Current and emerging wireless systems require adaptive transmissions to improve their throughput, to meet the QoS requirements or to maintain robust performance. However finding the optimal transmit parameters is getting more difficult due to the growing number of wireless devices that share the wireless medium and the increasing dimensions of transmit parameters, e.g., frequency, time and spatial domain. The performance of adaptive transmission policies derived from given measurements degrade when the environment changes. The policies need to either build up protection against those changes or tune themselves accordingly. Also, an adaptation for systems that take advantages of transmit diversity with finer granularity of resource allocation is hard to come up with due to the prohibitively large number of explicit and implicit environmental variables to take into account. The solutions to the simplified problems often fail due to incorrect assumptions and approximations. In this dissertation, we suggest two tools for adaptive transmission in changing complex environments. We show that adjustable robust optimization builds up protection upon the adaptive resource allocation in interference limited cellular broadband systems, yet maintains the flexibility to tune it according to temporally changing demand. Another tool we propose is based on a data driven approach called Support Vectors. We develop adaptive transmission policies to select the right set of transmit parameters in MIMO-OFDM wireless systems. While we don't explicitly consider all the related parameters, learning based algorithms implicitly take them all into account and result in the adaptation policies that fit optimally to the given environment. We extend the result to multicast traffic and show that the distributed algorithm combined with a data driven approach increases the system performance while keeping the required overhead for information exchange bounded. / text

<p>The thesis is largely built on a collection of published and submitted papers where the main focus is to analyze and optimize single-carrier adaptive coded modulation systems with and without antenna diversity. Multidimensional trellis codes are used as component codes. The majority of the analysis is done with both estimation and prediction errors being incorporated. Both channel estimation and prediction are performed using a pilot-symbol-assisted modulation scheme. Thus, known pilot symbols (overhead information) must be transmitted; which consumes power and also degrades system spectral efficiency. Both power consumption and pilot insertion frequency are optimized such that they are kept at necessary values to maximize system throughput without sacrificing the error rate performance. The results show that efficient and reliable system performance can be achieved over a wide range of the considered average channel quality. Going from a single-input single-output system to both spatially uncorrelated and correlated single-input multiple-ouput (SIMO) systems, and further to an uncorrelated multiple-input multiple-output (MIMO) diversity system, is the evolution of the thesis. In the SIMO case, maximum ratio combining is used to combine the incoming signals, whereas the signals are space-time combined in the MIMO diversity system. The multiple-input single-output system comes out as a special case of a MIMO system. Besides the spatially uncorrelated antenna array, the effect of spatial correlation is also considered in the SIMO case. In this case, only prediction error is considered and channel estimation is assumed to be perfect. At first, the impact of spatial correlation in a predicted system originally designed to operate on uncorrelated channels is quanitifed. Then, a maximum a posteriori (MAP)-optimal “space-time predictor” is derived to take spatial correlation into account. As expected, the results show that the throughput is still lower than the uncorrelated system, but the degradation is decreased when the MAP-optimal space-time predictor is used. Thus, by exploiting the correlation properly, the degradation can be reduced. By numerical examples, we demonstrate the potential effect of limiting the predictor complexity, of fixing the pilot spacing, as well as of assuming perfect estimation. The two first simplifications imply lower system complexity and feedback rate, whereas the last assumption is usually made to ease the mathematical analysis. The numerical examples indicate that all the simplifications can be done without serious impact on the predicted system performance.</p>

Cooperative wireless communication uses relays to enhance the capacity and reliability of data transmission. Adaptive transmission is typically used in conventional non-cooperative communications to exploit the time-varying nature of the wireless channel. In this thesis, we combine these two techniques. We consider decode-and-forward (DF) and amplify-and-forward (AF) relays. The wireless environment is modeled by using the Nakagami-m distribution. The achievable channel capacity with rate adaptive transmission is analytically derived for DF and AF cooperative networks. The performance of a DF cooperative network is analyzed with a constant power rate adaptive scheme consisting of a discrete set of transmission modes. The effect of decoding errors on DF cooperative networks is also analyzed. To this end, a new heuristic approximation of the total received signal-to-noise ratio at the destination is developed. This approximation enables simple yet accurate performance analysis. / Communications

