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... LD code in Figs. 5 and 6 with the BER curves in Fig. 7, we can see that our code from cyclic division algebras performs better than LD code by about 3 dB at 10 BER. From the capacity calculations of =-=[46]-=-, it can be seen that for 4 and 8 bits per channel use, i.e., with 4-QAM and 16-QAM our code is less than 1 dB away from the capacity. Fig. 8 gives the BER versus SNR for two transmit and 10 receive a...

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...n code-book [24] to encode the information bits and then spread the coded symbols to all transmit antennas. At the receiver, the maximum-likelihood (ML) -based detection and iterative decoding (e.g., =-=[25]-=-) are applied to decode the whole codeword. Alternatively, the capacity can also be achieved by using multiple variable-rate Gaussian code-books each for one of d data streams. At the receiver, these ...

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... capable of achieving near-ML performance with reasonable complexity. Moreover, the lattice decoders can be extended to support soft decision outputs and, hence, be used in an iterative MIMO receiver =-=[6]-=-. The lattice decoding algorithms have two kinds of implementation strategies, i.e., Fincke–Pohst strategy [4], [7], [8] and Schnorr–Euchner strategy [9]–[11]. To avoid confusion in this paper, the la...

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...ion on the transmitted frame. The reason is that such algorithms output an ordered 8 list of candidate codewords. One can then compute the soft outputs from this list using standard techniques (e.g., =-=[39]-=-, [40]). Here, we note that in the proposed joint detection and decoding framework, soft outputs are generally not needed. Another advantage of BrFS is that the complexity of certain decoders inspired...

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...ed on the sphere decoder [3]–[8], we describe a tunable MIMO detector with performance ranging from that of hard-output successive interference cancellation (SIC) [9] to that of max-log APP detection =-=[10]-=-. Tuning of the detector is achieved through log-likelihood ratio (LLR) clipping, channel matrix regularization, and imposing constraints on the maximum computational complexity of the decoder (i.e., ...

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...infeasible. Therefore, heuristics are often employed to obtain soft channel information [14]. [Also, see [15] and the references therein for a related work in the context of multi-user detection.] In =-=[16, 17]-=-, two variations of the sphere decoding algorithm have been proposed for estimating the soft information. In [17], sphere decoding has been employed to obtain a list of bit sequences that are “good” i...

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...-input soft-output detectors and decoders. Soft-output detection on multiple antenna (MIMO) channels can be achieved via an exhaustive list as in [3] or a limited size list of a spherical shape as in =-=[10]-=-. The APP detector based on an exhaustive list has a relatively large complexity, exponential in the number of transmit antennas and the number of bits per modulation symbol. On the contrary, an APP d...

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... for multi-antenna systems as the available capacity of a multi-input multi-output (MIMO) system with 4 transmit and 4 receive antennas at an SNR of 20 dB is approximately 12 bits per channel use [2]–=-=[4]-=-. A brute-force approach is therefore prohibitive. Recently, in [4]–[6] techniques from lattice decoding were applied to multi-antenna systems. These techniques reduce the complexity of the brute-forc...