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

Multi-agent simulation: new approaches to exploring space-time dynamics in GIS

As part of the long term quest to develop more disaggregate, temporally dynamic models of spatial behaviour, micro-simulation has evolved to the point where the actions of many individuals can be computed. These multi-agent systems/simulation(MAS) models are a consequence of much better micro data, more powerful and user-friendly computer environments often based on parallel processing, and the generally recognised need in spatial science for modelling temporal process. In this paper, we develop a series of multi-agent models which operate in cellular space.These demonstrate the well-known principle that local action can give rise to global pattern but also how such pattern emerges as the consequence of positive feedback and learned behaviour. We first summarise the way cellular representation is important in adding new process functionality to GIS, and the way this is effected through ideas from cellular automata (CA) modelling. We then outline the key ideas of multi-agent simulation and this sets the scene for three applications to problems involving the use of agents to explore geographic space. We first illustrate how agents can be programmed to search route networks, finding shortest routes in adhoc as well as structured ways equivalent to the operation of the Bellman-Dijkstra algorithm. We then demonstrate how the agent-based approach can be used to simulate the dynamics of water flow, implying that such models can be used to effectively model the evolution of river systems. Finally we show how agents can detect the geometric properties of space, generating powerful results that are notpossible using conventional geometry, and we illustrate these ideas by computing the visual fields or isovists associated with different viewpoints within the Tate Gallery.Our forays into MAS are all based on developing reactive agent models with minimal interaction and we conclude with suggestions for how these models might incorporate cognition, planning, and stronger positive feedbacks between agents

Iterative multi-user detection for OFDM using biased mutation assisted genetic algorithms

Space Division Multiple Access (SDMA) aided Orthogonal Frequency Division Multiplexing (OFDM) systems assisted by efficient Multi-User Detection (MUD) techniques have recently attracted intensive research interests. As expected, Maximum Likelihood (ML) detection was found to attain the best performance, although this was achieved at the cost of a high computational complexity. Forward Error Correction (FEC) schemes such as Turbo Trellis Coded Modulation (TTCM) can be efficiently amalgamated with SDMA-OFDM systems for the sake of improving the achievable performance without bandwidth expansion. In this contribution, a MMSE-aided Iterative GA (IGA) MUD is proposed for employment in a TTCM-assisted SDMA-OFDM system, which is capable of achieving a similar performance to that attained by its optimum ML-aided counterpart at a significantly lower complexity, especially at high user loads. Moreover, when the proposed novel Biased Q-function Based Mutation (BQM) scheme is employed, the IGA-aided system’s performance can be further improved by achieving an Eb/N0 gain of about 6dB in comparison to the TTCM-aided MMSE-SDMA-OFDM benchmarker system both in low- and high-throughput modem scenarios, respectively, while still maintaining a modest complexity

Iterative Joint Channel Estimation and Multi-User Detection for Multiple-Antenna Aided OFDM Systems

Multiple-Input-Multiple-Output (MIMO) Orthogonal Frequency Division Multiplexing (OFDM) systems have recently attracted substantial research interest. However, compared to Single-Input-Single-Output (SISO) systems, channel estimation in the MIMO scenario becomes more challenging, owing to the increased number of independent transmitter-receiver links to be estimated. In the context of the Bell LAyered Space-Time architecture (BLAST) or Space Division Multiple Access (SDMA) multi-user MIMO OFDM systems, none of the known channel estimation techniques allows the number of users to be higher than the number of receiver antennas, which is often referred to as a “rank-deficient” scenario, owing to the constraint imposed by the rank of the MIMO channel matrix. Against this background, in this paper we propose a new Genetic Algorithm (GA) assisted iterative Joint Channel Estimation and Multi-User Detection (GA-JCEMUD) approach for multi-user MIMO SDMA-OFDM systems, which provides an effective solution to the multi-user MIMO channel estimation problem in the above-mentioned rank-deficient scenario. Furthermore, the GAs invoked in the data detection literature can only provide a hard-decision output for the Forward Error Correction (FEC) or channel decoder, which inevitably limits the system’s achievable performance. By contrast, our proposed GA is capable of providing “soft” outputs and hence it becomes capable of achieving an improved performance with the aid of FEC decoders. A range of simulation results are provided to demonstrate the superiority of the proposed scheme. Index Terms—Channel estimation, genetic algorithm, multiple-input-multiple-output, multi-user detection, orthogonal frequency division multiplexing, space division multiple access

Control of spin coherence in nn-type GaAs quantum wells using strain

We show that the bulk-inversion-asymmetry-type strain-induced spin-orbit coupling can be used to effectively modify the Dresselhaus spin splitting in (001) GaAs quantum wells with small well width and the resulting spin lifetime can be increased by two orders of magnitude to nanoseconds under right conditions. The efficiency of this strain manipulation of the spin dephasing time under different conditions such as temperature, electric field and electron density is investigated in detail.Comment: 4 pages, 5 figures in eps forma

Slow subcarrier-hopped Space Division Multiple Access OFDM systems

Recently Space Division Multiple Access (SDMA) assisted Multi-Input-Multi-Output (MIMO) OFDM systems invoking Multi-User Detection (MUD) techniques have attracted substantial research interests, which are capable of exploiting both transmitter multiplexing gain and receiver diversity gain. Furthermore, the classic Frequency-Hopping (FH) technique can be effectively amalgamated with SDMA-OFDM systems, resulting in Frequency-Hopped (FH) SDMA-OFDM. In this paper we devise a Turbo Trellis Coded Modulation (TTCM) assisted subcarrier-based FH/SDMA-OFDM scheme, which may be able to fully exploit the attainable frequency diversity, while exhibiting a high Multi-User-Interference (MUI) resistance. In the high-throughput scenario investigated, the proposed Uniform Slow-SubCarrier-Hopped (USSCH) SDMA-OFDM system was capable of achieving 6dB Eb=N0 gain at the BER of 10¡4 over the conventional SDMA-OFDM system, while maintaining a similar complexity

A Sample of Quasars with Strong Nitrogen Emission Lines from the Sloan Digital Sky Survey

We report on 293 quasars with strong NIV] lambda 1486 or NIII] lambda 1750 emission lines (rest-frame equivalent width > 3 \AA) at 1.7 < z < 4.0 selected from the Sloan Digital Sky Survey (SDSS) Fifth Data Release. These nitrogen-rich (N-rich) objects comprise ~1.1% of the SDSS quasars. The comparison between the N-rich quasars and other quasars shows that the two quasar subsets share many common properties. We also confirm previous results that N-rich quasars have much stronger Lya and NV lambda 1240 emission lines. Strong nitrogen emission in all ionization states indicates high overall nitrogen abundances in these objects. We find evidence that the nitrogen abundance is closely related to quasar radio properties. The radio-loud fraction in the NIII]-rich quasars is 26% and in the NIV]-rich quasars is 69%, significantly higher than ~8% measured in other quasars with similar redshift and luminosity. Therefore, the high nitrogen abundance in N-rich quasars could be an indicator of a special quasar evolution stage, in which the radio activity is also strong.Comment: 8 pages, 4 figures; accepted by ApJ (ApJ June 10, 2008, v680 n1 issue