ITC/USA 2014 Conference Proceedings / The Fiftieth Annual International Telemetering Conference and Technical Exhibition / October 20-23, 2014 / Town and Country Resort & Convention Center, San Diego, CA / In this work we evaluated the performance of different water filling algorithms. We have selected four power allocation algorithms: Conventional water-filling (CWF), Constant power water-filling, Inverse Water-filling (IWF), and Adaptive Iterative Water-Filling (AIWF) algorithms. Capacity is the performance metric we used to compare the above algorithms by taking the optimality of transmission power allocation to each sub-channel into account. The power allocation can be calculated with a reference of the water level value that has different approaches for different algorithms. The water level can either be fixed once it is found, or it may be adaptive or different for different sub-channels. Hence, the results show that the adaptive iterative water filling (AIWF) algorithm has a better effect on the performance of MIMO-OFDM system by allocating power adaptively.

In conventional wireless channel models, there is no control on the gains of different subchannels. In such channels, the transmitted signal undergoes attenuation and phase shift and is subject to multi-path propagation effects. We herein refer to such channels as passive channels. In this dissertation, we study the problem of joint power allocation and channel design for a parallel channel which conveys information from a source to a destination through multiple orthogonal subchannels. In such a link, the power over each subchannel can be adjusted not only at the source but also at each subchannel. We refer to this link as an active parallel channel. For such a channel, we study the problem of sum-rate maximization under the assumption that the source power as well as the energy of the active channel are constrained. This problem is investigated for equal and unequal noise power at different subchannels. For equal noise power over different subchannels, although the sum-rate maximization problem is not convex, we propose a closed-form solution to this maximization problem. An interesting aspect of this solution is that it requires only a subset of the subchannels to be active and the remaining subchannels should be switched off. This is in contrast with passive parallel channels with equal subchannel signal-tonoise- ratios (SNRs), where water-filling solution to the sum-rate maximization under a total source power constraint leads to an equal power allocation among all subchannels. Furthermore, we prove that the number of active channels depends on the product of the source and channel powers. We also prove that if the total power available to the source and to the channel is limited, then in order to maximize the sum-rate via optimal power allocation to the source and to the active channel, half viii ix of the total available power should be allocated to the source and the remaining half should be allocated to the active channel. We extend our analysis to the case where the noise powers are unequal over different subchannels. we show that the sum-rate maximization problem is not convex. Nevertheless, with the aid of Karush-Kuhn-Tucker (KKT) conditions, we propose a computationally efficient algorithm for optimal source and channel power allocation. To this end, first, we obtain the feasible number of active subchannels. Then, we show that the optimal solution can be obtained by comparing a finite number of points in the feasible set and by choosing the best point which yields the best sum-rate performance. The worst-case computational complexity of this solution is linear in terms of number of subchannels. / UOIT

Spectrum access protocols have been proposed recently to provide flexible and efficient use of the available bandwidth. Game theory has been applied to the analysis of the problem to determine the most effective allocation of the users’ power over the bandwidth. However, prior analysis has focussed on Shannon capacity as the utility function, even though it is known that real signals do not, in general, meet the Gaussian distribution assumptions of that metric. In a non-cooperative spectrum sharing environment, the Shannon capacity utility function results in a water-filling solution. In this thesis, the suitability of the water-filling solution is evaluated when using non-Gaussian signalling first in a frequency non-selective environment to focus on the resource allocation problem and its outcomes. It is then extended to a frequency selective environment to examine the proposed algorithm in a more realistic wireless environment. It is shown in both scenarios that more effective resource allocation can be achieved when the utility function takes into account the actual signal characteristics. Further, it is demonstrated that higher rates can be achieved with lower transmitted power, resulting in a smaller spectral footprint, which allows more efficient use of the spectrum overall. Finally, future spectrum management is discussed where the waveform adaptation is examined as an additional option to the well-known spectrum agility, rate and transmit power adaptation when performing spectrum sharing.

