Resource Allocation for Multi Access MIMO Systems

Resource Allocation for Multi Access MIMO Systems

Shailendra Mishra (Kumaon Engineering College, India) and Durg Singh Chauhan (Uttrakhand Technical University, India)
DOI: 10.4018/978-1-4666-2163-3.ch014
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In this paper, the authors discuss the emergence of new technologies related to the topic of the high-speed packet data access in wireless networks. The authors propose an algorithm for MIMO systems that optimizes the number of the transmit antennas according to the user’s QoS. Scheduling performance under two types of traffic modes is also discussed: one is voice or web-browsing and the other is for data transfer and streaming data.
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Mimo Techniques

There are four unique multi-antenna MIMO techniques available to the system designer namely: spatial multiplexing (SM-MIMO), space-time coding (STC-MIMO), diversity systems (DIV-MIMO), smart antenna (SA-MIMO):

Spatial multiplexing maximizes the link capacity, for spatial multiplexing the number of receive antennas must be greater than or equal to the number of transmit antennas. It makes the receivers very complex, and therefore it is typically combined with orthogonal frequency-division multiplexing (OFDM) (Yang, 2005; Zhang & Letaief, 2004).The IEEE 802.16e standard incorporates MIMO-OFDMA. The IEEE 802.11n standard which is expected to be finalized soon, recommends MIMO-OFDM. Compared to spatial multiplexing systems, space-time code STC-MIMO systems provide robustness of communications without providing significant throughput gains against spatial multiplexing systems (Gesbert, Shafi, Shiu & Naguib, 2003). Moreover, to support fully the cellular environments MIMO research consortiums including IST-MASCOT, proposed to develop advanced MIMO communication techniques such as cross-layer MIMO, multi-user MIMO and ad-hoc MIMO.

Cross-layer MIMO enhances the performance of MIMO links by solving cross-layer problems occurred when the MIMO configuration is employed in the system (Jiang, Zhuang, & Shen, 2005). A Cross-layer technique has been enhancing the performance of SISO links as well. Examples of cross-layer techniques are Joint source-channel coding, Link adaptation, or adaptive modulation and coding (AMC), Hybrid ARQ (HARQ) and user scheduling. Multi-user MIMO can exploit multiple user interference powers as a spatial resource at the cost of advanced transmit processing while conventional or single-user MIMO uses only the multiple antenna dimension (Zhang & Letaief, 2004). Examples of advanced transmit processing for multi-user MIMO are interference aware precoding and SDMA-based user scheduling.

Ad-hoc MIMO is a useful technique for future cellular networks which considers wireless mesh networking or wireless ad-hoc networking. To optimize the capacity of ad-hoc channels, MIMO concept and techniques can be applied to multiple links between transmit and receive node clusters. Unlike multiple antennas at the single-user MIMO transceiver, multiple nodes are located in a distributed manner. So, to achieve the capacity of this network, techniques to manage distributed radio resources are essential like the node cooperation and dirty paper coding (DPC) (Caire & Shamai, 2003).

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