Mobile wireless broadband access networks are now becoming a reality, thanks to the emerging IEEE 802.16e standard. This kind of network offers different challenges when compared to the fixed ones, as power consumption becomes a major concern. In this standard, a strict organization of the downlink bursts is not guaranteed in the OFDMA frame and this may lead to extra power consumption for the receiver, decreasing the device's lifetime. In the present paper, we introduce an optimization algorithm capable of reducing the activity of each receiver in the system for decoding its addressed bursts, thanks to a better time-frequency organization of the bursts. We first work on fitting bursts within the smallest frame, and show that the minimal number of OFDM symbols is enough in 70 to 80% of the cases, while one extra is needed otherwise. Using a binary tree implementation of an exhaustive burst placement search, we also show that we can gain 20 to 30% in duty-cycling of the receivers by selecting the best configuration, hence gaining the corresponding energy. This holds for receivers decoding either their bursts only or all the bursts from the beginning of the frame up to their own bursts before sleeping, depending on the scenario. The full search is sustainable for up to 8 user bursts per frame.

Multiantenna systems and more particularly those operating on multiple input and multiple output (MIMO) channels are currently a must to improve wireless links spectrum efficiency and/or robustness. There exists a fundamental tradeoff between potential spectrum efficiency and robustness increase. However, multiantenna techniques also come with an overhead in silicon implementation area and power consumption due, at least, to the duplication of part of the transmitter and receiver radio frontends. Although the area overhead may be acceptable in view of the performance improvement, low power consumption must be preserved for integration in nomadic devices. In this case, it is the tradeoff between performance (e.g., the net throughput on top of the medium access control layer) and average power consumption that really matters. It has been shown that adaptive schemes were mandatory to avoid that multiantenna techniques hamper this system tradeoff. In this paper, we derive smartMIMO: an adaptive multiantenna approach which, next to simply adapting the modulation and code rate as traditionally considered, decides packet-per-packet, depending on the MIMO channel state, to use either space-division multiplexing (increasing spectrum efficiency), space-time coding (increasing robustness), or to stick to single-antenna transmission. Contrarily to many of such adaptive schemes, the focus is set on using multiantenna transmission to improve the link energy efficiency in real operation conditions. Based on a model calibrated on an existing reconfigurable multiantenna transceiver setup, the link energy efficiency with the proposed scheme is shown to be improved by up to 30% when compared to nonadaptive schemes. The average throughput is, on the other hand, improved by up to 50% when compared to single-antenna transmission.

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Ails/rae, -Next generation Wireless Local Area Networks (WLANs) have to cope with energy budgets se,'erely con strained by portability, autonomy and high integration re quirements. Practical power management approaches cur rently implemented aim at reducing the transceiver duty cycle. HowHer, recently developed energy-aware link adap· tation techniques, which trade oIT dynamically performance ,'crsus energy consumption, potentially bringing II factor.10 consumption reduction, promise to be more eITectin. Yet, to enable a meaningful trade'oIT, systems must present suffi cient energy-scillability, i.e. energy consumption benefit when reducing the performance requirements or the emi ronment constraints. This is not the case in current WLAN transceivers for which we show that duty cycle-based power management strategies are more effective. To make effective energy-aware link adaptation possible in future WLAN transceiwrs, we present techniques aiming at increasing their energy· scalability. Results show that a up to 7-fold energy consumption scalability can be achiewd, proyiding significant margin to get energy consumption reduction by Ildapting to the user requirements.