This research concerns analysis of system capacity, development of adaptive transmission schemes with known channel state information at the transmitter (CSIT) and design of new signal detection and channel estimation schemes with low complexity in some multiple antenna systems. We first analyze the sum-rate capacity of the downlink of a cellular system with multiple transmit antennas and multiple receive antennas assuming perfect CSIT. We evaluate the ergodic sum-rate capacity and show how the sum-rate capacity increases as the number of users and the number of receive antennas increases. We develop upper and lower bounds on the sum-rate capacity and study various adaptive MIMO schemes to achieve, or approach, the sum-rate capacity. Next, we study the minimum outage probability transmission schemes in a multiple-input-single-output (MISO) flat fading channel assuming partial CSIT. Considering two special cases: the mean feedback and the covariance feedback, we derive the optimum spatial transmission directions and show that the associated optimum power allocation scheme, which minimizes the outage probability, is closely related to the target rate and the accuracy of the CSIT. Since CSIT is obtained at the cost of feedback bandwidth, we also consider optimal allocation of bandwidth between the data channel and the feedback channel in order to maximize the average throughput of the data channel in MISO, flat fading, frequency division duplex (FDD) systems. We show that beamforming based on feedback CSI can achieve an average rate larger than the capacity without CSIT under a wide range of mobility conditions. We next study a SAGE-aided List-BLAST detection scheme for MIMO systems which can achieve performance close to that of the maximum-likelihood detector with low complexity. Finally, we apply the EM and SAGE algorithms in channel estimation for OFDM systems with multiple transmit antennas and compare them with a recently proposed least-squares based estimation algorithm. The EM and SAGE algorithms partition the problem of estimating a multi-input channel into independent channel estimation for each transmit-receive antenna pair, therefore avoiding the matrix inversion encountered in the joint least-squares estimation.

The thesis is largely built on a collection of published and submitted papers where the main focus is to analyze and optimize single-carrier adaptive coded modulation systems with and without antenna diversity. Multidimensional trellis codes are used as component codes. The majority of the analysis is done with both estimation and prediction errors being incorporated. Both channel estimation and prediction are performed using a pilot-symbol-assisted modulation scheme. Thus, known pilot symbols (overhead information) must be transmitted; which consumes power and also degrades system spectral efficiency. Both power consumption and pilot insertion frequency are optimized such that they are kept at necessary values to maximize system throughput without sacrificing the error rate performance. The results show that efficient and reliable system performance can be achieved over a wide range of the considered average channel quality. Going from a single-input single-output system to both spatially uncorrelated and correlated single-input multiple-ouput (SIMO) systems, and further to an uncorrelated multiple-input multiple-output (MIMO) diversity system, is the evolution of the thesis. In the SIMO case, maximum ratio combining is used to combine the incoming signals, whereas the signals are space-time combined in the MIMO diversity system. The multiple-input single-output system comes out as a special case of a MIMO system. Besides the spatially uncorrelated antenna array, the effect of spatial correlation is also considered in the SIMO case. In this case, only prediction error is considered and channel estimation is assumed to be perfect. At first, the impact of spatial correlation in a predicted system originally designed to operate on uncorrelated channels is quanitifed. Then, a maximum a posteriori (MAP)-optimal “space-time predictor” is derived to take spatial correlation into account. As expected, the results show that the throughput is still lower than the uncorrelated system, but the degradation is decreased when the MAP-optimal space-time predictor is used. Thus, by exploiting the correlation properly, the degradation can be reduced. By numerical examples, we demonstrate the potential effect of limiting the predictor complexity, of fixing the pilot spacing, as well as of assuming perfect estimation. The two first simplifications imply lower system complexity and feedback rate, whereas the last assumption is usually made to ease the mathematical analysis. The numerical examples indicate that all the simplifications can be done without serious impact on the predicted system performance.