Spectrum access protocols have been proposed recently to provide flexible and efficient use of the available bandwidth. Game theory has been applied to the analysis of the problem to determine the most effective allocation of the users’ power over the bandwidth. However, prior analysis has focussed on Shannon capacity as the utility function, even though it is known that real signals do not, in general, meet the Gaussian distribution assumptions of that metric. In a non-cooperative spectrum sharing environment, the Shannon capacity utility function results in a water-filling solution. In this thesis, the suitability of the water-filling solution is evaluated when using non-Gaussian signalling first in a frequency non-selective environment to focus on the resource allocation problem and its outcomes. It is then extended to a frequency selective environment to examine the proposed algorithm in a more realistic wireless environment. It is shown in both scenarios that more effective resource allocation can be achieved when the utility function takes into account the actual signal characteristics. Further, it is demonstrated that higher rates can be achieved with lower transmitted power, resulting in a smaller spectral footprint, which allows more efficient use of the spectrum overall. Finally, future spectrum management is discussed where the waveform adaptation is examined as an additional option to the well-known spectrum agility, rate and transmit power adaptation when performing spectrum sharing.

Spectrum access protocols have been proposed recently to provide flexible and efficient use of the available bandwidth. Game theory has been applied to the analysis of the problem to determine the most effective allocation of the users’ power over the bandwidth. However, prior analysis has focussed on Shannon capacity as the utility function, even though it is known that real signals do not, in general, meet the Gaussian distribution assumptions of that metric. In a non-cooperative spectrum sharing environment, the Shannon capacity utility function results in a water-filling solution. In this thesis, the suitability of the water-filling solution is evaluated when using non-Gaussian signalling first in a frequency non-selective environment to focus on the resource allocation problem and its outcomes. It is then extended to a frequency selective environment to examine the proposed algorithm in a more realistic wireless environment. It is shown in both scenarios that more effective resource allocation can be achieved when the utility function takes into account the actual signal characteristics. Further, it is demonstrated that higher rates can be achieved with lower transmitted power, resulting in a smaller spectral footprint, which allows more efficient use of the spectrum overall. Finally, future spectrum management is discussed where the waveform adaptation is examined as an additional option to the well-known spectrum agility, rate and transmit power adaptation when performing spectrum sharing.

Spectrum access protocols have been proposed recently to provide flexible and efficient use of the available bandwidth. Game theory has been applied to the analysis of the problem to determine the most effective allocation of the users’ power over the bandwidth. However, prior analysis has focussed on Shannon capacity as the utility function, even though it is known that real signals do not, in general, meet the Gaussian distribution assumptions of that metric. In a non-cooperative spectrum sharing environment, the Shannon capacity utility function results in a water-filling solution. In this thesis, the suitability of the water-filling solution is evaluated when using non-Gaussian signalling first in a frequency non-selective environment to focus on the resource allocation problem and its outcomes. It is then extended to a frequency selective environment to examine the proposed algorithm in a more realistic wireless environment. It is shown in both scenarios that more effective resource allocation can be achieved when the utility function takes into account the actual signal characteristics. Further, it is demonstrated that higher rates can be achieved with lower transmitted power, resulting in a smaller spectral footprint, which allows more efficient use of the spectrum overall. Finally, future spectrum management is discussed where the waveform adaptation is examined as an additional option to the well-known spectrum agility, rate and transmit power adaptation when performing spectrum sharing.

The purpose of this paper is to study the possibilities of the OFDM system adaptive modulation. The study presents mechanism for acquiring the necessary data of every frequency subchannel using pilot signals and application of these data for system adjustment. The paper introduces the water-filling principle for subchannel power allocation on the basis of their SNR and adaptive modulation mechanisms for variety of OFDM usage. These mechanisms are implemented into a MATLAB model. The code is then transformed into graphical representation and using USRP2, the signal is transmitted by real world channel.

Consider a generalized multiterminal source coding system, where 􏱡(l choose m) 􏱢 encoders, each m observing a distinct size-m subset of l (l ≥ 2) zero-mean unit-variance symmetrically correlated Gaussian sources with correlation coefficient ρ, compress their observation in such a way that a joint decoder can reconstruct the sources within a prescribed mean squared error distortion based on the compressed data. The optimal rate- distortion performance of this system was previously known only for the two extreme cases m = l (the centralized case) and m = 1 (the distributed case), and except when ρ = 0, the centralized system can achieve strictly lower compression rates than the distributed system under all non-trivial distortion constaints. Somewhat surprisingly, it is established in the present thesis that the optimal rate-distortion preformance of the afore-described generalized multiterminal source coding system with m ≥ 2 coincides with that of the centralized system for all distortions when ρ ≤ 0 and for distortions below an explicit positive threshold (depending on m) when ρ > 0. Moreover, when ρ > 0, the minimum achievable rate of generalized multiterminal source coding subject to an arbitrary positive distortion constraint d is shown to be within a finite gap (depending on m and d) from its centralized counterpart in the large l limit except for possibly the critical distortion d = 1 − ρ. / Thesis / Master of Applied Science (MASc)

