Adaptive Power Allocation and Control in Time-Varying Multi-Carrier MIMO Networks

In this paper, we examine the fundamental trade-off between radiated power and achieved throughput in wireless multi-carrier, multiple-input and multiple-output (MIMO) systems that vary with time in an unpredictable fashion (e.g. due to changes in the wireless medium or the users' QoS requirements). Contrary to the static/stationary channel regime, there is no optimal power allocation profile to target (either static or in the mean), so the system's users must adapt to changes in the environment "on the fly", without being able to predict the system's evolution ahead of time. In this dynamic context, we formulate the users' power/throughput trade-off as an online optimization problem and we provide a matrix exponential learning algorithm that leads to no regret - i.e. the proposed transmit policy is asymptotically optimal in hindsight, irrespective of how the system evolves over time. Furthermore, we also examine the robustness of the proposed algorithm under imperfect channel state information (CSI) and we show that it retains its regret minimization properties under very mild conditions on the measurement noise statistics. As a result, users are able to track the evolution of their individually optimum transmit profiles remarkably well, even under rapidly changing network conditions and high uncertainty. Our theoretical analysis is validated by extensive numerical simulations corresponding to a realistic network deployment and providing further insights in the practical implementation aspects of the proposed algorithm.Comment: 25 pages, 4 figure

Power control for predictable communication reliability in wireless cyber-physical systems

Wireless networks are being applied in various cyber-physical systems and posed to support mission-critical cyber-physical systems applications. When those applications require reliable and low-latency wireless communication, ensuring predictable per-packet communication reliability is a basis. Due to co-channel interference and wireless channel dynamics (e.g. multi-path fading), however, wireless communication is inherently dynamic and subject to complex uncertainties. Power control and MAC-layer scheduling are two enablers. In this dissertation, cross-layer optimization of joint power control and scheduling for ensuring predictable reliability has been studied. With an emphasis on distributed approaches, we propose a general framework and additionally a distributed algorithm in static networks to address small channel variations and satisfy the requirements on receiver-side signal-to-interference-plus-noise-ratio (SINR). Moreover, toward addressing reliability in the settings of large-scale channel dynamics, we conduct an analysis of the strategy of joint scheduling and power control and demonstrate the challenges. First, a general framework for distributed power control is considered. Given a set of links subject to co-channel interference and channel dynamics, the goal is to adjust each link\u27s transmission power on-the-fly so that all the links\u27 instantaneous packet delivery ratio requirements can be satised. By adopting the SINR high-delity model, this problem can be formulated as a Linear Programming problem. Furthermore, Perron-Frobenius theory indicates the characteristic of infeasibility, which means that not all links can nd a transmission power to meet all the SINR requirements. This nding provides a theoretical foundation for the Physical-Ratio-K (PRK) model. We build our framework based on the PRK model and NAMA scheduling. In the proposed framework, we dene the optimal K as a measurement for feasibility. Transmission power and scheduling will be adjusted by K and achieve near-optimal performance in terms of reliability and concurrency. Second, we propose a distributed power control and scheduling algorithm for mission-critical Internet-of-Things (IoT) communications. Existing solutions are mostly based on heuristic algorithms or asymptotic analysis of network performance, and there lack eld-deployable algorithms for ensuring predictable communication reliability. When IoT systems are mostly static or low mobility, we model the wireless channel with small channel variations. For this setting, our approach adopts the framework mentioned above and employs feedback control for online K adaptation and transmission power update. At each time instant, each sender will run NAMA scheduling to determine if it can obtain channel access or not. When each sender gets the channel access and sends a packet, its receiver will measure the current SINR and calculate the scheduling K and transmission power for the next time slot according to current K, transmission power and SINR. This adaptive distributed approach has demonstrated a signicant improvement compared to state-of-the-art technique. The proposed algorithm is expected to serve as a foundation for distributed scheduling and power control as the penetration of IoT applications expands to levels at which both the network capacity and communication reliability become critical. Finally, we address the challenges of power control and scheduling in the presence of large-scale channel dynamics. Distributed approaches generally require time to converge, and this becomes a major issue in large-scale dynamics where channel may change faster than the convergence time of algorithms. We dene the cumulative interference factor as a measurement of impact of a single link\u27s interference. We examine the characteristic of the interference matrix and propose that scheduling with close-by links silent will be still an ecient way of constructing a set of links whose required reliability is feasible with proper transmission power control even in the situation of large-scale channel dynamics. Given that scheduling alone is unable to ensure predictable communication reliability while ensuring high throughput and addressing fast-varying channel dynamics, we demonstrate how power control can help improve both reliability at each time instant and throughput in the long-term. Collectively, these ndings provide insight into the cross-layer design of joint scheduling and power control for ensuring predictable per-packet reliability in the presence of wireless network dynamics and uncertainties

