Enabling Technologies for Ultra-Reliable and Low Latency Communications: From PHY and MAC Layer Perspectives

© 1998-2012 IEEE. Future 5th generation networks are expected to enable three key services-enhanced mobile broadband, massive machine type communications and ultra-reliable and low latency communications (URLLC). As per the 3rd generation partnership project URLLC requirements, it is expected that the reliability of one transmission of a 32 byte packet will be at least 99.999% and the latency will be at most 1 ms. This unprecedented level of reliability and latency will yield various new applications, such as smart grids, industrial automation and intelligent transport systems. In this survey we present potential future URLLC applications, and summarize the corresponding reliability and latency requirements. We provide a comprehensive discussion on physical (PHY) and medium access control (MAC) layer techniques that enable URLLC, addressing both licensed and unlicensed bands. This paper evaluates the relevant PHY and MAC techniques for their ability to improve the reliability and reduce the latency. We identify that enabling long-term evolution to coexist in the unlicensed spectrum is also a potential enabler of URLLC in the unlicensed band, and provide numerical evaluations. Lastly, this paper discusses the potential future research directions and challenges in achieving the URLLC requirements

Robust Sensor Networks in Homes via Reactive Channel Hopping

Home area networks (HANs) consisting of wireless sensors have emerged as the enabling technology for important applications such as smart energy and assisted living. A key challenge faced in deploying robust wireless sensor networks (WSNs) for home automation applications is the need to provide long-term, reliable operation in the face of the varied sources of interference found in typical residential settings. To better understand the channel dynamics in these environments, we performed an in-depth empirical study of the performance of HANs in ten real-life apartments. Our empirical study leads to several key insights into designing robust HANs for residential environments. For example, we discover that there is not always a persistently good channel over 24 hours in many apartments; that reliability is strongly correlated across adjacent channels; and that interference does not exhibit cyclic behavior at daily or weekly timescales. Nevertheless, reliability can be maintained through a small number of channel hops. Based on these insights, we propose Adaptive and Robust Channel Hopping (ARCH) protocol, a lightweight receiver-oriented protocol which handles the dynamics of residential environments by reactively channel hopping when channel conditions have degraded. We evaluate our approach through a series of simulations based on real data traces as well as a testbed deployment in real-world apartments. Our results demonstrate that ARCH can reduce the number of packet retransmissions by a median of 42.3% compared to using a single, fixed wireless channel, and can enable up to a 2.2 X improvement in delivery rate on the most unreliable links in our experiment. Due to ARCH\u27s lightweight reactive design, this improvement in reliability is achieved with an average of 6 or fewer channel hops per link per day

Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey

Wireless sensor networks (WSNs) consist of autonomous and resource-limited devices. The devices cooperate to monitor one or more physical phenomena within an area of interest. WSNs operate as stochastic systems because of randomness in the monitored environments. For long service time and low maintenance cost, WSNs require adaptive and robust methods to address data exchange, topology formulation, resource and power optimization, sensing coverage and object detection, and security challenges. In these problems, sensor nodes are to make optimized decisions from a set of accessible strategies to achieve design goals. This survey reviews numerous applications of the Markov decision process (MDP) framework, a powerful decision-making tool to develop adaptive algorithms and protocols for WSNs. Furthermore, various solution methods are discussed and compared to serve as a guide for using MDPs in WSNs

Optimization and Learning in Energy Efficient Cognitive Radio System

Energy efficiency and spectrum efficiency are two biggest concerns for wireless communication. The constrained power supply is always a bottleneck to the modern mobility communication system. Meanwhile, spectrum resource is extremely limited but seriously underutilized. Cognitive radio (CR) as a promising approach could alleviate the spectrum underutilization and increase the quality of service. In contrast to traditional wireless communication systems, a distinguishing feature of cognitive radio systems is that the cognitive radios, which are typically equipped with powerful computation machinery, are capable of sensing the spectrum environment and making intelligent decisions. Moreover, the cognitive radio systems differ from traditional wireless systems that they can adapt their operating parameters, i.e. transmission power, channel, modulation according to the surrounding radio environment to explore the opportunity. In this dissertation, the study is focused on the optimization and learning of energy efficiency in the cognitive radio system, which can be considered to better utilize both the energy and spectrum resources. Firstly, drowsy transmission, which produces optimized idle period patterns and selects the best sleep mode for each idle period between two packet transmissions through joint power management and transmission power control/rate selection, is introduced to cognitive radio transmitter. Both the optimal solution by dynamic programming and flexible solution by reinforcement learning are provided. Secondly, when cognitive radio system is benefited from the theoretically infinite but unsteady harvested energy, an innovative and flexible control framework mainly based on model predictive control is designed. The solution to combat the problems, such as the inaccurate model and myopic control policy introduced by MPC, is given. Last, after study the optimization problem for point-to-point communication, multi-objective reinforcement learning is applied to the cognitive radio network, an adaptable routing algorithm is proposed and implemented. Epidemic propagation is studied to further understand the learning process in the cognitive radio network

