Increasing energy consumption is a result of the rapid growth in cellular communication technologies and a massive increase in the number of mobile terminals (MTs) and communication sites. In cellular communication networks, energy efficiency (EE) and spectral efficiency (SE) are two of the most important criteria employed to evaluate the performance of networks. A compromise between these two conflicting criteria is therefore required, in order to achieve the best cellular network performance. Fractional frequency reuse (FFR), classed as either strict FFR or soft frequency reuse (SFR), is an intercell interference coordination (ICIC) technique applied to manage interference when more spectrum is used, and to enhance the EE. A conventional cellular model's downlink is designed as a reference in the presence of inter-cell interference (ICI) and a general fading environment. Energy-efficient cellular models,such as cell zooming, cooperative BSs and relaying models are designed, analysed and compared with the reference model, in order to reduce network energy consumption without degrading the SE. New mathematical models are derived herein to design a distributed antenna system (DAS), in order to enhance the system's EE and SE. DAS is designed in the presence of ICI and composite fading and shadowing with FFR. A coordinate multi-point (CoMP) technique is applied, using maximum ratio transmission (MRT) to serve the mobile terminal (MT), with all distributed antenna elements (DAEs), transmit antenna selection (TAS) being applied to select the best DAE and general selection combining (GSC) being applied to select more than one DAE. Furthermore, a Cloud radio access network (C-RAN) is designed and analysed with two different schemes, using the high-power node (HPN) and a remote radio head (RRH), in order to improve the EE and SE of the system. Finally, a trade-off between the two conflicting criteria, EE and SE, is handled carefully in this thesis, in order to ensure a green cellular communication network.

It is well known that multiple antennas at the transmitter and receiver are imperative for reliable and high data-rate communication over wireless channels. However, these systems essentially need multiple radio frequency (RF) chains owing to multiple antennas, and hence pose challenges for applications with limited form-factor. Antenna Selection (AS) techniques alleviate this problem by using only a subset of the total available antennas and hence require only a few RF chains compared to the number of antennas. These systems operate in a closed-loop scenario, where the information fed back from the receiver is used for the transmit antenna subset selection. In contrast to this, a novel open-loop technique known as spatial modulation (SM) was recently proposed that uses a single RF-chain at the transmitter and achieves a higher spectral efficiency compared to single-input and AS based systems. The work in the thesis mainly focuses on the following aspects of SM system: Study of Mutual Information in SM systems operating in open-loop and closed-loop scenarios: We study the achievable mutual information in the SM system operating with finite and Gaussian input alphabet, and compare the results with that of the SIMO and AS based systems. Reduced-complexity maximum-likelihood (ML) decoding algorithms for SM systems: We propose ML-optimal sphere decoders for SM systems with arbitrary number of transmit antennas. Furthermore, a reduced-complexity ML detector is also proposed whose computational complexity is lowest among the known existing detectors in the literature. Transmit diversity techniques for SM systems: The conventional SM system achieves a transmit diversity order of one. We propose a complex interleaved orthogonal design baaed SM scheme that achieves a transit diversity order of two, while offering symbol-by- symbol ML decodability. Transmit antenna subset selection algorithms for SM systems: The SM system is considered in the closed-loop scenario, where only a subset of the total number of transmit antennas is chosen based on the information fed back by the receiver. Specifically, the Euclidean distance and capacity optimized antenna selection algorithms are studied in comparison with the conventional AS based systems. SM system operating in dispersive channels: The SM system operating in a dispersive channel with the aid of zero-padding is studied. It is shown that the SM system achieves full receive-diversity and multipath-diversity with ML decoding, but offers a decoding complexity that is exponential in the number of multipaths. Furthermore, a reduced complexity linear receiver is proposed that achieves achieves full multipath as well as receive-diversity, while offering a decoding complexity order same as that of the SM system operating in a frequency-flat channel.

Motivated by the increasing demand for higher data rates, coverage, and spectral efficiency, current and next generation wireless systems adapt transmission parameters and even who is being transmitted to, based on the instantaneous channel states. For example, frequency-domain scheduling(FDS) is an instance of adaptation in orthogonal frequency division multiple access(OFDMA) systems in which the base station opportunistically assigns different subcarriers to their most appropriate user. Likewise ,transmit antenna selection(AS) is another form of adaptation in which the transmitter adapts which subset of antennas it transmits with. Cognitive radio(CR), which is a next generation technology, itself is a form of adaptation in which secondary users(SUs) adapt their transmissions to avoid interfering with the licensed primary users(PUs), who own the spectrum. However, adaptation requires channel state information(CSI), which might not be available apriori at the node or nodes that are adapting. Further, the CSI might not be perfect due to noise or feedback delays. This can result in suboptimal adaptation in OFDMA systems or excessive interference at the PUs due to transmissions by the SUs in CR. In this thesis, we focus on adaptation techniques in current and next generation wireless systems and evaluate the impact of CSI –both perfect and imperfect –on it. We first develop a novel model and analysis for characterizing the performance of AS in frequency-selective OFDMA systems. Our model is unique and comprehensive in that it incorporates key LTE features such as imperfect channel estimation based on dense, narrow band demodulation reference signal and coarse, broad band sounding reference signal. It incorporates the frequency-domain scheduler, the hardware constraint that the same antenna must be used to transmit over all the subcarriers that are allocated to a user, and the scheduling constraint that the allocated subcarriers must all be contiguous. Our results show the effectiveness of combined AS and FDS in frequency-selective OFDMA systems even at lower sounding reference signal powers. We then investigate power adaptation in underlay CR, in which the SU can transmit even when the primary is on but under stringent interference constraints. The nature of the interference constraint fundamentally decides how the SU adapts its transmit power. To this end, assuming perfect CSI, we propose optimal transmit power adaptation policies that minimize the symbol error probability of an SU when they are subject to different interference and transmit power constraints. We then study the robustness of these optimal policies to imperfections in CSI. An interesting observation that comes out of our study is that imperfect CSI can not only increase the interference at the PU but can also decrease it, and this depends on the choice of the system parameters, interference, and transmit power constraints. The regimes in which these occur are characterized.