Transmit/receive beamformer design and power control in MIMO MC-CDMA systems

IEEE International Symposium on Circuits and Systems, Island of Kos, Greece, 21-24 May 2006In this paper, a joint transmitter and receiver beamformers design algorithm for downlink multiple input multiple output multicarrier code-division multiple access (MIMO MC-CDMA) system is proposed. The algorithm is iterative in nature where the transmitter beamformers and the receiver beamformers are determined alternately. The transmitter beamforming problem with a given receiver beamformer is formulated as a convex programming problem, which can be solved optimally using second order cone programming (SOCP), while the receiver beamforming problem is formulated as a constrained optimization problem with an analytical solution. The convergence of the algorithm is analyzed and the performance of the proposed algorithm is evaluated by computing simulation. © 2006 IEEE.published_or_final_versio

Transmitter adaptation for CDMA systems.

Kwan Ho-yuet.Thesis (M.Phil.)--Chinese University of Hong Kong, 2000.Includes bibliographical references (leaves 84-[87]).Abstracts in English and Chinese.Chapter 1 --- Introduction --- p.1Chapter 1.1 --- An Overview on Transmitter Optimization --- p.1Chapter 1.1.1 --- Transmitter Precoding Methods --- p.2Chapter 1.1.2 --- Chip Waveform Optimization --- p.3Chapter 1.1.3 --- Signature Sequence Adaptation --- p.3Chapter 1.2 --- Receiver Optimization --- p.5Chapter 1.3 --- Nonlinear Optimization with Constraints --- p.6Chapter 1.3.1 --- Lagrange Multiplier Methods --- p.6Chapter 1.3.2 --- Penalty Function Methods --- p.7Chapter 1.4 --- Outline of Thesis --- p.8Chapter 2 --- Transmitter Adaptation Scheme for AWGN Channels --- p.10Chapter 2.1 --- Introduction --- p.10Chapter 2.2 --- System Model --- p.12Chapter 2.3 --- Adaptation Algorithm --- p.13Chapter 2.3.1 --- Receiver optimization --- p.14Chapter 2.3.2 --- Single-user transmitter optimization --- p.18Chapter 2.3.3 --- Decentralized transmission scheme --- p.20Chapter 2.4 --- Modification of the sequence adaptation algorithm --- p.25Chapter 2.5 --- Performance Evaluation --- p.28Chapter 2.5.1 --- Performance of the decentralized scheme --- p.28Chapter 2.5.2 --- System Capacity with Target SNR Constraints --- p.29Chapter 2.5.3 --- Performance of modified sequences --- p.31Chapter 2.6 --- Summary --- p.33Chapter 3 --- Transmitter Adaptation Schemes for Rayleigh Fading Channels --- p.34Chapter 3.1 --- Introduction --- p.34Chapter 3.2 --- Sequence Adaptation for MC-CDMA Systems --- p.36Chapter 3.2.1 --- Multi-sequence MC-CDMA systems --- p.36Chapter 3.2.2 --- Single Sequence MC-CDMA systems --- p.41Chapter 3.2.3 --- Performance Evaluation --- p.45Chapter 3.3 --- Sequence Adaptation for Wideband CDMA System in Fading Channels --- p.50Chapter 3.3.1 --- System Model and Algorithm Development --- p.50Chapter 3.3.2 --- Performance Evaluation --- p.56Chapter 3.4 --- Summary --- p.60Chapter 4 --- Practical Issues on Sequence Adaptation --- p.61Chapter 4.1 --- Introduction --- p.61Chapter 4.2 --- Preliminary --- p.62Chapter 4.3 --- Sequence Adaptation Algorithm with Perfect Estimation of SNR --- p.63Chapter 4.4 --- Performance Evaluation --- p.68Chapter 4.4.1 --- Typical Behaviour Analysis --- p.71Chapter 4.4.2 --- Average Performance Analysis --- p.72Chapter 4.5 --- Sequence Adaptation Algorithm with imperfect estimation of pre- vious state SNR --- p.75Chapter 4.6 --- Performance Evaluation --- p.77Chapter 4.7 --- Summary --- p.79Chapter 5 --- Conclusions and Future Works --- p.81Chapter 5.1 --- Conclusions --- p.81Chapter 5.2 --- Future Works --- p.83Bibliography --- p.8

New adaptive transmission schemes for MC-CDMA systems.

