The fifth generation (5G) of cellular networks for beyond 2020 envisages to handle two new use cases in Machine-Type Communications (MTC), namely Ultra-Reliable Low Latency Communications (URLLC) and massive MTC (mMTC) . In MTC, MTC devices autonomously communicate with minimum human cooperation , . 5G communication technology should be flexible enough to support ultra-reliable low latency communications by guaranteeing reliability greater than . Key challenges and requirements of 5G technology such as latency, data rate, energy and cost issues are discussed in more details in , , .
In recent years, MTC has gained much attention from the mobile network operators, equipment vendors and academic researchers due to such novel communication paradigm, the capability of exchanging short data messages and also being cost-effective, energy efficient, ; reliable and within a stringent delay requirement. MTC takes advantage of several distinctive properties such as group-based communications, low mobility, time-controlled, time-tolerant and secure connection which are at the same time challenging tasks since technically advanced solutions are needed to deliver the required tasks. Within the application requirements, hence, opening up different research areas is currently being carried out in academia, industry, and standards bodies . Current technologies cover a small range of applications and services while the upcoming MTC should be able to cover a broad range of services with multiple forms of data traffic in order to deal with different service requirements as data rate, latency, reliability, energy consumption and security , . Future MTC improvements will be conspicuous in health-care, logistics, process automation, transportation, e.g , . In mMTC a huge number of devices in a specific domain are connected to the cellular network with low-rate and low-power connectivity, different quality-of-service (QOS) requirements and high reliability to support demanding situations, e.g. smart meters, actuators , .
Moreover, MTC services have to met stringent timing constraints from few seconds to even excessively low end-to-end deadlines in mission critical communications , connection between vehicles, remote control of robots in addition to an extreme low end-to-end latency in the scope of less than a millisecond which is a key enabler in several services including cloud connectivity, industrial control, road safety , , . Latency refers to the time duration between transferring the message from the transmitter and receiving correctly at the receiver where some messages drop due to the buffer overflows, unsuccessful synchronizations, unsuccessful decoding which result in unlimited delay . Hence, we can define the reliability as the probability of successful transmission under the predetermined delay constraint , . In URLLC, high probability of successful transmission indicates low outage probability (or packet drop) while the opposite does not always hold as the reliability is restricted to a specific latency budget due to the limited amount of channel uses . Hence, one of the major requirements of URLLC is a extremely low outage probability under a very demanding latency budget where retransmissions are not always available. In the use of short messages under URLLC, new robust channel codes are needed; otherwise, the performance of the system will be even further away from the Shannon limit with long data packets .
Under Shannon’s channel coding theorem, error-free communication is attained when the blocklength goes to infinity . For instance, authors in , provide a tight approximation of achievable coding rate under finite blocklength (FB) regime and indicate a noticeable performance loss compared to the Shannon coding. This motivates us to analyze the performance of MTC under FB regime since in URLLC, due to the equal packet length of metadata and information bits, an unsuccessful encoding of the metadata decreases the system efficiency . In the past few years, several works have studied different aspects of FB coding since majority of the theoretical results assume infinite blocklength (IFB). For instance, authors in , examine some possible FB coding schemes which may be applied in 5G technology. They show that novel coding schemes with better minimum distance between the codewords, improve the efficiency of system at the cost of more sophisticated decoders. Moreover, the performance of spectrum sharing networks with FB codes are studied in . The blocklength of information bits highly affects the system quality where an optimal power allocation technique improves the system efficiency with short message transmissions. Furthermore, authors in , propose a new power allocation technique, so-called modified water-filling in order to maximize the lower bound of the coding rate with short packet transmission compared to the common water-filling method. In addition, performance of ARQ protocol in terms of throughput and average latency is studied in . Authors determine the optimal lengths of the codeword which minimize the latency and maximize the throughput per-user for an specific number of information bits. They illustrate that with optimal codes, the shorter the codeword is, the lower outage probability attains.
