Throughput Optimal Scheduling with Dynamic Channel Feedback

It is well known that opportunistic scheduling algorithms are throughput optimal under full knowledge of channel and network conditions. However, these algorithms achieve a hypothetical achievable rate region which does not take into account the overhead associated with channel probing and feedback required to obtain the full channel state information at every slot. We adopt a channel probing model where $\beta$ fraction of time slot is consumed for acquiring the channel state information (CSI) of a single channel. In this work, we design a joint scheduling and channel probing algorithm named SDF by considering the overhead of obtaining the channel state information. We first analytically prove SDF algorithm can support $1+\epsilon$ fraction of of the full rate region achieved when all users are probed where $\epsilon$ depends on the expected number of users which are not probed. Then, for homogenous channel, we show that when the number of users in the network is greater than 3, $\epsilon > 0$, i.e., we guarantee to expand the rate region. In addition, for heterogenous channels, we prove the conditions under which SDF guarantees to increase the rate region. We also demonstrate numerically in a realistic simulation setting that this rate region can be achieved by probing only less than 50% of all channels in a CDMA based cellular network utilizing high data rate protocol under normal channel conditions.Comment: submitte

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig

Concurrent Channel Probing and Data Transmission in Full-duplex MIMO Systems

An essential step for achieving multiplexing gain in MIMO downlink systems is to collect accurate channel state information (CSI) from the users. Traditionally, CSIs have to be collected before any data can be transmitted. Such a sequential scheme incurs a large feedback overhead, which substantially limits the multiplexing gain especially in a network with a large number of users. In this paper, we propose a novel approach to mitigate the feedback overhead by leveraging the recently developed Full-duplex radios. Our approach is based on the key observation that using Full-duplex radios, when the base-station (BS) is collecting CSI of one user through the uplink channel, it can use the downlink channel to simultaneously transmit data to other (non-interfering) users for which CSIs are already known. By allowing concurrent channel probing and data transmission, our scheme can potentially achieve a higher throughput compared to traditional schemes using Half-duplex radios. The new flexibility introduced by our scheme, however, also leads to fundamental challenges in achieving throughout optimal scheduling. In this paper, we make an initial effort to this important problem by considering a simplified group interference model. We develop a throughput optimal scheduling policy with complexity $O((N/I)^I)$, where $N$ is the number of users and $I$ is the number of user groups. To further reduce the complexity, we propose a greedy policy with complexity $O(N\log N)$ that not only achieves at least 2/3 of the optimal throughput region, but also outperforms any feasible Half-duplex solutions. We derive the throughput gain offered by Full-duplex under different system parameters and show the advantage of our algorithms through numerical studies.Comment: Technical repor

Joint Channel Probing and Proportional Fair Scheduling in Wireless Networks

The design of a scheduling scheme is crucial for the efficiency and user-fairness of wireless networks. Assuming that the quality of all user channels is available to a central controller, a simple scheme which maximizes the utility function defined as the sum logarithm throughput of all users has been shown to guarantee proportional fairness. However, to acquire the channel quality information may consume substantial amount of resources. In this work, it is assumed that probing the quality of each user's channel takes a fraction of the coherence time, so that the amount of time for data transmission is reduced. The multiuser diversity gain does not always increase as the number of users increases. In case the statistics of the channel quality is available to the controller, the problem of sequential channel probing for user scheduling is formulated as an optimal stopping time problem. A joint channel probing and proportional fair scheduling scheme is developed. This scheme is extended to the case where the channel statistics are not available to the controller, in which case a joint learning, probing and scheduling scheme is designed by studying a generalized bandit problem. Numerical results demonstrate that the proposed scheduling schemes can provide significant gain over existing schemes.Comment: 26 pages, 8 figure