by Yin-Man Lee.Thesis (M.Phil.)--Chinese University of Hong Kong, 1999.Includes bibliographical references (leaves 82-[87]).Abstracts in English and Chinese.Chapter 1 --- Introduction --- p.1Chapter 1.1 --- Overview of MC-CDMA --- p.1Chapter 1.2 --- System Model --- p.3Chapter 1.3 --- Receiver Optimization --- p.7Chapter 1.4 --- Transmitter Optimization --- p.9Chapter 1.5 --- Nonlinearly Constrained Optimization --- p.10Chapter 1.6 --- Outline of Thesis --- p.11Chapter 2 --- Centralized Transmitter Optimization for MC-CDMA Systems --- p.13Chapter 2.1 --- Introduction --- p.13Chapter 2.2 --- Problem Development --- p.15Chapter 2.3 --- Lagrangian Optimization Approaches --- p.16Chapter 2.3.1 --- Penalty Function Method --- p.17Chapter 2.3.2 --- Barrier Function Method --- p.19Chapter 2.3.3 --- Powell's Method and Augmented Lagrangian Method --- p.21Chapter 2.4 --- Optimal FDMA System --- p.23Chapter 2.5 --- Modified Centralized Optimization Schemes --- p.25Chapter 2.6 --- Performance --- p.27Chapter 2.6.1 --- Typical Behavior --- p.27Chapter 2.6.2 --- Average Performance --- p.32Chapter 2.7 --- Summary --- p.38Chapter 3 --- Decentralized Transmitter Optimization for MC-CDMA Sys- tems --- p.39Chapter 3.1 --- Introduction --- p.39Chapter 3.2 --- System Model --- p.41Chapter 3.3 --- Optimization --- p.42Chapter 3.3.1 --- Receiver Optimization --- p.43Chapter 3.3.2 --- Single-user Transmitter Optimization --- p.44Chapter 3.3.3 --- Decentralized Transmission Scheme --- p.45Chapter 3.3.4 --- Multirate Transmission with Decentralized Transmission Scheme --- p.47Chapter 3.4 --- Performance --- p.48Chapter 3.5 --- Summary --- p.57Chapter 4 --- Performance Evaluation of Various Adaptive Transmission Schemes --- p.59Chapter 4.1 --- Introduction --- p.59Chapter 4.2 --- Comparison of Different Adaptive Transmission Schemes --- p.61Chapter 4.3 --- Adaptive Transmission Schemes with K > M --- p.64Chapter 4.4 --- Modified Adaptive Transmission Scheme with Graceful Degrada- tion in the SNR --- p.68Chapter 4.5 --- Summary --- p.71Chapter 5 --- Conclusions and Future Work --- p.73Chapter 5.1 --- Conclusions --- p.73Chapter 5.2 --- Future Work --- p.75A The Hungarian Method for Optimal Frequency Assignment --- p.76Bibliography --- p.8

Multiuser MIMO-OFDM for Next-Generation Wireless Systems

This overview portrays the 40-year evolution of orthogonal frequency division multiplexing (OFDM) research. The amelioration of powerful multicarrier OFDM arrangements with multiple-input multiple-output (MIMO) systems has numerous benefits, which are detailed in this treatise. We continue by highlighting the limitations of conventional detection and channel estimation techniques designed for multiuser MIMO OFDM systems in the so-called rank-deficient scenarios, where the number of users supported or the number of transmit antennas employed exceeds the number of receiver antennas. This is often encountered in practice, unless we limit the number of users granted access in the base station’s or radio port’s coverage area. Following a historical perspective on the associated design problems and their state-of-the-art solutions, the second half of this treatise details a range of classic multiuser detectors (MUDs) designed for MIMO-OFDM systems and characterizes their achievable performance. A further section aims for identifying novel cutting-edge genetic algorithm (GA)-aided detector solutions, which have found numerous applications in wireless communications in recent years. In an effort to stimulate the cross pollination of ideas across the machine learning, optimization, signal processing, and wireless communications research communities, we will review the broadly applicable principles of various GA-assisted optimization techniques, which were recently proposed also for employment inmultiuser MIMO OFDM. In order to stimulate new research, we demonstrate that the family of GA-aided MUDs is capable of achieving a near-optimum performance at the cost of a significantly lower computational complexity than that imposed by their optimum maximum-likelihood (ML) MUD aided counterparts. The paper is concluded by outlining a range of future research options that may find their way into next-generation wireless systems

