Claude Esteban en la revista Vuelta

performance of the ML SDM detection schemes. Indeed, we demonstrated in Section 3.4.3 that both the SDM detection method based on the SIC as well as the GA-MMSE detector [100] are capable of satisfying these requirements.

In Chapter 4 we have focused our attention on a family of potent Reduced Search Algorithm (RSA) aided space-time processing methods, the members of which exhibit a particularly advantageous trade-off between the achievable performance and the associated computational complexity, namely the family of the Sphere Decoding-aided SDM detection methods. Consequently, a set of novel OHRSA-aided SDM detection methods was outlined in Section 4.2. Specifically, in Section 4.2.1 we derived the OHRSA-aided ML SDM detector, which benefits from the optimal performance of the ML SDM detector [28], while exhibiting a relatively low computational complexity, which is only slightly higher than that required by the low-complexity MMSE SDM detector [28]. To elaborate a little further, in Section 4.2.2 we derived a bit-based OHRSA-aided ML SDM detector, which allows us to apply the OHRSA method of Section 4.2 in high-throughput systems, which employ multi-level modulation schemes, such asM-QAM [28].

In Section 4.2.3 we deduced the OHRSA-aided Max-Log-MAP SDM detector, which allows for an efficient evaluation of the soft-bit information and therefore results in highly efficient turbo decoding. Un- fortunately however, in comparison to the OHRSA-aided ML SDM detector of Section 4.2.2 the OHRSA- aided Max-Log-MAP SDM detector of Section 4.2.3 still exhibits a substantially higher complexity. Con- sequently, in Section 4.2.5 we derive an approximate Max-Log-MAP method, namely the SOPHIE SDM detector. The SOPHIE SDM detector combines the advantages of both the OHRSA-aided ML and OHRSA- aided Log-MAP SDM detectors of Sections 4.2.2 and 4.2.3, respectively. Specifically, it exhibits a similar performance to that of the optimal Max-Log-MAP detector, while imposing a modest complexity, which is only slightly higher than that required by the low-complexity MMSE SDM detector [28]. The computa- tional complexity as well as the achievable performance of the SOPHIE SDM detector of Section 4.2.5 were analysed and quantified in Sections 4.2.5.1 and 4.2.5.2, respectively.

Our related conclusions were summarized in Section 4.3. Specifically, based on Figure 4.11 and we re- ported achieving a BER of10−4at SNRs ofγ=4.2, 9.2and14.5in high-throughput 8x8 rate-1₂turbo-coded

M = 4, 16and64-QAM systems communicating over a dispersive Rayleigh fading channel. Additionally, recall from Figure 4.10 that we reported achieving a BER of 10−4 at SNRs ofγ = 9.5, 16.3and 22.8in high-throughput rank-deficient 4x4, 6x4 and 8x4 rate-1₂ turbo-coded 16-QAM systems, respectively.

6.1.3 Iterative Reciever Architecture

In Chapter 5 we derived an iterative, so-called turbo multi-antenna-multi-carrier (MAMC) receiver archi- tecture. Following the philosophy of turbo processing [26], our turbo SDM-OFDM receiver of Figure 5.1

6.1.3. Iterative Reciever Architecture 179

comprises a succession of detection modules, which iteratively exchange soft bit-related information and thus facilitate a substantial improvement of the overall system performance.

More specifically, our turbo SDM-OFDM receiver comprises three major components, namely, the soft- feedback decision-directed channel estimator discussed in detail in Section 2.9, followed by the soft-input- soft-output OHRSA Log-MAP SDM detector derived in Section 4.2.3 as well as a soft-input-soft-output serially concatenated turbo code [27]. Consequently, in Figures 5.3–5.14 of Chapter 5 we analyzed the achievable performance of each individual constituent component of our turbo receiver, as well as the achiev- able performance of the entire amalgamated iterative system. We aimed at identifying the optimum system configuration, while considering various design trade-offs, such as the achievable BER performance, the attainable data-rate as well as the associated computational complexity.

In Section 5.4.2.4 we demonstrated that our turbo SDM-OFDM system employing the MIMO-DDCE scheme of Section 2.9 as well as the OHRSA Max-Log-MAP SDM detector of Section 4.2.3 remains ef- fective in channel conditions associated with high mobile speeds of up to 130 km/h, which corresponds to the OFDM-symbol normalized Doppler frequency of 0.006. Additionally, in Figure 5.13 we reported a vir- tually error-free performance for a rate1/2turbo-coded 8x8-QPSK-OFDM system, exhibiting an effective throughput of 8 MHz_·8 bits/s/Hz=64 Mbps and having a pilot overhead of only 10% at an SNR of 7.5dB and a normalized Doppler frequency of 0.003, which corresponds to a mobile terminal speed of about 65 km/h.

In conclusion, we would like to offer the following important observations. The potential performance gain achievable by an iterative multi-antenna multi-carrier system may be dissected into several major re- gions, where we may identify the diversity gain region, the detection gain region as well as the iterative

gain region. Consider the BER versus SNR performance curves depicted in Figure 6.2.

• Firstly, the diversity gain region may be associated with the interval spanning the SNR values of Figure 6.2, which lie between the performance curves 1 and 2 corresponding to the scenarios of low and high diversity ranks1, respectively. Correspondingly, the achievable diversity gain may be realized by attaining a sufficient diversity rank contributed by the combination of the channel and waveform parameters. This phenomenon is exemplified, for instance, by Figure 5.8 of Section 5.3.

• The detection gain region may be identified as the region of the SNR values located between the performance curves 2 and 3 of Figure 6.2, which correspond to the systems employing for example a linear MMSE detector and a near-optimum Max-Log-MAP detector, respectively. The achievable detection gain may be realized by the means of employing an efficient MIMO detection method rem-

1_{Quantitatively speaking, the low diversity rank channel is a channel, where the distribution of the total channel energy is}

6.2. Future work 180