In the past, NLOS propagation was proven to be a source of distortion for radio-based positioning systems due to the lack of temporal and spatial resolution of previous cellular systems. Hence every NLOS component was perceived as a perturbation for localization. Even though 5G is not yet standardized, a strong proposal, which has the potential to overcome the problem of limited temporal and spatial resolution, is the massive MIMO millimeter wave technology. We reconsider the role of NLOS components for position and orientation estimation in 5G millimeter wave MIMO systems. Our analysis is based on the concept of Fisher information. We show that, for sufficiently high temporal and spatial resolution, NLOS components always provide position and orientation information which consequently increases position and orientation estimation accuracy. Additionally, we show that the information gain of NLOS components depends on the actual location of the reflector or scatter. Our numerical examples suggest that NLOS components are most informative about the position and orientation of a mobile terminal when corresponding reflectors or scatterers are illuminated with narrow beams.

Situational awareness in wireless networks refers to the availability of position information on transmitters and receivers as well as information on their propagation environments to aid wireless communications. In millimeter wave massive multiple-input multiple-output communication systems, situational awareness can significantly improve the quality and robustness of communications. In this paper, we establish a model that describes the statistical dependency between channel state information and the position, orientation, and clock offset of a user equipment along with the locations of features in the propagation environment. Based on this model, we introduce COMPAS (Concurrent Mapping, Positioning, and Synchronization), an inference engine that can provide accurate and reliable situational awareness in future communication systems. Numerical results show that COMPAS is able to infer the positions of an unknown and time-varying number of features in the propagation environment and, at the same time, estimate the position, orientation, and clock offset of a user equipment.

Timely message delivery is a key enabler for Internet of Things (IoT) and cyber-physical systems to support wide range of context-dependent applications. Conventional time-related metrics (e.g. delay and jitter) fails to characterize the timeliness of the system update. Age of information (AoI) is a time-evolving metric that accounts for the packet inter-arrival and waiting times to assess the freshness of information. In the foreseen large-scale IoT networks, mutual interference imposes a delicate relation between traffic generation patterns and transmission delays. To this end, we provide a spatiotemporal framework that captures the peak AoI (PAoI) for large scale IoT uplink network under time-triggered (TT) and event triggered (ET) traffic. Tools from stochastic geometry and queueing theory are utilized to account for the macroscopic and microscopic network scales. Simulations are conducted to validate the proposed mathematical framework and assess the effect of traffic load on the PAoI. The results unveil a counter-intuitive superiority of the ET traffic over the TT in terms of PAoI, which is due to the involved temporal interference correlations. Insights regarding the network stability frontiers and the location-dependent performance are presented. Key design recommendations regarding the traffic load and decoding thresholds are highlighted.

This paper uses the reconstruction-computation-quantization (RCQ)paradigm to decode low-density parity-check (LDPC) codes. RCQ facilitates dynamic non-uniform quantization to achieve good frame error rate (FER) performance with very low message precision. For message-passing according to a flooding schedule, the RCQ parameters are designed by discrete density evolution. Simulation results on an IEEE 802.11 LDPC code show that for 4-bit messages, a flooding Min Sum RCQ decoder outperforms table-lookup approaches such as information bottleneck (IB) or Min-IB decoding, with significantly fewer parameters to be stored. Additionally, this paper introduces layer-specific RCQ, an extension of RCQ decoding for layered architectures. Layer-specific RCQ uses layer-specific message representations to achieve the best possible FER performance. For layer-specific RCQ, this paper proposes using layered discrete density evolution featuring hierarchical dynamic quantization (HDQ) to design parameters efficiently. Finally, this paper studies field-programmable gate array (FPGA) implementations of RCQ decoders. Simulation results for a (9472, 8192) quasi-cyclic (QC) LDPC code show that a layered Min Sum RCQ decoder with 3-bit messages achieves more than a 10% reduction in LUTs and routed nets and more than a 6% decrease in register usage while maintaining comparable decoding performance, compared to a 5-bit offset Min Sum decoder.

In high-throughput applications, low-complexity and low-latency channel decoders are inevitable. Hence, for low-density parity-check (LDPC) codes, message passing decoding has to be implemented with coarse quantization—that is, the exchanged beliefs are quantized with a small number of bits. This can result in a significant performance degradation with respect to decoding with high-precision messages. Recently, so-called information-bottleneck decoders were proposed which leverage a machine learning framework (i.e., the information bottleneck method) to design coarse-precision decoders with error-correction performance close to high-precision belief-propagation decoding. In these decoders, all conventional arithmetic operations are replaced by look-up operations. Irregular LDPC codes for next-generation fiber optical communication systems are characterized by high code rates and large maximum node degrees. Consequently, the implementation complexity is mainly influenced by the memory required to store the look-up tables. In this paper, we show that the complexity of information-bottleneck decoders remains manageable for irregular LDPC codes if our proposed construction approach is deployed. Furthermore, we reveal that in order to design information bottleneck decoders for arbitrary degree distributions, an intermediate construction step which we call message alignment has to be included. Exemplary numerical simulations show that incorporating message alignment in the construction yields a 4-bit information bottleneck decoder which performs only $0.15$ dB worse than a double-precision belief propagation decoder and outperforms a min-sum decoder.

Massive Internet of Things (IoT) is foreseen to introduce plethora of applications for a fully connected world. Heterogeneous traffic is envisaged, where packets generated at each device should be differentiated and served according to their priority. This paper develops a novel priority-aware spatiotemporal mathematical model to characterize massive IoT networks with uplink prioritized multi-stream traffic (PMT). Stochastic geometry is utilized to account for the macroscopic network wide mutual interference between the coexisting devices. Discrete time Markov chains (DTMCs) are employed to track the microscopic evolution of packets within each priority queue. To provide a systematic and tractable model, we decompose the prioritized queueing model at each device to a single-queue system with server vacation. To this end, the IoT PMT network is modeled as spatially interacting vacation queues. Dedicated and shared channel priority-aware access strategies are presented. A priority-agnostic scheme is used as a benchmark to highlight the impact of prioritized uplink transmission on the performance of different priorities in terms of transmission probabilities and delay. Additional performance metrics as average number of packets, peak age of information, delay distribution, and Pareto frontiers for different parameters are presented, which give insights on stable operation of uplink IoT networks with PMT.