3 Andrews, J.G., Ganti, R.K., Haenggi, M. et al., A primer on spatial modeling and analysis in wireless networks. IEEE Communications Magazine, vol. 48, no. 11, pp. 156–163, November 2010.
4 Andrews, J.G., Buzzi, S., Choi, W. et al., What will 5G be? IEEE Journal on Selected Areas of Communications, vol. 32, no. 6, pp. 1065–1082, June 2014.
5 Aquilina, P., Cirik, A.C., and Ratnarajah, T., Weighted sum rate maximization in full‐duplex multi‐user multi‐cell MIMO networks. IEEE Transactions on Communications, vol. 65, no. 4, pp. 1590–1608, April 2017.
6 Axell, E., Leus, G., and Larsson, E.G., Spectrum sensing for cognitive radio: State‐of‐the‐art and recent advances. IEEE Signal Processing Magazine, vol. 29, no. 3, pp. 101–116, May 2012.
7 Belikaidis, I., Georgakopoulos, A., Demestichas, P. et al. Multi‐RAT dynamic spectrum access for 5G heterogeneous networks: The speed‐5G approach. IEEE Wireless Communications, vol. 24, no. 5, pp. 14–22, October 2017.
8 Chen, S. and Zhao, J., The requirements, challenges, and technologies for 5G of terrestrial mobile telecommunication. IEEE Communications Magazine, vol. 52, no. 5, pp. 36–43, May 2014.
9 Du, B., Pan, C., Zhang, W., and Chen, M., Distributed energy‐efficient power optimization for CoMP systems with max‐min fairness. IEEE Communications Letters, vol. 18, no. 6, pp. 999–1002, June 2014.
10 Elmasry, G., McClatchy, D., Heinrich, R., and Delaney, K., A software defined networking framework for future airborne connectivity. 2017 Integrated Communications, Navigation and Surveillance Conference (ICNS), Herndon, VA, 2017, pp. 1–19.
11 Haider, F. and Gao, X., Cellular architecture and key technologies for 5G wireless communication networks. IEEE Communications Magazine, vol. 52, no. 2, pp. 122–130, May 2014.
12 He, S., Huang, Y., Jin, S., and Yang, L., Coordinated beamforming for energy efficient transmission in multi‐cell multi‐user systems. IEEE Transactions on Communications, vol. 61, no. 12, pp. 4961–4971, December 2013.
13 Hong, X., Wang, J., and Wang, C.X., Cognitive radio in 5G: A perspective on energy‐spectral efficiency tradeoff. IEEE Communications Magazine, vol. 52, no. 7, pp. 46–53, July 2014.
14 Ismail, M. and Zhunag, W., A distributed multi‐service resource allocation algorithm in heterogeneous wireless access medium. IEEE Journal on Selected Areas in Communications, vol. 30, no. 2, pp. 425–432, February 2012.
15 Koudouridis, G. and Soldati, P., spectrum and network density management in 5G ultra‐dense networks. IEEE Wireless Communications, pp. 30–37, October 2017.
16 Mahmood, N., Sarret, M.G., Berardinelli, G., and Mogensen, P., Full duplex communications in 5G Small CELLS. In 2017 13th International Wireless Communications and Mobile Computing Conference (IWCMC), Valencia, 2017, pp. 1665‐1670.
17 Ng, D., Lo, E., and Schober, R., Energy‐efficient resource allocation in multi‐cell OFDMA systems with limited backhaul capacity. IEEE Transactions on Wireless Communications, vol. 11, no. 10, pp. 3618–3631, October 2012.
18 Osseiran, A., Boccardi, F., and Braun, V., Scenarios for 5G mobile and wireless communications: The vision of the METIS project. IEEE Communications Magazine, vol. 52, no. 5, pp. 26–35, May 2014.
