In this paper, we introduce a novel power control strategy based on the total received power in the uplink of cooperative base stations (CBSs). Power control via channel inversion is common practice in the uplink of cellular systems as it maintains constant received signal power at the controlling base station (BS) regardless of the position of the mobile station (MS). Unlike the conventional cellular system, CBSs share information in detecting the MSs and they are shown to increase the system capacity. We propose a power control approach based on sustaining the total received power in CBSs. We investigate the performance of this method through simulations and show that it reduces the intercell interference in the CBS systems, saves power and improves the BER performance in both coded and uncoded scenarios.

We introduce a novel framework for soft-input, soft-output (SISO) equalization in frequency selective multipleinput multiple-output (MIMO) channels based on the well-known belief propagation (BP) algorithm. As in the BP equalizer, we model the multipath channels using factor graphs (FGs) where the transmitted and received signals are represented by the function and variable nodes respectively. The edges connecting the function and variable nodes illustrate the dependencies of the multipath channel and soft decisions are developed by exchanging information on these edges iteratively. We incorporate powerful techniques such as groupwise iterative multiuser detection (IMUD), probabilistic data association (PDA) and sphere decoding (SD) in order to reduce the computational complexity of BP equalizer with relatively small degradation in performance. The computational complexity of this new reduced-complexity BP (RCBP) equalizer grows linearly with block size and memory length of the channel. The proposed framework has a flexible structure that allows for parallel as well as serial detection. We will illustrate through simulations that the RCBP equalizer can even handle overloaded scenarios where the channel matrix is rank deficient, and it can achieve excellent performance by applying iterative equalization using the low-density parity check codes (LDPC).

System-wide multiuser detection (MUD), in which all base stations (BSs) of a cellular system cooperate to detect the data of all mobile stations (MSs), has much promise. However, little is known at present about practical techniques or their performance. An attractive method is belief propagation (BP), with message exchange between nearby cooperating BSs over a backbone landline network, but its performance is known only for a greatly simplified network model and its computation load grows exponentially with the number of interfering MS symbols at each BS. In this paper, we present a reduced complexity variation of BP (RCBP) and show that its performance is close to or identical to that of BP in the simplified network. We also observe excellent performance by iterative multiuser detection and decoding of low-density parity check codes (LDPC). Furthermore, we examine RCBP performance in a realistic wireless network model, with path loss, shadowing, fading and power control. These results, though poorer than those of the simplified network, show that system-wide MUD with cooperating BSs provides great improvement compared with conventional systems.

We propose a reduced-complexity belief propagation (RCBP) receiver for joint detection in the uplink of cellular multi-access networks with base station cooperation. Using local message passing algorithms based on belief propagation (BP) has been shown to be very promising for distributed base station processing. Unfortunately, the computational complexity of the BP algorithm grows exponentially with the number of interfering users at each base station. Reducing the computational load of marginalizing the a posteriori probabilities (APP) in BP nodes is challenging because this problem is generally rank deficient, and most well-known sub-optimal detection methods are not efficient in such conditions. In this paper we use an iterative group-wise multiuser detection algorithm to efficiently reduce the complexity of BP receiver. We evaluate the performance and complexity of the proposed RCBP algorithm under decomposed and clustered base station scenarios via simulations.

We consider a narrowband multiuser system in which several transmitting users are received through a synchronous, flat fading channel at an antenna array. The paper introduces a multiuser detection technique that combines groupwise detection with iteration. It is well suited to overloaded conditions, where there are more transmitter antennas than receive antennas. The soft decisions it uses internally also make it suitable as a detector in a concatenated structure. Its performance and computation can be traded off through selection of group size. A variant of the algorithm achieves further computation reduction by incorporating a soft sphere detection core.

We introduce a symbol by symbol, soft-input soft-output (SISO) multiuser detector for frequency selective multiple-input multiple-output (MIMO) channels. The basic principle of this algorithm is to extract a posteriori probabilities (APP) of all interfering symbols at each symbol interval and then feed these updated APP as a priori probabilities (apP) for joint APP extraction in the next symbol interval. Unlike near-optimal block oriented sphere decoding (SD) and soft decision equalization (SDE), the computational complexity of this updating APP (UA) algorithm is linear in the number of symbols. However, the exponential computational load of optimal joint APP extraction makes the basic UA impractical. To decrease computations we replace the optimal joint APP extractor by a groupwise SISO multiuser detector with a soft sphere decoding core. The resulting reduced complexity updating APP (RCUA) equalizer is flexible in different situations and outperforms the traditional sub-optimal MMSE-DFE without increasing the computational costs substantially.