, management, information retrieval) (e.g., [17, 98, 116, 117, 163, 216]). A simplified evolution scheme of morphological analysis based design approaches is shown in Fig. 7.

Fig. 7

Evolution of morphological analysis approaches

Here the combinatorial framework (as combinatorial modular morphological design) is based on Hierarchical Multicriteria Morphological Design (HMMD) method (e,g., [115,116,117]). A basic simplified version of HMMD is used. In HMMD method (combinatorial synthesis), the considered system consists of the following: (1) systems parts or components (modules) and corresponding design alternatives (DAS) for each component; (2) interconnection/compatibility (IC) of DAs which are included into the same system or the system part. The basic assumptions of HMMD are the following: (a) a tree-like structure of the system; (b) a composite estimate for system quality that integrates components (subsystems, parts, modules) qualities and qualities of IC (compatibility) across subsystems; (c) monotonic criteria for the system and its components (parts, modules); and (d) quality estimates of system components and IC are evaluated by the same ordinal scales. The designations are:  (1) design alternatives (DAs) for nodes of the model;  (2) priorities of DAs (\(r=\overline{1,k}\); 1 corresponds to the best level of quality);  (3) an ordinal compatibility estimate for each pair of DAs (\(w=\overline{0,l}\); l corresponds to the best level of quality). The phases of HMMD are:

Phase 1 Design of the tree-like system model.

Phase 2 Generation of DAs for leaf nodes of the model.

Phase 3 Hierarchical selection and composing of DAs into composite DAs for the corresponding higher level of the system hierarchy.

Phase 4 Analysis and improvement of composite DAs (i.e., composite system solutions).

Thus, system S consisting of m parts (components) \(P(1),\ldots ,P(i),\ldots ,P(m)\) is considered. A set of design alternatives (DAs) is generated for each system part above (i.e., leaf node). The problem is:

Find composite design alternative   \(S=S(1)\star \cdots \star S(i)\star \cdots \star S(m)\): one representative design alternative S(i) for each system component/part  P(i) (\(i=\overline{1,m}\)) with non-zero  IC estimates between the representative DAs.

A discrete domain of the integrated system excellence is based on the vector:   \(N(S)=(w(S);n(S))\),  where w(S) is the minimum of pairwise compatibility between DAs which correspond to different system components (i.e., \(\forall \quad P_{j_{1}}\) and \( P_{j_{2}}\), \(1 \le j_{1} \ne j_{2} \le m\)) in S,  \(n(S)=(n_{1},\ldots ,n_{r},\ldots n_{k})\),  where \(n_{r}\) is the number of DAs of the rth quality in S  (\(\sum ^{k}_{r=1} n_{r} = m\)). Nondominated by N(S) composite solutions are searched for (i.e., Pareto-efficient solutions). The problem is NP-hard and enumerative methods may be used (e.g., while taking into account a reduced dimension by problem partition/decomposition) and heuristics. A simplified numerical example of morphological design is shown in Fig. 8: ordinal estimates of DAs are depicted in parentheses in Fig. 8a, positive ordinal compatibility estimates are depicted in Fig. 8b. The Pareto-efficient solutions are: \(S_{1}=X_{2}\star Y_{1}\star Z_{2}\), \(N(S_{1}) = (2;2,0,1)\); \(S_{2}=X_{3}\star Y_{1}\star Z_{3}\), \(N(S_{2}) = (3;1,1,1)\). \(S_{3}=X_{3}\star Y_{2}\star Z_{1}\), \(N(S_{3}) = (1;3,0,0)\).

Note, HMMD approach versions and their applications by various real-world examples are described in many publications (e.g., [116, 117]).

Fig. 8

Illustrative example of hierarchical morphological design

Example of Composite Challenge System

Figure 9 illustrates the design scheme of challenge system for 5G technology (while taking into account the topic “optimization methods”). The scheme solving components are based on heuristic (engineering) methods.