19 Rodriguez, J. (ed.), Fundamentals of 5G Mobile Networks, Wiley, 2015. ISBN 9781118867525.
20 SPEED‐5G Public Deliverable, D3.2: SPEED‐5G enhanced functional and system architecture, scenarios and performance evaluation metrics. ICT‐671705, H2020‐ICT‐2014‐2, June 2016.
21 Venturino, L., Zappone, A., Risi, C., and Buzzi, S., Energy‐efficient scheduling and power allocation in downlink OFDMA networks with base station coordination. IEEE Transaction on Wireless Communications, vol. 14, no. 1, pp. 1–14, 2015.
22 Weber, S., Yang, X., Andrews, J.G., and de Veciana, G., Transmission capacity of wireless ad hoc networks with outage constraints. IEEE Transactions on Information Theory, vol. 51, no. 12, pp. 4091–4102, December 2005.
23 Yang, C., Li, J., Guizani, M. et al., Advanced spectrum sharing in 5G cognitive heterogeneous networks. IEEE Wireless Communications, vol. 23, no. 2, pp. 94–101, April 2016.
24 Zhang, J. and Andrews, J.G., Distributed antenna systems with randomness. IEEE Transaction on Wireless Communications, vol. 7, no. 9, pp. 3636–3646, September 2008.
25 Zhang, Z, Zhang, W., Zeadally, S, Wang, Y., and Liu, Y., Cognitive radio spectrum sensing framework based on multi‐agent architecture for 5G networks. IEEE Wireless Communications, vol. 22, no. 6, pp. 34–39, December 2015.
Notes
1 1 5G literature often uses the term dynamic spectrum management (DSM) instead of dynamic spectrum access.
2 2 Technically mm‐wave starts at 30 GHz, but it is common in 5G literature to consider the band above 24 GHz to be the mm‐wave band.
3 3 Many military communications waveforms use a mix of TDMA and FDMA, creating time and frequency slots for the allocated spectrum that can be shared between network nodes in omni‐directional transmission. Part 1 of this book showed how military communications MANETs evolved to use directional sectored antennas to be able to transmit and receive on the same frequency simultaneously based on directionality. 5G introduces many antenna technologies that enhance this FD capability for commercial wireless systems.
4 4 FD and concurrent sensing can mitigate the hidden node problem explained in Chapter 2.
5 5 Figure 5.4 is an oversimplification of spatial separation. Base stations before 5G used sectored antennas allowing for frequency reusability, which is not illustrated in this figure.
6 6 There are different terminologies in the literature that expresses the signal to noise ratio. SNR is one term that stands for signal to noise ratio; SNIR is another term that expresses signal to noise interference ratio. SINR is a third term that stands for signal to interference plus noise ratio. SIR is a fourth term for signal to interference ratio where interference can be additive noise and/or from another user using the same frequency. While in the previous chapters we used the term SNIR because military communications seek to distinguish noise from interference that can be malicious, in this chapter we will use the term SIR to emphasize the role of SI in 5G.
7 7 Notice that not all transmitting nodes cause interference. Φ is the subset of all transmitting nodes that can introduce interference at location o.
8 8 Metrics such as the probability of granting a connection when requested, the probability of keeping a connection during the duration of the session, and the probability of meeting the data rate defined in the service agreement can be used to quantify the reliability of the 5G network.
9 9 Notice that β can be a tunable parameter. Some literature uses the expression “link closure” to indicate that the condition SIR > β is met.
10 10 Theoretically, one can always trade data rate for FEC to achieve link closure under low SIR. Practically, there is a computational power limitation for the use of FEC. A 5G node has limited modes of turbo code and can't keep trading data rate for lower SIR to achieve link closure. Also, a link is requested with a specific minimum data rate. The result of Equation 6.1 defines Γ in Equation 6.2.
11 11 Notice how β is related to the data rate. A link can always trade off bandwidth for higher dB gain using error control coding.
12 12 Note that different flows may be requesting different data rates.
13 13