智能天線外文翻譯--智能天線技術(shù)的未來無線系統(tǒng)趨勢與挑戰(zhàn)（原文）

1、IEEE Communications Magazine ? September 2004 90 0163-6804/04/$20.00 © 2004 IEEEWIRELESS WORLD RESEARCH FORUMINTRODUCTIONThe development of a truly personal communi- cations space will rely on the design of next- ge

2、neration wireless systems based on a whole new concept of fast, reconfigurable networks, supporting features such as high data rates, user mobility, adaptability to varying network conditions, and integration of a number

3、 of wireless access technologies, and offering new user-centric flexible service paradigms, relying on the exploitation of new resources such as cross-layer and contextual information. Thenew challenges require the consi

4、deration of certain enabling technologies, such as smart antennas, under new performance objectives and design constraints. Smart antenna systems consist of multiple antenna elements at the transmitting and/or receiving

5、side of the communication link, whose signals are processed adaptively in order to exploit the spatial dimension of the mobile radio channel. Depending on whether the pro- cessing is performed at the transmitter, receive

6、r, or both ends of the communication link, the smart antenna technique is defined as multiple- input single-output (MISO), single-input multi- ple-output (SIMO), or multiple-input multiple-output (MIMO). Exploitation

7、of the spatial dimension can increase the capacity of the wireless network by improving link quality through the mitigation of a number of impair- ments of mobile communications, such as multi- path fading and co-channel

8、 interference, and by increasing the data rate through the simultane- ous transmission of multiple streams by differ- ent antennas. Until now, in the design of second- and third- generation wireless systems, smart antenn

9、a capa- bility was considered an add-on feature, and optimization of the trade-off between complexi- ty/cost and performance enhancements was not performed during the design phase. Adoption of smart antenna techniques in

10、 future-generation wireless systems would require the smart anten- na feature to be an inherent part of the system design in order to provide the expected benefi- cial impact on efficient use of the spectrum, minimizatio

11、n of the cost of establishing new wireless networks, enhancement of the quality of service, and realization of reconfigurable, robust, and transparent operation across multitechnolo- gy wireless networks. To this end cur

12、rent research effort in the area is focusing on the fol- lowing critical issues: ? The design and development of advanced smart antenna processing algorithms that allow adaptation to varying propagation and network condi

13、tions and robustness against network impairments ? The design and development of innovative smart antenna strategies for optimization ofAngeliki Alexiou, Bell Laboratories, Lucent TechnologiesMartin Haardt, Ilmenau Unive

14、rsity of TechnologyABSTRACTThe adoption of smart antenna techniques in future wireless systems is expected to have a significant impact on the efficient use of the spectrum, the minimization of the cost of estab- lishing

15、 new wireless networks, the optimization of service quality, and realization of transpar- ent operation across multitechnology wireless networks. Nevertheless, its success relies on two considerations that have been ofte

16、n over- looked when investigating smart antenna tech- nologies: first, the smart antennas features need to be considered early in the design phase of future systems (top-down compatibility); second, a realistic performan

17、ce evaluation of smart antenna techniques needs to be per- formed according to the critical parameters associated with future systems requirements (bottom-up feasibility). In this article an overview of the benefits of a

18、nd most recent advances in smart antenna transceiver architec- ture is given first. Then the most important trends in the adoption of smart antennas in future systems are presented, such as reconfig- urability to varying

19、 channel propagation and network conditions, cross-layer optimization, and multi-user diversity, as well as challenges such as the design of a suitable simulation methodology and the accurate modeling of channel characte

20、ristics, interference, and implementation losses. Finally, market trends, future projections, and the expected financial impact of smart antenna systems deployment are discussed.Smart Antenna Technologies for Future Wire

21、less Systems: Trends and ChallengesIEEE Communications Magazine ? September 2004 92SMART ANTENNA TRANSCEIVER ARCHITECTURESIn a multiple-transmit multiple-receive antenna system as illustrated in Fig. 2, the data block to

22、 be transmitted is encoded and modulated to symbols of a complex constellation. Each symbol is then mapped to one of the transmit antennas (spatial multiplexing) after space-time weighting of the antenna elements. After