In this thesis, the process of optimizing Cooperative Spectrum Sensing in Cognitive Radio has been investigated in fast-fading environments where simulation results have shown that its performance is limited by the Probability of Reporting Errors. By proposing a transmit diversity scheme using Differential space-time block codes (D-STBC) where channel state information (CSI) is not required and regarding multiple pairs of Cognitive Radios (CR's) with single antennas as a virtual MIMO antenna arrays in multiple clusters, Differential space-time coding is applied for the purpose of decision reporting over Rayleigh channels. Both Hard and Soft combination schemes were investigated at the fusion center to reveal performance advantages for Hard combination schemes due to their minimal bandwidth requirements and simplistic implementation. The simulations results show that this optimization process achieves full transmit diversity, albeit with slight performance degradation in terms of power with improvements in performance when compared to conventional Cooperative Spectrum Sensing over non-ideal reporting channels. Further research carried out in this thesis shows performance deficits of Cooperative Spectrum Sensing due to interference on sensing channels of Cognitive Radio. Interference Alignment (IA) being a revolutionary wireless transmission strategy that reduces the impact of interference seems well suited as a strategy that can be used to optimize the performance of Cooperative Spectrum Sensing. The idea of IA is to coordinate multiple transmitters so that their mutual interference aligns at their receivers, facilitating simple interference cancellation techniques. Since its inception, research efforts have primarily been focused on verifying IA's ability to achieve the maximum degrees of freedom (an approximation of sum capacity), developing algorithms for determining alignment solutions and designing transmission strategies that relax the need for perfect alignment but yield better performance. With the increased deployment of wireless services, CR's ability to opportunistically sense and access the unused licensed frequency spectrum, without causing harmful interference to the licensed users becomes increasingly diminished, making the concept of introducing IA in CR a very attractive proposition. For a multiuser multiple-input-multiple-output (MIMO) overlay CR network, a space-time opportunistic IA (ST-OIA) technique has been proposed that allows spectrum sharing between a single primary user (PU) and multiple secondary users (SU) while ensuring zero interference to the PUs. With local CSI available at both the transmitters and receivers of SUs, the PU employs a space-time WF (STWF) algorithm to optimize its transmission and in the process, frees up unused eigenmodes that can be exploited by the SU. STWF achieves higher performance than other WF algorithms at low to moderate signal-to-noise ratio (SNR) regimes, which makes it ideal for implementation in CR networks. The SUs align their transmitted signals in such a way their interference impairs only the PU's unused eigenmodes. For the multiple SUs to further exploit the benefits of Cooperative Spectrum Sensing, it was shown in this thesis that IA would only work when a set of conditions were met. The first condition ensures that the SUs satisfy a zero interference constraint at the PU's receiver by designing their post-processing matrices such that they are orthogonal to the received signal from the PU link. The second condition ensures a zero interference constraint at both the PU and SUs receivers i.e. the constraint ensures that no interference from the SU transmitters is present at the output of the post-processing matrices of its unintended receivers. The third condition caters for the multiple SUs scenario to ensure interference from multiple SUs are aligned along unused eigenmodes. The SU system is assumed to employ a time division multiple access (TDMA) system such that the Principle of Reciprocity is employed towards optimizing the SUs transmission rates. Since aligning multiple SU transmissions at the PU is always limited by availability of spatial dimensions as well as typical user loads, the third condition proposes a user selection algorithm by the fusion centre (FC), where the SUs are grouped into clusters based on their numbers (i.e. two SUs per cluster) and their proximity to the FC, so that they can be aligned at each PU-Rx. This converts the cognitive IA problem into an unconstrained standard IA problem for a general cognitive system. Given the fact that the optimal power allocation algorithms used to optimize the SUs transmission rates turns out to be an optimal beamformer with multiple eigenbeams, this work initially proposes combining the diversity gain property of STBC, the zero-forcing function of IA and beamforming to optimize the SUs transmission rates. However, this solution requires availability of CSI, and to eliminate the need for this, this work then combines the D-STBC scheme with optimal IA precoders (consisting of beamforming and zero-forcing) to maximize the SUs data rates.

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