Cooperative diversity techniques for high-throughput wireless relay networks

Relay communications has attracted a growing interest in wireless communications with application to various enhanced technologies. This thesis considers a number of issues related to data throughput in various wireless relay network models. Particularly, new implementations of network coding (NC) and space-time coding (STC) techniques are investigated to offer various means of achieving high-throughput relay communications. Firstly, this thesis investigates different practical automatic repeat request (ARQ) retransmission protocols based on NC for two-way wireless relay networks to improve throughput efficiency. Two improved NC-based ARQ schemes are designed based on go-back-N and selective-repeat (SR) protocols. Addressing ARQ issues in multisource multidestination relay networks, a new NC-based ARQ protocol is proposed and two packet-combination algorithms are developed for retransmissions at relay and sources to significantly improve the throughput. In relation to the concept of channel quality indicator (CQI) reporting in two-way relay networks, two new efficient CQI reporting schemes are designed based on NC to improve the system throughput by allowing two terminals to simultaneously estimate the CQI of the distant terminal-relay link without incurring additional overhead. The transmission time for CQI feedback at the relays is reduced by half while the increase in complexity and the loss of performance are shown to be negligible. Furthermore, a low-complexity relay selection scheme is suggested to reduce the relay searching complexity. For the acknowledgment (ACK) process, this thesis proposes a new block ACK scheme based on NC to significantly reduce the ACK overheads and therefore produce an enhanced throughput. The proposed scheme is also shown to improve the reliability of block ACK transmission and reduce the number of data retransmissions for a higher system throughput. Additionally, this thesis presents a new cooperative retransmission scheme based on relay cooperation and NC to considerably reduce the number of retransmission packets and im- prove the reliability of retransmissions for a more power efficient and higher throughput system with non-overlapped retransmissions. Moreover, two relay selection schemes are recommended to determine the optimised number of relays for the retransmission. Finally, with respect to cognitive wireless relay networks (CWRNs), this thesis proposes a new cooperative spectrum sensing (CSS) scheme to improve the spectrum sensing performance and design a new CSS scheme based on NC for three-hop CWRNs to improve system throughput. Furthermore, a new distributed space-time-frequency block code (DSTFBC) is designed for a two- hop nonregenerative CWRN over frequency-selective fading channels. The proposed DSTFBC design achieves higher data rate, spatial diversity gain, and decoupling detection of data blocks at all destination nodes with a low-complexity receiver structure

Rateless Space-Time Block Codes for 5G Wireless Communication Systems

This chapter presents a rateless space-time block code (RSTBC) for massive multiple-input multiple-output (MIMO) wireless communication systems. We discuss the principles of rateless coding compared to the fixed-rate channel codes. A literature review of rateless codes (RCs) is also addressed. Furthermore, the chapter illustrates the basis of RSTBC deployments in massive MIMO transmissions over lossy wireless channels. In such channels, data may be lost or are not decodable at the receiver end due to a variety of factors such as channel losses or pilot contamination. Massive MIMO is a breakthrough wireless transmission technique proposed for future wireless standards due to its spectrum and energy efficiencies. We show that RSTBC guarantees the reliability of the system in such highly lossy channels. Moreover, pilot contamination (PC) constitutes a particularly significant impairment in reciprocity-based multi-cell systems. PC results from the non-orthogonality of the pilot sequences in different cells. In this chapter, RSTBC is also employed in the downlink transmission of a multi-cell massive MIMO system to mitigate the effects of signal-to-interference-and-noise ratio (SINR) degradation resulting from PC. We conclude that RSTBC can effectively mitigate such interference. Hence, RSTBC is a strong candidate for the upcoming 5G wireless communication systems

C-RAN CoMP Methods for MPR Receivers

The growth in mobile network traffic due to the increase in MTC (Machine Type Communication) applications, brings along a series of new challenges in traffic routing and management. The goals are to have effective resolution times (less delay), low energy consuption (given that wide sensor networks which are included in the MTC category, are built to last years with respect to their battery consuption) and extremely reliable communication (low Packet Error Rates), following the fifth generation (5G) mobile network demands. In order to deal with this type of dense traffic, several uplink strategies can be devised, where diversity variables like space (several Base Stations deployed), time (number of retransmissions of a given packet per user) and power spreading (power value diversity at the receiver, introducing the concept of SIC and Power-NOMA) have to be handled carefully to fulfill the requirements demanded in Ultra-Reliable Low-Latency Communication (URLLC). This thesis, besides being restricted in terms of transmission power and processing of a User Equipment (UE), works on top of an Iterative Block Decision Feedback Equalization Reciever that allows Multi Packet Reception to deal with the diversity types mentioned earlier. The results of this thesis explore the possibility of fragmenting the processing capabilities in an integrated cloud network (C-RAN) environment through an SINR estimation at the receiver to better understand how and where we can break and distribute our processing needs in order to handle near Base Station users and cell-edge users, the latters being the hardest to deal with in dense networks like the ones deployed in a MTC environment