23、transmission through the wireless channel, demultiplexing, weighting, demodulation, and decoding is per- formed at the receiver in order to recover the transmitted data. A large number of transmission schemes over MISO o

24、r MIMO channels have been proposed in the literature, designed to maximize spectral efficiency and link quality through the maximiza- tion of diversity, data rate, and signal-to-interfer- ence-and-noise ratio (SINR). Eac

25、h of these schemes relies on a certain amount of channel state information (CSI) available at the transmit- ter and/or receiver side. CSI at the transmitter can be made available through feedback or can be obtained based

26、 on estimation of the receive channel. The former approach introduces the trade-off between feedback channel bandwidth and CSI accuracy, whereas in the latter channel reciprocity issues in frequency-division duplex (FDD)

27、 systems should be accounted for. CSI at the receiver can be obtained using training-basedor blind techniques, which exploit other proper- ties of the received signal, such as constant enve- lope and the finite alphabet.

28、 Transmission schemes that do not require CSI at the transmitter exploit the spatial dimension by either introducing coding on the spatial domain or employing spatial multiplexing gain. The for- mer approach, space-time

29、coding [1], increases redundancy over space and time, as each anten- na transmits a differently encoded fully redun- dant version of the same signal. The received signal is detected using a maximum likelihood (ML) decode

30、r. Space-time codes were originally developed in the form of space-time trellis codes (STTCs), which required a multidimensional Viterbi algorithm for decoding at the receiver. These codes can provide diversity equal to

31、the number of transmit antennas as well as coding gain depending on the complexity of the code without loss in bandwidth efficiency. Space-time block codes (STBCs) offer the same diversity as STTCs but do not provide cod

32、ing gain. Howev- er, STBCs are often the preferred solution over STTCs, as their decoding only requires linear processing. STBCs for two transmit antennas (proposed by Alamouti [5]) have been adopted as part of third-gen

33、eration (3G) standards. Space-time coding techniques assume in princi- ple perfect CSI at the receiver. Nevertheless, unitary and differential space-time coding has been proposed [6], which does not require CSI at either

34、 side of the communication link. Layered space-time architectures exploit the spatial multiplexing gain by sending indepen- dently encoded data streams in diagonal layers as originally proposed in [7] or in horizontal la

35、y- ers, the so-called Vertical Bell Labs Layered Space-Time (V-BLAST) scheme [8], depicted in Fig. 3. The receiver must demultiplex the spatial channels in order to detect the transmitted sym- bols. Various techniques ha

36、ve been used for this purpose, such as zero-forcing (ZF) that uses sim- ple matrix inversion, but results are poor when the channel matrix is ill conditioned; minimum mean square error (MMSE), more robust in that sense b

37、ut provides limited enhancement if knowledge of the noise/interference is not used; and maximum likelihood (ML), which is optimal in the sense that it compares all possible combi- nations of symbols but can be too comple

38、x, espe- cially for high-order modulation. Transmission schemes that require perfect CSI at the transmitter optimize SINR by focusing ener- gy in the desired directions, minimizing energy toward all other directions and

39、satisfying transmit power constraints (as illustrated in Fig. 4). Beam- forming allows spatial access to the radio channel by means of different approaches, considering either short-term properties (e.g., directional par

40、ameters) or long-term properties (e.g., second- order statistics) of the radio channel. In the majority of cases it is reasonable to assume that only partial CSI is available at the transmitter. Hybrid schemes that combi

41、ne space- time coding and beamforming have been pro- posed; these introduce precoding to exploit the available CSI when optimizing a certain criterion (e.g., pairwise error probability) [9]. Robust trans- mit beamforming

42、 schemes have also been proposed that take into account CSI estimation errors andI Figure 2. Smart antenna transceiver architecture.M Tx antennas N Rx antennasChannel H Data Coding Modulation Space-time weighting Spatial

43、 multiplexingData Demultiplexing Weighting Demodulation DecodingI Figure 3. Layered architectures: a) in diagonal BLAST (D-BLAST) the bit- stream/antenna association is periodically cycled; b) in vertical BLAST (V- BLAST