Fig. 9

General design scheme of challenge system for 5G technology

5G Technology Challenges

Table 2 contained an illustrative list of basic literature sources on 5G systems and corresponding challenges/key technologies. In addition, it may be reasonable to point out some basic resources in contemporary communication systems, for example: (a) time, (b) energy, (c) spectrum, and (d) cost. The corresponding design/management problems are the following: (1) sharing/location/allocation (e.g., devices, channels, frequency, energy), (2) caching, (3) configuration/reconfiguration (e.g., network topology), (4) adaptation (e.g., utilization modes), and (5) selection of the best modes, components, subsystems: selection of radio access technology, network node(s) (relay node(s), devices, channel, basic station, cloud, data center, access network, handoff/ handover strategies, etc).

Note, key requirements for 5G communication technology are described in [10, 52]: (1) high data rates, (2) low latency, (3) low energy consumption, (4) high stability, (5) improved connectivity and reliability, and (6) improved security. The following main network quality criteria (criteria or objective functions) are usually considered [184]: (1) peak data rate, (2) geographical area coverage, (3) spectral efficiency, (4) QoS, (5) QoE, (6) easy of connectivity, (7) energy-efficiency, (8) latency, (9) reliability, (10) fairness of users, (11) implementation complexity, and (12) system lifetime (maximization).

Table 2 Survey publications on 5G systems (challenges/key technologies, related issues)

Four integrated lists of challenges for 5G technology (based on recent literature) are presented in Table 3. Note, our basic illustrative set of information items as technology challenges for 5G communications was described in [120]. An examined general hierarchical structure of composite 5G challenge system is depicted in Fig. 10 [120].

Table 3 Challenges for 5G technology and CloudIoT
Fig. 10

General hierarchical structure of composite 5G challenge system

Optimization Based Composite 5G Challenge System

Tables 4 and 5 contain the selected 5G challenges (local DAs) based on topic “optimization”. Here, the illustrative estimates are based on expert judgment: (1) ordinal scale [1, 2, 3] for DAs, (2) ordinal scale [0, 1, 2, 3] for DAs compatibility. Note, the high values of DAs compatibility correspond to the case when the challenges are based on the same (or close) optimization model/approach.

The examined structure of the composite (modular) system is (Fig. 11, design alternative \(X_{0}\) corresponds to the absence of an activity, illustrative ordinal estimates of DAs are shown in parentheses as priorities in Tables 4 and 5):

0.\(S^{I} = A^{1} \star A^{2}\star A^{3} \star A^{4}\star A^{5} \) :

1.\( A^{1} = B^{1} \star B^{2} \):

1.1.\(B^{1}\) (IoT):  \(B^{1}_{0}(3)\), \(B^{1}_{1}(2)\), \(B^{1}_{2}(2)\), \(B^{1}_{3}(3)\), \(B^{1}_{4}(2)\), \(B^{1}_{5}(2)\), \(B^{1}_{6}(2)\), \(B^{1}_{7}(1)\), \(B^{1}_{8}(3)\), \(B^{1}_{9}(1)\);

1.2.\(B^{2}\) (D2D):  \(B^{2}_{0}(3)\), \(B^{2}_{1}(2)\), \(B^{2}_{2}(2)\), \(B^{2}_{3}(2)\), \(B^{2}_{4}(1)\), \(B^{2}_{5}(2)\), \(B^{2}_{6}(1)\), \(B^{2}_{7}(2)\), \(B^{2}_{8}(2)\), \(B^{2}_{9}(2)\), \(B^{2}_{10}(2)\).

2.\( A^{2} = B^{3} \) (RAT):  \(B^{3}_{0}(3)\), \(B^{3}_{1}(1)\), \(B^{3}_{2}(3)\), \(B^{3}_{3}(2)\), \(B^{3}_{4}(2)\), \(B^{3}_{5}(2)\).