1.1 On the Impact of Cooperative Diversity
Cooperative diversity provides the possibility of high data rate; while improving the reliability. In cooperative networks, intermediate nodes transfer the message from the source to destination . Cooperative technique exploits the spatial diversity gain to reduce the impact of wireless fading from multipath propagation. The major advantage of this technique is that the several independent copies of a signal arrive at the destination without installing collocated antennas at the source or receiver in addition of a better signal quality, better coverage, greater capacity and lower transmit power , . The most conventional cooperative scheme is decode-and-forward (DF), where the auxiliary node, namely relay, decodes, encodes and retransmits the message . Cooperative schemes are categorized as fixed, adaptive and feedback schemes . In the fixed protocol, relay always forwards the message to destination while in adaptive protocol, the relay retransmits the message under a predefined threshold rule which enables that to communicate independently or not. In the feedback protocol, if the destination requests, the cooperation takes place . During the past few years, the efficiency of cooperative networks has been investigated in several system and channel models. Authors in , propose a method that meet the high reliability and latency requirements through taking the advantage of cooperative relaying technique. Moreover, authors in , provide a comprehensive study regarding the exiting cooperative schemes and analyze the performance of each scheme. Relaying performance of quasi-static Rayleigh channels where the channel gains of the direct link and relaying are combined at the destination, is studied in . They indicate that the performance loss increases if the outage probability of the source-to-relay link is higher than the overall outage probability. The efficiency of multi-relay DF scenario under the assumption of perfect channel-state-information (CSI) and partial CSI is provided in . Authors show that with perfect CSI, the throughput of IFB is smaller than the throughput with FB coding. Authors in , examine the throughput of a multi-hop relaying network under FB and IFB regimes with two assumptions: target overall outage probability constant coding rate. They illustrate that there is different but optimal number of hops which maximize the throughput for either FB or IFB assumptions. In addition, they indicate that the FB-throughput is quasi-concave in the overall outage probability and coding rate. Furthermore, authors illustrate that the multi-hop network is less affected by the blocklength under the constant coding rate assumption compared to the target overall outage probability scenario. Moreover,  studies the performance of DF relay network in dissimilar Rayleigh fading channels. Although authors attain the closed form expression of the outage probability, but they do not consider the impact of finite blocklength coding. Furthermore, authors in , study the achievable coding rate and ergodic capacity of non-orthogonal amply-and-forward (AF) multi relay network subject a total average power constraint (TAPC) and an individual average power constraint (IAPC). They indicate that the ergodic capacity can be attained by an iterative water-filling-based algorithm. In addition, they show that in a multi relay NAF network, the transmit power at the source should be equally allocated in all broadcasting phases to cover the capacity at sufficiently high SNRs.
Moreover, in our previous work , introduces relaying as means to achieve ultra-reliable. We study the performance of cooperative relaying protocols, supposing Rayleigh fading channels. We show that relaying technique improves the reliability and how we can meet the ultra-reliable communication requirements. We examine the impact of coded blocklength and number of information bits on the probability of successful transmission. In addition, it is shown that relaying requires less transmit power compared to the direct transmission (DT) to enable ultra-reliable under FB regime. We also provide an approximation to the outage probability in closed form. We extend our work in , by considering ultra-reliable MTC with incremental relaying technique in . We define the overall outage probability in each studied relaying scheme, assuming Nakagami- fading. We investigate the impact of fading severity and power allocation factor on the outage probability. We also provide the outage probability in closed form. Our works in  and  show that cooperative diversity os useful to meet URLLC requirements.
1.2 Energy Efficiency of Cooperative Communication
Another key characteristic of wireless communications that highly affect the performance of 5G networks, is the energy efficiency (EE) due to the limited energy resources in energy-constraint networks , . EE which has been widely studied recently literatures, is defined as the ratio of successfully transmitted bits to the total consumed energy ,. Hence, reducing the amount of energy-per-bit, improves EE at low SNR regime , particularly in wireless networks where the batteries which are not rechargeable or easy to charge, supply the wireless components . The reason which motivates us to study EE in the context of URLLC is that,URLLC is achieved at the cost of high transmit power , , but we aim to show that cooperative diversity alleviates these demands.
In early works, authors in , examine EE in tactile Internet under queuing and transmission delays to design an energy efficient resource allocation strategy. They propose an optimal resource allocation strategy where the average total consumed power under stringent latency constraint equals to that with unlimited queuing latency requirement with plenty of transmit antennas. Moreover, , provides a comprehensive overview of energy-efficient networks, and determines the trade-off between energy efficiency and spectrum efficiency and their applications in 5G networks.
1.3 Our Contribution
In this work, we further study three cooperative protocols, namely DF, selection combining (SC) and MRC. Furthermore, we indicate the superiority of MRC over SC and DF protocols in terms of coding rate and reliability. We also show the optimal value of power allocation at the source and relay in each of studied protocols. Moreover, we examine the minimum latency and energy efficiency in cooperative schemes under two different power allocation constraints.
The following are considered the contributions of this work.