Game Theoretic Models for Power Control in Wireless Networks

Τα τελευταία χρόνια, η τεχνολογία των κινητών επικοινωνιών έχει εξελιχθεί ραγδαία εξαιτίας των αυξανόμενων απαιτήσεων των χρηστών, όπως είναι η πρόσβαση σε υπηρεσίες του Διαδικτύου μέσω των ασύρματων έξυπνων κινητών συσκευών καθώς και οι απαιτήσεις σε καλύτερη ποιότητα στις προσφερόμενες υπηρεσίες. Σήμερα, οι συσκεύες αυτές χρησιμοποιούν την τέταρτη γενιά δικτύων (4Gή LTE) καθώς αντικαθιστάτην τρίτη γενιά δικτύων (3G) και προσφέρει στους χρήστες βελτιωμένες υπηρεσίες με υψηλότερες ταχύτητες. Οι ασύρματες έξυπνες κινητές συσκευές (smartphones) έχουν μεγάλη ζήτηση στην αγορά, για το λόγο αυτό γίνεται προσπάθεια να εξελιγχθούν σε επίπεδο ενεργειακής κατανάλωσης, ώστε ο χρήστης να μην χρειάζεται να επαναφορτίζει τη συσκευή του σε τακτά χρονικά διαστήματα. Η θεωρία παιγνίων παρέχει πολύτιμα μαθηματικά εργαλεία όπου μπορούν να χρησιμοποιηθούν για την επίλυση των διαφόρων προβλημάτων που αντιμετώπιζουν τα ασύρματα δίκτυα. Στην παρούσα διπλωματική εργασία μελετάτε το πρόβλημα του ελέγχου ισχύος εκπομπής (powercontrol). Συγκεκριμένα, μελετάμε παιγνιοθεωρητικά μοντέλα για έλεγχο ισχύος εκπομπής σε ασύρματα δίκτυα (CDMA& LTE). Η μελέτη μας επικεντρώνεται στα μη συνεργατικά παίγνια και υποθέτουμε ότι οι χρήστες του δικτύου (αποστολείς, παραλείπτες) είναι εγωιστές και ορθολογιστές. Στη συνέχεια, εισάγουμε αλγορίθμους μάθησης, regretlearningalgorithms, καθώς και την σύνδεση τους με την θεωρία παιγνίων. Τέλος, ερευνάμε τις διάφορες regretlearningτεχνικές εφαρμόζοντάς τες στο πρόβλημα του powercontrolστα ασύρματα δίκτυα επόμενης γενιάς.In recent years, the technology of mobile communications has evolved rapidly due to increasing requirements, such as access to Internet services via mobile phones and requirements better quality services. Nowadays, the devices use the Long Term Evolution (LTE), which called also as 4G networks. The fourth generation (4G) networks replace the third networks generation (3G) and offer to users improved services at higher speeds. Mobile devices to access the Internet, such as smartphones, tablet PCs and netbooks are in high demand in the market for it is an effort to develop in energy consumption level, that the user does not need recharge the device at regular time intervals. Game theory provides valuable mathematical tools that can be used to solve problems of wireless communication networks and can be applied to multiple layers of wireless networks. In this thesis, we study power control issue and consider it at the physical layer of wireless networks. Specifically, we study game theoretic models for power control in wireless communication networks (CDMA & LTE). In the game theory, we have focused in the non-cooperative power control games and assumed that both transmitters and receivers are selfish and rational. In addition, we insert regret learning techniques and their connection with the game theory. Finally, we investigate the regret learning techniques applied to the problem of power control in the next generation networks

Convergence Time of Power-Control Dynamics ∗

We study two (classes of) distributed algorithms for power control in a general model of wireless networks. There are n wireless communication requests or links that experience interference and noise. To be successful a link must satisfy an SINR constraint. The goal is to find a set of powers such that all links are successful simultaneously. A classic algorithm for this problem is the fixed-point iteration due to Foschini and Miljanic [8], for which we prove the first bounds on worst-case running times – after roughly O(n log n) rounds all SINR constraints are nearly satisfied. When we try to satisfy each constraint exactly, however, convergence time is infinite. For this case, we design a novel framework for power control using regret learning algorithms and iterative discretization. While the exact convergence times must rely on a variety of parameters, we show that roughly a polynomial number of rounds suffices to make every link successful during at least a constant fraction of all previous rounds.