This research concerns analysis of system capacity, development of adaptive transmission schemes with known channel state information at the transmitter (CSIT) and design of new signal detection and channel estimation schemes with low complexity in some multiple antenna systems. We first analyze the sum-rate capacity of the downlink of a cellular system with multiple transmit antennas and multiple receive antennas assuming perfect CSIT. We evaluate the ergodic sum-rate capacity and show how the sum-rate capacity increases as the number of users and the number of receive antennas increases. We develop upper and lower bounds on the sum-rate capacity and study various adaptive MIMO schemes to achieve, or approach, the sum-rate capacity. Next, we study the minimum outage probability transmission schemes in a multiple-input-single-output (MISO) flat fading channel assuming partial CSIT. Considering two special cases: the mean feedback and the covariance feedback, we derive the optimum spatial transmission directions and show that the associated optimum power allocation scheme, which minimizes the outage probability, is closely related to the target rate and the accuracy of the CSIT. Since CSIT is obtained at the cost of feedback bandwidth, we also consider optimal allocation of bandwidth between the data channel and the feedback channel in order to maximize the average throughput of the data channel in MISO, flat fading, frequency division duplex (FDD) systems. We show that beamforming based on feedback CSI can achieve an average rate larger than the capacity without CSIT under a wide range of mobility conditions. We next study a SAGE-aided List-BLAST detection scheme for MIMO systems which can achieve performance close to that of the maximum-likelihood detector with low complexity. Finally, we apply the EM and SAGE algorithms in channel estimation for OFDM systems with multiple transmit antennas and compare them with a recently proposed least-squares based estimation algorithm. The EM and SAGE algorithms partition the problem of estimating a multi-input channel into independent channel estimation for each transmit-receive antenna pair, therefore avoiding the matrix inversion encountered in the joint least-squares estimation.

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This work starts from the proposition that it is beneficial to conserve communication energy in Wireless Sensor Networks (WSNs). For WSNs there is an added incentive for energy-efficient communication. The power supply of a sensor is often finite and small. Replenishing the power may be impractical and is likely to be costly. Wireless Sensor Networks are an important area of research. Data about the physical environment may be collected from hostile or friendly environments. Data is then transmitted to a destination without the need for communication cables. There are power and resource constraints upon WSNs, in addition WSN networks are often application specific. Different applications will often have different requirements. Further, WSNs are a shared medium system. The features of the MAC (Medium Access Control) protocol together with the application behaviour shape the communication states of the node. As each of these states have different power requirements the MAC protocol impacts upon the operation and power consumption efficiency. This work focuses on the development of an energy conservation protocol for WSNs where direct communication between sources and a base station is feasible. Whilst the multi-hop approach has been regarded as the underlying communication paradigm in WSNs, there are some scenarios where direct communication is applicable and a significant amount of communication energy can be saved. The Power & Reliability Aware Protocol has been developed. Its main objectives are to provide efficient data communication by means of energy conservation without sacrificing required reliability. This has been achieved by using direct communication, adaptive power adaptation and intelligent scheduling. The results of simulations illustrate the significance of communication energy and adaptive transmission. The relationship between Received Signal Strength Indicator (RSSI) and Packet Reception Rate (PRR) metrics is established and used to identify when power adaptation is required. The experimental results demonstrate an optimal region where lower power can be used without further reduction in the PRR. Communication delays depend upon the packet size whilst two-way propagation delay is very small. Accurate scheduling is achieved through monitoring the clock drift. A set of experiments were carried out to study benefits of direct vs. multi-hop communication. Significant transmitting current can be conserved if the direct communication is used. PoRAP is compared to Sensor-MAC (S-MAC), Berkeley-MAC (B-MAC) and Carrier Sense Multiple Access (CSMA). Parameter settings used in the Great Duck Island (GDI) a production habitat monitoring WSNs were applied. PoRAP consumes the least amount of energy.

