Patent Publication Number: US-8542640-B2

Title: Inter-cell approach to operating wireless beam-forming and user selection/scheduling in multi-cell environments based on limited signaling between patterns of subsets of cells

Description:
PRIORITY 
     The present patent application claims priority to and incorporates by reference the corresponding provisional patent application Ser. No. 61/092,722, titled, “An Inter-Cell Approach to Operating Wireless Beam-forming and User Selection/Scheduling in Multi-Cell Environments Based on Limited Signaling between Patterns of Subsets of Cells” filed on Aug. 28, 2008. 
    
    
     FIELD 
     The present invention relates to the field of wireless downlink transmission in a multi-cell wireless environment. 
     BACKGROUND 
     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 user 4  and user 5  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 ( 200   a ,  200   b  and  200   c ) use a first subset of frequencies, a second subset of cells ( 210   a ,  210   b  and  210   c ) use a second subset of frequencies and a third subset of cells ( 220   a ) 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 user 4  and user 5  have interfering cells which are now further away. As an example, users in cell  200   a  have the nearest interfering cells as cells  200   b  and  200   c , and do not experience interference from  200   a ,  210   a  or  210   b . 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 BS 1 , BS 2 , and BS 3  to jointly signal together to serve user 4  and user 5 . 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. 
     SUMMARY 
     Methods, systems and apparatuses for operating a multiple-cell network, each cell having at least one wireless transmission entity are disclosed. The multiple cells are divided into subsets of cells, each subset having one or more cells. The subsets of cells each have an associated priority that can be changed over transmission resources. The cells from the same subset are able to operate their associated transmission processes independent of each other without, for example, requiring exchange of transmission parameter or other information between such cells of the same subset. 
     Cells of lower priority utilize transmissions and transmission parameters from cells of higher priority in determining their own transmission parameters. Scheduling and transmission parameter operations for the multi-cell system are performed starting with a highest priority subset of cells, and ending with a lowest priority subset of cells. The scheduling and transmission parameter information is forwarded from each of the base stations in the subset of cells having a higher priority to selected number of base stations in a subset of cells having a lower priority. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The present invention will be understood more fully from the detailed description given below and from the accompanying drawings of various embodiments of the invention, which, however, should not be taken to limit the invention to the specific embodiments, but are for explanation and understanding only. 
         FIG. 1  illustrates a conceptual arrangement of multiple cells in a wireless system which would apply to the case of centrally located base-stations with omni-directional antennas. 
         FIG. 2  illustrates a conceptual arrangement of cells utilizing a frequency reuse factor of three. 
         FIG. 3  illustrates conceptual arrangement (pattern) of cells which can apply to frequency reuse factor one, but where cell subsets have associated priorities in a multi-step coordination algorithm. 
         FIG. 4  is a flow diagram of one embodiment for a technique for operating a cell-based wireless network using limited signaling between cells. 
         FIG. 5  is a flow diagram of one embodiment of a technique for operating a cell-based wireless network where subset priorities are dynamically modified. 
         FIG. 6  is a block diagram of one embodiment of a base station. 
     
    
    