The rest of this paper is organized as follows. Section 2 presents the system model. Section 3 discusses the cooperative diversity and examines the outage probability in three cooperative schemes considered in this work, and Section 4.1 presents some numerical results regarding the performance of studied cooperative schemes under URR. Section 4.2 investigates the energy efficiency of considered cooperative schemes and presents some numerical results. Finally, Section 5 concludes the paper. The important abbreviation and symbols are listed in Table 1.
|bpcu||Bit per Channel Use|
|CSI||Channel State Information|
|DF||Decode and Forward|
|EPA||Equal Power Allocation|
|mMTC||Massive Machine-Type Communication|
|MRC||Maximum Ratio Combining|
|OPA||Optimal Power Allocation|
|Probability Density Function|
|QOS||Quality of Service|
|SNR||Signal to Noise Ration|
|URLLC||Ultra-Reliable Low Latency Communication|
|URR||Ultra reliable Region|
|Probability Density Function|
|Inverse of -Function|
|Exponential Euler’s Number|
|Number of Channel Uses|
|Logarithm to the Base 2|
|Number of Channel Uses for Source|
|Number of Channel Uses for Relay|
|Distance of Source-Destination Link|
|Distance of Source-Relay Link|
|Distance of Relay-Destination Link|
|Power of Relay|
|Power of Source|
|Power of Transmitter|
|Power of Receiver|
|Power of Amplifier|
|Probability of Successful Transmission|
|Maximum Total Power|
|Power of Noise|
|Average Power Constraint|
|Power Allocation Factor|
|Maximum Coding Rate|
2.1 System Model
Fig. illustrates a DF relaying scenario including a source , destination and a decode-forward relay . We normalize the distance of to as m, and that can move in a straight line between and , while the distance between and is denoted by and the distance of the relaying link is denoted by . The links denoted by the following random variables , and represent the -, - and - links respectively, and each transmission uses channel uses where . This means that channel uses in the broadcasting phase and channel uses for the relaying phase. In this scenario, first sends the message to the and in the broadcasting phase and if successfully decodes the message, forwards it to the in the relaying phase . The received signals in the broadcasting phase are denoted as and , and only if collaborates with , the received signal at is as follow 
where is the transmitted signal with power and is the AWGN noise with power where . Quasi-static Rayleigh fading channels in the -, - and - links are denoted as , and , respectively. In this work, we consider two distinct power constraints, namely ) EPA where equal powers are allocated to and and ) OPA where total power of and is equal to the maximum power. In a DF-based relaying protocol, the instantaneous SNR depends on the total power constraint , which is given by , and , where is the power allocation factor considered to provide a fair comparison between DT and cooperative transmissions and with EPA. Hence, the average SNR in each link is , and .
2.2 Performance Analysis of Single-Hop Communication under the Finite Blocklength Regime
In this section we revisit the concept of FB coding. In a single hope communication, first information bits are mapped to a sequence, namely codeword including
symbols. Afterwards, the created codeword passes the wireless channels and channel outputs map into the estimate of the information bits. Thus, for a single-hop communication with blocklength, outage probability and the average power constraint , where holds, the maximum coding rate of AWGN channel in bits is calculated as
which holds for the AWGN channels where the channel coefficient is equal to one. While for quasi-static fading channels, we attain the outage probability as follow 
Note that (4) is accurate for , as proved for AWGN channels [19, Figs. and ], as well as for fading channels as discussed in . In addition, in the relaying schemes, we assume that can encode information bits into channel uses, while uses channel uses. Hence, and could employ more sophisticated encoding technique than , .
2.3 Closed-Form Expression of the Outage Probability
Unfortunately, (4) does not have a closed-form expression, but it can be tightly approximated as we shall see next.
The outage probability is approximated as
where and , where .
Moreover, we compare the accuracy of linearized Q-function in (6) to original Q-function in (3) as indicated in Fig.. The difference between these two plots does not have a noticeable impact on the outage probability since we find the approximated outage probability in (5) via integrating over the SNR range and due to the symmetric property of the function as evinced by Fig., regions that show the difference between the original and linearized Q-function, cancel each other and so, this difference becomes negligible as illustrated in Fig.. Thus, we can notice that error defined by is approximately equal to zero which shows the accuracy of the linearized Q-function applied in the closed form expression of the outage probability. Fore example, the maximum error over the entire SNR range is about . Similar conclusion holds for other values of .
3 The Proposed Method
In this section, we investigate the outage probability of cooperative DF, SC and MRC protocols under the FB regime. The direct transmission model is used here as the basis of the comparison.
3.1 Direct Transmission
The source sends the message directly to the destination, where , with average SNR , where the outage probability is calculated as in (5) but with and with .
3.2 Dual Hop Decode-and-Forward (DF)
In this scheme, since the - distance is too large, it assumes that the direct link is in the outage; thus, always collaborates with the source. Hence, sends the message to both and in the broadcasting phase. Then, transfers the message to . The overall outage probability is given by
where and are calculated according to (5). Notice that we update with , and with , , respectively. This scenario can be analyzed as selection combining (SC) or maximum ratio combining (MRC) depending on how the destination combines the original transmitted signal and the retransmitted signal.
