Adaptive Transmission for OFDM

To respond to dynamic channel conditions caused by fading, shadowing, and other time-varying disturbances, orthogonal frequency division multiplexing (OFDM) packet radio systems should adapt transmission parameters on a packet-by-packet basis to maintain or improve performance over the channel. For this to be possible, there are three key ideas that must be addressed: first, how to determine the subchannel conditions; second, which transmission parameters should be adapted; and third, how to adapt those parameters intelligently. In this thesis, we propose a procedure for determining relative subchannel quality without using any traditional channel measurements. Instead, statistics derived solely from subcarrier error counts allow subchannels to be ranked by order of estimated quality; this order can be exploited for adapting transmission parameters. We investigate adaptive subcarrier power allocation, adaptive subcarrier modulation that allows different subcarriers in the same packet to use different modulation formats, and adaptive coding techniques for OFDM in fading channels. Analysis and systems simulation assess the accuracy of the subcarrier ordering as well as the throughput achieved by the proposed adaptive transmission protocol, showing good performance across a wide range of channel conditions

The Multi-Input Multi-Output (MIMO) Channel Modeling, Simulation and Applications

This thesis mainly focus on the Multi-Input Multi-Output (MIMO) channel modeling, simulation and applications. There are several ways to design a MIMO channel. Most of the examples are given in Chapter 2, where we can design channels based on the environments and also based on other conditions. One of the new MIMO channel designs based on physical and virtual channel design is discussed in Unitary-Independent- Unitary (UIU) channel modeling. For completeness, the different types of capacity are discussed in details. The capacity is very important in wireless communication. By understanding the details behind different capacity, we can improve our transmission efficiently and effectively. The level crossing rate and average duration are discussed.One of the most important topics in MIMO wireless communication is estimation. Without having the right estimation in channel prediction, the performance will not be correct. The channel estimation error on the performance of the Alamouti code was discussed. The design of the transmitter, the channel and the receiver for this system model is shown. The two different types of decoding scheme were shown - the linear combining scheme and the Maximum likelihood (ML) decoder. Once the reader understands the estimation of the MIMO channel, the estimation based on different antenna correlation is discussed. Next, the model for Mobile-to-Mobile (M2M) MIMO communication link is proposed. The old M2M Sum-of-Sinusoids simulation model and the new two ring models are discussed. As the last step, the fading channel modeling using AR model is derived and the effect of ill-conditioning of the Yule-Walker equation is also shown. A number of applications is presented to show how the performance can be evaluated using the proposed model and techniques

Interference Mitigation Framework for Cellular Mobile Radio Networks

For today's cellular mobile communication networks, the needed capacity is hard to realize without much more of (expensive) bandwidth. Thus new standards like LTE were developed. LTE advanced is in discussion as the successor of LTE and cooperative multipoint transmission (CoMP) is one of the hot topics to increase the system's capacity. System simulations often show only weak gains of the signal-to-interference ratio due to high interference from noncooperating cells in the downlink. This paper presents an interference mitigation framework to overcome the hardest issue, that is, the low penetration rate of mobile stations that can be served from a cluster composed of their strongest cells in the network. The results obtained from simulation tools are discussed with values resulting from testbed on the TU Dresden. They show that the theoretical ideas can be transferred into gains on real systems

Scheduling algorithms for next generation cellular networks

Next generation wireless and mobile communication systems are rapidly evolving to satisfy the demands of users. Due to spectrum scarcity and time-varying nature of wireless networks, supporting user demand and achieving high performance necessitate the design of efficient scheduling and resource allocation algorithms. Opportunistic scheduling is a key mechanism for such a design, which exploits the time-varying nature of the wireless environment for improving the performance of wireless systems. In this thesis, our aim is to investigate various categories of practical scheduling problems and to design efficient policies with provably optimal or near-optimal performance. An advantage of opportunistic scheduling is that it can effectively be incorporated with new communication technologies to further increase the network performance. We investigate two key technologies in this context. First, motivated by the current under-utilization of wireless spectrum, we characterize optimal scheduling policies for wireless cognitive radio networks by assuming that users always have data to transmit. We consider cooperative schemes in which secondary users share the time slot with primary users in return for cooperation, and our aim is to improve the primary systems performance over the non-cooperative case. By employing Lyapunov Optimization technique, we develop optimal scheduling algorithms which maximize the total expected utility and satisfy the minimum data rate requirements of the primary users. Next, we study scheduling problem with multi-packet transmission. The motivation behind multi-packet transmission comes from the fact that the base station can send more than one packets simultaneously to more than one users. By considering unsaturated queueing systems we aim to stabilize user queues. To this end, we develop a dynamic control algorithm which is able to schedule more than one users in a time slot by employing hierarchical modulation which enables multi-packet transmission. Through Lyapunov Optimization technique, we show that our algorithm is throughput-optimal. We also study the resulting rate region of developed policy and show that it is larger than that of single user scheduling. Despite the advantage of opportunistic scheduling, this mechanism requires that the base station is aware of network conditions such as channel state and queue length information of users. In the second part of this thesis, we turn our attention to the design of scheduling algorithms when complete network information is not available at the scheduler. In this regard, we study three sets of problems where the common objective is to stabilize user queues. Specifically, we first study a cellular downlink network by assuming that channels are identically distributed across time slots and acquiring channel state information of a user consumes a certain fraction of resource which is otherwise used for transmission of data. We develop a joint scheduling and channel probing algorithm which collects channel state information from only those users with su±ciently good channel quality. We also quantify the minimum number of users that must exist to achieve larger rate region than Max-Weight algorithm with complete channel state information. Next, we consider a more practical channel models where channels can be time-correlated (possibly non-stationary) and only a fixed number of channels can be probed. We develop learning based scheduling algorithm which tracks and predicts instantaneous transmission rates of users and makes a joint scheduling and probing decision based on the predicted rates rather than their exact values. We also characterize the achievable rate region of these policies as compared to Max-Weight policy with exact channel state information. Finally, we study a cellular uplink system and develop a fully distributed scheduling algorithm which can perform over general fading channels and does not require explicit control messages passing among the users. When continuous backoff time is allowed, we show that the proposed distributed algorithm can achieve the same performance as that of centralized Max-Weight algorithm in terms of both throughput and delay. When backoff time can take only discrete values, we show that our algorithm can perform well at the expense of low number of mini-slots for collision resolution

Receive Antenna Selection for Time-Varying Channels Using Discrete Prolate Spheroidal Sequences

Receive antenna selection (AS) has been shown to maintain the diversity benefits of multiple antennas while potentially reducing hardware costs. However, the promised diversity gains of receive AS depend on the assumptions of perfect channel knowledge at the receiver and slowly time-varying fading. By explicitly accounting for practical constraints imposed by the next-generation wireless standards such as training, packetization and antenna switching time, we propose a single receive AS method for time-varying fading channels. The method exploits the low training overhead and accuracy possible from the use of discrete prolate spheroidal (DPS) sequences based reduced rank subspace projection techniques. It only requires knowledge of the Doppler bandwidth, and does not require detailed correlation knowledge. Closed-form expressions for the channel prediction and estimation error as well as symbol error probability (SEP) of M-ary phase-shift keying (MPSK) for symbol-by-symbol receive AS are also derived. It is shown that the proposed AS scheme, after accounting for the practical limitations mentioned above, outperforms the ideal conventional single-input single-output (SISO) system with perfect CSI and no AS at the receiver and AS with conventional estimation based on complex exponential basis functions