     DETAILED DESCRIPTION 
     In the following description, numerous specific details are set forth. However, embodiments of the invention may be practiced without these specific details. In other instances, well-known circuits, structures and techniques have not been shown in detail in order not to obscure the understanding of this description. 
     As discussed above, current multi-cell networks suffer from several limitations including, for example, the following. The throughput of a multi-cell wireless system with no coordination between cells can be interference limited. This is due to inter-cell interference (ICI) which limits the rate the system can support. For MIMO systems, the per-cell throughput is also limited, and often can not be made to grow significantly either with increased transmission power and/or increased numbers of deployed antennas due to this ICI. 
     Systems with full coordination or coordination among clusters of cells, whereby groups of cells (base-stations) jointly signal and serve users over multiple cells, can reduce inter-cell interference to increase per-cell throughput. However, such joint signaling approaches can require large amounts of channel-state information, and large overheads to share information between cells. In addition, there are implementation challenges such as maintaining timing and synchrony between coordinated transmissions between various cells. 
     Multi-user MIMO systems form a group of options for signaling both within cells and in cluster based systems. These systems allow multiple users to be served simultaneously, exploiting the spatial dimensions when using multiple transmit antennas. Many such techniques, such as linear zero forced beamforming (LZFB), apply signals to beams, where such beams calculated as a function of the channel state of their users. Such techniques often require full channel-state information in order to form beams with which to serve users. In general such information is of the form of complex vectors of channel coefficients. Obtaining such information for all users, including those not eventually scheduled, can be a large overhead in a multi-cell environment. Accuracy and sensitivity with respect to this information is also an issue. 
     Described herein are techniques known that may be referred to as “Random” or “Opportunistic” beamforming. In various embodiments, beams are not calculated, but either selected at random or selected from a codebook of such beams. To serve users, a system does not require full channel state information from users. Rather, a system often requires only an indication of the useful signal level with respect to the beam, or beams, being used. 
     Described herein are methods, architectures and techniques of operating a joint beam-forming and user-selection system across multiple cells for downlink communication in a multi-cell environment. The method outlines a system which coordinates beam and user selection across multiple cells, but is strictly cellular in the sense that users are served (i.e. given their intended data) only from the signals from one cell. There is provided methods, architectures and techniques to control the level and effect of inter-cell interference through a partially coordinated multi-cell process of user scheduling and beam selection. In one embodiment, the method includes multi-step techniques that can scale to large numbers of cells, specifying which operations remain as independent operations within a cell, and which operations, with what cells, require information exchange. The control of interference results in tangible benefits such as improving the rate the multi-cell system can support, in terms of sum throughput and/or individual user rates and/or fairness. 
     The methods, architectures and techniques described herein also significantly reduce the amount of inter-cell coordination and signaling required for operation as compared to prior techniques such as Network MIMO and coordinated MU-MIMO systems over clusters of cells. 
     In various embodiments, combinations of the following principles may be utilized: 1) dividing cells into different subsets whereby cells within the same subset are made to operate independently of each other; 2) operating the decision making processes, such as beam selections, users to schedule, and rates to schedule users, in an ordered fashion from one subset to the next subset; 3) using the ordered process to achieve and define potential limited and staged information exchanges between cells in different subsets; 4) leveraging beam-forming and beam-pilots to simplify the type of, or even eliminate, backbone signaling (nature and amount of information) required by and shared between cells; 5) varying the order of subsets of cells in cells across transmission resources, e.g. time slots and/or frequency bands or spreading codes in CDMA, so as to ensure fairness between subsets. 
     Many of these features, in particular 1)-4), allow many cells and processes to operate as if cells were independent isolated cells. This ability to operate some or all processes independently significantly reduces the system&#39;s complexity. Sufficient signaling (e.g. implicitly by wireless pilots or explicitly by inter-cell backbone communication) is maintained between only limited subsets of cells. Such signaling is used for purposes such as, for example, to allow for the control of interference with a small system overhead. Such limits and clear specification of signaling also allows for a system which can scale with increased numbers of cells. 
     Some portions of the detailed descriptions which follow are presented in terms of algorithms and symbolic representations of operations on data bits within a computer memory. These algorithmic descriptions and representations are the means used by those skilled in the data processing arts to most effectively convey the substance of their work to others skilled in the art. An algorithm is here, and generally, conceived to be a self-consistent sequence of steps leading to a desired result. The steps are those requiring physical manipulations of physical quantities. Usually, though not necessarily, these quantities take the form of electrical or magnetic signals capable of being stored, transferred, combined, compared, and otherwise manipulated. It has proven convenient at times, principally for reasons of common usage, to refer to these signals as bits, values, elements, symbols, characters, terms, numbers, or the like. 
     It should be borne in mind, however, that all of these and similar terms are to be associated with the appropriate physical quantities and are merely convenient labels applied to these quantities. Unless specifically stated otherwise as apparent from the following discussion, it is appreciated that throughout the description, discussions utilizing terms such as “processing” or “computing” or “calculating” or “determining” or “displaying” or the like, refer to the action and processes of a computer system, or similar electronic computing device, that manipulates and transforms data represented as physical (electronic) quantities within the computer system&#39;s registers and memories into other data similarly represented as physical quantities within the computer system memories or registers or other such information storage, transmission or display devices. 
     The description also relates to apparatus for performing the operations herein. This apparatus may be specially constructed for the required purposes, or it may comprise a general purpose computer selectively activated or reconfigured by a computer program stored in the computer. Such a computer program may be stored in a computer readable storage medium, such as, but is not limited to, any type of disk including floppy disks, optical disks, CD-ROMs, and magnetic-optical disks, read-only memories (ROMs), random access memories (RAMs), EPROMs, EEPROMs, magnetic or optical cards, or any type of media suitable for storing electronic instructions, and each coupled to a computer system bus. 
     The algorithms and displays presented herein are not inherently related to any particular computer or other apparatus. Various general purpose systems may be used with programs in accordance with the teachings herein, or it may prove convenient to construct more specialized apparatus to perform the required method steps. The required structure for a variety of these systems will appear from the description below. In addition, the present invention is not described with reference to any particular programming language. 
     Described herein is a process applied to a network of wireless base-stations or wireless transmitting entities, each station or entity serving a geographical area (e.g. cell) or group of users. We shall describe the process as it applies to the case that geographic areas are deemed as cells. The process allows the multi-cell network to control the inter-cell interference users experience by controlling the inter-cell interference between subsets of cells. This can result in increased wireless throughputs. 
     The process itself can do so with reduced signaling exchanges between cells and only partial coordination between some cells. In particular, cells are divided into subsets, which are given an ordered priority for each transmission opportunity (scheduling decision and associated data transmission to a user). In one embodiment, within each subset of cells, there are no such signaling exchanges required between cells. That is, cells within each subset operate independently. Between different subsets there can be signaling exchange requirements, and these are clearly specified by the relative priority of the subsets. Furthermore, the types of information exchanges used between, individual cells often requires less overhead and less accuracy than many alternative MU-MIMO techniques. 
     The network operates by dividing the cells into a number of subsets. For each transmission opportunity the subsets are ordered CellSet(1), CellSet(2), . . . , CellSet(N), where at least one subset has more than one member (one cell). Within any subset, e.g. CellSet(j), member cells are allowed to operate independently of each other. Thus there is no need to share signals or user information between such cells greatly reducing system complexity with respect to fully coordinated systems (effectively a system for which each subset consists of only one cell). 
       FIG. 3  illustrates such an example where there are three subsets. The grouping of cells in  FIG. 3  is not in relation to specifying of frequencies as in the example of  FIG. 2 . Rather, the grouping of cells in  FIG. 3  indicates subsets of cells. The system of  FIG. 3  can have any frequency reuse including a frequency reuse of one. The subsets do not specify transmission resources. 
     As illustrated in  FIG. 3 , one subset has cells  300   a ,  300   b ,  300   c ,  300   d , and another subset has cells  310   a ,  310   b ,  310   c ,  310   d , and another subset has cells  320   a ,  320   b ,  320   c ,  320   d . In contrast to  FIG. 2 , all cells, in all subsets may use the same transmission resources, e.g. the same frequencies at the same time. 
     Cells in each subset, i.e. subset “CellSet(k)”, make operational decisions independently of each other. These include their individual user scheduling decisions, beamforming solution calculations, and signaling required to obtain information necessary for these processes. Such operations for a cell may assume any instantaneous operation or decision of cells within the same subset, though they can assume some nominal, i.e. average, behavior such as an average interference level. Once such independent processes are complete for all cells in a subset, each cell in CellSet(k) sends a relatively small amount of information to a small number, (e.g. three) cells in the subset of next priority, i.e. CellSet(k+1). The network can also indirectly send, i.e. forward, relevant information from CellSet(1), . . . , CellSet(k−1) to cells in CellSet(k+1). To do this a cell in CellSet(k) may forward information it may have received from cells from CellSet(k−1), and so on. By induction it is possible for information about some cells in CellSet(1) to reach some cells in CellSet(k+1), k&gt;1. Note, it is not necessary for all cells in CellSet(k) to communicate to all cells in CellSet(k+n), for n=1, 2, . . . , N−k. It is also not necessary for direct communication between a cell in CellSet(k) and one in CellSet(k+n) for n≧2. For example, cell  300   a  in  FIG. 3  may only have to send information to cells  310   a  and  310   b.    
     Based on this limited inter-cell information exchange, each cell in CellSet(k+1) makes its own independent decisions (independent of other cells in the subset). These include user scheduling decisions and beamforming solutions. Such decisions can be required to consider the effect of such decisions (i.e. of the beams it chooses, designs and/or schedules) on relevant scheduled users in cells from prior subsets Cellset(1), . . . , CellSet(k). In general, a cell in CellSet(k+n), n≧1, tries to limit the interference it creates to scheduled users in a select number of cells from CellSet(1), . . . , CellSet(k). 
     Once scheduling in a CellSet(k+1) is complete, the process continues as before with each cell in CellSet(k+1) passing a relatively small amount of information to a small number of cells in CellSet(k+2). Cells in CellSet(k+2) make their independent (or each other) decisions and the process continues until all cells in all subsets have made scheduling decisions. In one embodiment, at this point transmission begins. 
     Between subsets, inter-cell information exchange can be direct, can be implicit, or can be both. Implicit exchanges are supported, for example, through present or past pilot signals that are present in probing wireless transmissions. For example, cells in CellSet(k) may transmit a number of pilot signals on beams they select. When a pilot signal is applied to a beam it provides a signal with which a recipient can measure the effect, e.g. strength as seen through a channel, of that beam. Given the broadcast nature of the wireless medium such pilots can be sensed by user terminal in cells in subsets CellSet(k), CellSet(k+1), . . . without direct signaling between base-stations. Such pilots can also be sensed by user terminal in cells in subsets CellSet(k−1), CellSet(k−2). The pilots can be used to estimate interference with respect to such pilots without any cell in CellSet(k) having to send direct transmissions to any cell of any other subset. 
     The user terminal, having information with respect to a beam pilot, can forward that information to the station of its serving cell. They can also choose not to forward such information but rather to combine such information with information from other beam pilots and send information which is a function of collective pilot information. A user terminal may also choose not to send any such information to its station. 
     Direct information exchanges between cells are of the form of the identifying indices (ID)s of scheduled users, beam IDs, etc. In one embodiment, these are (simple) integer valued quantities. Note, in such direct inter-cell exchanges all cells in CellSet(j) do not communicate with all cells in CellSet(k), k&gt;j. In one embodiment, information for each cell in CellSet(j) only propagates (either directly or through forwarding of such information) to a group of cells in the sets CellSet(j+1), CellSet(j+2), . . . . 
     Furthermore, in the embodiment described the cells in CellSet(j) are the only cells sending such information directly to cells in CellSet(j+1). In fact, in the embodiment there may be no need for direct communication between any cell in CellSet(j) and any cell in CellSet(j+n) for n≧2. Cells in CellSet(j) not only sends its own information to cells in CellSet(j+1), it can also forward information it may have gotten from cells in CellSet(j−1). One can see this enables cells from CellSet(j−n), n&gt;1, to indirectly send information to cells in CellSet(j). With these features the direct inter-cell overhead is greatly reduced. Again, many other exchanges are implicit, as supported through the sensing of beam pilots. 
     Pilot-based information exchanges are efficient means of obtaining inter-cell “Channel Quality Information” CQI and (optionally) Channel State Information (CSI). This information is utilized in making scheduling decisions and selecting beams. In some embodiments the CQI information can represent the energy a user terminal sees from a beam pilot. The CQI information can also be a function of information collected from many beam pilots. 
     To describe embodiments of CQI information a case that assumes users (user terminals) within each cell each have one receive antenna is presented. User terminal can also be equipped with multiple antennas. However in this description, and without loss in generality, descriptions based on one receive antenna simplify the exposition. 
     Downlink transmissions from the “M” antennas in each cell, possibly located at the base-station, to UTs in the cell are made by creating a number of beams and placing transmitted data streams (streams of coded and modulated symbols as known to those familiar with the state of the art) on such beams. Within any cell a beam “b k ”, or beamforming vector, is a complex M vector, which we will consider without loss in generality to be of unit norm (scaling to non-unit norm can be accounted for in the data-streams, or power applied to a stream).
 