3.3 Selection Combining (SC)
In this protocol, starts to collaborate with if the destination confirms that the source transmission was unsuccessful and so, the destination requests for retransmission from to receive the frame correctly. Cooperation occurs if decodes the received message from correctly and so, transfers the message to . Thereafter, if confirms that the transmission from is also failed, requests for the next subsequent message from . Thus, the outage probability happens only if both - and - links are in outage , . The overall outage is given by
where is equal to (5) where is updated with and with .
3.4 Maximum Ratio Combining (MRC)
In this scenario, relay always collaborates with the source and so, the channel gains of - and - links are combined at the receiver. Thus, the aggregated SNR is bigger than the primary attempted transmission rate as the - transmission failed. In addition, the outage probability occurs if - or - transmission fails. Hence, the instantaneous SNR is , . The outage probability is 
where is the outage probability of the source-to-relay-to destination link, notice that the term refers to the probability that D was not able to decode S message alone. In order to calculate the (9), first we need to attain the PDF of , and then we calculate the outage probability as proposed in proposition 1. To do so, let denote the sum of two independently distributed exponential random variables (RV), and . Then, is 
Since the RVs are independent, the proof is straightforward solution of .
The outage probability of the MRC of the - and - links , is equal to
where, and are specified in (6), and , , , , , and .
3.5 Asymptotic Analysis
The outage probability in (5) can be defined as as the SNR goes to infinity, where . Thus, the approximated asymptotic outage probability per link in Rayleigh fading channels is , where is a function of and [36, §10]. Thereafter, we resort to Taylor series as approaches zero as , and so, and attain an asymptotic expression as [36, §11]. The asymptotic expression of after maximum ratio combining of and transmissions is given in [42, §7]. Therefore, the outage probability is approximated as , resorting to series expansion as .
4 Numerical Results and Discussion
4.1 URLLC via Cooperative Diversity
In this section we show some numerical results of cooperative relaying transmission under FB regime. First, we show the impact of coding rate on the probability of successful transmission where MRC protocol outperforms DF, SC and DT in terms of reliability. We also indicate the minimum latency required to support URLLC. Thereafter, we show the optimal value of power allocation factor for each of studied protocols. In addition, we compare the performance of cooperative relaying to DT in terms of power consumption and blocklength to perform under the UR region (URR). We verify the accuracy of our analytical model through the Monte-Carlo simulations. Unless stated; otherwise, assume maximum transmit power per link as dB, , , and is in between and , with . The URR is shaded purple area in the following plots, and its most loose constraint is denoted with a red line where the outage probability is , thus reliability is feasible.
4.1.1 Reliability vs. Coding Rate
Fig. compares the probability of successful transmission () as a function of coding rate in URR. We can clearly see that MRC supports URLLC with higher coding rates compared to DT, DF and SC schemes which is more evident with short packet lengths under the FB regime. Hence, MRC is less affected by the coding rate growth under the URR. For instance, with and , MRC covers reliability, while SC provides equal reliability as MRC but with lower coding rate as and . In addition, with , reliability decreases to and with and for DF and DT schemes, respectively. Thus, URLLC is feasible via the cooperative schemes and we can apply each of these schemes based on our requirements such as reliability, packet length and number of transmitted information bits.
In Fig. we examine the impact of power allocation factor on the probability of successful transmission. As mentioned earlier in Section 2, in order to provide a fair comparison between DT and cooperative schemes, we allocate powers to and according to the power allocation factor. In DF, outage probability is minimized via equal power allocation strategy while in SC and MRC, we exploit additional diversity of the direct link; thus, less power should be allocated to as shown in Fig.. We also illustrate that URLLC is feasible through the cooperative schemes, particularly with SC and MRC where the outage probability is minimized to and , respectively. As we indicated in our previous work in , these results holds for other values of SNR and coding rate.
In Fig. we compare the ultra-reliable performance of cooperative schemes to DT in terms of transmit power under equal power allocation constraint. We can clearly see the power gain attained via the cooperative protocols at high SNR regime where there is huge performance gap between cooperative schemes and DT. In addition, we indicate that MRC and SC protocols perform closely in the entire SNR range and consumes less transmit power to communicate under the URR in comparison to DF and DT.
In addition, we indicate the possibility of using asymptotic expressions in ultra-reliable region. In other words, at high SNR regime, the maximum achievable coding rate (2) converges the asymptotically long codewords as , where . In Fig. we show that the asymptotic expressions approach the analytical results as the transmit power increases.
Fig. indicates the performance advantage of cooperative schemes over DT. Cooperative schemes exploit diversity gain which decreases the outage probability remarkably. As we expected, the outage probability decreases in blocklength. In addition, SC and MRC protocols are able to support URLLC under FB regime with very short packet lengths.
