Abstract:
A system for predicting portions of the spectrum to be available for communications. Data of spectrum usage over time and availability may be obtained. An analysis of the data may be made and then a prediction may be inferred as to the present and future availability of various portions of the spectrum for use. The system may increase the usability of the spectrum.

Description:
BACKGROUND  
       [0001]     The present invention relates to wireless communications, and particularly to spectrum use for such communications. More particularly, the invention relates to use in a crowded spectrum.  
         [0002]     The wireless spectrum is becoming crowded with increasing traffic for commercial, civilian and military use. There appears to be a need to achieve greater accessibility to unused portions of the spectrum without encountering unforeseen obstacles. SUMMARY  
         [0003]     The invention involves predicting portions of the spectrum to be available for communications. Data of spectrum usage over time and availability may be obtained. An analysis of the data may be made and then a prediction may be inferred as to the present and future availability of various portions of the spectrum for use. The invention may increase the usability of the spectrum. 
     
    
     BRIEF DESCRIPTION OF THE DRAWING  
       [0004]      FIG. 1  is a block diagram of a system that may be utilized for predictive modeling for spectrum use;  
         [0005]      FIG. 2   a  is a graph showing frequency usage over time;  
         [0006]      FIG. 2   b  is a graph revealing a prediction of success of transmission versus time;  
         [0007]      FIG. 3  illustrates frequency hopping as a graph of frequency slots versus time slots;  
         [0008]      FIG. 4  is a graph of a predictive model contour;  
         [0009]      FIG. 5  is a block diagram of a predictive model controller having an input of parameters relating to spectrum usage and computing spectrum availability for use by a transmitter/receiver device; and  
         [0010]      FIG. 6  illustrates a model predictive control for frequency hopping which is illustrated in the form of frequency slots versus time slots. 
     
    
     DESCRIPTION  
       [0011]     There may be holes, portions or frequencies available in a crowded spectrum. The term “holes” in the present description may mean portions available for present and future use in the spectrum. These holes in the spectrum may be exploited. However, the holes could be dynamic; for instance, a device may be transmitting at different frequencies at unscheduled times or at the same frequency on an infrequent basis. If the holes could be predicted, an intelligent wireless system could guarantee performance and secure communication in the face of a crowded spectrum, system uncertainties, jamming signals and interference.  
         [0012]     A model of system use of a spectrum may be built with its basis in time measurements and times of which frequencies are being used and their amount of usage. The measurements may be transcribed into a topology of frequency use with a mathematical model. The model may be stochastic, i.e., involving a statistical and probability approach. The model may also include heuristics to be input by the user, in that the model be self-corrective. It may be adaptive in that it can “learn” from usage in a communication system.  
         [0013]     The model may be used predictively to determine where the next hole (i.e, next available frequency slot) in the spectrum will be with a reasonable level of confidence, i.e., degree of probability. Then a transmission may be made at the noted frequency hole during the predicted time of availability. The present control system may monitor and record the successes and failures of transmission, and react to failures, jamming or other interference of transmission.  
         [0014]     A stochastic model may be used to internalize the topology of frequency use. Afterwards, the model may be invoked at certain discrete intervals to predict an occurrence of and/or when and where the holes in the spectrum will be. The control system may then determine whether a transmission at the predicted hole or frequency is successful. If not successful, the system may take remedial action by retransmitting (if the interfering signal&#39;s duration is known or internalized in the stochastic model) or by looking for other holes that can be used for transmitting messages.  
         [0015]     The stochastic model may use a variety of tools to internalize the frequency topology. Such tools may include Markov processes (hidden or embedded in some instances). A suite of predictive tools that may be used for the model includes model predictive control (MPC), internal model control (IMC), and stochastic control techniques. The tools may be used in the same manner that they be used in predicting computer usage. Computer usage predicting may be noted in an article entitled “Real-Time Adaptive Resource Management”, by A. Pavan et al., “Integrated Engineering”, pp. 2-4, Computer, July 2001.  
         [0016]     The stochastic model and control algorithms may be embedded in the control system or device that is used for transmission and/or reception of signals. The model may be also distributed among a set of transmission devices to ensure redundancy in the event of failure of some devices in the set or network.  
         [0017]      FIG. 1  is a block diagram of a system  10  that may be utilized for predictive modeling for spectrum use. From a spectrum/frequency information mechanism  27 , a signal  11  may be designated as “u” incorporating frequency usage over time, which would include the times and durations of use at the respective frequencies of the spectrum. Signal  11  may go to a system model  12 . An output signal  14  from system model  12  may be 
 ŷ 
 which provides a prediction of success of transmission, as noted by indication  57 , or a figure of metric like Quality of Service (QoS). QoS may include success of transmission, timeliness of the message (or latency) and the integrity of it. Signal  11  may also go to a communication system  13  which may include a transmitter  26  to be used. Transmitter  26  may receive its control and monitoring from the communication system  13  via a connection  56 . Transmitter  26  may provide its frequency and time usage of the spectrum to the communication system  13  via connection  59 . The frequency and time usage of the spectrum may go from communication system  13  to spectrum/frequency information mechanism  27  via connection  28 . An output signal  15  from communication system  13  may be “y” which indicates the actual success of a transmission, as noted by indication  58 , or QoS. Signals  14  and  15  may go to an adder-subtracter  16  where signal  14  may be subtracted from signal  15  to result in an error signal  17  which may be fed to system model  12  to adjust and/or update the prediction (or system) model. The error signal  17  may be the difference between the actual success of transmission and the predicted success of transmission. The signal  17  may also have a corrective effect on the system model  12  and its output  14 . 
 