 b   k =( b   ik   ,b   2k , . . . ,  b   Mk ) T  
 
Each element in the vector is complex term representing a scaling and phase term that is applied to an individual antenna. For transmission a signal stream s k  and power p k  is applied to this beam (beamforming vector). Here the stream itself is a row vector, say over time “t”, i.e. s k =s k  (t 1 ), s k  (t 2 ), s k  (t 3 ), . . . , where t 1 , t 2 , t 3  are slots in time. The power p k , p k ≧0, is a real valued scalar. A number “m” of such beams can be scheduled for transmission concurrently and in the same band. With this the signal “S” radiated from the transmission antennas in the cell at this time and in the band has form
 
             S   =       ∑     j   =   1     m     ⁢           ⁢       p   j     ⁢     s   j     ⁢     b   j               
The channel between UT(j)&#39;s single receive antenna and the M transmission antennas of the base-station is a complex M vector h j , of form
 
 h   j =( h   ij   ,h   2j , . . . ,  h   Mj ) T  
 
This is termed the Channel State Information (CSI) for UT(j). It includes a number of effects such as path loss, fading, etc. With these quantities, one can describe the received signal S i     k    that UT(i k ) sees from the transmission antennas as
 
               S     i   k       =       ∑     j   =   1     m     ⁢           ⁢         p   j       ⁢       s   j     ⁡     (       b   j     ,     h   j       )                 
where (x,y) denotes the mathematical inner-product between the two vectors. Assume the data signal s k  on beam b k , k:1≦k≦m, is intended for the i k -th user terminal, UT(i k ), which is scheduled by its base-station to receive such data. Within the sum “S i     k   ” above, the effective signal term received by UT(i k ) is that from beam b k , i.e.
 
Intended Signal=√{square root over ( p   k )} s   k ( b   k   ,h   i     k   )
 
and the effective interference received are terms from the unintended signals, i.e.
 
               Interference   ⁢           ⁢   Signal     =       ∑     j   ≠   k       ⁢           ⁢         p   j       ⁢       s   j     ⁡     (       b   j     ,     h     i   k         )                 
If we assume all signal streams s k  are of unit norm, and are all mathematically independent of each other, then the useful signal energy UT(i k ) sees from the transmission is
 
Useful Signal Energy= p   k |( b   k   ,h   i     k   )| 2  
 
and the interference energy is
 
               Interference   ⁢           ⁢   Energy     =       ∑     j   ≠   k       ⁢           ⁢       p   j     ⁢            (       b   j     ,     h     i   k         )          2               
Here, in one embodiment, the terms of form
 
 p   j |( b   j   ,h   i     k   )| 2    j= 1 , . . . , m  
 
are what we call Channel Quality Information (CQI) terms for UT(i k ) with respect to the beam p j  of this cell. The sum-terms “Useful Signal Energy” and “Interference Energy” represent another form of CQI which is a function of multiple individual energy measures. Each term, whether as a sum or an individual term with respect to a single beam, is a real positive valued scalar. They are often sufficient to describe effect signal and interference energies seen by users. There is no need to know individual Channel State Information (CSI) to estimate or obtain these energies. Also, the CQI information itself is of the form of scalar valued indices and thus requires less overhead to describe with sufficient accuracy than multi-dimensional, positive and complex, CSI terms. Base-stations can also send pilots so that the CSI itself can be estimated.
 
