In this paper, we investigate iterative detection and decoding, a.k.a. turbo detection, for multiple-input multiple-output (MIMO) transmission. Specifically, we consider using a low complexity soft-in/soft-out MIMO detector based on belief propagation over a pair-wise graph that accepts a priori information feedback from a channel decoder. Simulation results confirm that considerable performance improvement can be obtained with only a few detection-and-decoding iterations if convolutional channel coding is used. A brief estimate is given of the overall complexity of turbo detectors, to verify the key argument that the performance of a maximum a posteriori (MAP) detector (without turbo iteration) can be achieved, at much lower computation cost, by using the low complexity soft-in/soft-out MIMO detector under consideration.

This letter addresses antenna ordering to improve the performance of the MIMO detectors in [4], where two low complexity MIMO detectors have been proposed based on either fully-connected or ring type pair-wise Markov random field (MRF). The former was shown to be better than the latter, while being more complex. The objective of this letter is to make the performance of the detector based on ring-type MRF (with complexity of O(2M 22m)) close to or better than that of fully-connected MRF (with complexity of O(M (M-1)22m)), by applying appropriate antenna ordering. The simulation results validate the proposed antenna ordering methods.

The system level performance of a superposition coded broadcast/unicast service overlay system is considered. Cellular network for unicast service only is considered as interference limited system, where increasing the transmission power does not help improve the network throughput especially when the frequency reuse factor is close to 1. In such cases, the amount of power that does not contribute to improving the throughput can be considered as "unused." This situation motivates us to use the unused power for broadcast services, which can be efficiently provided in OFDM based single frequency networks as in digital multimedia broadcast systems. In this paper, we investigate the performance of such a broadcast/unicast overlay system in which a single frequency broadcast service is superimposed over a unicast cellular service. Alternative service multiplexing using FDM/TDM is also considered for comparison.

Recent works on MIMO receiver design were mainly focused on sphere decoding, which provides a trade-off between the performance and complexity by suitably choosing the “radius” or the number of candidates in the search space. Meanwhile, another approach, called poly-diagonalization and trellis detection, has been proposed to compromise the complexity and performance. In this paper, we compare various MIMO receiver algorithms in terms of both performance and complexity. The performance is evaluated in a frequency selective fading channel environment on the basis of orthogonal frequency division multiplexing with channel coding, for which the generation of soft decision values is crucial. The simulations show that the poly-diagonalization approach matches the performance of sphere decoding at similar computational complexity.

The impact of co-channel deployment of femtocells on existing macro-cellular systems is investigated considering the use of techniques to resolve the loud neighbor problem. There are several approaches to this aim, for example, femtocell power control, interference coordination, and opening access to femtocells. Of these, coordinated scheduling, including power control, and their impact will be the main focus of this work. In the context of 3GPP-LTE, we examine under various operational scenarios the performance in terms of the average and 5% worst user throughput, a useful measure of fairness among users, both for femto and macro cells. Although recent studies have shown that co-channel femtocell has a minor impact on the macrocell performance in average sense, a non-negligible percentage of users may lose their opportunity to get satisfactory data service and, hence, we focus more on the 5% worst users.