A conceptual example of an arrangement of cells is shown in FIG. 1. Each cell (e.g., 100, 110, 120 . . . 190) includes a subset of transmit antennas and base stations of a larger wireless network. In FIG. 1 there is one subset of antennas per cell, and one base-station per cell, with each subset of antennas is located at a base-station. The subset of transmit antennas and base stations serve mobile wireless devices operating with the geographical region of the cell.
The term “cell” herein refers a geographic area in which a subset of transmit antennas, or a subset of base-stations, jointly transmit signals produced by a single common physical layer mechanism over these antennas to a subset of users. Antennas within each cell transmit useful signals only to users within that cell. In many prior art systems cellular systems cells often operate independently on many operations, e.g. in scheduling users and creating transmissions on their respective antennas.
In general, the transmitted signals can be generated by using one in a number of possible transmission techniques, e.g. single-input single-output (SISO) transmission; multiple input multiple output (MIMO) transmission; and, multi-user MIMO (MU-MIMO) transmission whereby multiple antennas coordinate a joint concurrent transmission to multiple users. The underlying structure of transmissions can be based on, for example, Orthogonal Frequency Division Multiplexing (OFDM), Code Division Multiple Access (CDMA), etc. In some networks, beam-forming is used, i.e. MIMO or MISO is used to form beams to concurrently serve different users).
In any such cellular scenario, if neighboring base stations (cells) use the same transmission resource (e.g. the same frequency band at the same time), users in a cell will experience interference from other cells. Such interference, often termed “inter-cell interference”, can be quite extreme near the edges of cells, thus limiting performance in such areas. For example, users such as user4 and user5 in FIG. 1 can experience high inter-cell interference levels. This is a classic problem with cell structures, and is true for SISO, MIMO and MU-MIMO transmissions.
Alleviating the effect of inter-cell interference is a very important problem, particularly in systems with multiple antennas, as in Multiple Input Multiple Output (MIMO) systems, Multiple Input Single Output (MISO) systems, and Single Input Multiple Output (SIMO) systems. Without interference, i.e. in an isolated cell free from inter-cell interference, MIMO techniques in principle can allow one to consider a system where the transmission rates in terms of bits/sec/Hz scale linearly with the number of transmit antennas. Here the rate (or throughput) of a system is generally linked to a term of form “log(1+S/noise)”, where “S” is a signal “energy” term which can be made to grow in such a way that throughput scales almost linearly with the number of transmit antennas used. Given this, MIMO systems have the potential to produce very large transmission rates in the order of many bits/sec/Hz especially when used with moderate to large numbers of antennas. Note, the term “noise” is random noise that may be present in the channel, such as thermal noise. We can assume “noise=1” with S scaled appropriately.
However, with interference the transmission rate has a different general form of “log(1+S/(1+Q))”. Here “Q” is the interference energy term, which includes energy from inter-cell interference, including interference from MIMO systems in adjacent cells. Given the nature of MIMO systems, the effect of interference terms can, similar to the signal term “S”, also grow fast with the number of antennas. As a result the growth of the effective ratio “S/Q” is much smaller than that of “S” in the isolated cell case, and throughput of the MIMO systems in a multi-cell environment can be severely degraded relative to that predicted for the case of isolated cells without interference.
There are many methods for alleviating multi-cell interference. Some techniques include, for example, frequency reuse which controls interference by dividing transmission resources over cells. Specifically the idea is to constrain adjacent cells not to use the same time/code/frequency resource, or sufficiently to ensure cells using the same resource are geographically separated. This is what happens in a classic cellular “frequency reuse” pattern where resources are divided in terms of frequencies, as illustrated in FIG. 2, or in an OFDM system which constrains cells to use different tones at different times, or as in a CDMA system which constrains cells to use different codes.
FIG. 2 illustrates a prior art frequency reuse cell arrangement. The example of FIG. 2 illustrates a frequency reuse factor of three. In this example, a first subset of cells (200a, 200b and 200c) use a first subset of frequencies, a second subset of cells (210a, 210b and 210c) use a second subset of frequencies and a third subset of cells (220a) use a third subset of frequencies.
In the classic cellular system example illustrated in FIG. 2, the network can utilize three different frequencies (with a frequency reuse factor of three) so that no two neighboring cells use the same frequency. The separation between cells (distance separation) that use the same frequency helps in reducing the interference between cells (the “inter-cell interference”). Specifically, users such as user4 and user5 have interfering cells which are now further away. As an example, users in cell 200a have the nearest interfering cells as cells 200b and 200c, and do not experience interference from 200a, 210a or 210b. However the efficiency of the system can be hurt because the frequency-reuse reduces the effective number of frequencies (the bandwidth) used for signaling information to users in each cell. With a frequency reuse factor of “F”, the rate of such a system scales as “(1/F)log(1+S/Q)”. Therefore, even when one reduces the effect of “Q”, the price paid in the pre-log scaling of “1/F” can offset such benefits. Furthermore there are additional losses in efficiency by not exploiting diversity among frequencies. This can further reduce the effective rates a user may receive even more.
Therefore, while these techniques are simple and effective in controlling interference, they are not necessarily the most efficient methods since they can be overly conservative limiting the potential reuse of transmission resources for the “S” term. This is also particularly true for MIMO systems where the use of multiple antennas allows one to consider division of resources also in space, not only in the time, frequency or code domains.
Another method to control interference is to have all cells coordinate and jointly design their transmissions with each other. For example, Network MIMO systems can create joint transmissions whereby signals radiated from multiple base-stations are jointly created across such base-stations and can be intended for uses in multiple cells. For example, a NW-MIMO system for FIG. 1 may allow BS1, BS2, and BS3 to jointly signal together to serve user4 and user5. This is an example of limited coordination over a cluster of cells. In the extreme a NW-MIMO system may provide full coordination over cells. Multi-user MIMO (MU-MIMO) techniques are effective techniques to consider for such systems since they can create transmissions operating across multiple cells, and also implicitly control interference. Specifically some of these techniques, e.g. Linear Zero Forcing, use knowledge of the channel state between users and transmission antennas to jointly control both signal and interference to each of the scheduled users. Such techniques however can require large amounts of channel state information (CSI) in the form of vectors of complex-valued numbers. This can be a significant system overhead. When MU-MIMO is used with user selection, such CSI often has to be obtained for a large pool of users from which a subset is to be selected. Therefore, in the final transmission, full CSI has been obtained from many users that are not scheduled. As the number of transmit antennas and users grow, the overhead grows and can be quite large.
Such fully coordinated and/or large MU-MIMO multi-cell systems may not be practical in some deployments. The complexity of coordinating all antennas, problems of asynchronony in reception of signals from highly geographically separated antennas to any given user, and the amount and latency of information that needs to be shared between remote base-stations (antennas) over the backbone infra-structure, can make such interference control techniques difficult to scale over many cells.
It is therefore of interest to consider cellular systems, whereby antennas in each cell only serve users in the cell, but for which some coordination between cells allows for control of inter-cell interference (ICI). Such techniques are being considered in the “Coordinated Multi-Point” (CoMP) effort within 3GPP LTE. There are multiple ideas also in the research community. Often such ideas look at systems within small clusters of coordinating cells, or in small deployments of cells.
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