     Any base-station can send pilots so that such CQI can be estimated with respect to its transmissions. A pilot is broadcast on the wireless medium, thus a pilot sent by a station in “cella” can also be received by users in cells other than cella. Note that the channel vector now more generally is a function of not only the user but which base-station sending the pilot. For “users)” and base-station “n” the CSI is more generally a complex M vector h j   n , of form
 
 h   j   n =( h   1j   n   ,h   2j   n   , . . . , h   Mj   n ) T  
 
     Note, users cells in a cell of a given cell subset can in fact use past pilots which are sent from other cells of subsets with lower priority, i.e. cells in prior cell subsets in the process order. The user terminal knows when such pilots are transmitted and then sense them. To keep the exposition simple, and without explicitly writing all quantities, it should be clear to those familiar with the art that beams b k  and powers p k , can also be indexed additionally by the originating base-station index “n”. Thus the CSI and CQI information mentioned previously can now be indexed additionally by “n”, referring to the base-station (cell) in question. One such CQI value would refer to a particular beam intended for user(j) transmitted from its own base-station. All other beams from its base-station not intended for this user terminal, and all other beams from other stations, become interference. Pilots, whether from a user&#39;s cell or other cells, enable one cell to provide information to users in another cell about interference levels, as measured through CQI values. This form of implicit inter-cell information exchange helps simplify inter-cell signaling. 
     By knowing the user IDs of the scheduled users in CellSet(1), . . . , CellSet(k), a cell “cella” in CellSet(k+1) is able to limit the request of relevant inter-cell CQI information to a subset of the scheduled users in a subset of cells in CellSet(1), . . . , CellSet(k). Information, for example, includes CQI, both sum and individual, such users measure with respect to beam-pilots sent by “cella” in CellSet(k+1). 
     By knowing the beam IDs of the scheduled beams in CellSet(1), . . . , CellSet(k), a cell in CellSet(k+1) is able to request inter-cell information from its own users for only on the subset of the scheduled beams from cells in CellSet(1), . . . , CellSet(k). Note, all the users in the cell have obtained this information from when cell pilots were sent by cells in CellSet(1), . . . , CellSet(k). In one embodiment, feedback of either type of information may happen within the regular feedback exchanges within the cell. In another embodiment such information does not need to be individual CQI values but simply the sum over all CQI values, i.e. sum over all beams and channels to respective stations of the values p j |(b j ,h i     k   )| 2 . 
     Such collected information would be a measure of the interference level “Q”, mentioned in the background section, as seen by the “user” in a cell in CellSet(k+1) and coming from all interfering cells in CellSet(1), . . . , CellSet(k). Note, because a step-wise priority of subsets of cells is utilized, the measure ignores interference that comes from other cells within CellSet(k+1). 
     For cells in the same subset, in one embodiment subsets are chosen as in  FIG. 3  to constrain (limit to a low level) the level of interference between cells in the same subset. For example, cells in the subset may be geographically separated. In one embodiment the order of subsets specifies a requirement that cells in Subset(k) limit the interference they produce to users in subsets Subset(j), j&lt;k. 
     The ordered process limits the required information exchange and allows the system to scale well with the number of cells. However, in general, the ordered process of the system would give an advantage to Subset(k) over subsets Subset(k+1), . . . , Subset(k+N) because Subset(k) does not consider users or beams in Subset(k+n), n≧1. However, the order and definition of the subsets can be different in different frequency bands and time instances. For example, if there are two subsets Subset(A) and Subset(B), for some time instances or frequency bands, Subset( 1 )=Subset(A) and Subset( 2 )=Subset(B), and for other time instances of frequency bands, Subset( 1 )=Subset(B) and Subset( 2 )=Subset(A). The latency required for inter-cell communication and steps from each Subset(k)→Subset(k+1), implicit or direct, may be controlled by staggering such exchanges and pilots with respect to on-going transmissions. 
     One such subset pattern that may be used is similar in spirit to that described in the frequency reuse pattern in  FIG. 2 ; however, the operation fundamentally differs in the sense that all cells can use the same time and frequency resources. That is frequency reuse factor one can be used. The pattern is for ordering purposes and subset identification purposes only. Such a pattern is illustrated in  FIG. 3  using N=3 subsets labeled “A” (cells A(1)= 300   a , A(2)= 300   b , A(3)= 300   c  and A(4)= 300   d ), “B” (cells B(1)= 310   a , B(2)= 310   b , B(3)= 310   c  and B(4)= 310   d ), “C” (cells C(1)= 320   a , C(2)= 320   b , C(3)= 320   c  and C(4)= 320   d ). The example of  FIG. 3  provides a limited number of cells and a limited number of sub-sets; however, any number of cells and any number of subsets can be supported. Cells within a subset may be chosen in a way so that the cells, due to geographic separation, shadowing, etc, are assumed to interfere minimally with each other. Cells from different subsets however do interfere with each other. 
       FIG. 4  is a flow diagram of one embodiment for a step-wise technique for operating a cell-based wireless network using limited signaling between cells. In one embodiment, each cell is equipped with a number of transmission antennas. These can be located at a single base station, multiple coordinating base-stations, or be distributed geographically in the cell as in a distributed antenna system (DAS). Cells may have the same or different numbers of antennas. 
     In the examples that follow, the system is described as having cells with a single base-station and the same number of antennas “M” in each cell without loss in generality; however, alternate configurations can also be supported. Users (e.g., user terminals, user terminal, hereinafter “UTs”) within each cell can also be equipped with multiple antennas. However in our description, for simplicity and without loss in generality, user terminals (UTs) have a single receive antenna each. 
     The cells of the wireless network (or system) are divided into subsets,  400 . The description of  FIG. 3  is one such possible division; however, different cell arrangements in terms of, for example, numbers of cells and/or numbers of subsets, can also be supported. Without loss in generality we will use labels “A”, “B”, “C”, . . . to refer to un-ordered subsets. 
     The cell subsets are ordered,  410 . The order can be considered a mapping of labels “A”, “B”, “C”, and so on, to ordered indices 1, 2, 3, . . . where “1” has higher priority than “2”, “2” has higher priority than “3”, and so on. Subset ordering may be pre-programmed into the base stations and/or ordering may be accomplished by operation of an external controller that can communicate with the base stations. We will describe, without loss in generality, the operation with three labels “A”, “B” and “C” to three ordered indices 1, 2, and 3. 
     In one embodiment, the order and assignment of such subset labels “A”, “B” and “C” can vary. For example, for one time instance and/or frequency band we can consider that CellSet(1)=CellSet(A), CellSet(2)=CellSet(B), CellSet(3)=CellSet(C). In another we can have CellSet(1)=CellSet(B), CellSet(2)=CellSet(B), CellSet(3)=CellSet(A), etc. The first order is used in the description that follows, referring to the A cells as those forming CellSet(1), with B cells in CellSet(2) and the C cells CellSet(3) without loss in generality. 
     Also note that subset definitions are not required to follow the pattern illustrated in the figures. There can also be N=2, or N&gt;3, subsets. Also, while the following description of the network involves a sequential process from CellSet(1)→CellSet(2)→CellSet(3), the process can follow in general a tree structure. For example, if there are N=5 subsets, one process could be made of two branches 
     CellSet(1)→CellSet(2)→CellSet(3) 
     CellSet(1)→CellSet(2)→CellSet(4)→CellSet(5) 
     which implies a network where CellSet(3) and CellSet(4) both depend on CellSet(1) and CellSet(2), but do are not (in the system design) made to depend on each other. 
     Beam selection and scheduling is performed for the first subset “CellSet(1)”, by  420 . Here each cell in CellSet(1) sends its beam pilots, collects CQI from its users (only its users) with respect to such pilots, and decides which users to schedule and which beams and powers to assign to such users. Each cell may have a fixed preset set of beams b 1 , . . . , b m  to consider. The definition of such beams could also be random, or be a random selection, from transmission slot to transmission slot. During this process users in CellSet(2) and CellSet(3) can sense the beam pilots from (some) cells in CellSet(1). 
     In the pilot, scheduling and beam selection process the base stations in CellSet(1) operate independently of each other and of all other cells in CellSet(k), k≧2. Referring to  FIG. 3 , and assuming for this time/frequency instance CellSet(1)=CellSet(A), this means Cells A(1), A(2), A(3) and A(4) operate independently from each other and do not send or share information to each other. They also operate independently of all “B” and “C” cells. If UT(a) is assigned beam “k” and a power p k , the effective signal to interference ratio the UT(a) would see has form 
               SINR   ⁡     (     k   ,   a     )       =         p   k     ⁢            (       b   k     ,     h   a       )          2         1   +       ∑     j   ≠   k       ⁢           ⁢       p   j     ⁢            (       b   j     ,     h   a       )          2                   
This ratio can be calculated at the base-station given the CQI feedback from users in the cell. In one embodiment, if some CQI terms are not fed back (e.g. deemed too small by the UT), they are either set to zero or some small value. In another embodiment the user does not send back individual CQI terms, but only sends back the SINR.
 