         [0018]     The signal  14  may be fed to a controller  18  to provide a prediction of success of transmission or QoS at a particular frequency at a certain time, or a plurality thereof. Signal  14  may have an adjusting effect on the controller  18  relative to an output signal  19 . Signal  15  may be input to controller  18  to indicate if there was an actual success of transmission or QoS. Signal  19  may be output from controller  18  to provide input for a possible change of the frequency and time of usage by communication system  13 . Signal  19  may also be input to system model  12 .  
         [0019]      FIGS. 2   a  and  2   b  are graphs having curves  21  and  22 , respectively, of u (frequency usage) over or versus time, and 
 ŷ 
 (prediction of success of transmission) over or versus time t. One may note that if u is constant over time as shown with curve  21  in  FIG. 2   a , the system model  12  output 
 ŷ 
 of QoS or prediction of success of transmission curve  22  of  FIG. 2   b  may be non-constant over time t. This could happen due to interference signals in the spectrum. The time scale may be marked off in equal increments which are similar for curves  21  and  22 . One may ask what should be the next u value be to maximize the QoS value signal 
 ŷ 
 QoS may depend on a transmitter&#39;s use of a hole in the spectrum and what other transmitter may be using that particular hole and at what times. Here is where the prediction may come in. At any one time, much of the spectrum may be in use. Some areas of the spectrum may be more crowded than other areas. If the present predictive modeling system were used by all actual and prospective spectrum users, usage of the spectrum could be increased many times. 
 
         [0020]     Prediction may involve predictive de-confliction. A success factor may involve several parameters of significance which are those of QoS such as latency, i.e., time delay. Even though the transmission may be successful, it may not be of much good if it is slow getting to its expected recipient and its lateness results in the transmission being of less or no value. There may be a factor of message integrity to consider in transmissions. The message may succeed but there may be one bad bit in a digital transmission, which may affect the integrity of the message in the transmission. Integrity of the message may be of particular concern in a secure communication where the transmission succeeds but the encryption or decryption does not work.  
         [0021]     Signal  11  u may indicate a particular frequency that a transmitter is using over time or it may indicate amplitude and frequency usage at certain moments and durations of time. The transmitter may be hopping frequencies; for example, it may hop to preset frequencies at prescribed times. A software program may be utilized to perform such frequency hopping. Graph  23  of  FIG. 3  shows an example of frequency hopping which is illustrated in the form of frequency slots versus time slots. The duration of the time slots may be in the range of milliseconds. Thus, the transmitter may hop from one frequency to another many times a second or minute. The transmitter and receiver operations should be configured relative to this graph of information, as applicable, which may be in a form of a table. However, the table may change dynamically. The actual usages u indicated by signal  11  may dynamically change the table in accordance with the overall system  10  of  FIG. 1 . The signal  11  u may be a case of frequency hopping or the frequency at which the transmitter is broadcasting. Prediction of holes in a spectrum may be useful for planning frequency hopping. Hopping may involve encryption and integrity of the messages being sent. There may be some redundancy as desired in certain circumstances.  
         [0022]     The error output  17  of overall system  10  may update and adjust the system model  12  providing the prediction signal  14 . The prediction signal  14  
 
