Multi-connectivity (MC) is regarded as one of the key features that meet the requirements of ultra-reliable low-latency communications for 5th generation networks, as it provides multiple diversity branches. Recently, we evaluated the decoding reliability of various MC setups and different combining algorithms by analyzing the resulting outage probabilities. In this work, we are interested in how much the performance is affected by correlated fading. Specifically, we analyze the outage probability of MC systems with joint decoding reception operating over correlated quasi-static Rayleigh and Nakagami-m fading channels. Our main contributions are as follows: (i) deriving the exact outage probability in integral form and the asymptotic outage probability at high signal-to-noise ratio (SNR) in closed form; (ii) deriving the correlation loss, which quantifies the extra SNR required under correlated fading as compared with the independent scenario; and (iii) evaluating frame-error rates of quasi-cyclic low-density parity-check codes by Monte-Carlo simulations. Our results show that the correlation loss is marginal for a low to moderate level of correlation, whereas the diversity gain is not affected by correlation whatsoever. Furthermore, we find that the correlation loss is independent of the degrees of freedom encapsulated by the Nakagami-m fading parameter.

Multi-connectivity (MC) is regarded as one of the key features that meet the requirements of ultra-reliable low-latency communications for 5th generation networks, as it provides multiple diversity branches. Recently, we evaluated the decoding reliability of various MC setups and different combining algorithms by analyzing the resulting outage probabilities. In this work, we are interested in how much the performance is affected by correlated fading. Specifically, we analyze the outage probability of MC systems with joint decoding reception operating over correlated quasi-static Rayleigh fading channels. Our main contributions are as follows: (i) deriving the exact outage probability in integral form and the asymptotic outage probability at high signal-to-noise ratio (SNR) in closed form; (ii) deriving the correlation loss, which quantifies the extra SNR required under correlated fading as compared with the independent scenario; and (iii) evaluating frame-error rates of quasi-cyclic low-density parity-check codes by Monte-Carlo simulations. Our results show that the correlation loss is marginal for a low to moderate level of correlation, whereas the diversity gain is not affected by correlation whatsoever.

Multi-connectivity is a key enabler for realtime applications demanding high reliability such as connected vehicles. Employing macro-diversity with distributed transceivers has the advantage of mitigating large-scale losses such as shadowing, but may incur time offsets between packets, requiring the receiver to synchronize to each packet individually. Since packet detection is prerequisite for any downstream receiver processing, synchronization can become a bottleneck to achieving high reliability. In this paper, we propose a concept to improve receiver performance in macro-diversity multi-connectivity networks in case of time offsets between packets, for instance, due to loose synchronization of distributed transmitters. By buffering the inputs of parallel receiver paths and allowing for iterative synchronization, successfully detected packets can serve as extended correlation sequence to detect previously undetected packets which thereby become available to diversity combining. Taking link-level simulations of IEEE 802.11 (WLAN) as an example, we demonstrate the efficacy of such Joint Synchronization (JS) and provide first numerical results. We see an SNR gain of about 1 dB in the mid-SNR range, which is equivalent to a packet error rate reduction by an order of magnitude for four-fold diversity. With power consumption in mind, we consider the trade-off between implementation complexity and the gain of JS. We conclude that JS is a viable backwards-compatible approach to improve diversity combining of delayed packets in multi-connectivity networks.

We consider the lossy transmission of a single source over parallel additive white Gaussian noise channels with independent quasi-static fading, which we term the lossy multi-connectivity problem. We assume that only the decoder has access to the channel state information. Motivated by ultra-reliable and low latency communication requirements, we are interested in the finite blocklength performance of the problem, i.e., the minimal excess-distortion probability of transmitting k source symbols over n channel uses. By generalizing non-asymptotic bounds by Kostina and Verdú for the lossy joint source-channel coding problem, we derive non-asymptotic achievability and converse bounds for the lossy multi-connectivity problem. Using these non-asymptotic bounds and under mild conditions on the fading distribution, we derive approximations for the finite blocklength performance in the spirit of second-order asymptotics for any discrete memoryless source under an arbitrary bounded distortion measure. Furthermore, in the achievability part, we analyze the performance of a universal coding scheme by modifying the universal joint source-channel coding scheme by Csiszár and using a generalized minimum distance decoder. Our results demonstrate that the asymptotic notions of outage probability and outage capacity are in fact reasonable criteria even in the finite blocklength regime. Finally, we illustrate our results via numerical examples.