     The rate with which a user can be served can be a function of these SINR values. Thus, a cell, having such SINR values, through CQI terms, can make decisions on which users to schedule, which beams to assign to which users, etc. Such multi-user scheduling processes are known to those familiar with the state of the art. The invention uses such scheduling processes. 
     Referring back to  FIG. 4 , information gained through beam selection and scheduling of CellSet(1) is exchanged with base stations in other cell sets,  430 . In one embodiment, once users and beams have been scheduled the identity of such beams and users may be sent to cells in CellSet(2). For illustration we consider this CellSet(2) to be the “B” cells in  FIG. 3 . In one embodiment, the actual beam coefficients do not need to be sent, only pilots. If the beams have different power levels than represented in the beam-pilots, beam-pilots are adjusted to reflect such power levels. 
     In one embodiment, each “A” cell sends information to only some “B” cells. For example, Cell A(1) sends information only to B-cells that it may receive (some minimal level of) interference from. In  FIG. 3 , for example, we one can consider that A(1)= 300   a  sends information only to adjacent B cells, i.e. to B(1)= 310   a  and B(2)= 310   b . Similarly, A(2)= 300   b  sends to B(1)= 310   a , and so on. With this B(1) would receive information from A(1), A(2), and A(3). 
     Referring back to  FIG. 4 , information received from CellSet(1) can be used for beam selection and scheduling for CellSet(2). In one embodiment, beam selection mechanism for CellSet(2) is different than beam selection for CellSet(1). The different beam selection mechanism may function to enable cells in CellSet(2) to adjust beams in order to reduce interference to users scheduled in cells of CellSet(1). Cells in CellSet(2) operate independently of each other. Referring to the example of  FIG. 3 , this means Cells B(1), B(2), B(3) and B(4) operate independently from each other and do not send or share information to each other. 
     In one embodiment, for a given B-Cell, say B(1)= 310   a  for illustration (all B-cells follow a similar procedure with respect to its considered A-cells), the system may create beams or select beams to as to minimize interference to the users it knows that are scheduled in A(1)= 300   a  A(2)= 300   b , and A(3)= 300   c . It may not consider A(4)= 300   d  given geographic separation. In such embodiments A(4) would not be required to send any information to B(1). Let their be “P” users in A(1), A(2), and A(3) that are scheduled, and the station in B(1) is made aware of, and let them be labeled UTA(x 1 ), . . . , UTA(x P ). 
     In one embodiment, a cell&#39;s base station, e.g. the station in B(1)= 310   a , sends a number of channel-pilots and obtains Channel State Information (CSI) from those scheduled users in the relevant A-cells, e.g. in cells A(1)= 300   a , A(2)= 300   b  and A(3)= 300   c . Note, B-cell B(2)= 310   b  may also require CSI information from some of those same users, specifically scheduled users in A(1)= 300   a . This information is with respect to a different channel h j   n  as mentioned before, where “n” identifies the base-station (or cell). Pilots in different B-cells can be made to operate orthogonally and/or in some predetermined fashion in time and frequency so as not to interfere with each other. 
     Once the base station of B(1) obtains the required CSI information between UTA(x 1 ), . . . , UTA(x P ) and its transmit antennas, the base station may create or select (from a pool) a set of beams to minimize interference to such users. In one embodiment, the CSI, the channel, between UT(x i ) and the base-station of B(1) is designated as z i . Note, z i  is a complex M-vector as h j , of form
 
 z   j =( z   ij   ,z   2j   , . . . , z   Mj ) T  
 
In one embodiment the beams are created based on this information. A process to do so is to form a matrix
 
