# US 8,787,186 B2 · Mobility in a distributed antenna system

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- Number: US 8,787,186 B2
- Document ID: US8787186B2
- Status: Granted
- Kind: B2
- Application number: 13/161,706
- Filed: 2011-06-16
- Published: 2012-01-05
- Granted: 2014-07-22
- Earliest claimed: 2008-12-12
- Assignees: BlackBerry Limited
- Applicants: Thomas Aloysuis Sexton; Charles Arthur Cliff; Mark Pecen; Paul James Lusina
- Inventors: Thomas Aloysuis Sexton; Charles Arthur Cliff; Mark Pecen; Paul James Lusina
- Web page: https://patentgrep.com/patent/US8787186B2

## CPC

- H03M 7/30: Compression (speech analysis-synthesis for redundancy reduction G10L19/00; for image communication H04N); Expansion; Suppression of unnecessary data, e.g. redundancy reduction
- H04W 4/02: Services making use of location information
- H04L 5/005: {of common pilots, i.e. pilots destined for multiple users or terminals}
- H04L 25/0232: {by interpolation between sounding signals}
- H04L 25/0242: {using matrix methods}
- H04L 27/0006: {Assessment of spectral gaps suitable for allocating digitally modulated signals, e.g. for carrier allocation in cognitive radio (for spectrum sharing between different networks H04W16/14)}
- H04L 67/12: specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
- H04H 20/38: Arrangements for distribution where lower stations, e.g. receivers, interact with the broadcast
- H04L 69/28: Timers or timing mechanisms used in protocols
- H04W 72/21: in the uplink direction of a wireless link, i.e. towards the network
- H04W 88/02: Terminal devices
- H03M: CODING; DECODING; CODE CONVERSION IN GENERAL (using fluidic means F15C4/00; optical analogue/digital converters G02F7/00; coding, decoding or code conversion, specially adapted for particular applications, see the relevant subclasses, e.g. G01D, G01R, G06F, G06T, G09G, G10L, G11B, G11C, H04B, H04L, H04M, H04N; ciphering or deciphering for cryptography or other purposes involving the need for secrecy G09C)
- H04W: WIRELESS COMMUNICATION NETWORKS (broadcast communication H04H; communication systems using wireless links for non-selective communication, e.g. wireless extensions H04M1/72)
- H04L: TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION (arrangements common to telegraphic and telephonic communication H04M)
- H04H: BROADCAST COMMUNICATION (multiplex communication H04J; pictorial communication aspects of broadcast systems H04N)

## Abstract

A device, method or system implements operations to receive compressed samples of wireless transmissions from a plurality of user equipment (UEs) traveling through different communication regions in a wireless network, and detect information in the wireless transmissions of the UEs based on the compressed samples.

## Claims

### Claim 1

A method comprising:
receiving samples of wireless transmissions from a plurality of user equipment (UEs) traveling through different communication regions in a wireless network, wherein the communication regions are pilot zones, and each pilot zone is assigned a corresponding set of pilots signals for use by UEs when traveling through the corresponding pilot zone, each of the wireless transmissions being based on an S-sparse set of vectors;
detecting information in the wireless transmissions of the UEs based on the samples;
constructing an overall matrix of estimated channels for the UEs, wherein the information in the wireless transmission of the UEs is detected based on at least the constructed matrix and the samples; and
estimating a channel for each of the UEs based on pilot signals employed by the UEs,
wherein two or more of the different communication regions are assigned the same set of pilot signals.

### Claim 2 (depends on 1)

The method according to claim 1, further comprising:
identifying a presence of the UEs in the different communication regions at any given point in time; and
identifying communication parameters used by the UEs in the different communication regions.

### Claim 3 (depends on 1)

The method according to claim 1, wherein the detecting information comprises implementing a linear program to solve a L1 minimization problem.

### Claim 4 (depends on 1)

The method according to claim 1, wherein the detecting information operation comprises:
estimating a transmit vector for the wireless transmissions based on the samples and the estimated channels; and
demodulating the estimated transmit vector to obtain information in the wireless transmissions of the UEs.

### Claim 5 (depends on 4)

The method according to claim 4, further comprising:
re-modulating the information to obtain a re-modulated transmit vector;
re-estimating channels for the UEs based on the re-modulated transmit vector;
estimating a transmit vector for a subsequent transmission period based on the samples and the re-estimated channels for the UEs; and
demodulating the estimated transmit vector to obtain information in the wireless transmissions of the UEs.

### Claim 6 (depends on 1)

The method according to claim 1, wherein the detecting operation employs a linear program using a sparse representation matrix and a sensing matrix, and the matrices are selected to reduce at least a Candes Noise Coefficient.

### Claim 7 (depends on 1)

The method according to claim 1, wherein the wireless network includes remote regional processors associated with respective communication regions, and the samples are received from the remote regional processors.

### Claim 8 (depends on 7)

The method according to claim 7, wherein each of the remote regional processors allocates communication parameters for use by UEs in its respective communication region.

### Claim 9

A method comprising:
receiving samples taken of a wireless transmission from user equipment (UE) traveling from a first pilot zone to a second pilot zone in a wireless network having a plurality of pilot zones, each pilot zone having an assigned set of pilot signal, the wireless transmission including a pilot signal selected from the set of pilot signals by the UE based on a scheduled assignment and being based on an S-sparse combination of a set of vectors;
identifying a presence of the UE in the second pilot zone;
identifying the selected pilot signal to be used by the UE in the second pilot zone based on the scheduled assignment; and
detecting information in the wireless transmission from the set of samples,
wherein the samples are sampled at different geographic locations in the second region at a given point in time, and
wherein the scheduled assignment is based on a system clock and a UE identifier (ID).