3.\( A^{3} = B^{4} \star B^{5} \):

3.1.\(B^{4}\) (W-SDN):  \(B^{4}_{0}(3)\), \(B^{4}_{1}(1)\), \(B^{4}_{2}(1)\), \(B^{4}_{3}(2)\), \(B^{4}_{4}(2)\);

3.2.\(B^{5}\) (NFV):  \(B^{5}_{0}(3)\), \(B^{5}_{1}(2)\), \(B^{5}_{2}(2)\), \(B^{5}_{3}(2)\), \(B^{5}_{4}(2)\), \(B^{5}_{5}(2)\), \(B^{5}_{6}(2)\), \(B^{5}_{7}(2)\), \(B^{5}_{8}(2)\), \(B^{5}_{9}(2)\), \(B^{5}_{10}(1)\).

4.\( A^{4} = B^{6} \star B^{7} \):

4.1.\(B^{6}\) (Big Data and Mobile Cloud Computing):  \(B^{6}_{0}(3)\), \(B^{6}_{1}(1)\), \(B^{6}_{2}(2)\), \(B^{6}_{3}(2)\), \(B^{6}_{4}(3)\), \(B^{6}_{5}(2)\), \(B^{6}_{6}(2)\), \(B^{6}_{7}(1)\), \(B^{6}_{8}(2)\), \(B^{6}_{9}(1)\), \(B^{6}_{10}(2)\);

4.2.\(B^{7}\) (Mobile edge computing):  \(B^{7}_{0}(3)\), \(B^{7}_{1}(3)\), \(B^{7}_{2}(2)\), \(B^{7}_{3}(3)\), \(B^{7}_{4}(3)\), \(B^{7}_{5}(1)\).

5.\( A^{5} = B^{8} \star B^{9} \star B^{10} \star B^{11} \):

5.1.\(B^{8}\) (Green Communication):  \(B^{8}_{0}(3)\), \(B^{8}_{1}(3)\), \(B^{8}_{2}(3)\), \(B^{8}_{3}(1)\), \(B^{8}_{4}(3)\), \(B^{8}_{5}(2)\), \(B^{8}_{6}(3)\), \(B^{8}_{7}(3)\), \(B^{8}_{8}(3)\), \(B^{8}_{9}(2)\);

5.2.\(B^{9}\) (Massive MIMO):  \(B^{9}_{0}(3)\), \(B^{9}_{1}(3)\), \(B^{9}_{2}(3)\), \(B^{9}_{3}(3)\), \(B^{9}_{4}(2)\), \(B^{9}_{5}(1)\), \(B^{9}_{6}(2)\), \(B^{9}_{7}(2)\), \(B^{9}_{8}(2)\);

5.3.\(B^{10}\) (UDN):  \(B^{10}_{0}(3)\), \(B^{10}_{1}(1)\), \(B^{10}_{2}(2)\), \(B^{10}_{3}(2)\), \(B^{10}_{4}(2)\), \(B^{10}_{5}(2)\);

5.4.\(B^{11}\) (Millimeter Wave and TeraHertz):  \(B^{11}_{0}(3)\), \(B^{11}_{1}(1)\), \(B^{11}_{2}(2)\), \(B^{11}_{3}(3)\), \(B^{11}_{4}(2)\).

Illustrative ordinal estimates of DAs pair compatibilities (based on author expert judgment) are contained in Tables 6, 7, 8, 9, 10, and 11.

Fig. 11

Morphological hierarchical structure of composite 5G challenge system

Table 4 5G alternative improvement activities based on optimization approaches, part 1
Table 5 5G alternative improvement activities based on optimization approaches, part 2

The following Pareto-efficient composite DAs for system parts are obtained:

  1. 1.

    For subsystem \(A^{1}\)\(A^{1}_{1}= B^{1}_{7}\star B^{2}_{6}\), \(N(A^{1}_{1}) = (3; 2,0,0)\).

  2. 2.

    For subsystem \(A^{2}\)\(A^{2}_{1}= B^{3}_{1}\).

  3. 3.

    For subsystem \(A^{3}\)\(A^{3}_{1}= B^{4}_{1}\star B^{5}_{10}\), \(N(A^{3}_{1}) = (3; 2,0,0)\).

  4. 4.