 Z=[z   1   z   2    . . . z   P ]
 
and create a symmetric matrix
 
 ZZ*  
 
     This matrix has real valued eigenvalues. If the “m” beams for Cell B(1) are selected as the “m” eigenvectors corresponding the “m” eigenvalues with minimum absolute value, then these unit norm beams would be the selection that would minimize the sum interference to UTA(x 1 ), . . . , UTA(x P ). If M&gt;P, some eigenvalues would be zero and therefore some of the beams can be selected to as to ensure they create effectively zero interference to these users. 
     In another embodiment a B cell creates more beams than it will eventually schedule. Specifically, the cell can consider “L”, L&gt;m, beams, and allow the base station of cell B(1) to also do some internal beam selection with respect to its users, and in the process described above, in order to finally select a subset of m beams that is good with respect to both its cell and neighboring A cells. 
     Another selection is simply to not to create beams but rather to select beams. Beams can be selected as a subset of beams from a pool (also known as a codebook) of pre-designed beams. The selection is made so as to minimize the sum interference to UTA(x 1 ), . . . , UTA(x P ). Another selection is simply to select a subset of beams from a pool of pre-designed so as to minimize the maximum interference to any of the UTA(x 1 ), . . . , UTA(x P ). 
     Another method of beam selection is to use only CQI. This procedure may be included within the User Selection in CellSet(2) procedure described below. Here the base station of the B-cell has a pre-determined pool of beams. The base station sends beam pilots, and then obtains CQI information both from its users (as in the intra-cell procedure) and also from UTA(x 1 ), . . . , UTA(x P ). The CQI from UTA(x 1 ), . . . , UTA(x P ) informs the B-cell base station as to the interference it will create to such users for a given beam selection. The base station then constrains its user and beam selection so as to select beams that do not interfere beyond a certain level to UTA(x 1 ), . . . , UTA(x P ). 
     Beam selection and scheduling is performed for CellSet(2),  440 . In one embodiment, user selection in each cell in CellSet(2) is done as described above, given the beam as selected in the steps above (one of the two options). There is however one extra process to consider which is in addition to the process for CellSet(1) which may be used to improve performance to B-Cell users. This process uses signaling already performed in the user and beam selection for A-cells. 
     In one embodiment, when A-cells signaled its own users with its beams, B-cell users in range of any such transmission were able to obtain CQI with respect to all such beams. B-cell users in a given B-cell, e.g. B(1), may receive CQI information with respect to beams in the A-cells near to it, e.g. A(1), A(2) and A(3). As with the channel-pilots, beam pilots from different A-cells can be made to operate over time and frequency so as not to interfere with each other. 
     When beam pilots in B(1) are sent to its users, the identity of the scheduled beams in A(1), A(2) and A(3) may also be sent. In one embodiment, when B(1) users send CQI information of B(1) beams back to the base-station of B(1), they also send CQI information for these (and only for these) scheduled A-Cell beams. This extra CQI may be used to revise the interference level calculation for B(1) users. 
     For example, if the A cell beams have energy abeamp 1 , . . . , abeamp P , the beams are beamp 1 , beamp P , and these beams are seen by a user UT(l k ) in B(1) where beamp 1  comes through a channel h tk  to the user from the A-cell antennas, the true interference a such a user sees comes from both beams within its cells and beams in adjacent cells. 
               New   ⁢           ⁢   Interference   ⁢           ⁢   Energy     =         ∑     j   ≠   k       ⁢           ⁢       p   j     ⁢            (       b   j     ,     h     l   k         )          2         +       ∑   t     ⁢           ⁢       abeamp   t     ⁢            (       abeam   t     ,     h     t   k         )          2                 
This sum “New Interference Energy” value is a form of CQI which is sufficient to define quantities such as “Q” in rate determinations by the station in B(1). Users may only send back such information. Knowing users possible rates B(1) can make its scheduling and beam decisions.
 
     Furthermore, C-cells in Subset( 3 ) may also create interference to users in B(1), interference which is unknown at this point in the stepwise process since cells in Subset( 3 ) have not made any decisions. In some embodiments allowing for some nominal level of such interference in the CQI can improve system performance. One way to do so is to add an additional term “C(l k )” to account for this unknown interference, e.g. as in 
               New   ⁢           ⁢   Interference   ⁢           ⁢   2     =         ∑     j   ≠   k       ⁢           ⁢       p   j     ⁢            (       b   j     ,     h     l   k         )          2         +       ∑   t     ⁢           ⁢       abeamp   t     ⁢            (       abeam   t     ,     h     t   k         )          2         +     C   ⁡     (     l   k     )               
Either of these is used in the SINR calculation when scheduling B(1)&#39;s users. Note, only the CQI value |(abeam t ,h tk )| needs to be sent, and at times it is sufficient to send some of the sum quantities. The base station in cell B(1) does not need to know the individual channels.
 
     In one embodiment, once users and beams have been scheduled the identity of such beams and users is sent to cells in CellSet(3),  450 . In one embodiment, each “A” and “B” cell may send information to only some “C” cells. For example, Cells A(1) and B(1) may send information (either directly or by forwarding) only to C-cells that it may receive (some minimal level of) interference from. Because some B-cells may have A-cell information required by a C-cell, the B-cells may forward this information. That is, A-cells do not have to send this information directly. 
     In one embodiment, the base station in cell B(1)= 310   a  sends information only to C(1)= 320   a , C(2)= 320   b  and C(3)= 320   c . Similarly, B(2) sends to C(1), and so on. C(1) requires information from A(1), A(2) and A(4). Note, B(1) already has the information about A(1), and so forwards this information, along with its own, to C(1). 
     The base stations on CellSet(2) send information to other cells,  450 . Similar to what was done in CellSet(2), beams in each cell in CellSet(3) are chosen so as not to interfere (either in a sum, maximum and/or other sense) to users in CellSet(1) and CellSet(2). The procedure is similar to that described above for CellSet(2), except that the users “UTA(x 1 ), . . . , UTA(x P )” come from both A-cell and B-cells. Once the beams are designed/selected, each C-cell base station operates independently to select its users. 
     Beam selection and scheduling is performed for CellSet(3),  460 . The beam selection and scheduling is performed in a similar manner as for CellSet(2) described above, but utilizing information available to the base stations in CellSet(3), i.e. using information form various cells in CellSet(1) and CellSet(2). The example of  FIGS. 3 and 4  describe only three subsets of cells; however, any number of subsets can be supported. If additional subsets are supported, information is exchanged with these additional cells as described above. 
     Note, when transmission actually occurs,  460 , at the end of the complete scheduling and decision process, an A-cell user may receive interference form B and C cells (and possibly from A-cells, though the system ignores these as being small). In one embodiment these unknown interference levels can be accounted for in  420  by adding a term to the inter-cell interference similar to what was done in  440  and  460  above. For example, assume the signal s k  on beam b k  is intended for a scheduled the i k -th user terminal, UT(i k ), in an A-cell. We will ignore for simplicity of exposition the index “n” for such a cell in beam, power and CSI quantities. Also assume that the total B-cell+C-cell to the A-cell interference this user expects to receive is BC(i k ), Then one can replace the interference energy in the SINR calculation by the below quantity. 
     
