Link adaptation techniques, where the modulation, coding rate, and/or other signal transmission parameters are dynamically adapted to the changing channel conditions, have recently emerged as powerful tools for increasing the data rate and spectral efficiency of wireless datacentric networks. While there has been significant progress on understanding the theoretical aspects of time adaptation in LA protocols, new challenges surface when dynamic transmission techniques are employed in broadband wireless networks with multiple signaling dimensions. Those additional dimensions are mainly frequency, especially in multicarrier systems, and space in multiple-antenna systems, particularly multiarray multiple-input multiple-output communication systems. In this article we give an overview of the challenges and promises of link adaptation in future broadband wireless networks. We suggest guidelines to help in the design of robust, complexity/cost-effective algorithms for these future wireless networks.

Abstract-We present a general framework to quantify the data throughput capabilities of a wireless communication system when it combines: 1) multiple transmit signals; 2) adaptive modulation for each signal; and 3) adaptive array processing at the receiver. We assume a noise-limited environment, corresponding to either an isolated cell or a multicell system whose out-of-cell interference is small compared with the thermal noise. We focus on the user data throughput, in bits per second/Hertz (bps/Hz), and its average over multipath fading, which we call the user spectral efficiency. First, an analysis method is developed to find the probability distribution and mean value of the spectral efficiency over the user positions and shadow fadings, both as a function of user distance from its serving base station and averaged over the cell coverage area. We assume fading conditions and receiver processing that lend themselves to closed-form analysis. The resulting formulas are simple and straightforward to compute, and they provide a number of valuable insights. Next, we run Monte Carlo simulations, both to confirm the analysis and to treat cases less amenable to simple analysis.A key contribution of this paper is a simple formula for the mean spectral efficiency in terms of the propagation exponent, mean signal-to-noise ratio at the cell boundary, number of antennas, and type of coding. Under typical propagation conditions, the mean spectral efficiency using three transmit and three receive antennas ranges from 19.2 bps/Hz (uncoded) to 26.8 bps/Hz (ideally coded), highlighting the potential benefits of multiple transmissions combined with adaptive techniques. This is much higher than the spectral efficiencies for a link using a single transmitter and a threefold receive diversity under the same conditions, where the range is from 8.77 bps/Hz to 11.4 bps/Hz. Moreover, the latter results are not nearly as practical to achieve, as they call for large signal constellations that would be highly vulnerable to impairments.

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We describe a simulation study of a cellular system using multiple-input multiple-output (MIMO) antenna techniques along with adaptive modulation and aggressive frequency reuse. We show, for the case of 3 transmit and 3 receive antennas, how much MIMO systems outperform systems with receive-diversity-only when noise dominates. When co-channel interference from surrounding cells dominates, the differences shrink, as do the absolute numbers. We quantify these reductions for the specific cases studied, and discuss further areas of research.

Abstract-We investigate the high spectral efficiency capabilities of a cellular data system that combines the following: 1) multiple transmit signals, each using a separately adaptive modulation; 2) adaptive array processing at the receiver; and 3) aggressive frequency reuse (reuse in every cell). We focus on the link capacity between one user and its serving base station, for both uncoded and ideally coded transmissions. System performance is measured in terms of average data throughput, where the average is over user location, shadow fading, and fast fading. We normalize this average by the total bandwidth, call it the mean spectral efficiency, and show why this metric is a useful representation of system capability. We then quantify it, using simulations, to characterize multiple-input multiple-output systems performance for a wide variety of channel conditions and system design options.