Wireless Communication Under Imperfect Source/Channel Information - Chen, Fangzhou (ProQuest Dissertations & Theses, 2017)
The rapid development of demand for wireless data has lead to the development of numerous new applications, which the existing networks were not designed to handle. Different applications may have substantially different quality of service (QoS) requirements involving reliability, security, delay and throughput. Regardless of the required QoS, the perfect knowledge of source/channel conditions can always help to improve the performance. However, due to the intractability and unpredictability of the wireless communication environment, well-performed techniques under imperfect knowledge of source/channel information are more desirable from practical point of view, which is the focus of this research dissertation. First, we focus on wireless communication under imperfect source information problem. We consider a system in which two nodes take correlated measurements of a random source with time-varying and unknown statistics. The observations of the source at the first node are to be losslessly replicated with a given probability of outage at the second node, which receives data from the first node over a constant-rate errorless channel. We develop a system and associated strategies for joint distributed source coding (encoding and decoding) and transmission control in order to achieve low end-to-end delay. Slepian-Wolf coding in its traditional form cannot be applied in our scenario, since the encoder requires the joint statistics of the observations and the associated decoding delay is very high. We analytically evaluate the performance of our strategies and show that the delay achieved by them are order optimal, as the conditional entropy of the source approaches to the channel rate. We also evaluate the performance of our algorithms based on real-world experiments using two cameras recording videos of a scene at different angles. Having realized our schemes, we demonstrated that, even with a very low-complexity quantizer, a compression ratio of approximately 50% is achievable for lossless replication at the decoder, at an average delay of a few seconds. Second, we shift our focus on addressing wireless communication under imperfect knowledge of channel information problems, faced in power control for cellular networks. Coordinated Multipoint (CoMP) promised substantial throughput gain for next-generation cellular systems. However, realizing this gain is costly in terms of pilots and backhaul bandwidth, and may require substantial modifications in physicallayer hardware. Target in efficient throughput gain, we develop a novel coordinated power control scheme for uplink cellular networks called Checks and Balances (C & B), which checks the received signal strength of one user and its generated interference to neighboring base stations, and balances the two. C & B has some highly attractive advantages: C & B (i) can be implemented easily in software, (ii) does not require to upgrade non-CoMP physical-layer hardware, (iii) allows for fully distributed implementation for each user equipment (UE), and (iv) does not need extra pilots or backhaul communications. We evaluate the throughput performance of C& B on an uplink LTE system-level simulation platform, which is carefully calibrated with Huawei. Our simulation results show that C& B achieves much better throughput performance, compared to several widely-used power control schemes. Lastly, we focus on adaptive modulation and coding (AMC). In this dissertation, we propose a new rate adaptation method that consists of two parts: a data-guided physical layer abstraction model and a recursive SINR estimation and AMC control algorithm. The key features of this new rate adaptation method are three-fold: (i) Accurate and robust modeling: The block error rate (BLER) calculated from the abstracted physical layer model precisely matches with the BLER generated from an LTE link-level simulator under various scenarios (including different LTE channel models, SINR regimes, and user mobility speeds). (ii) Low complexity: The abstracted physical layer model has very simple analytical expressions, and our algorithm can be realized with only a few computations. (iii) Fast convergence: Under static channel conditions, the SINR estimation error of our algorithm decays to zero at the fastest speed among all algorithms that achieve the throughput-optimal rate selection. Under dynamic channel conditions, simulation results obtained from the LTE link-level simulator show that the performance of our algorithm is much better than several state-of-the-art algorithms, and is close to the performance of an AMC control algorithm with perfect channel estimation.