       
         
           
             
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     Once all of the subsets of cells are configured, transmission can occur in all cells,  470 . Transmission from the “M” antennas in each cell to UTs in the cell is made by applying as previously described data signals to respective beams. creating a number of beams 
     As mentioned above, once a set of potentially scheduled beams, b 1 , . . . , b L  has been chosen or selected at random by a station, the base-station in the cell transmits each beam as a pilot to the users at a nominal power “q k ”. This “q” can be determined by a power level “p k ”, which may at the point of sending the pilot be determined or still yet undetermined. Each user, on receiving the beam pilots calculates an estimate for the CQIs with respect to the beams. For UT(a) this is the set
 
 CQI ( j,a )= q   j |( b   j   ,h   a )| 2  j=1, . . . , L
 
     If the power “q k ” is a nominal level, the base-station, knowing or making the final decision on “p k ” values can adjust such CQI terms accordingly without having to send additional pilots. In some steps of scheduling such powers “q k ” may be set to some (equal) nominal power “β”, from which adjustments to CQI for actual values “p k ” can be made simply by the ratio “p k /β”. 
     Users feedback CQI information to various base-stations in the multi-step process outlined above. In some embodiments the UT(a) sends back all such individual q j |(b j ,h a )| 2  like terms of such information. In some embodiments each UT may choose to ignore some of the terms (beams) if the signal level is sufficiently small so as to either be negligible in terms of potential interference or insufficient to support a users data stream. 
     As mentioned before, CQI is obtained for other base-stations, where an index “n” refines the identity of beam, power, channel and CQI quantities with respect to a base-station identity “n”. For beams a user knows it can not use (or will not be assigned to use) to support its own signaling stream, such beams are potential forms of interference to the user. If the user knows such beams are being scheduled, e.g. if such beams are from cells of higher priority than its own cell, and thus will become interference, it may be sufficient in some embodiments that the user not send the individual CQI values but a sum over many such CQI values, as shown in sum “Interference” quantities. 
     If UT(a) is assigned beam “k” and a power p k , the effective signal to interference ratio the UT(a) would see has form 
               SINR   ⁡     (     k   ,   a     )       =         p   k     ⁢   q   ⁢            (       b   k     ,     h   a       )          2         q   +     q   ⁢       ∑     j   ≠   k       ⁢           ⁢       p   j     ⁢            (       b   j     ,     h   a       )          2                     
As mentioned this ratio can be calculated at the base-station given the CQI feedback from users in the cell. Such SINR estimates may also be calculated by the user itself. In some embodiments the user calculates this SINR value and sends this to the base-station. In some embodiments, if the SINR value is too low, the user may choose not to send any such information to the base-station.
 
     The supported rate UT(a) can receive can be determined as function of SINR values for this user. The SINR is in fact the term, or strongly related to the term, “S/Q” mentioned in the background section. Here for example,
 
Supported Rate( k,a )=Function( SINR ( k,a ))
 
For example, one such function in terms of bits/sec/Hz could have a form
 
Supported Rate( k,a )=log 2 (1 +SINR ( k,a ))−offset( SINR ( k,a ))
 
In one embodiment, the offset term is used to convert an ideal achievable rate to a practical rate. In general, each SINR value is mapped, either by a function, or a lookup table, to a supported rate.
 
     In one embodiment, at any given transmission slot, a selection of users, UT(i 1 ), . . . , UT(i m ) is made to be served by beams. If L=m, i.e. all beams are to be used, and UT(i j ) is given beam b j , selection can be done to maximize 
               Proportional   ⁢           ⁢   Fair   ⁢           ⁢   Rate     =       ∑     j   =   1     m     ⁢           ⁢       w   ⁡     (     i   j     )       ⁢           ⁢   Supported   ⁢           ⁢   Rate   ⁢           ⁢     (     j   ,     i   j       )               
This selection is in line with a classic proportional fair sharing (PFS) system, known to those skilled in the art. The weight “w(a)” assigned to UT(a) to ensure the desired fairness between users seeing different average channel conditions.
 
     If m&lt;L subset of beams is to be selected in addition to a subset of users. Consider that beam c j  is assigned to UT(i j ), then both a subset of beam indices c 1 , . . . , c m  and a set of users UT(i 1 ), . . . , UT(i m ), is selected to maximize 
     
       
         
           
             
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     In one embodiment, the search over beam indices and user indices in each cell is in general a combinatorial process. This can be complex for moderate numbers of users and/or moderate numbers of beams. However, one process that works well is to limit the search to considering only one beam for each UT. To do this over the K users, UT(a), a=1, . . . , K, for each UT(a) one beam from the L beams is selected. Label the index of this beam r a . The selection of UT(a) in scheduling implies that this beam will be used for this user. This system makes sense since it is often the case that only a single beam provides a good signal term to a user. 
     A nominal power β is assumed for the beam. Given this UT(a) has a nominal supported rate based on this beam and power as described before. The users are then selected in a greedy fashion whereby at step “n” UT(i n ) is selected so that maximizes the partial sum 
               ∑     j   =   1     n     ⁢           ⁢       w   ⁡     (     i   j     )       ⁢           ⁢   Supported   ⁢           ⁢   Rate   ⁢           ⁢     (       r   j     ,     i   j       )             
given the previous selections i 1 , . . . , i n−1 , and under the restriction that this n-th (new) user does not use the same beam as any of the other previously selected users UT(i 1 ), . . . , UT(i n−1 ).
 