ŷ
 
 may be sent to the controller  18  as guidance in forming the signal  19  indicating available frequencies and times for the transmitter of the actual communication system  13  to use. The controller  18  may do a multi-step prediction far ahead of the present moment, which provides the best control of spectrum selection or frequency hopping. This approach may be an optimization of frequency hopping. Such action may be in real-time. The simulation may be faster than real time to determine the control action to take at the present time. Changes from moment to moment of the predictions and their bases may be taken into account. 
 
         [0023]      FIG. 4  illustrates the real world  52  during t RW  up to t o =0 and a prediction of what the system might be able to do after t o =0 in the simulated world  53 , for instance, in the 5 time slots up to t=1 to the right as shown by curve  24  along simulated time  54 . At time line  55 , the input for the controller  18  may again be computed and implemented. At t=1, the prediction may be recomputed, i.e., updated. That may be needed since there are ongoing environmental changes, frequency usage changes, and so on. The prediction may be updated for the next 5time slots. The number of time slots for each prediction or update may be arbitrary.  
         [0024]     For time line  54 , the prediction may be a of a predictive model contour  24  at the output  14  of the system model  12 . System model  12  of overall system  10  may be realized with model predictive control (MPC), internal model control (IMC), or other like software and stochastic control techniques.  
         [0025]     Relative to predictions, there may be a receding horizon control (RHC) in which the prediction horizon may recede if transmission time is limited. In other words, predictions are not made beyond the time that the transmission is scheduled to stop. Here, the overall system  10  may go into a terminal state. Although in some frequency spectrums, usage has no terminal state, e.g., cell telephones.  
         [0026]     There may be a number of transmitter/receiver (T/R) devices connected with a centralized predictive modeling system which may have a central processor making decisions for assigning frequencies for these devices. However, the T/R devices may be decentralized and the decisions for assigning the frequencies be distributed to each device. Some de-confliction among the various devices may be needed. So even if the decisions for frequencies are decentralized, they are not necessarily totally decentralized. Each of the T/R devices may have a spectrum analyzer and a processor for making its own decisions about frequency use. There may be interconnections among the devices. Each may take into account the whole frequency spectrum or some a priori assigned portions of the spectrum to various T/R devices.  
         [0027]     Frequency selection by a T/R device may depend much on who is broadcasting in the particular geographical area where the specific T/R device is located. An analogous situation may be a railway system having various geographical areas where each train is located. A specific train may have a particular itinerary which may involve certain geographical areas that it may be going through relative to getting to its destination. There may be an interchange of information. Theoretically, the centralization and decentralization approaches should result in the same answers, whether a frequency selection for a pair of transmitter and receiver devices or a rail selection for a train. The centralized approach may be regarded for selecting the global optimum for all units. The decentralized approach may be regarded for selecting the local optimum for the local unit having a mission. The latter may often have more concern for the local environment rather than the global environment. Decentralization may become less expensive than centralization. Decentralization may also be computationally simpler. The decentralized system may provide greater probabilities for selected frequencies for an individual T/R device than the centralized system.  
         [0028]     If there are two sets of transmitter/receiver devices wanting to use the same frequency, there may be a negotiation involving time-share on that frequency which may be similar to track-share of a railway system. One may incorporate partitioning time/frequency/code (PTFC) to resolve conflicts between the sets. There may be a code with established techniques for distributing information. So there may be code distribution among the sets or units. Some approaches that may be used are code divisional multiplexing (CDM) with application for cell phones, and time domain multiplexing (TDM). There may be a software-defined radio which involves and is leveraged by the present adaptive predictive model control (PMC). The PMC may be adaptive in that it is improving at every time-instant and helps one to find and use quick and efficient solutions successfully in a decentralized system.  
         [0029]     One end goal is a rapid deployment of wireless networks in a new environment. This may be a good use. A bad use may be the jamming of certain frequencies and making holes in the jamming for one&#39;s own information or use. Such jamming may be coded much like the enigma machine approach used during WWII. The other side of a conflict may jam GPS and communication signals. There may be noise in the regular signals, possibly including a code in them.  
