We propose an enhanced precision channel state acquisition scheme for downlink Multi-User MIMO (MU-MIMO) transmission with a large number of transmit antennas at the base station. The proposed scheme is an attempt to harvest the Channel State Information (CSI) at the transmitter by incrementally improving the precision of the channel knowledge. We show how, in an LTE-Advanced system, CSI harvesting mechanism followed by the conventional Zero-Forcing Beam-Forming (ZFBF) results in improved MU-MIMO performance, relative to the current method for acquiring CSI at the transmitter. The proposed progressive CSI harvesting scheme is also shown to yield significant saving in terms of feedback overhead. The implications of the larger MIMO systems and higher MU-MIMO ranks, and sensitivity to higher mobility are also discussed.

We consider downlink multi-user MIMO transmission of a wireless system in which the base station is equipped with a large number of antennas. We propose two downlink schemes based on random beamforming and subspace tracking and show that while reducing signaling overhead, these schemes can improve the system performance compared to conventional systems with a smaller number of antennas at the base station.

This paper shows how cognitive radio (CR) can help to optimize system power consumption of multiple input multiple output (MIMO) communication systems. Leveraging results from information theory and capabilities of a CR (e.g., the awareness of the component capabilities and characteristics), a theoretical framework is developed to minimize the system power consumption of MIMO systems while still considering radiated power. This paper mathematically formulates the system power consumption minimization problem under a sum rate constraint for MIMO systems. The impact of channel correlation and partial channel state information at the transmitter is considered. Numerical algorithms are developed to solve the constrained optimization problem. The simulation results show that significant power savings (e.g., up to 75% for a 4 x 4 MIMO system with Class A power amplifiers) can be achieved compared to conventional power allocation schemes. The results also show that the more computationally efficient suboptimal heuristic algorithms can achieve power savings comparable to the exhaustive search algorithm.

Direct-conversion receiver (DCR) architecture has received considerable attention recently owing to its portable architecture and superior performance in terms of power and cost over its super-heterodyne counterpart. Flicker noise is one of the major impairments which severely affects the performance of the system. In this work, we investigate the use of signal processing techniques in the mitigation of flicker noise in OFDM-based systems which employ DCR architecture. The statistical properties of flicker noise are exploited to develop adaptive signal processing algorithms that reduce the effect of flicker noise in DCRs. Results indicate that signal processing algorithms can provide significant performance gain under low SNR conditions.

Digital pre-distortion is a powerful technique for combating nonlinearity in power amplifiers. Nonlinear response identification of the amplifier is one of the primary objectives in all pre-distortion schemes. If the estimation performance of the adaptive algorithm is not fast and accurate enough, the system will not be able to satisfy the stringent timing and spectral requirements of modern standards. This problem is exacerbated in modern communication systems which are rapidly converging towards a multi-carrier scheme. In such schemes, due to high peak to average power ratios, the convergence of pre-distorters worsens significantly. This paper highlights the problem of slow convergence in multi-carrier systems, and proposes a novel solution, which consists of implementing a twin feedback loop, one including the power amplifier and the other including the pre- distortion system itself. The goal is to leverage the learning curve of the pre-distorter itself and build on what is already known from the partially converged characteristics. Simulations with standard multi-carrier schemes indicate that such a method can deliver vastly improved performance over conventional systems.

In this paper, we show how cognitive radio can help minimize energy consumption of a wireless mobile communication device. We propose an energy optimization framework using cognitive radio for a given quality of service requirement based on the channel and the radio capabilities. The cognitive radio not only adjusts modulation, coding, and radiated power, as with conventional adaptive modulation, but also adjusts component characteristics (e.g., power amplifier characteristics) so that the radio operates with the highest energy efficient possible way. Simulation results show that significant energy savings (up to 75%) can be achieved compared to conventional adaptive modulation. This framework also can be applied to optimize radio operations to achieve additional goals.

Cellular transmission standards such as GSM and IS-54 produce data streams with varying degrees of significance at the source encoder level. We propose an unequal error protection scheme based on a non-uniform signal set which provides the more important data with a preferential Euclidean distance. The unequal error protection is partly accomplished by the modulator in contrast to the conventional systems where the channel encoder is solely responsible. The coding gain resulting from the asymmetric modulation can be translated into a reduction in the complexity of the channel encoder; specifically a reduction by more than half in the number of encoder states can be expected. We study the interaction between the code complexity and the modulation asymmetry in quantitative terms. For differentially coherent systems, a /spl pi//6-shifted differential embedded QPSK is proposed. Decentralizing the bit protection culminates in an extra degree of freedom which in turn introduces more flexibility into the system design.