Multi-connectivity (MCo) is considered to be a key strategy for enabling reliable transmissions and enhanced data rates in fifth-generation mobile networks, as it provides multiple links from source to destination. In this paper, we quantify the communication performance of MCo in terms of outage probability and throughput. For doing so, we establish a simple, yet accurate analytical framework at high signal-to-noise ratio (SNR), in which the number of links, the spectral efficiency, the path loss, and the SNR are incorporated, giving new insights into the potentials of MCo as compared with the single-connectivity (SCo). These are our main contributions: 1) finding the exact coding gain of the outage probability for parallel block-fading channels; 2) quantifying the performance improvement of MCo over SCo in terms of SNR gain; and 3) comparing optimal and suboptimal combining algorithms for MCo at the receiver side, namely joint decoding, selection combining, and maximal-ratio combining, also in terms of SNR gain. In addition, we apply our analytical framework to real field channel measurements and thereby illustrate the potential of MCo to achieve high reliability and high data rates in real cellular networks.

In this paper, we are interested in the lossy transmission of a single source over parallel additive white Gaussian noise channels with independent quasi-static fading and receiver channel state information. We call this the lossy multi-connectivity problem. Motivated by the ultra-reliable and low latency communication requirements, we consider the finite blocklength performance of lossy multi-connectivity. By generalizing the non-asymptotic bounds of Kostina and Verdti for the lossy joint source-channel coding problem, we derive nonasymptotic achievability and converse bounds for the lossy multi-connectivity problem. Using these non-asymptotic bounds, under mild conditions on the fading distribution, we derive good approximations for the finite blocklength performance in the spirit of second-order asymptotics for any discrete memoryless source under any bounded distortion measure. Our results demonstrate that the asymptotic notions of outage probability and outage capacity are actually good criteria even in the finite blocklength regime. Finally, we illustrate our results via numerical examples.

Multi-connectivity is considered to be key for enabling reliable transmissions and enhancing data rates in future wireless networks. In this work, we quantify the communication performance by the outage probability and the system throughput. We establish a remarkably simple, yet accurate analytical framework based on joint decoding to describe the outage probability and the system throughput depending on the number of links, the modulation scheme, the code rate, the bandwidth, and the received signal-to-noise ratio. To investigate the tradeoff between the outage probability and the system throughput we define two modes to either achieve low outage probabilities or high system throughput which we refer to as the diversity and the multiplexing mode, respectively. We then establish a rate-reliability tradeoff analysis based on time sharing between both modes.

Multi-connectivity is considered to be key for enabling reliable transmissions in future wireless networks. Transmission reliability depends on the used combining algorithm such as joint decoding (JD), maximum selection combining (MSC), and maximum ratio combining (MRC). To compare the performance of these combining algorithms we derive their outage probabilities based on distributed source coding. The outage probability is analytically described depending on the number of links, the modulation scheme, the code rate, and the received signal-to-noise-ratios (SNR). We show that JD requires less transmit power than MRC and MSC to achieve a given target outage probability.

The so-called chief executive officer problem suggests that the source message can be recovered at the destination by merging a set of corrupted replicas forwarded by multiple relays, as long as these replicas are sufficiently correlated with the original message. In this paper, we build on Slepian-Wolf's correlated source coding theorem to design a simple, yet efficient power allocation scheme for a multirelay system, in which the direct link is unavailable to convey information. In such a system, the replicas forwarded by the relays are allowed to contain intra-link errors due to previous unreliable hops, and the destination is supposed to retrieve the source message by jointly decoding all received replicas. Importantly, the proposed power allocation is asymptotically optimal at high signal-to-noise ratio.