Ant-colony-based multiuser detection for multifunctional-antenna-array-assisted MC DS-CDMA systems

A novel Ant Colony Optimization (ACO) based Multi-User Detector (MUD) is designed for the synchronous Multi-Functional Antenna Array (MFAA) assisted Multi-Carrier Direct-Sequence Code-Division Multiple-Access (MC DS-CDMA) uplink (UL), which supports both receiver diversity and receiver beamforming. The ACO-based MUD aims for achieving a bit-error-rate (BER) performance approaching that of the optimum maximum likelihood (ML) MUD, without carrying out an exhaustive search of the entire MC DS-CDMA search space constituted by all possible combinations of the received multi-user vectors. We will demonstrate that regardless of the number of the subcarriers or of the MFAA configuration, the system employing the proposed ACO based MUD is capable of supporting 32 users with the aid of 31-chip Gold codes used as the T-domain spreading sequence without any significant performance degradation compared to the single-user system. As a further benefit, the number of floating point operations per second (FLOPS) imposed by the proposed ACO-based MUD is a factor of 108 lower than that of the ML MUD. We will also show that at a given increase of the complexity, the MFAA will allow the ACO based MUD to achieve a higher SNR gain than the Single-Input Single-Output (SISO) MC DS-CDMA system. Index Terms—Ant Colony Optimization, Multi-User Detector, Multi-Functional Antenna Array, Multi-Carrier Direct-Sequence Code-Division Multiple-Access, Uplink, Near-Maximum Likelihood Detection

Investigating the relation between optimum guard interval and channel delay spread for a MC-CDMA system

This paper demonstrates a novel approach to determining the optimum guard interval for a multicarrier code division multiple access (MC-CDMA) system. Analytical expressions for useful and interference power are derived as a basis for comparison. From these, an expression for the signal-to-noise ratio of a detected bit is derived and used to determine the optimum guard interval for a given channel profile and system parameters. In contrast to other works, we use channel models based on actual measurements and we highlight important differences from theoretical models to support our approach. From our results, we propose an empirical rule for optimum guard intervals given prevailing channel parameters. We show that the optimum guard interval can be selected as the delay window that includes 95% and 99% multipath power for Es /N0 = 10 dB and Es /N0 = 20 dB, respectively. In our case, the optimum guard interval was between 2 τrms and 4 τrms for Es /N0 = 10 dB and between 3 τrms and 6.4 τrms for Es/N0 = 20 dB

MC-CDMA aided multi-user space-time shift keying in wideband channels

In this paper, we propose multi-carrier code division multiple access (MC-CDMA)-aided space-time shift keying (STSK) for mitigating the performance erosion of the classic STSK scheme in dispersive channels, while supporting multiple users. The codewords generated by the STSK scheme are appropriately spread in frequency-domain (FD) and transmitted over a number of parallel frequency-?at subchannels. We propose a new receiver architecture amalgamating the single-stream maximum-likelihood (ML) detector of the STSK system and the multiuser detector (MUD) of the MC-CDMA system. The performance of the proposed scheme is evaluated for transmission over frequency-selective channels in both uncoded and channel-coded scenarios. The results of our simulations demonstrate that the proposed scheme overcomes the channel impairments imposed by wideband channels and exhibits near-capacity performance in a channel-coded scenario

Ant-Colony-Based Multiuser Detection for MC DS-CDMA Systems

In this contribution we present a novel ant colony optimization (ACO) based multi-user detector (MUD) designed for synchronous multi-carrier direct sequence code division multiple access (MC DSCDMA) systems. The operation of the ACO-based MUD is based on the behaviour of the ant colony in nature. The ACO-based MUD aims for achieving the same bit-error-rate (BER) performance as the optimum maximum likelihood (ML) MUD, without carrying out an exhaustive search of the entire MC DS-CDMA search space constituted by all possible combinations of the received multi-user vectors. We will demonstrate that the system is capable of supporting almost as many users as the number of chips in the spreading sequence, while searching only a small fraction of the entire ML search space. It will also be demonstrated that the number of floating point operations per second is a factor of 108 lower for the proposed ACO-based MUD than that of the ML MUD, when supporting K = 32 users in a MC DS-CDMA system employing 31-chip Gold codes as the T-domain spreading sequence