Fig. indicates the total minimum latency required for URLLC under the FB regime with two distinct power constraints as ) EPA: , where , and ) OPA: . The choices of the minimum latency and optimal powers are in such a way that minimizes the outage probability constraint to a specific interval of interest and holds the power constraints which gives the optimal values of and , and is a nonlinear optimization problem as follows 111We solve the optimization problem numerically with the Matlab function . Interior point algorithm is used to solve the nonlinear optimization problem ..
We set the minimum blocklength to 100 since (4) is accurate for , as proved for AWGN channels [19, Figs. and ] as well as for fading channels as discussed in , and to a maximum of so to reduce the search range, and to be within URLLC boundaries.
It shows that DT is not able to cope with the stringent latency constraint and need a large tolerance of delay; thus, we resort to cooperative protocols in order to reduce the latency in URR. It can be clearly seen that SC works highly better than DF and performs closely to MRC in the entire range but with higher latency requirements when we allocate equal powers to the and . We also indicate that under OPA constraint, SC outperforms MRC in terms of channel uses and is more energy efficient than MRC as we discuss about it in the following section, while with equal power allocation strategy, MRC requires less channel uses and consumes less transmit power as we can see in Fig.. Here, with equal power allocation strategy, the total power of and may be less than the maximum total power ( dB) but in Fig. we force and to be equal with total power of . Therefore, according to the simulations, when bpcu and dB, higher reliability is feasible in Fig. compared to Fig..
4.2 Energy Efficiency Analysis
Energy efficiency (EE) determines the trade-off between throughput gains and total energy consumed. Let us first define the total energy consumption per bit of each scenario. The total power consumed includes power of transmission with no dependency on the distance of relay nodes, consumed power in radio frequency(RF) circuitry and also coding rate , . Here, we ignore the baseband processing consumption since its value is negligible in comparison to the energy consumption of RF circuitry .
Then the total energy consumption per bit of a single-hop transmission is
where is the power amplifier consumption for a single-hop transmission and is the drain efficiency of the amplifier, and are the power consumed for transmitting and receiving in the internal circuitry, respectively. In a similar way, we can also find the total power consumption of multi-hop schemes by determining the outage probability on - link in each cooperative schemes.
4.2.1 Cooperative Transmissions
The total power consumption for DF protocol depends on the outage probability of - link as follow
where the first term indicates that the consumed energy on the - link, while the second term shows that could decode the message correctly and send the packet to .
In the case of SC and MRC, the total power consumption is formulated as follow
where and is calculated by (5) accordingly to each method. The additional in each term of (17) compared to the (16), corresponds to the transmission of , which is heard by both and and destination decodes - and - transmissions, simultaneously.
Hence, the EE for each protocol is formulated as
Furthermore, as observed from Fig., is non-convex in the SNR, while the outage probability is monotonically decreasing in the SNR and energy consumption is monotonically increasing, which is observed in Figs. and , respectively. We maximize the energy efficiency as follow
This problem is equivalent to minimize the outage probability with respect to , and blocklength . Since we aim to compare the performance of cooperative schemes, we do not focus on the proposal of a particular solution, but we resort to numerically efficient algorithm. Therefore, we resort to implemented in Matlab and use interior point algorithm to solve the nonlinear optimization problem as detailed in . We consider outage probability threshold in an interval of interest as . At each outage probability value, we numerically determine , and blocklength that maximize the energy efficiency. We apply the numerical optimization due to the nonlinear constraint on the outage probability .
Fig. compares the energy efficiency of cooperative schemes in terms of probability of successful transmission under two distinct power constraints. In this paper, we assume mW, mW and the drain efficiency according to the power consumption values reported in . Under EPA strategy, MRC is the most energy efficient scenario among other cooperative scenarios as it consumes less transmit power shown in Fig., and has lower latency in URR while under OPA, SC becomes the most energy efficient protocol as we show in Fig. it reduces the latency and the total power consumption is less than that of MRC. Since Fig. indicates that in order to perform in URR, we should allocate more power to the source where is equal to and for SC and MRC, respectively. Hence, more power is allocated to the source of MRC than that of SC; thus, MRC becomes less energy efficient compared to SC under OPA strategy.
Fig. compares the total consumed energy in each of studied cooperative scenarios under EPA and OPA strategies. Under EPA, as we expected MRC is superior and consumes less transmit power compared to DF and SC protocols, while with OPA, SC outperforms MRC and becomes most energy efficient protocol. In addition, with the maximum transmit power of dB, and no feasible solutions are found for DF protocol under stringent reliability requirements, which evinces the need for more sophisticated cooperative protocols. Feasible solutions are found if the transmit power increases, but it would be spectrally and energy insufficient.
