An overview of emerging technologies for 5G: Full-Duplex relaying cognitive radio networks, device-to-device communications and cell-free massive MIMO

received in the previous phase, to 
 the receiver. At the same time, relay also receives signal x(t) from the transmitter. Due to 
 operating in FD mode, the receiving side of the relay is interfered by the transmitting side. 
At the relay, after receiving signal, the relay forwards it to destination based on two 
common protocols: amplify-and-forward (AF) and decode-and-forward (DF). The 
advantages of AF protocol is that this process is simpler than DF. However, interference 
and noise of the transmitter-relay hop are also amplified, which results in decreasing 
SINR value at the destination. 
 AF
The instantaneous end-to-end SNRs of a two-hop AF relaying system, denoted by gee2 
 DF
and of a two-hop DF relaying system, gee2 are given by [29], [30], 
 AF ggsr rd DF
 gee2 , ge2 e min{ g sr ,g rd }, 
 1 ggsr rd
where gsr and grd are the instantaneous SNRs of the first hop from source (transmitter) 
to relay and the second hop from relay to destination, respectively. 
Interestingly, a new concept of relay station has been proposed in 3GPP standard [31] 
which consists of two kinds of relay: moving relay nodes [32], [33] and relay-users [34]. 
This concept provides great potential for 5G because it deals with the high implement 
cost of fixed relay. 
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Full-duplex relaying communication in CRNs is introduced with the aim of inheriting 
the advantages of CNs and FD system having been intensively and widely studied in the 
literature [6], [12], [36]. Fig. 8 demonstrates the case of a FD relaying system in CRNs, 
where the secondary network is a FD relaying system, operating under underlay 
scheme. Due to the transmit power constraint of ST, in many cases the secondary 
receiver (SR) can not directly receive signal from the secondary transmitter (ST), so that 
a relay node is required. To enhance data rate and spectral efficiency, FD relay (FD-R) 
is considered instead of HD relay. Certainly, FD-R has to perform a self-interference 
suppression scheme to overcome the self-interference phenomenon at the relay. 
Notably, the spectrum allocation of D2D communication is mostly managed by BS, 
whereas CRNs is fully autonomous by SUs. Thus, SUs are able to detect the usable 
spectrum, select the best one, and adjust parameters such as pre-coding and transmit power. 
Figure 8. FDR in CRN system (ST: Secondary transmitter, FD-R : Ful-duplex relay, 
SR: Secondary receiver) 
4. Device-to-Device Communications 
Along with massive MIMO and CRNs, D2D communication has emerged as a 
promising solution for improving spectral efficiency of cellular networks (CelNs) [37]. 
It also provides a solution for decreasing overload of data traffic over CelNs and 
reducing transmission delay by directly communicating among devices [37]. Based on 
the kind of spectrum, D2D can be classified into two groups including in-band and out-
band D2D communication [8]. 
4.1. Out-band D2D communications 
For out-band D2D communication, D2D devices (DUs) use unlicensed spectrum, such 
as Wi-fi, Bluetooth and ZigBee, for transmitting and receiving data. The benefit of this 
type is no interference between D2D and CelN. 
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In terms of spectrum access policies, a device can occupy an unlicensed spectrum under the 
management of CelNs, called controlled out-band communication, or without any 
involvement of CelNs, called autonomous out-band communication [8]. For the former, the 
D2D communication performance can be controlled to improve the reliability and to ensure 
QoS. In contrast, autonomous out-band communication independently operates from CelN, 
which means that less overhead of CelNs, but the QoS cannot be controlled [38]. 
4.2. In-band D2D communications 
Different from out-band, DUs share spectrum with cellular users (CelUs) in two modes: 
underlay mode (UMod) and overlay mode (OMod) [8]. The advantage of this paradigm 
is that CelNs are able to manage performance of D2D communications. 
For OMod, the radio and time resources are allocated to DUs and CelUs such that there 
is no interference caused by D2D communication on CelNs and vice versa. As a result, 
interference management is not needed for this case, but the spectrum efficiency is not 
optimal and CelUs cannot exploit the full capacity of CelNs that can provide [8]. 