         [0030]     A model based control may do a prediction from a certain one time such as to. It may be rather easy to implement in the present invention a transmitter/receiver device, a sensor, plug and play, some numbers, slots opening up, autonomous selection, and/or reconfiguration by the controller whether it be centralized or decentralized.  
         [0031]     An example of a system for model prediction of spectrum use may include a stochastic model of spectrum use base on a time-sequence usage of frequencies, an adapting model based on environmental conditions (i.e., present usage, future usage, spots, locations and interference), model based controller development and a model predictive controller.  
         [0032]      FIG. 5  reveals a schematic of a multiple of transmitter/receiver devices in conjunction with a model predictive controller  29 . Three T/R devices  25  are shown but there could be many more or fewer T/R devices using the spectrum that a T/R device  26  would like to use. Outputs indicating the usage of the various frequencies of the various T/R devices  25  as signals  35  may go to a spectrum/frequency (usage) information mechanism  27 . An illustrative example of finding a hole for a T/R device  26  that one may want to use is shown. The T/R device  26  may output a signal  28  indicating its spectrum use. Signal  28  may go to the information mechanism  27  and the model predictive controller  29 . From the spectrum usage information of the T/R devices  25  and  26 , an output signal  31  representing that information may go to a miniaturized spectrum analyzer  32 . The spectrum may be analyzed in view of the T/R device usage. Analysis results in the form of a signal  33  may go to a hole estimator  34 , which in view of the spectrum analysis results, particularly as accumulated over time, may provide a history of holes and estimates of where the holes in the spectrum appear and at what times and durations. The hole estimator  34  may send estimates, based on the information in signal  33 , as a signal  36  to the model predictive controller  29 .  
         [0033]     A spectrum predictor  37  along with a signal  39  from a disturbance model  38  may predict “surge events”, interruptions and upcoming transmissions in the spectrum, and provide that information as a signal  41  to controller  29 . A mechanism  42  may provide a Markov process for hole dynamics as a signal  43  to the controller  29  to aid the controller in dealing with the estimation of holes signal  36  from hole estimator  34  in conjunction with the other signals  28  and  41  received by the controller  29 . Controller  29  may use a spectrum model and a history of holes to determine the frequency hole most likely to be empty for the next “x” milliseconds, seconds or minutes. A signal  44  indicating a broadcast frequency selected or a frequency hop sequence in view what is predicted to be available may be sent to the T/R device  26  to be used. Also controller  29  may indicate with a signal  45  to device  26  how many seconds (i.e., x seconds or the like) that the hole or holes (if a hop sequence) specified in signal  44  will likely be available. Also, signal  45  from controller  29  may indicate the future times that certain holes will likely be available.  
         [0034]      FIG. 6  reveals an approach of the model predictive controller  29 . As noted above, spectrum usage and/or hole availability information may be provided to controller  29 . The controller may use observed past and present spectrum usage and a history of holes as shown by curve  46  to form a model for prediction. The model may be used for predicting the availability of the spectrum for usage. The predictions may use the model for the next “h” steps (with an assumed input and noise profile). The h steps may extend for a horizon length “h” as shown by line  47  in  FIG. 6 . Predicting for the future as represented by simulated time (i.e., t+1, t+2, t+3,... t+h) may be shown by the predictive model contour  24 . That may be the “predict” stage  48  which is the first phase of the model predictive control as shown in the spectrum usage or hole availability versus real time graph with time steps t and t+1 shown on the abscissa axis. The next stage  49  may involve the use of the predictions to compute an optimal input at “t+1”. At the next stage  50 , the computed input  51  may be implemented at “t+1”. Occasionally, the model that approximates the profile  46  may be updated or adapted, such as every 15 minutes or so.  
         [0035]     In the present specification, some of the material may be of a hypothetical or prophetic nature although stated in another manner or tense.  
         [0036]     Although the invention is described with respect to at least one illustrative embodiment, many variations and modifications will become apparent to those skilled in the art upon reading the present specification. It is therefore the intention that the appended claims be interpreted as broadly as possible in view of the prior art to include all such variations and modifications.