Cochannel interference (CCI) is a major impairment in narrowband cellular systems. To increase the spectral efficiency of the narrowband systems, identical carrier frequencies are reused in distant cells. The interference rejection capability of the receiver determines this frequency reuse and is therefore critical. In this thesis, we propose an improved demodulation scheme, employing high-resolution frequency estimation techniques, for continuous phase modulated (CPM) signals in presence of CCI. Minimum shift keying (MSK), which is a special case of CPM, is a very popular modulation format around the world. Frequency detectors, such as the limiter-discriminator permit the non-coherent demodulation of MSK signals. High-resolution frequency estimation appears as a very attractive alternative to the conventional non-coherent frequency detectors. The frequency estimation methods that we have studied are based on autoregressive modeling. The contributions of this thesis include the implementation of various demodulation schemes employing parametric frequency estimation. The use of the Viterbi algorithm as a non-linear equalization technique to mitigate intersymbol interference is considered. We verified that the model-based sequence estimation schemes outperform the conventional non-coherent receivers for MSK with AWGN, flat fading, and CCI. Demodulator diversity is also investigated as a way to combat interference. An improved technique combining the proposed model-based receiver and the conventional coherent receiver is implemented and simulated in presence of CCI. / Master of Science

In a multiuser system, several signals are transmitted simultaneously within the same frequency band. This can result in significant improvements both in spectral efficiency and system capacity. However, a detrimental effect of the shared transmissions (both in time and bandwidth), is that the signal received at the base station (BS) or access point (AP) suffers from cochannel interference (CCI) and inter-symbol interference (ISI). This situation presents challenges to receiver design. To combat the destructive nature of multipath fading, a receiver often employs multiple antennas to collect the faded superimposed versions of the transmitted signals. The multiple signals are combined and processed in such a way that the effects of CCI and ISI are minimized and the desired information is reliably recovered. The situation is even more challenging when the system is operating under overload, i.e. when there are fewer receive antennas than there are transmitted signals. Multiuser detection (MUD) is used to simultaneously estimate the information sent by the transmitters. To do this, the receiver exploits differences among the cochannel signals (through unique spatial signatures in this case). We consider a cochannel communication system where multiple transmitted signals arrive at a receiver (equipped with multiple receive antennas) after propagating through a Rayleigh fading channel. It is assumed that the receiver is operating in an overloaded scenario. For such systems, an optimum maximum a posterior probability (MAP) detector estimates the transmitted signal by maximizing the probability of correct decision. The MAP detector reduces to the maximum likelihood (ML) detector when all the transmitted signals are equiprobable. The computational complexity of both MAP and ML detectors increases exponentially with the number of transmitted signals and the channel memory. For large systems suffering severe CCI and ISI, this is clearly not a good choice for real-time implementation due to the associated computational expenses. The main factors that influence the complexity of MAP / ML detection are: (i) the number of transmitted signals (or equivalently the number of users sharing the system resources), (ii) modulation alphabet size, and (iii) length of the channel memory. On the other hand, linear detection approaches fail to offer acceptable performance while other nonlinear sub-optimum approaches incur high computational costs for reasonably improved system performance and exhibit an irreducible error-floor at medium to high signal to noise ratio (SNR) values. We develop receiver signal processing techniques for the frequency-flat fading channel (where all the multipaths of the transmitted signal arrive at the receiver within a symbol period). We develop an ant colony optimization (ACO) assisted soft iterative detection approach for binary phase-shift keying (BPSK) modulated signals which employs a simplified MAP criteria to extract the most probable signals from the search space. The structure of the receiver is such that it can continue operating under overloaded conditions. The technique achieves near maximum likelihood (ML) performance in critically loaded cases using much lower complexity. For the challenging case of overload it still offers performance close to ML at low to moderate SNR values. Second, an integrated framework comprising of ACO metaheuristic and a recursively defined ML search criteria is developed to handle multilevel modulations. The proposed receiver is capable of achieving near-ML performance for the considered system with significant savings in computational complexity. The receiver framework is independent of the system loading condition, and therefore it remains suitable for overloaded scenarios. Due to the branch and bound nature of the algorithm, an exact expression for the complexity cannot be determined. Instead, an upper bound on computational complexity is developed.