In this paper, we assess the relay communication under the finite blocklength regime under Rayleigh fading. Performance of three relaying scenario, namely DF, SC and MRC is compared to direct transmission under two distinct power constraints so-called EPA and OPA. Based on the outage probability analysis of each transmission protocol, we show that relaying improves the probability of a successful transmission and guarantees ultra-high reliability with FB codes. MRC protocol is less affected by the coding and provide higher reliability compared to DT and two other relaying scenarios. In addition, we numerically show the optimal power allocation for the relaying protocols under study so to operate in URR. Our results shows that operation at URR is feasible by allocating more power to the source; however, relay node is considered to provide additional diversity gain compared to the DT which is more evident at high SNR regime. We compare the studied cooperative schemes in terms of latency and energy efficiency under the two distinct power constraints. According to the results, with equal power allocation at source and relay, MRC is the most energy efficient protocol with lower latency and power consumption compared to the other scenarios while SC has the highest energy efficiency and lowest latency under optimal power allocation strategy. Moreover, we provide the outage probability in closed form and prove the accuracy and appropriateness of our analytical model through numerical results. Finally, in our future work, we will focus on the impact of imperfect channel state information on URLLC.
Availability of data and material
The manuscript is self-contained. Simulations description and parameters are provided in details in Section 4.
The authors declare that they have no competing interests.
This work has been partially supported by Finnish Funding Agency for Technology and Innovation (Tekes), Huawei Technologies, Nokia and Anite Telecoms, and Academy of Finland (under Grant no. 307492)
All authors have contributed to this manuscript and approved the submitted manuscript.
Centre for Wireless Communications (CWC), University of Oulu, Finland
-  P. Popovski, J. J. Nielsen, C. Stefanovic, E. de Carvalho, E. Ström, K. F. Trillingsgaard, A.-S. Bana, D. M. Kim, R. Kotaba, J. Park et al., “Ultra-reliable low-latency communication (urllc): Principles and building blocks,” arXiv preprint arXiv:1708.07862, 2017.
-  H. Shariatmadari, R. Ratasuk, S. Iraji, A. Laya, T. Taleb, R. Jäntti, and A. Ghosh, “Machine-type communications: current status and future perspectives toward 5G systems,” IEEE Communications Magazine, vol. 53, no. 9, pp. 10–17, 2015.
-  Y. Mehmood, C. Görg, M. Muehleisen, and A. Timm-Giel, “Mobile m2m communication architectures, upcoming challenges, applications, and future directions,” EURASIP Journal on Wireless Communications and Networking, vol. 2015, no. 1, p. 250, 2015.
-  A. E. Kalør, R. Guillaume, J. J. Nielsen, A. Mueller, and P. Popovski, “Network slicing for ultra-reliable low latency communication in industry 4.0 scenarios,” arXiv preprint arXiv:1708.09132, 2017.
-  J. G. Andrews, S. Buzzi, W. Choi, S. V. Hanly, A. Lozano, A. C. Soong, and J. C. Zhang, “What will 5G be?” IEEE Journal on selected areas in communications, vol. 32, no. 6, pp. 1065–1082, 2014.
-  Z. Dawy, W. Saad, A. Ghosh, J. G. Andrews, and E. Yaacoub, “Toward massive machine type cellular communications,” IEEE Wireless Communications, vol. 24, no. 1, pp. 120–128, 2017.
-  B. Lee, S. Park, D. J. Love, H. Ji, and B. Shim, “Packet structure and receiver design for low-latency communications with ultra-small packets,” in Global Communications Conference (GLOBECOM), 2016 IEEE. IEEE, 2016, pp. 1–6.
-  T. Taleb and A. Kunz, “Machine type communications in 3GPP networks: potential, challenges, and solutions,” IEEE Communications Magazine, vol. 50, no. 3, 2012.
-  M. Condoluci, M. Dohler, G. Araniti, A. Molinaro, and K. Zheng, “Toward 5G densenets: architectural advances for effective machine-type communications over femtocells,” IEEE Communications Magazine, vol. 53, no. 1, pp. 134–141, 2015.
-  J. F. Monserrat, G. Mange, V. Braun, H. Tullberg, G. Zimmermann, and Ö. Bulakci, “Metis research advances towards the 5g mobile and wireless system definition,” EURASIP Journal on Wireless Communications and Networking, vol. 2015, no. 1, p. 53, 2015.
-  C. Bockelmann, N. Pratas, H. Nikopour, K. Au, T. Svensson, C. Stefanovic, P. Popovski, and A. Dekorsy, “Massive machine-type communications in 5G: Physical and mac-layer solutions,” IEEE Communications Magazine, vol. 54, no. 9, pp. 59–65, 2016.
