Purpose Higher education institutions (HEIs) should play a fundamental role in achieving the international 2030 sustainable development (SD) agenda. Quality education is the fourth of the sustainable development goals (SDGs), and one of the targets related to this is to ensure that by 2030 all learners acquire the knowledge and skills needed to promote SD. Therefore, the SDGs provide a motive for HEIs to integrate SD concepts into their day-to-day practices. This study aims to introduce a framework for HEIs’ sustainable development assessment. Such a framework guides HEIs and educational leaders to support their countries’ commitments to achieving the SDGs. Design/methodology/approach This paper presents the results of a case study analysis of the role and successful techniques of HEIs in achieving SD in three countries, namely, Germany, Japan and Egypt. Primary data was collected by semi-structured interviews with three Cairo University officials, while secondary data was collected by reviewing the universities' official websites, reports, publications and related papers. This study introduces a novel framework for HEIs' SD analysis and assessment, which guides HEIs and educational leaders to support SD to fulfill their countries' commitments to achieving the SDGs. This framework is based on the following five categories: strategic direction and institutional working practices, supporting students, supporting university staff competencies, supporting society's stakeholders and networking and sustainable campus. Consideration is given to the potential role of HEIs to support SD in each of these areas. Findings Cairo University could learn from the novel and pioneer practices of the Leuphana University of Lüneburg, and the University of Tokyo to fill in the gaps it has in different roles. It can also put more effort into adopting the suggested higher education programs of Egypt's Vision 2030. Research limitations/implications This paper is limited to a case analysis comparing three countries, Germany, Japan and Egypt. Second, this study has not considered school education, which is equally essential in countries' SD. Practical implications HEIs can use the framework and the findings in this paper to evaluate their current roles in supporting SD, identify the gaps and take actions accordingly to address their weaknesses. Originality/value The paper compares three universities, one in each of the case study countries. It draws conclusions that identify ways in which the paper's framework and findings can guide SD practice in HEIs internationally, especially those in the developing world.

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Sparse code multiple access (SCMA) is a promising non‐orthogonal multiple access scheme for cellular Internet of things (IoT) due to its ability to support massive connectivity, grant‐free transmission and scalability. Inspired by the recent developments of deep learning for physical layer communications, we present a design of an uplink SCMA receiver using deep learning. We propose the use of recurrent neural networks (RNNs) for joint channel estimation and multiuser data detection of uplink SCMA under time‐varying Rayleigh channel using a single deep learning structure. The use of RNNs enables the receiver to learn the time correlation between the received samples with a very low pilot density and with low complexity. Compared with the conventional SCMA receiver, the simulation results show that the proposed deep learning‐based receiver can achieve BER performance similar to that of the conventional SCMA receivers (such as sparse pilot channel estimator and message‐passing algorithm detector) with very low pilot density and with much lower complexity. Moreover, the proposed SCMA receiver shows good resilience to small changes in the receiver speed (second‐order channel statistics) which enables the proposed deep learning receiver to work over a reasonable range of receiver speeds without parameter tuning. Fine tuning the network parameters to capture the variations of the channel, during online transmission using small data sets and small training period, is also checked and provides additional BER benefits.