CN111034076A

CN111034076A - Candidate control channel blind detection

Info

Publication number: CN111034076A
Application number: CN201780093871.4A
Authority: CN
Inventors: 弓宇宏; 郝鹏; 左志松
Original assignee: ZTE Corp
Current assignee: ZTE Corp
Priority date: 2017-08-11
Filing date: 2017-08-11
Publication date: 2020-04-17
Anticipated expiration: 2037-08-11
Also published as: WO2019028830A1; CN111034076B

Abstract

One or more devices, systems, and/or methods are provided for performing blind detection on candidates (e.g., control channels), determining and/or selecting candidate patterns, and/or determining one or more candidate (e.g., control channel) locations. For example, a plurality of candidate styles may be determined based on a set of bitmaps. A candidate style may be selected from the plurality of candidate styles. The node may perform blind detection of (e.g., candidate control) channels based on the candidate pattern.

Description

Candidate control channel blind detection

Background

A communication link between wireless nodes, such as between a User Equipment (UE) and a Base Station (BS), may be assisted using a Physical Downlink Control Channel (PDCCH) search space. The UE may need to perform blind detection on one or more candidate control channels corresponding to one or more aggregation levels and/or control regions. However, the UE and/or the BS may have limited and/or varying resources and/or may have limited and/or varying capabilities.

Disclosure of Invention

In accordance with the present disclosure, one or more devices and/or methods are provided for performing blind detection on candidate control channels. In one example, a plurality of candidate patterns (patterns) may be determined based on a set of bitmaps. A candidate style may be selected from the plurality of candidate styles. Blind detection of the candidate control channel may be performed based on the candidate pattern.

In one example, a starting candidate location associated with a search space may be selected. One candidate style may be selected from a plurality of candidate styles. Blind detection of the candidate control channel may be performed based on the candidate pattern and the starting candidate position.

In one example, a starting candidate location associated with a search space may be selected. The candidate pattern may be determined based on the starting candidate position and a value. Blind detection of the candidate control channel may be performed based on the candidate pattern.

In one example, a plurality of candidate styles may be determined based on a set of bitmaps. A candidate style may be selected from the plurality of candidate styles. An indication corresponding to blind detection of the candidate control channel may be generated based on the candidate pattern. The indication may be communicated to the node.

In one example, a starting candidate location associated with a search space may be selected. One candidate style may be selected from a plurality of candidate styles. An indication corresponding to blind detection of the candidate control channel may be generated based on the candidate pattern and the starting position. The indication may be communicated to the node.

In one example, a starting candidate location associated with a search space may be selected. A candidate pattern may be determined based on the starting candidate position and a value. An indication corresponding to blind detection of the candidate control channel may be generated based on the candidate pattern. The indication may be communicated to the node.

Drawings

Although the techniques provided herein may be embodied in alternative forms, the specific embodiments shown in the drawings are merely a few examples, which are intended to be complementary to the descriptions provided herein. These embodiments should not be construed in a limiting sense, such as to limit the claims appended hereto.

Fig. 1A is a flow diagram illustrating an example method of selecting a candidate style.

Fig. 1B is a flow diagram illustrating an example method of selecting a candidate style.

Fig. 1C is a flow diagram illustrating an example method of determining candidate styles.

Fig. 1D is a flow diagram illustrating an example method of selecting a candidate style.

Fig. 1E is a flow diagram illustrating an example method of selecting a candidate style.

Fig. 1F is a flow diagram illustrating an example method of determining candidate styles.

Fig. 2 is a diagram showing one or more examples of candidate styles.

Fig. 3 is a diagram illustrating one or more examples of candidate styles.

Fig. 4 is a diagram showing one or more examples of candidate styles.

Fig. 5 is a diagram showing one or more examples of candidate styles.

Fig. 6 is a diagram illustrating an example of one or more candidate styles and/or one or more bitmaps.

Fig. 7 is a diagram showing one or more examples of candidate styles.

Fig. 8 is a diagram showing one or more examples of candidate styles.

Fig. 9 is a diagram showing one or more examples of candidate styles.

Fig. 10 is a diagram showing one or more examples of candidate styles.

Fig. 11 is a diagram showing one or more examples of candidate styles.

Fig. 12 is a diagram illustrating an example of one or more candidate styles and/or one or more bitmaps.

Fig. 13 is a diagram illustrating an example of one or more candidate styles and/or one or more bitmaps.