There has been phenomenal interest in applying space-time coding techniques in wireless communications in the last two decades. In general, the benefit of applying space-time codes in multiple-input, multiple-output (MIMO) wireless channels is an increase in transmission reliability or system throughput (capacity). However, such a benefit cannot be obtained in some wireless systems where size or other constraints preclude the use of multiple antennas. As such, wireless relay communications has recently been proposed as a means to provide spatial diversity in the face of this limitation. In this approach, some users or relay nodes assist the transmission of other users information. This dissertation contributes to the advancement of wireless relay communications by investigating the performance of various relaying signal processing methods under different practical fading environments. In particular, it examines two main relaying methods, namely decode-and-forward (DF) and amplify-and-forward (AF).<p> For DF, the focus is on the diversity analysis of relaying systems under various practical protocols when detection error at relays is taken into account. In order to effectively mitigate the phenomenon of error propagation, the smart relaying technique proposed by Wang et al. in [R1] is adopted. First, diversity analysis of a single-relay system under the scenario that only the relay is allowed to transmit in the second time slot (called Protocol II) is carried out. For Nakagami and Hoyt generalized fading channels, analytical and numerical results are provided to demonstrate that the system always obtains the maximal diversity when binary phase shift keying (BPSK) modulation is used. Second, a novel and low-complexity relaying system is proposed when smart relaying and equal gain combing (EGC) techniques are combined. In the proposed system, the destination requires only the phases of the channel state information in order to detect the transmitted signals. For the single-relay system with M-ary PSK modulation, it is shown that the system can achieve the maximal diversity under Nakagami and Hoyt fading channels. For the K-relay system, simulation results suggest that the maximal diversity can also be achieved. Finally, the diversity analysis for a smart relaying system under the scenario when both the source and relay are permitted to transmit in the second time slot (referred to as Protocol I) is presented. It is shown that Protocol I can achieve the same diversity order as Protocol II for the case of 1 relay. In addition, the diversity is very robust to the quality of the feedback channel as well as the accuracy of the quantization of the power scaling implemented at the relay.<p> For AF, the dissertation considers a fixed-gain multiple-relay system with maximal ratio combining (MRC) detection at the destination under Nakagami fading channels. Different from the smart relaying for DF, all the channel state information is assumed to be available at the destination in order to perform MRC for any number of antennas. Upperbound and lowerbound on the system performance are then derived. Based on the bounds, it is shown that the system can achieve the maximal diversity. Furthermore, the tightness of the upperbound is demonstrated via simulation results. With only the statistics of all the channels available at the destination, a novel power allocation (PA) is then proposed. The proposed PA shows significant performance gain over the conventional equal PA.

There has been phenomenal interest in applying space-time coding techniques in wireless communications in the last two decades. In general, the benefit of applying space-time codes in multiple-input, multiple-output (MIMO) wireless channels is an increase in transmission reliability or system throughput (capacity). However, such a benefit cannot be obtained in some wireless systems where size or other constraints preclude the use of multiple antennas. As such, wireless relay communications has recently been proposed as a means to provide spatial diversity in the face of this limitation. In this approach, some users or relay nodes assist the transmission of other users information. This dissertation contributes to the advancement of wireless relay communications by investigating the performance of various relaying signal processing methods under different practical fading environments. In particular, it examines two main relaying methods, namely decode-and-forward (DF) and amplify-and-forward (AF).<p> For DF, the focus is on the diversity analysis of relaying systems under various practical protocols when detection error at relays is taken into account. In order to effectively mitigate the phenomenon of error propagation, the smart relaying technique proposed by Wang et al. in [R1] is adopted. First, diversity analysis of a single-relay system under the scenario that only the relay is allowed to transmit in the second time slot (called Protocol II) is carried out. For Nakagami and Hoyt generalized fading channels, analytical and numerical results are provided to demonstrate that the system always obtains the maximal diversity when binary phase shift keying (BPSK) modulation is used. Second, a novel and low-complexity relaying system is proposed when smart relaying and equal gain combing (EGC) techniques are combined. In the proposed system, the destination requires only the phases of the channel state information in order to detect the transmitted signals. For the single-relay system with M-ary PSK modulation, it is shown that the system can achieve the maximal diversity under Nakagami and Hoyt fading channels. For the K-relay system, simulation results suggest that the maximal diversity can also be achieved. Finally, the diversity analysis for a smart relaying system under the scenario when both the source and relay are permitted to transmit in the second time slot (referred to as Protocol I) is presented. It is shown that Protocol I can achieve the same diversity order as Protocol II for the case of 1 relay. In addition, the diversity is very robust to the quality of the feedback channel as well as the accuracy of the quantization of the power scaling implemented at the relay.<p> For AF, the dissertation considers a fixed-gain multiple-relay system with maximal ratio combining (MRC) detection at the destination under Nakagami fading channels. Different from the smart relaying for DF, all the channel state information is assumed to be available at the destination in order to perform MRC for any number of antennas. Upperbound and lowerbound on the system performance are then derived. Based on the bounds, it is shown that the system can achieve the maximal diversity. Furthermore, the tightness of the upperbound is demonstrated via simulation results. With only the statistics of all the channels available at the destination, a novel power allocation (PA) is then proposed. The proposed PA shows significant performance gain over the conventional equal PA.