     In one embodiment, the greedy algorithm starts by selecting the first user, and continues till all “m” users are selected. This provides a fast, efficient, method for making a good joint beam and user selection. The process can be extended to N&gt;3 subsets, with cells in CellSet(k) sending its own information and forwarded user ID and beam ID information from CellSet(1), . . . , CellSet(k−1) to cells in CellSet(k+1). 
     In addition to reducing overheads via the cell subsets, the type of inter-cell communication also greatly helps to simplify the system. The inter-cell information shared between cells as described is in two forms. The first is that which is sent directly between cells, say from a “Cell(i) in CellSet(k)” to a “Cell(j) in Cellset(k+1)”, via the existing “backbone” communication link between cells (or between base-stations in different cells). This information includes which users (the user identifications) of the users scheduled for transmission in “Cell(i)”, and possibly also, if there is also a choice of beams, which beam (beam indices) are to be scheduled and at which power. This is a small amount of information relative to channel state information and beamforming coefficients, both of which are vectors of complex valued terms. 
     The second type of information that is exchanged (from Cell(i) to Cell(j)) is implicit information received indirectly from beams or channel pilots sent from base-stations/antennas in “Cell(i)”. Specifically, when “Cell(i)” send pilot beams, pilots which are used to get Channel Quality Information (CQI) from its own users, users in Cell(j) note also the channel quality with respect to those beams. Channel quality information, for the beams scheduled in Cell(i), are sent to the base-station of Cell(j) when Cell(j) gets feedback from its own users with respect to its own beams. 
     If Cell(i) did beam selection, the CQI feedback required with respect to Cell(i) beams is only for the scheduled beams in Cell(i). Finally, since Cell(j) knows the users scheduled in Cell(i), it requests also, when it sends channel pilots, such users to send Channel State Information (CSI) and/or CQI feedback to the Cell(j) base-station. Only scheduled users in Cell(i), which can sufficiently sense such pilots (are in interference/transmission range Cell(j)), need to send such feedback. Users which are not scheduled in Cell(i), or see signals from the base-station in Cell(j) are low levels, will not or implicitly can not respond with such information. 
       FIG. 5  is a flow diagram of one embodiment of a technique for operating a cell-based wireless network where subset priorities are dynamically modified. The technique of  FIG. 5  may be applied to groups of cells as described above. 
     The multiple cells are divided into multiple subsets,  510 . The description of  FIG. 5  is based on the cell arrangement illustrated in  FIG. 3 ; however, different cell arrangements in terms of, for example, numbers of cells and/or numbers of subsets, can also be supported. 
     The cell subsets are ordered,  510 . Subset ordering may be pre-programmed into the base stations and/or ordering may be accomplished by operation of an external controller that can communicate with the base stations. 
     Beam selection and scheduling is performed for the first subset,  520 . In one embodiment, the scheduling is performed by the base stations in the cells of the first subset. Each cell may have a fixed preset set of beams b 1 , . . . , b n . to consider. The definition of such beams could also be random, or be a random selection, from transmission slot to transmission slot. 
     Information gained through beam selection and scheduling of CellSet(1) is exchanged with base stations in other cell sets,  530 . In one embodiment, once users and beams have been scheduled the identity of such beams and users may be sent to cells in CellSet(2). For illustration we consider this CellSet(2) to be the “B” cells in  FIG. 3 . In one embodiment, the beam coefficients do not need to be sent, only pilots. If the beams have different power levels than represented in the beam-pilots, beam-pilots are adjusted to reflect such power levels. In one embodiment, each “A” cell sends information to only some “B” cells. For example, Cell A(1) sends information only to B-cells that it may receive (some minimal level of) interference from. 
     Beam selection and scheduling is performed for CellSet(2),  540 . In one embodiment, user selection in each cell in CellSet(2) is done as described above, given the beam as selected in the steps above. 
     In one embodiment, once users and beams have been scheduled the identity of such beams and users is sent to cells in CellSet(3),  550 . In one embodiment, each “A” and “B” cell may send information to only some “C” cells. For example, Cell A(1) and B(1) may send information only to C-cells that it may receive (some minimal level of) interference from. Because some B-cells may have A-cell information required by a C-cell, the B-cells may forward this information. 
     Beam selection and scheduling is performed for CellSet(3),  560 . The beam selection and scheduling is performed in a similar manner as for CellSet(2) described above, but utilizing information available to the base stations in CellSet(3). The example of  FIGS. 3 and 5  describe only three subsets of cells; however, any number of subsets can be supported. If additional subsets are supported, information is exchanged with these additional cells as described above. 
     Once all of the subsets of cells are configured, transmission can occur in all cells,  570 . In one embodiment, after the subsets of cells are configured, the priorities of the subsets may be modified,  580 . That is, the subset groups may be reorganized with one or more of the cells being assigned a different priority than during the previous initialization sequence. 
     In response to the modification of the priorities,  580 , a subsequent initialization sequence may be performed. In this subsequent initialization sequence the highest priority group of cells operates as described above and provides information to the next highest priority group of cells. The process continues as outlined above until all cells are initialized. 
       FIG. 6  is a block diagram of one embodiment of a base station. The base station illustrated in  FIG. 6  is intended to represent a range of base stations (e.g., for a macrocell, for a picocell). Alternative base stations may include more, fewer and/or different components. A mobile wireless device including, for example, for cellular telephones, wireless data communications, etc., may have the same or a similar architecture. 
     Base station  600  may include bus  605  or other communication device to communicate information, and processor  610  coupled to bus  605  that may process information. While base station  600  is illustrated with a single processor, base station  600  may include multiple processors and/or co-processors. Base station  600  further may include random access memory (RAM) or other dynamic storage device  620 , coupled to bus  605  and may store information and instructions that may be executed by processor  610 . For example, the process of  FIG. 6  may be implemented as instructions stored in memory  620  that are executed by processor  610 . Memory  620  may also be used to store temporary variables or other intermediate information during execution of instructions by processor  610 . 
     Base station  600  may also include read only memory (ROM) and/or other static storage device  630  coupled to bus  605  that may store static information and instructions for processor  610 . Data storage device  640  may be coupled to bus  605  to store information and instructions. Data storage device  640  such as a magnetic disk or optical disc and corresponding drive may be coupled to base station  600 . 
     Base station  600  further may include network interface(s)  680  to provide access to a network. Network interface(s)  680  may include, for example, a wireless network interface having antenna  685 , which may represent one or more antenna(e) that may communicate utilizing OFDM protocols. Network interface(s)  680  may also include, for example, a wired network interface to communicate with remote devices via network cable  687 , which may be, for example, an Ethernet cable, a coaxial cable, a fiber optic cable, a serial cable, or a parallel cable. 
     A computer-readable medium includes any mechanism that provides (e.g., memory  620 , ROM  630 , storage device  640 ) content (e.g., computer executable instructions) in a form readable by an electronic device (e.g., a computer, a personal digital assistant, a cellular telephone). For example, a computer-readable medium includes read only memory (ROM); random access memory (RAM); magnetic disk storage media; optical storage media; flash memory devices, etc. 
     Reference in the specification to “one embodiment” or “an embodiment” means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the invention. The appearances of the phrase “in one embodiment” in various places in the specification are not necessarily all referring to the same embodiment. 
     Whereas many alterations and modifications of the present invention will no doubt become apparent to a person of ordinary skill in the art after having read the foregoing description, it is to be understood that any particular embodiment shown and described by way of illustration is in no way intended to be considered limiting. Therefore, references to details of various embodiments are not intended to limit the scope of the claims which in themselves recite only those features regarded as essential to the invention.