Fig. 14 is a diagram showing one or more examples of candidate styles.

Fig. 15 is a diagram illustrating one or more candidate location examples.

Fig. 16 is a scenario illustration involving an example configuration of a Base Station (BS) that may utilize and/or implement at least a portion of the techniques provided herein.

Fig. 17 is a scenario illustration involving an example configuration of a User Equipment (UE) that may utilize and/or implement at least a portion of the techniques provided herein.

Fig. 18 is a scenario illustration featuring an example non-transitory computer-readable medium in accordance with one or more provisions set forth herein.

Detailed Description

The subject matter now will be described more fully hereinafter with reference to the accompanying drawings, which form a part hereof, and which show, by way of illustration, specific example embodiments. This description is not intended to be an extension or a detailed discussion of known concepts. Details that are generally known to one of ordinary skill in the relevant art may be omitted or may be processed in a simplified manner.

The following subject matter may be embodied in a variety of different forms, such as methods, devices, components, and/or systems. Accordingly, the present subject matter is not intended to be construed as limited to any of the example embodiments set forth herein. Rather, the example embodiments are provided for illustration only. Such embodiments may take the form of, for example, hardware, software, firmware, or any combination thereof.

One or more computing devices and/or techniques are provided for performing blind detection on candidate control channels. For example, a User Equipment (UE) may connect to a network via a Base Station (BS) of the network (e.g., wireless communication). The search space may include (e.g., locations of) one or more Physical Downlink Control Channels (PDCCHs). To receive data from the BS (e.g., and/or the network), the UE may need to perform blind detection on the candidate control channels. The UE and/or BS may be required to determine (e.g., randomly) one or more candidate patterns corresponding to one or more candidate control channels. However, there may be limited (e.g., search space) resources for the UE to perform blind detection of the one or more candidate control channels. Thus, in accordance with one or more techniques herein, determining (e.g., and/or selecting) the one or more candidate patterns and/or selecting one or more candidate locations may be facilitated by allowing a UE to perform blind detection on one or more candidate control channels using available resources.

An example method 100A of selecting a candidate style is shown in fig. 1A. At 105A, the first node may determine a plurality of candidate styles based on the set of bitmaps. In some examples, the first node may comprise a UE. The set of bitmaps may correspond to the plurality of candidate styles. For example, a first bitmap in the set of bitmaps may correspond to a first candidate pattern in the plurality of candidate patterns.

The plurality of candidate patterns may correspond to one or more numbers of Control Channel Elements (CCEs) (e.g., corresponding to a search space). For example, the plurality of candidate patterns may include an aggregation level 8 pattern, where each candidate (e.g., control channel) corresponds to eight CCEs. Alternatively and/or additionally, the plurality of candidate patterns may include an aggregation level 4 pattern, where each candidate corresponds to four CCEs. Alternatively and/or additionally, the plurality of candidate patterns may include an aggregation level 2 pattern, where each candidate corresponds to two CCEs. Alternatively and/or additionally, the plurality of candidate patterns may include an aggregation level 1 pattern, where each candidate corresponds to one CCE.

In some examples, the plurality of candidate patterns may include candidate patterns corresponding to a control region (e.g., CORESET) including 16 CCEs (e.g., 16 aggregation level 1 candidates, eight aggregation level 2 candidates, four aggregation level 4 candidates, two aggregation level 8 candidates, etc.). Alternatively and/or additionally, the plurality of candidate patterns may include candidate patterns corresponding to a control region including 32 CCEs (e.g., 32 aggregation level 1 candidates, 16 aggregation level 2 candidates, eight aggregation level 4 candidates, four aggregation level 8 candidates, etc.). Alternatively and/or additionally, the plurality of candidate patterns may include candidate patterns corresponding to a control region including a number of CCEs greater than 32 (e.g., 64 CCEs, 128 CCEs, 256 CCEs, etc.).

In some examples, the first node may use the set of bitmaps to determine the plurality of candidate styles. For example, a first bitmap of the set of bitmaps may be used by the first node to determine a first candidate pattern. The first bitmap may include the most significant bits (MIB) (e.g., bits of the first bitmap associated with higher importance levels are determined as compared to one or more other bits in the first bitmap). The candidate corresponding to the starting candidate position may be determined by the first node based on the value (e.g., 1,0) of the MIB. For example, a candidate corresponding to the starting candidate position may be selected based on the MIB having a first value (e.g., 1). Alternatively and/or additionally, the candidate corresponding to the starting candidate position may not be selected based on the MIB having the second value (e.g., 0).