For UMod, both DUs and CelUs can use the same frequency and the same time slot for 
their transmissions, resulting in existing interference among them. Thus, the system 
needs to implement an additional scheme to partly or totally cancel the interference, 
making the system more complicated. With a sufficiently good interference 
management, the spectrum efficiency of this mode is improved significantly [38]. 
TABLE 2. Some key features of in-band and out-band D2D communications 
 Advantages In-band Out-band 
 underlay overlay control auto 
 Enable to control the performance of DUs Y Y Y N 
 Improve spectral efficiency Y Y N N 
 CelNs’ spectral efficiency is optimal Y N NA NA 
 Enable DUs and simultaneously operate on the same 
 Y N NA NA 
 spectrum of CelNs 
Along with EE, reducing energy consumption and prolonging battery life are also two 
important aspects in 5G visions [2]. Among many solutions, wireless power transfer 
(WPT) and energy harvesting (EH) have drawn great attention from the research 
community. The main concept of EH is to harvest and convert unused energy to useful 
energy to charge batteries. For natural energies such as solar and wind, they are free but 
strongly depend on weather and unable to exactly predict, meanwhile wireless 
communication systems require high reliability. For that reason, radio frequency (RF) 
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 Thu Dau Mot University Journal of Science – Volume 2 – Issue 4-2020 
signals, which are independent of natural conditions, become a potential resource 
[39],[40]. For WPT, energy is wirelessly transmitted to an intended receiver and it is 
usually fully controlled by transmitters. 
Although EH and WPT have been considered as a promising technique, there still are 
some challenges that need to be solved to make it practical. For example, the amount of 
harvested energy is very small. Thus, the application of EH and WPT, recently, has 
been considered in D2D communications due to low-power devices and short 
transmission. 
Figure 9. An example of EH based D2D communication system. 
5. Conclusions 
Throughout the paper, we have presented a comprehensive study on the emerging 
techniques for 5G including FDR in CRNs, D2D communications and CF massive 
MIMO. Some potential directions could be developed from the paper are listed as 
follows: 
 Due to the randomness of location of users in CF massive MIMO, deploying 
 stochastic geometry theory to model the system is an potential topic. 
 Resource allocation for multigroup multicast CF massive MIMO to reduce the 
 inter-user interference. 
 In the near future, the need of directly communicating between devices is 
 predicted to increase. Thus, considering D2D system in CF massive MIMO is 
 also a promising direction. 
 The development of low power devices makes energy harvesting become 
 more practical. However, EHC in CF massive MIMO has not been studied in 
 the literature. 
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References 
[1] D. Molkdar, W. Featherstone, S. Lambotharan (2002). An overview of EGPRS: the packet 
 data component of EDGE. J. Electron. Commun. Eng., 14(1),21–38. 
[2] GSMA Intelligence (2014). Understanding 5G: Perspectives on future technological 
 advancements in mobile. White paper. 
[3] M. Agiwal and A. Roy and N. Saxena (2016). Next Generation 5G Wireless Networks: A 
 Comprehensive Survey. IEEE Commun. Surveys Tuts., 18(3), 1617–1655. 
[4] F. Akyildiz and W. y. Lee and M. C. Vuran and S. Mohanty (2008). A survey on spectrum 
 management in cognitive radio networks. IEEE Commun. Mag., 46(4), 40–48. 
[5] B. Wang and K. J. R. Liu (2011). Advances in cognitive radio networks: A survey. IEEE J. 
 Sel. Topics Signal Process, 5(1), 5–23. 
[6] M. Amjad and F. Akhtar and M. H. Rehmani and M. Reisslein and T. Umer (2017). Full-
 Duplex Communication in Cognitive Radio Networks: A Survey. IEEE Commun. Surveys 
 Tuts, 19(4), 2158–2191. 
[7] Z. Zhang and K. Long and A. V. Vasilakos and L. Hanzo (2016). Full-Duplex Wireless 
 Communications: Challenges, Solutions, and Future Research Directions. Proc. of the 
 IEEE, 104(7), 1369–1409. 
