The future connectivity landscape, and notably, the 5G wireless systems will feature Ultra-Reliable Low Latency Communication (URLLC). The coupling of high reliability and low latency requirements in URLLC use cases makes the wireless access design very challenging, in terms of both the protocol design and of the associated transmission techniques. This paper aims to provide a broad perspective on the fundamental tradeoffs in URLLC, as well as the principles used in building access protocols. Two specific technologies are considered in the context of URLLC: massive MIMO and multi-connectivity, also termed interface diversity . This paper also touches on the importance of the proper statistical methodology for designing and assessing extremely high-reliability levels.
Acquisition of the CSI at the receiver is usually perceived as less critical than at the transmitter as the delay between channel estimation and data detection is short. However, extreme cases of mobility at the user side might require an alternative to coherent detection, especially if URLLC is the target. Hence, non-coherent detection methods can be an asset in that case. A particularly simple method @cite_69 that greatly benefits from the presence of a massive number of antennas is based on energy detection at the uplink of a massive MIMO system. The principle is to send a single stream of data, collect and aggregate the energy from all antennas. Detection is performed based on the average channel energy across the antenna array, which tends to a deterministic quantity for localized movements of the user and is therefore much more robust to user mobility than coherent detection. In addition, an efficient constellation design has been proposed in @cite_49 that is able to benefit from the advantages of coherent communications at low mobility while switching to energy detection to ensure reliable communications at high mobility.
@cite_69 @cite_49
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The stringent requirements of ultra-reliable low-latency communications (URLLC) require rethinking of the physical layer transmission techniques. Massive antenna arrays are seen as an enabler of the emerging 5th generation systems, due to increases in spectral efficiency and degrees of freedom for transmissions, which can greatly improve reliability under demanding latency requirements. Massive array coherent processing relies on accurate channel state information (CSI) in order to achieve high reliability. In this paper, we investigate the impact of imperfect CSI in a single-input multiple-output (SIMO) system on the coherent receiver. An amplitude-phase keying (APK) symbol constellation is proposed, where each two symmetric symbols reside on distinct power levels. The symbols are demodulated using a dual-stage non-coherent and coherent detection strategy, in order to improve symbol reliability. By means of analysis and simulation, we find an adequate scaling of the constellation and show that for high signal-to-noise ratio (SNR) and inaccurate CSI regime, the proposed scheme enhances receiver performance. We study in this paper the network spectral efficiency of a multiple-input multiple-output (MIMO) ad hoc network with K simultaneous communicating transmitter-receiver pairs. Assuming that each transmitter is equipped with t antennas and each receiver with r antennas and each receiver implements single-user detection, we show that in the absence of channel state information (CSI) at the transmitters, the asymptotic network spectral efficiency is limited by r nats s Hz as Krarrinfin and is independent of t and the transmit power. With CSI corresponding to the intended receiver available at the transmitter, we demonstrate that the asymptotic spectral efficiency is at least t+r+2radictr nats s Hz. Asymptotically optimum signaling is also derived under the same CSI assumption, i.e., each transmitter knows the channel corresponding to its desired receiver only. Further capacity improvement is possible with stronger CSI assumption; we demonstrate this using a heuristic interference suppression transmit beamforming approach. The conventional orthogonal transmission approach is also analyzed. In particular, we show that with idealized medium access control, the channelized transmission has unbounded asymptotic spectral efficiency under the constant per-user power constraint. The impact of different power constraints on the asymptotic spectral efficiency is also carefully examined. Finally, numerical examples are given that confirm our analysis Channel state information (CSI) in systems with multiple antennas at both sides of the transmission link (multiple input multiple output, MIMO) is beneficial for many transmission schemes. However, a transceiver station can only estimate the channel transfer functions (CTFs) of the channels over which the received signals propagate which are different from the channels over which the transmitted signals will propagate in FDD systems. This paper shows a technique to obtain the CSI of the channels over which the transmitted signal will propagate based on the CSI of the channels over which the received signals propagate. By means of estimating the directions of arrival (DOAs) and times of arrival (TOAs), the shift of the previously estimated CSI to the CSI which cannot be estimated based on training signals can be calculated. The quality of the proposed algorithm depends on the quality of the parameter estimation. In scenarios with many scatterers the estimation of the DOAs and TOAs may not be precise enough or there may be paths with similar parameters which cannot be resolved. Thus, the proposed algorithm performs better in non-rich scattering environments. Large multiple-input multiple-output (MIMO) networks promise high energy efficiency, i.e., much less power is required to achieve the same capacity compared to the conventional MIMO networks if perfect channel state information (CSI) is available at the transmitter. However, in such networks, huge overhead is required to obtain full CSI especially for Frequency-Division Duplex (FDD) systems. To reduce overhead, we propose a downlink antenna selection scheme, which selects S antennas from M > S transmit antennas based on the large scale fading to serve K ≤ S users in large distributed MIMO networks employing regularized zero-forcing (RZF) precoding. In particular, we study the joint optimization of antenna selection, regularization factor, and power allocation to maximize the average weighted sum-rate. This is a mixed combinatorial and non-convex problem whose objective and constraints have no closed-form expressions. We apply random matrix theory to derive asymptotically accurate expressions for the objective and constraints. As such, the joint optimization problem is decomposed into subproblems, each of which is solved by an efficient algorithm. In addition, we derive structural solutions for some special cases and show that the capacity of very large distributed MIMO networks scales as O(KlogM) when M→∞ with K, S fixed. Simulations show that the proposed scheme achieves significant performance gain over various baselines.