In some examples, the set of bitmaps includes a first number of bitmaps corresponding to a second number of candidate patterns included in the plurality of candidate patterns. The first number (e.g., of bitmaps) may be equal to the second number (e.g., of candidate patterns). In some examples, a second node (e.g., a network and/or a BS) (e.g., and/or a first node) may define a set of bitmaps. Alternatively and/or additionally, the second node may transmit a message to the first node comprising a set of bitmaps (e.g., corresponding to one or more control regions and/or one or more aggregation levels). In some examples, the message may include a Radio Resource Control (RRC) message and/or a different type of message.

In some examples, the first node may receive the message (e.g., including the set of bitmaps). In some examples, at 110A, the first node may select a candidate style from a plurality of candidate styles. In some examples, the first node may randomly select the candidate pattern (e.g., using a hash function). The first node may select a bitmap (e.g., of a set of bitmaps) corresponding to the candidate pattern. The first node may randomly select the bitmap.

In some examples, the candidate pattern may be used to determine one or more candidates corresponding to a first control region that includes a first number of CCEs. Alternatively and/or additionally, the candidate pattern may be adjusted to correspond to a second candidate pattern of a second control region including a second number of CCEs. In some examples, the candidate style may be used to determine one or more candidates corresponding to a first aggregation level. Alternatively and/or additionally, the candidate pattern may be adjusted to correspond to a third candidate pattern of a second aggregation level comprising a third number of CCEs.

Alternatively and/or additionally, one or more demodulation reference signal (DMRS) channel estimation results may be used for corresponding to the first control region and/or (e.g., reused for corresponding to) the second control region. Alternatively and/or additionally, one or more DMRS channel estimation results may be used to correspond to a first aggregation level and/or (e.g., reused to correspond to) a second aggregation level.

At 115A, the first node may (e.g., then) perform blind detection on a candidate (e.g., control channel) based on the candidate pattern. For example, the first node may determine one or more candidates corresponding to a first value (e.g., 1) based on the bitmap. The first node may perform blind detection on the one or more candidates. The first node may determine, based on the bitmap, one or more resources corresponding to a second value (e.g., 0) that are not candidates (e.g., control channel candidates).

An example method 100B of selecting a candidate style is shown in fig. 1B. At 105B, the first node may select a starting candidate location associated with the search space. In some examples, the first node may comprise a UE. In some examples, the starting candidate location may correspond to an aggregation level 8 candidate (e.g., corresponding to eight CCEs). Alternatively and/or additionally, the starting candidate location may correspond to an aggregation level 4 candidate (e.g., corresponding to four CCEs). Alternatively and/or additionally, the starting candidate location may correspond to an aggregation level 2 candidate (e.g., corresponding to two CCEs). Alternatively and/or additionally, the starting candidate position may correspond to an aggregation level 1 candidate (e.g., to one CCE). Alternatively and/or additionally, the starting candidate location may correspond to a control region that includes a number of CCEs greater than eight (e.g., 16,32,64, etc.).

In some examples, the first node may randomly select a starting candidate location. For example, the first node may select a starting candidate location based on a hash function. In some examples, the starting candidate location is selected based on the number of CCEs in the control region and/or the aggregation level. In some examples, the hash function may be defined as:

alternatively and/or additionally, the hash function may be defined as:

in some examples, L may represent an aggregation level corresponding to the starting candidate location and/or a number of CCEs included within a control region corresponding to the starting candidate location. Alternatively and/or additionally, L may represent a highest aggregation level and/or a maximum (e.g., highest) number of CCEs corresponding to the candidate pattern, the CCEs corresponding to a control region (e.g., by the first node) determined to include a greater number of CCEs than one or more other control regions associated with the candidate pattern. In some examples, i may be defined as:

i＝0,…,L-1

alternatively and/or additionally, m may be defined as:

m＝0,…,M^(L)-1

in some examples, M^(L)May represent the identified candidate index. Alternatively and/or additionally, k may represent a (e.g., non-negative) time unit index (e.g., an integer). Y is_kCan be defined as:

Y_k＝(A·Y_k-1)modD

in some examples, A,D and/or Y_k-1May be defined by the first node and/or the second node (e.g., network and/or BS) (e.g., a 39827, D65537). Alternatively and/or additionally, Y_k-1May be based on (e.g., 16 bit) Radio Network Temporary Identifier (RNTI) (e.g., n)_RNTI)。

In some examples, the first node may determine a plurality of candidate styles based on the set of bitmaps. The set of bitmaps may correspond to the plurality of candidate styles. For example, a first bitmap in the set of bitmaps may correspond to a first candidate pattern in the plurality of candidate patterns.