[8] Asadi and Q. Wang and V. Mancuso (2014). A Survey on Device-toDevice 
 Communication in Cellular Networks. IEEE Commun. Surveys Tuts., 16(4), 1801–1819. 
[9] H. Q. Ngo and A. Ashikhmin and H. Yang and E. G. Larsson and T. L. Marzetta (2015). 
 Cell-Free Massive MIMO: Uniformly great service for everyone. in Proc. Int. Workshop 
 Signal Process. Adv. Wireless Commun., pp. 201–205. 
[10] H. Q. Ngo and L. N. Tran and T. Q. Duong and M. Matthaiou and E. G. Larsson (2018). 
 On the Total Energy Efficiency of Cell-Free Massive MIMO. IEEE Trans. Green 
 Commun. Netw, 2(1), 25–39. 
[11] H. Q. Ngo and A. Ashikhmin and H. Yang and E. G. Larsson and T. L. Marzetta (2017). 
 Cell-Free Massive MIMO Versus Small Cells. IEEE Trans. Wireless Commun, 16(3), 
 1834–1850. 
[12] H. Ju and E. Oh and D. Hong (2009). Improving efficiency of resource usage in two-hop 
 full duplex relay systems based on resource sharing and interference cancellation. IEEE 
 Trans. Wireless Commun., 8(8), 3933–3938. 
[13] R. Lopez-Valcarce and E. Antonio-Rodriguez and C. Mosquera and F. Perez-Gonzalez 
 (2012). An Adaptive Feedback Canceller for Full-Duplex Relays Based on Spectrum 
 Shaping. IEEE J. Sel. Areas Commun, 30(8), 1566–1577. 
[14] G. Interdonato and H. Q. Ngo and E. G. Larsson and P. Frenger (2016). How Much Do 
 Downlink Pilots Improve Cell-Free Massive MIMO? IEEE Global Communications 
 Conference (GLOBECOM), p.1–7. 
[15] M. Bashar, K. Cumanan, A. G. Burr, H. Q. Ngo, and M. Debbah (2018). Cell-free 
 Massive MIMO with limited backhaul, in Proc. IEEE ICC, pp. 1–7. 
 360 
 Thu Dau Mot University Journal of Science – Volume 2 – Issue 4-2020 
[16] S. Shamai and B. M. Zaidel (2001). Enhancing the cellular downlink capacity via co-
 processing at the transmitting end. in Proc. IEEE VTC-Spring, vol. 3, pp. 1745–1749. 
[17] Osseiran, J. F. Monserrat, and P. Marsch (2016). 5G Mobile and Wireless Communications 
 Technology. Cambridge University Press, ch. 9, Coordinated Multi-Point Transmission in 
 5G. 
[18] Shidong Zhou and Ming Zhao and Xibin Xu and Jing Wang and Yan Yao (2003). 
 Distributed wireless communication system: a new architecture for future public wireless 
 access. IEEE Commun. Mag., 41(3), 108–113. 
[19] J. Mitola and G. Q. Maguire (1999). Cognitive radio: making software radios more 
 personal. IEEE Pers. Commun., 6(4), 13–18. 
[20] G. I. Tsiropoulos and O. A. Dobre and M. H. Ahmed and K. E. Baddour (2016). Radio 
 Resource Allocation Techniques for Efficient Spectrum Access in Cognitive Radio 
 Networks. IEEE Commun. Surv. Tutor, 18(1), 824–847. 
[21] Goldsmith and S. A. Jafar and I. Maric and S. Srinivasa (2009). Breaking Spectrum 
 Gridlock With Cognitive Radios: An Information Theoretic Perspective. Proc. IEEE, 
 97(5), 894–914. 
[22] V. Valenta and R. Marsˇalek and G. Baudoin and M. Villegas ´ and M. Suarez and F. 
 Robert (2010). Survey on spectrum utilization in Europe: Measurements, analyses and 
 observations. in Proc. 5th Int. Conf. CrownCom, Jun. 2010. 
[23] S. Haykin (2005). Cognitive radio: brain-empowered wireless communications. IEEE J. 
 Sel. Areas Commun., 23(2), 201–220. 