-  P. Popovski, “Ultra-reliable communication in 5G wireless systems,” in 1st International Conference on 5G for Ubiquitous Connectivity (5GU), 2014 . IEEE, 2014, pp. 146–151.
-  B. Singh, O. Tirkkonen, Z. Li, M. A. Uusitalo, and R. Wichman, “Selective multi-hop relaying for ultra-reliable communication in a factory environment,” in 27th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC), 2016 IEEE . IEEE, 2016, pp. 1–6.
-  A. Biral, H. Huang, A. Zanella, and M. Zorzi, “On the impact of transmitter channel knowledge in energy-efficient machine-type communication,” in Globecom Workshops (GC Wkshps), 2016 IEEE. IEEE, 2016, pp. 1–7.
-  G. Durisi, T. Koch, and P. Popovski, “Toward massive, ultrareliable, and low-latency wireless communication with short packets,” Proceedings of the IEEE, vol. 104, no. 9, pp. 1711–1726, 2016.
-  O. Yilmaz, “Ultra-reliable and low-latency 5G communication,” in European Conference on Networks and Communications (EuCNC), vol. 2016, 2016.
-  M. Sybis, K. Wesolowski, K. Jayasinghe, V. Venkatasubramanian, and V. Vukadinovic, “Channel coding for ultra-reliable low-latency communication in 5G systems,” in 84th Vehicular Technology Conference (VTC-Fall), 2016 IEEE . IEEE, 2016, pp. 1–5.
-  Y. Hu, J. Gross, and A. Schmeink, “On the performance advantage of relaying under the finite blocklength regime,” IEEE Communications Letters, vol. 19, no. 5, pp. 779–782, 2015.
-  Y. Polyanskiy, H. V. Poor, and S. Verdú, “Channel coding rate in the finite blocklength regime,” IEEE Transactions on Information Theory, vol. 56, no. 5, pp. 2307–2359, 2010.
-  O. Iscan, D. Lentner, and W. Xu, “A comparison of channel coding schemes for 5G short message transmission,” in Globecom Workshops (GC Wkshps), 2016 IEEE. IEEE, 2016, pp. 1–6.
-  B. Makki, T. Svensson, and M. Zorzi, “Finite block-length analysis of spectrum sharing networks,” in International Conference on Communications (ICC), 2015 IEEE . IEEE, 2015, pp. 7665–7670.
-  J.-H. Park and D.-J. Park, “A new power allocation method for parallel awgn channels in the finite block length regime,” IEEE Communications Letters, vol. 16, no. 9, pp. 1392–1395, 2012.
-  R. Devassy, G. Durisi, P. Popovski, and E. G. Strom, “Finite-blocklength analysis of the arq-protocol throughput over the gaussian collision channel,” in 6th International Symposium on Communications, Control and Signal Processing (ISCCSP), 2014 . IEEE, 2014, pp. 173–177.
-  S. Ikki and M. H. Ahmed, “Performance analysis of cooperative diversity wireless networks over nakagami-m fading channel,” IEEE Communications letters, vol. 11, no. 4, 2007.
-  E. Zimmermann, P. Herhold, and G. Fettweis, “On the performance of cooperative relaying protocols in wireless networks,” Transactions on Emerging Telecommunications Technologies, vol. 16, no. 1, pp. 5–16, 2005.
-  F. Mansourkiaie and M. H. Ahmed, “Cooperative routing in wireless networks: A comprehensive survey,” IEEE Communications Surveys & Tutorials, vol. 17, no. 2, pp. 604–626, 2015.
-  V. N. Swamy, S. Suri, P. Rigge, M. Weiner, G. Ranade, A. Sahai, and B. Nikolić, “Cooperative communication for high-reliability low-latency wireless control,” in International Conference on Communications (ICC), 2015 IEEE . IEEE, 2015, pp. 4380–4386.
-  Y. Hu, A. Schmeink, and J. Gross, “Relaying with finite blocklength: Challenge vs. opportunity,” in Sensor Array and Multichannel Signal Processing Workshop (SAM), 2016 IEEE. IEEE, 2016, pp. 1–5.
-  F. Du, Y. Hu, L. Qiu, and A. Schmeink, “Finite blocklength performance of multi-hop relaying networks,” in International Symposium on Wireless Communication Systems (ISWCS), 2016. IEEE, 2016, pp. 466–470.
-  P. Nouri, H. Alves, and M. Latva-aho, “On the performance of ultra-reliable decode and forward relaying under the finite blocklength,” in European Conference on Networks and Communications (EuCNC), 2017. IEEE, 2017, pp. 1–5.
-  P. Nouri, H. Alves, R. Demo Souza, and M. Latva-aho, “Ultra-reliable short message cooperative relaying protocols under nakagami-m fading,” in International Symposium on Wireless Communication Systems (ISWCS), 2017. IEEE, 2017.
