Due to the reuse factor reduction, the same frequencies are reused in adjacent neighboring cells, which causes an attendant increase in co-channel interference (CCI). CCI has already become the limiting factor in the performance of orthogonal frequency division multiplexing (OFDM) based cellular systems. Joint maximum likelihood sequence estimation (JMLSE) based interference cancellation algorithms have been under intense research. However, despite the fact that the error probability of JMLSE is critical for analyzing the performance, to the best of our knowledge, the mathematical expression has not been derived for MQAM-OFDM yet. Direct computation of the error probability involves integrating a multi-dimensional Gaussian distribution that has no closed-form solution. Therefore, an alternative way is to upper and lower bound the error probability with computable quantities. In this paper, firstly, both the upper and the conventional lower error probability bounds of JMLSE are derived for MQAM-OFDM systems based on a genie-aided receiver. Secondly, in order to reduce the gap between the conventional lower bound and the simulation results, a tighter lower bound is derived by replacing the genie with a less generous one. Thirdly, those derived error probability bounds are generalized to the receiver diversity scheme. These error probability bounds are important new analytical results that can be used to provide rapid and accurate estimation of the BER performance over any MQAM scheme and an arbitrary number of interferers and receive antennas.

Due to the reuse factor reduction, the attendant increase in co-channel interference (CCI) becomes the limiting factor in the performance of the orthogonal frequency division multiplexing (OFDM) based cellular systems. In the previous work, we proposed the least mean square-blind joint maximum likelihood sequence estimation (LMS-BJMLSE) algorithm, which is effective for CCI cancellation in OFDM systems with only one receive antenna. However, LMS-BJMLSE requires a long training sequence (TS) for channel estimation, which reduces the transmission efficiency. In this paper, we propose a subcarrier identification and interpolation algorithm, in which the subcarriers are divided into groups based on the coherence bandwidth, and the slowest converging subcarrier in each group is identified by exploiting the correlation between the mean-square error (MSE) produced by LMS and the mean-square deviation (MSD) of the desired channel estimate. The identified poor channel estimate is replaced by the interpolation result using the adjacent subcarriers' channel estimates. Simulation results demonstrate that the proposed algorithm can reduce the required training sequence dramatically for both the cases of single interference and dual interference. We also generalize LMS-BJMLSE from single antenna to receiver diversity, which is shown to provide a huge improvement.

Software-Defined Radio (SDR) represents a major paradigm shift in the design of radios, allowing a large fraction of the functionality to be implemented through programmable signal processing devices, enabling the radio to change its operating parameters to accommodate new air interface, features and capabilities. However, the actual realization of innovative and software-reconfigurable receiver diversity at mobile handsets in intermediate frequency band to provide wide-ranging benefits, including more effective filtered result, less cost of the mixed channel access, improved capacity, better link reliability, and reduced power consumption, has been slowed down largely due to an absence of effective architecture reducing the complexity of adaptive combining algorithms. This paper proposes a novel reconfigurable architecture for adaptive space diversity at handsets in MC-CDMA (multicode code-division multiple-access) systems. The key to which is the development of a valid and effective alternative to the time-consuming multiplication operation and despreading acquisition. A software definable algorithm can become a multiplier-free architecture if it can restrict the weight factors to power-of-two values and repetitive gradient search procedure to contain shift operations and predicate functions. The results of numerical simulation and experimentation confirm the expectation that the constrained approach should perform comparably to, but not better than the traditional diversity algorithm. That is, the feasibility of SDR depends on its trading some performance for reduced computational complexity, improved area efficiency and less power consumption.

A combining method for receiver diversity, followed by a Bayesian decision feedback equalizer, is proposed. This eigenvector based combining maximizes the desired part energy of combined channel, on which the equalizer performance mainly depends. The validity of the proposed method is demonstrated by simulations.