[24] S. Srinivasa and S. A. Jafar (2007). Cognitive Radios for Dynamic Spectrum Access - The 
 Throughput Potential of Cognitive Radio: A Theoretical Perspective. IEEE Commun. 
 Mag., 45(5), 73–79. 
[25] M. O. Hasna and M. S. Alouini (2003). End-to-end performance of transmission systems 
 with relays over Rayleigh-fading channels. IEEE Trans. Wireless Commun., 2(6), 1126–
 113. 
[26] S. Ikki and M. H. Ahmed (2007). Performance Analysis of Cooperative Diversity Wireless 
 Networks over Nakagami-m Fading Channel. IEEE Commun. Lett., 11(4),334–336. 
[27] H. A. Suraweera and H. K. Garg and A. Nallanathan, “Performance Analysis of Two Hop 
 Amplify-and-Forward Systems with Interference at the Relay,” IEEE Commun. Lett., vol. 
 14, no. 8, pp. 692–694, Aug. 2010. 
[28] J. H. Lee and O. S. Shin (2010). Full-duplex relay based on block diagonalisation in 
 multiple-input multiple-output relay systems. IET Comm., 4(15), 1817–1826. 
[29] J. N. Laneman and D. N. C. Tse and G. W. Wornell (2004). Cooperative diversity in 
 wireless networks: Efficient protocols and outage behavior. IEEE Trans. Inf. Theory, 
 50(12), 3062–3080. 
[30] T. Q. Duong and Vo Nguyen Quoc Bao and H. j. Zepernick (2009). On the performance of 
 selection decode-and-forward relay networks over Nakagami-m fading channels. IEEE 
 Commun. Lett., 13(3), 172–174. 
 361 
Toan Xuan Doan, Thanh Quoc Trinh – Volume 2 – Issue 4-2020, p. 348-362. 
[31] S. Yang and X. Xu and D. Alanis and S. Xin Ng and L. Hanzo (2016). Is the Low-
 Complexity Mobile-Relay-Aided FFR-DAS Capable of Outperforming the High-
 Complexity CoMP?. IEEE Trans. Veh. Technol., 65(4), 2154–2169. 
[32] M. S. Pan and T. M. Lin and W. T. Chen (2015). An Enhanced Handover Scheme for 
 Mobile Relays in LTE-A High-Speed Rail Networks. IEEE Trans. Veh. Technol., 64(2), 
 743–75. 
[33] D. Hwang and D. I. Kim and S. K. Choi and T. J. Lee (2015). UE Relaying Cooperation 
 Over D2D Uplink in Heterogeneous Cellular Networks. IEEE Trans. Commun., 63(12), 
 4784–4796. 
[34] S. S. Nam and M. S. Alouini and S. Choi (2018). Iterative Relay Scheduling With Hybrid 
 ARQ Under Multiple User Equipment (Type II) Relay Environments. IEEE Access, vol. 6, 
 pp. 6455–6463. 
[35] M. G. Khafagy, M. S. Alouini, and S. A¨ıssa (2018). Full-duplex relay selection in 
 cognitive underlay networks. IEEE Trans. Commun., pp. 1–1. 
[36] D. Feng and L. Lu and Y. Yuan-Wu and G. Y. Li and S. Li and G. Feng (2014). Device-to-
 device communications in cellular networks. IEEE Commun. Mag., 52(4), 49–5. 
[37] F. Jameel and Z. Hamid and F. Jabeen and S. Zeadally and M. A.Javed (2018). A Survey 
 of Device-to-Device Communications: Research Issues and Challenges. IEEE Commun. 
 Surveys Tuts., pp. 1–1,. 
[38] S. Sudevalayam and P. Kulkarni (2011). Energy Harvesting Sensor Nodes: Survey and 
 Implications. IEEE Commun. Surveys Tuts., 13(3), 443–461. 
[39] R. V. Prasad and S. Devasenapathy and V. S. Rao and J. Vazifehdan (2014). Reincarnation 
 in the Ambiance: Devices and Networks with Energy Harvesting. IEEE Commun. Surveys 
 Tuts., 16(1), 195–213. 
 362