### Claim 10 (depends on 9)

The method of claim 9, wherein the first and second zones are assigned a different set of pilot signals.

### Claim 11

A communication system comprising:
a plurality of remote samplers {1, . . . , j, . . . , z} where z is a number of remote samplers, a jth remote sampler capturing N samples during a word interval and transforming the N samples into Mj samples denoted collectively as yj using a sensing matrix Φj where Mj<N/2 and M=M1+ . . . +Mj+ . . . +Mz; and
a solver for receiving samples {y1, . . . , yj, . . . , yz} from the remote samplers and formulating a soft estimate of a collection of data vectors {{tilde over (x)}1, . . . , {tilde over (x)}k, . . . , {tilde over (x)}l} based on knowledge of transmission matrices {Ψ1, . . . , Ψk, . . . , Ψl} where l is a number of user equipment {1, . . . , k, . . . , l},
wherein a transmission matrix Ψk is incoherent within a sensing matrix Ψj for all pairs k, j according to a Restricted Isometry Property.

### Claim 12 (depends on 11)

The system according to claim 11, wherein a quantized vector {circumflex over (x)}k is associated with a soft vector {tilde over (x)}k using a sparsity value sk.

### Claim 13 (depends on 12)

The system according to claim 12, further comprising a plurality of regional leaf brains for receiving samples from remote samplers in their respective regions, wherein a regional leaf brain from the plurality of regional leaf brains assigns a value sk to a kth user equipment.

### Claim 14 (depends on 13)

The system according to claim 13, wherein the plurality of regional leaf brains includes a first regional leaf brain and a second regional leaf brain, the first regional leaf brain assigning a sparsity value sk to a first user equipment, and the second regional leaf brain assigning a value sk+1 to a second user equipment.

### Claim 15 (depends on 13)

The system according to claim 13, wherein the plurality of regional leaf brains includes a first regional leaf brain and a second regional leaf brain, the first regional leaf brain assigning to a kth user equipment a transmission matrix Ψreuse and a second regional leaf brain assigning to a k+1th user equipment the matrix Ψreuse, wherein the solver is aware of the assignments and the solver estimates a complex path gain from the kth user equipment to the jth remote sampler.

### Claim 16 (depends on 13)

The system according to claim 13, wherein the kth user equipment uses a pilot matrix Ψk in a given word interval at a time tw where:
(1) the kth user equipment determines the pilot matrix Ψk by evaluating a formula using tw or by reading a table using tw, and
(2) a (k+l)th user equipment determines a pilot matrix Ψk+1 by evaluating a formula using tw or by reading a table using tw.

### Claim 17 (depends on 16)

The system according to claim 16, wherein Ψk+1 is identical to Ψk at a time t=tw.

### Claim 18 (depends on 16)

The system according to claim 16, wherein during certain word intervals a set of transmission matrices Ψ comprises pilot matrices and a kth element xk is a pattern with sparsity sk=1 and xk is known to the solver.

### Claim 19 (depends on 18)

The system according to claim 18, wherein the set of transmission matrices Ψ comprises columns of a Discrete Fourier Transform matrix.

### Claim 20 (depends on 18)

The system according to claim 18, wherein the set of transmission matrices Ψ comprises columns generated using a pseudo random number generator.

### Claim 21 (depends on 11)

The system according to claim 11, wherein the solver creates a soft estimate {tilde over (x)} using a zero forcing method.

### Claim 22 (depends on 11)

The system according to claim 11, wherein the solver creates a soft estimate using a maximum-likelihood method.

### Claim 23

A method of operating user equipment comprising:
learning a number of uplink antennas passed per second; and
apportioning an uplink transmission bandwidth between data, signaling and pilot transmissions based on the learned number,
wherein a data transmission comprises s-sparse transmission based on a pseudo-random number-based transmission matrix Ψ which is repopulated at certain time intervals.

### Claim 24

A communication system comprising:
a plurality of remote samplers {1, . . . , j, . . . , z} where z is a number of remote samplers, a jth remote sampler capturing N samples during a word interval and transforming the N samples into Mj samples denoted collectively as yj using a sensing matrix Ψj where Mj<N/2 and M=M1+ . . . +Mj+ . . . +Mz; and
a solver for receiving samples {y1, . . . , yj, . . . , yz} from the remote samplers and formulating a soft estimate of a collection of data vectors {{tilde over (x)}1, . . . , {tilde over (x)}k, . . . , {tilde over (x)}l} based on a knowledge of transmission matrices {Ψ1, . . . , Ψk, . . . , Ψl} where l is a number of user equipment {1, . . . , k, . . . , l},
wherein the remote samplers perform sampling of a sparse or non-sparse data transmission.

### Claim 25 (depends on 24)

The communication system according to claim 24, wherein an entropy of a transmitted user constellation is either 16-bits or 32-bits.

### Claim 26 (depends on 24)

The communication system according to claim 24, wherein the non-sparse data transmission includes a checksum.

### Claim 27 (depends on 26)

The communication system according to claim 26, wherein the solver computes a checksum from data formulated from the received samples, and deletes blocks of data where a checksum fails.

### Claim 28

A communication system comprising:
a plurality of remote samplers for receiving wireless signals transmitted by one or more user equipment using a transmission matrix from one or more transmission matrices, each of the remote samplers capturing N samples during a word interval and transforming the N samples into M samples, where M<N/2, using a sensing matrix from one or more sensing matrices; and
a solver for receiving the M samples from each of the remote samplers and formulating a soft estimate of data vectors based on a knowledge of the transmission matrices used by each of the one or more user equipment,
wherein all pairs of a sensing matrix and transmission matrix satisfy a Restricted Isometry Property.