Motivated by the demand of increasing data rate in wireless communication, multiple-antenna communication is becoming a key technology in the next generation wireless system. This dissertation considers three different aspects of multipleantenna communication. The first part is signal detection in the multiple-input multiple-output (MIMO) communication. Some low complexity near optimal detectors are designed based on an improved version of Bell Laboratories Layered Space-Time (BLAST) architecture detection and an iterative space alternating generalized expectation-maximization (SAGE) algorithm. The proposed algorithms can almost achieve the performance of optimal maximum likelihood detection. Signal detections without channel knowledge (noncoherent) and with co-channel interference are also investigated. Novel solutions are proposed with near optimal performance. Secondly, the error exponent of the distributed multiple-antenna communication (relay) in the windband regime is computed. Optimal power allocation between the source and relay node, and geometrical relay node placement are investigated based on the error exponent analysis. Lastly, the quality of service (QoS) of MIMO/single-input single- output(SISO) communication is studied. The tradeoff of the end-to-end distortion and transmission buffer delay is derived. Also, the SNR exponent of the distortion is computed for MIMO communication, which can provide some insights of the interplay among time diversity, space diversity and the spatial multiplex gain.

Motivated by the demand of increasing data rate in wireless communication, multiple-antenna communication is becoming a key technology in the next generation wireless system. This dissertation considers three different aspects of multipleantenna communication. The first part is signal detection in the multiple-input multiple-output (MIMO) communication. Some low complexity near optimal detectors are designed based on an improved version of Bell Laboratories Layered Space-Time (BLAST) architecture detection and an iterative space alternating generalized expectation-maximization (SAGE) algorithm. The proposed algorithms can almost achieve the performance of optimal maximum likelihood detection. Signal detections without channel knowledge (noncoherent) and with co-channel interference are also investigated. Novel solutions are proposed with near optimal performance. Secondly, the error exponent of the distributed multiple-antenna communication (relay) in the windband regime is computed. Optimal power allocation between the source and relay node, and geometrical relay node placement are investigated based on the error exponent analysis. Lastly, the quality of service (QoS) of MIMO/single-input single- output(SISO) communication is studied. The tradeoff of the end-to-end distortion and transmission buffer delay is derived. Also, the SNR exponent of the distortion is computed for MIMO communication, which can provide some insights of the interplay among time diversity, space diversity and the spatial multiplex gain.

Opportunistic spectrum access has become a high priority research area in the past few years. The motivation behind this actively researched area is the fact that the limited spectrum available is currently being utilized in an inefficient way. The complete wireless spectrum is assigned and reserved, but not necessarily being used. At the same time, the demand for innovation in wireless technology is growing. Since there is no room in the wireless spectrum to allocate significant frequency bands for future wireless technologies, the only recourse is to increase utilization of the spectrum. To achieve this, we must find a way to share the spectrum. Spectrum sharing techniques will require coordination between all the layers of the protocol stack. The network and the wireless medium are inextricably linked and, thus, both must be considered when optimizing wireless network performance. Unfortunately, interactions in the wireless medium can lead to non-convex problems which have been shown to be NP-hard. Techniques must be developed to tackle the optimization problems that arise from wireless network analysis. In this document we focus on analyzing the spectrum sharing problem from two perspectives: cooperative game theory and non-convex optimization. We develop a cooperative game theory model to analyze a scenario where nodes in a multi-hop wireless network need to agree on a fair allocation of spectrum. We show that in high interference environments, the utility space of the game is non-convex, which may make some optimal allocations unachievable with pure strategies. However, we show that as the number of channels available increases, the utility space becomes close to convex and thus optimal allocations become achievable with pure strategies. We propose the use of the NBS and show that it achieves a good compromise between fairness and efficiency, using a small number of channels. We also propose a distributed algorithm for spectrum sharing and show that it achieves allocations reasonably close to the NBS. In our game theory analysis, we studied the possible outcomes of the spectrum sharing problem and propose the NBS as a desirable outcome and propose an algorithm to achieve the NBS spectrum allocation. However, the expression used to compute the NBS is a non-convex optimization problem. We propose an optimization model to solve a class of problems that incorporate the non-convex dynamics of the wireless medium that occur when the objective is a function of SINR. We present two case studies to show the application of the model to discrete and continuous optimization problems. We propose a branch and bound heuristic, based on the RLT, for approximating the solution of continuous optimization problems. Finally, we present results for the continuous case study. We show simulation results for the heuristic compared to a time constrained mixed integer linear program (MILP) as well as a nonlinear optimization using random starting points. We show that for small networks the MILP achieves the optimal in reasonable time and the heuristic achieves a value close to the optimal. We also show that for large networks the heuristic outperforms the MILP as well as the nonlinear search. / Ph. D.

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