Complexity-Aware Scheduling for an LDPC Encoded C-RAN Uplink

Centralized Radio Access Network (C-RAN) is a new paradigm for wireless networks that centralizes the signal processing in a computing cloud, allowing commodity computational resources to be pooled. While C-RAN improves utilization and efficiency, the computational load occasionally exceeds the available resources, creating a computational outage. This paper provides a mathematical characterization of the computational outage probability for low-density parity check (LDPC) codes, a common class of error-correcting codes. For tractability, a binary erasures channel is assumed. Using the concept of density evolution, the computational demand is determined for a given ensemble of codes as a function of the erasure probability. The analysis reveals a trade-off: aggressively signaling at a high rate stresses the computing pool, while conservatively backing-off the rate can avoid computational outages. Motivated by this trade-off, an effective computationally aware scheduling algorithm is developed that balances demands for high throughput and low outage rates.Comment: Conference on Information Sciences and Systems (CISS) 2017, to appea

Dynamic Time-domain Duplexing for Self-backhauled Millimeter Wave Cellular Networks

Millimeter wave (mmW) bands between 30 and 300 GHz have attracted considerable attention for next-generation cellular networks due to vast quantities of available spectrum and the possibility of very high-dimensional antenna ar-rays. However, a key issue in these systems is range: mmW signals are extremely vulnerable to shadowing and poor high-frequency propagation. Multi-hop relaying is therefore a natural technology for such systems to improve cell range and cell edge rates without the addition of wired access points. This paper studies the problem of scheduling for a simple infrastructure cellular relay system where communication between wired base stations and User Equipment follow a hierarchical tree structure through fixed relay nodes. Such a systems builds naturally on existing cellular mmW backhaul by adding mmW in the access links. A key feature of the proposed system is that TDD duplexing selections can be made on a link-by-link basis due to directional isolation from other links. We devise an efficient, greedy algorithm for centralized scheduling that maximizes network utility by jointly optimizing the duplexing schedule and resources allocation for dense, relay-enhanced OFDMA/TDD mmW networks. The proposed algorithm can dynamically adapt to loading, channel conditions and traffic demands. Significant throughput gains and improved resource utilization offered by our algorithm over the static, globally-synchronized TDD patterns are demonstrated through simulations based on empirically-derived channel models at 28 GHz.Comment: IEEE Workshop on Next Generation Backhaul/Fronthaul Networks - BackNets 201

Multiuser Switched Diversity Scheduling Schemes

Multiuser switched-diversity scheduling schemes were recently proposed in order to overcome the heavy feedback requirements of conventional opportunistic scheduling schemes by applying a threshold-based, distributed, and ordered scheduling mechanism. The main idea behind these schemes is that slight reduction in the prospected multiuser diversity gains is an acceptable trade-off for great savings in terms of required channel-state-information feedback messages. In this work, we characterize the achievable rate region of multiuser switched diversity systems and compare it with the rate region of full feedback multiuser diversity systems. We propose also a novel proportional fair multiuser switched-based scheduling scheme and we demonstrate that it can be optimized using a practical and distributed method to obtain the feedback thresholds. We finally demonstrate by numerical examples that switched-diversity scheduling schemes operate within 0.3 bits/sec/Hz from the ultimate network capacity of full feedback systems in Rayleigh fading conditions.Comment: Accepted at IEEE Transactions on Communications, to appear 2012, funded by NPRP grant 08-577-2-241 from QNR

Coverage Protocols for Wireless Sensor Networks: Review and Future Directions

The coverage problem in wireless sensor networks (WSNs) can be generally defined as a measure of how effectively a network field is monitored by its sensor nodes. This problem has attracted a lot of interest over the years and as a result, many coverage protocols were proposed. In this survey, we first propose a taxonomy for classifying coverage protocols in WSNs. Then, we classify the coverage protocols into three categories (i.e. coverage aware deployment protocols, sleep scheduling protocols for flat networks, and cluster-based sleep scheduling protocols) based on the network stage where the coverage is optimized. For each category, relevant protocols are thoroughly reviewed and classified based on the adopted coverage techniques. Finally, we discuss open issues (and recommend future directions to resolve them) associated with the design of realistic coverage protocols. Issues such as realistic sensing models, realistic energy consumption models, realistic connectivity models and sensor localization are covered

CARES: computation-aware scheduling in virtualized radio access networks

In a virtualized Radio Access Network (RAN), baseband processing is performed by software running in cloudcomputing platforms. However, current protocol stacks were not designed to run in this kind of environments: the high variability on the computational resources consumed by RAN functions may lead to eventual computational outages (where frames are not decoded on time), severely degrading the resulting performance. In this paper, we address this issue by re-designing two key functions of the protocol stack: (i) scheduling, to select the transmission of those frames that do not incur in computational outages, and (ii) modulation and coding scheme (MCS) selection, to downgrade the selected MCS in case no sufficient computational resources are available. We formulate the resulting problem as a joint optimization and compute the (asymptotically) optimal solution to this problem. We further show that this solution involves solving an NP-hard problem, and propose an algorithm to obtain an approximate solution that is computationally efficient while providing bounded performance over the optimal. We thoroughly evaluate the proposed approach via simulation, showing that it can provide savings as high as 80% of the computational resources while paying a small price in performance.The work of University Carlos III of Madrid was supported by the H2020 5G-MoNArch project (Grant Agreement No. 761445) and the work of NEC Europe Ltd. by the 5G-Transformer project (Grant Agreement No. 761536)

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