-  D. Qiao, M. C. Gursoy, and S. Velipasalar, “Energy efficiency of fixed-rate wireless transmissions under queueing constraints and channel uncertainty,” in Global Telecommunications Conference, 2009. GLOBECOM 2009. IEEE. IEEE, 2009, pp. 1–6.
-  M. C. Gursoy, “On the capacity and energy efficiency of training-based transmissions over fading channels,” IEEE Transactions on Information Theory, vol. 55, no. 10, pp. 4543–4567, 2009.
-  G. Wu, C. Yang, S. Li, and G. Y. Li, “Recent advances in energy-efficient networks and their application in 5G systems,” IEEE Wireless Communications, vol. 22, no. 2, pp. 145–151, 2015.
-  C. She and C. Yang, “Energy efficient design for tactile internet,” in International Conference on Communications in China (ICCC), 2016 IEEE/CIC . IEEE, 2016, pp. 1–6.
-  E. Dosti, M. Shehab, H. Alves, and M. Latva-aho, “Ultra reliable communication via cc-harq in finite block-length,” in European Conference on Networks and Communications (EuCNC), 2017 . IEEE, 2017, pp. 1–5.
-  E. Dosti, U. L. Wijewardhana, H. Alves, and M. Latva-aho, “Ultra reliable communication via optimum power allocation for type-i arq in finite block-length,” arXiv preprint arXiv:1701.08617, 2017.
-  W. Yang, G. Durisi, T. Koch, and Y. Polyanskiy, “Quasi-static multiple-antenna fading channels at finite blocklength,” IEEE Transactions on Information Theory, vol. 60, no. 7, pp. 4232–4265, 2014.
-  Y. Hu, A. Schmeink, and J. Gross, “Blocklength-limited performance of relaying under quasi-static Rayleigh channels,” IEEE Transactions on Wireless Communications, vol. 15, no. 7, pp. 4548–4558, 2016.
-  B. Makki, T. Svensson, and M. Zorzi, “Finite block-length analysis of the incremental redundancy harq,” IEEE Wireless Communications Letters, vol. 3, no. 5, pp. 529–532, 2014.
-  J. N. Laneman, D. N. Tse, and G. W. Wornell, “Cooperative diversity in wireless networks: Efficient protocols and outage behavior,” IEEE Transactions on Information theory, vol. 50, no. 12, pp. 3062–3080, 2004.
-  H. Alves, R. D. Souza, G. Fraidenraich, and M. E. Pellenz, “Throughput performance of parallel and repetition coding in incremental decode-and-forward relaying,” Wireless Networks, vol. 18, no. 8, pp. 881–892, 2012.
-  H. Alves, R. D. Souza, G. Brante, and M. E. Pellenz, “Performance of type-i and type-ii hybrid arq in decode and forward relaying,” in 73rd Vehicular Technology Conference (VTC Spring), 2011 IEEE . IEEE, 2011, pp. 1–5.
-  P. Athanasios, “Probability, random variables, and stochastic processes,” 2017.
-  I. S. Gradshteyn and I. M. Ryzhik, Table of integrals, series, and products. Academic press, 2014.
-  R. A. Waltz, J. L. Morales, J. Nocedal, and D. Orban, “An interior algorithm for nonlinear optimization that combines line search and trust region steps,” Mathematical programming, vol. 107, no. 3, pp. 391–408, 2006.
-  G. G. de Oliveira Brante, M. T. Kakitani, and R. D. Souza, “Energy efficiency analysis of some cooperative and non-cooperative transmission schemes in wireless sensor networks,” IEEE Transactions on Communications, vol. 59, no. 10, pp. 2671–2677, 2011.
-  H. Alves, R. D. Souza, and G. Fraidenraich, “Outage, throughput and energy efficiency analysis of some half and full duplex cooperative relaying schemes,” Transactions on Emerging Telecommunications Technologies, vol. 25, no. 11, pp. 1114–1125, 2014.
-  S. Cui, A. J. Goldsmith, and A. Bahai, “Energy-constrained modulation optimization,” IEEE transactions on wireless communications, vol. 4, no. 5, pp. 2349–2360, 2005.
-  Tran, Tuyen X and Tran, Nghi H and Bahrami, Hamid Reza and Sastry, Shivakumar, “On achievable rate and ergodic capacity of NAF multi-relay networks with CSI,” IEEE transactions on communications, vol. 62, no. 5, pp. 1490-1502, 2014.
-  Beaulieu, Norman C and Hu, Jeremiah, “A closed-form expression for the outage probability of decode-and-forward relaying in dissimilar Rayleigh fading channels,” IEEE Communications Letters, vol. 10, no. 12, 2006.