    For subsystem \(A^{4}\)\(A^{4}_{1}= B^{6}_{1}\star B^{7}_{5}\), \(N(A^{4}_{1}) = (3; 2,0,0)\); \(A^{4}_{2}= B^{6}_{9}\star B^{7}_{5}\), \(N(A^{4}_{2}) = (3; 2,0,0)\).

  5. 5.

    For subsystem \(A^{5}\)\(A^{5}_{1}= B^{8}_{3} \star B^{9}_{5} \star B^{10}_{1} \star B^{11}_{1} \), \(N(A^{5}_{1}) = (1;4,0,0)\); \(A^{5}_{2}= B^{8}_{8} \star B^{9}_{7} \star B^{10}_{5} \star B^{11}_{2} \), \(N(A^{5}_{2}) = (3;0,3,1)\).

Figure 12 illustrates quality of two Pareto-efficient solutions for subsystem \(A^{5}\). Further, compatibility estimates are not used for composition of the resultant system solutions. The obtained resultant solutions (composite 5G challenge systems based on optimization approaches) are:

$$ S^{I}_{1}= A^{1}_{1} \star A^{2}_{1} \star A^{3}_{1} \star A^{4}_{1} \star A^{5}_{1};$$
$$ S^{I}_{2}= A^{1}_{1} \star A^{2}_{1} \star A^{3}_{1} \star A^{4}_{1} \star A^{5}_{2};$$
$$ S^{I}_{3}= A^{1}_{1} \star A^{2}_{1} \star A^{3}_{1} \star A^{4}_{2} \star A^{5}_{1}; {\text{ and}} $$
$$ S^{I}_{4}= A^{1}_{1} \star A^{2}_{1} \star A^{3}_{1} \star A^{4}_{4} \star A^{5}_{2}.$$
Fig. 12

Quality of \(A^{5}\)


This paper describes a combinatorial framework for selection and composition of technology challenges/key technologies based on optimization methods to design a composite technology challenge system. A numerical illustrative example for an optimization based modular technology challenges system in 5G communications is presented.

Note, the paper suggests firstly an important step to structuring the processes of the system improvement and/or system paradigm shift to design a hierarchical modular technology challenge system (instead of listing the prospective challenges and key technologies). Evidently, the suggested approach can be used for complex technological systems in various domains.

It may be reasonable to consider the following future research directions: (1) investigation of technology challenge systems in various computer science/engineering domains; (2) special study for designing a hierarchy (or ontology) of technology challenges; (3) examination of the combinatorial framework under uncertainty; (4) designing a special computer-aided system to support the suggested combinatorial framework; (5) study of composite innovations as combination of modular innovation activities (e.g., challenges/key technologies); (6) study of multi-stage composite technology challenge(s) systems; and (7) using the suggested combinatorial framework and its applications in computer science/engineering education (e.g., for student projects).

Table 6 Estimates of ordinal compatibility between DAs for \(A^{1} = B^{1} \star B^{2}\)
Table 7 Estimates of ordinal compatibility between DAs for \(A^{3} = B^{4} \star B^{5}\)
Table 8 Estimates of ordinal compatibility between DAs for \(A^{4} = B^{6} \star B^{7}\)
Table 9 Estimates of ordinal compatibility between DAs for \(A^{5} = B^{8} \star B^{9} \star B^{10} \star B^{11} \), part 1
Table 10 Estimates of ordinal compatibility between DAs for \(A^{5} = B^{8} \star B^{9} \star B^{10} \star B^{11}\), part 2
Table 11 Estimates of ordinal compatibility between DAs for \(A^{5} = B^{8} \star B^{9} \star B^{10} \star B^{11}\), part 3


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The research was done at Institute for Information Transmission Problems of Russian Academy of Sciences (IITP RAS) and supported by the Russian Government (Contract No 14.W03.31.0019).

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Levin, M.S. Composite Technology Challenge System for Optimization in 5G Communications. SN COMPUT. SCI. 1, 221 (2020).

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  • Combinatorial framework
  • Technology challenge system
  • Heuristic
  • 5G communications