The plurality of candidate patterns may correspond to one or more numbers of CCEs (e.g., corresponding to a search space). For example, the plurality of candidate patterns may include an aggregation level 8 pattern (e.g., where each candidate corresponds to eight CCEs), an aggregation level 4 pattern (e.g., where each candidate corresponds to four CCEs), an aggregation level 2 pattern (e.g., where each candidate corresponds to two CCEs), and/or an aggregation level 1 pattern (e.g., where each candidate corresponds to one CCE).

In some examples, the plurality of candidate patterns may include candidate patterns corresponding to control regions including 16 CCEs (e.g., 16 aggregation level 1 candidates, eight aggregation level 2 candidates, four aggregation level 4 candidates, two aggregation level 8 candidates, etc.). Alternatively and/or additionally, the plurality of candidate patterns may include candidate patterns corresponding to a control region including 32 CCEs (e.g., 32 aggregation level 1 candidates, 16 aggregation level 2 candidates, eight aggregation level 4 candidates, four aggregation level 8 candidates, etc.). Alternatively and/or additionally, the plurality of candidate patterns may include candidate patterns corresponding to a control region including a number of CCEs greater than 32 (e.g., 64 CCEs, 128 CCEs, 256 CCEs, etc.).

In some examples, the first node may use the set of bitmaps to determine the plurality of candidate styles. For example, a first bitmap of the set of bitmaps may be used by the first node to determine a first candidate pattern. The first bitmap may include the MIB (e.g., a bit of the first bitmap determined to be associated with a higher importance level than one or more other bits in the first bitmap). The candidate corresponding to the starting candidate location may be determined by the first node (e.g., and/or the second node) based on a value (e.g., 1,0) of the MIB. For example, a candidate corresponding to the starting candidate position may be selected based on the MIB having a first value (e.g., 1). Alternatively and/or additionally, the candidate corresponding to the starting candidate position may not be selected based on the MIB having the second value (e.g., 0).

In some examples, the set of bitmaps includes a first number of bitmaps corresponding to a second number of candidate patterns included in the plurality of candidate patterns. The first number (e.g., of bitmaps) may be equal to the second number (e.g., of candidate patterns). In some examples, the second node (e.g., and/or the first node) may define a set of bitmaps. Alternatively and/or additionally, the second node may transmit a message to the first node comprising a set of bitmaps (e.g., corresponding to one or more control regions and/or one or more aggregation levels). In some examples, the message may include an RRC message and/or a different type of message.

In some examples, the first node may receive the message (e.g., including the set of bitmaps). In some examples, the first node may select one candidate style from a plurality of candidate styles at 110B. In some examples, the first node may randomly select the candidate pattern (e.g., using a hash function). The first node may select a bitmap (e.g., of a set of bitmaps) corresponding to the candidate pattern. The first node may randomly select the bitmap.

In some examples, the candidate pattern and/or the starting candidate location may be used to determine one or more candidates corresponding to a first control region comprising a first number of CCEs and/or one or more candidates corresponding to a first aggregation level. In some examples, the plurality of candidate patterns may include a second candidate pattern for determining one or more candidates corresponding to a second control region including a second number of CCEs and/or one or more candidates corresponding to a second aggregation level.

Alternatively and/or additionally, one or more DMRS channel estimation results may be used to correspond to the first control region and/or (e.g., reused to correspond to) the second control region. Alternatively and/or additionally, one or more DMRS channel estimation results may be used to correspond to a first aggregation level and/or (e.g., reused to correspond to) a second aggregation level.

At 115B, the first node may (e.g., then) perform blind detection on a candidate (e.g., control channel) based on the candidate pattern and/or the starting candidate location. For example, the first node may determine one or more candidates corresponding to a first value (e.g., 1) based on the bitmap. The first node may perform blind detection of the one or more candidates. The first node may determine, based on the bitmap, one or more resources corresponding to a second value (e.g., 0) that are not candidates (e.g., control channel candidates).

An example method 100C of determining candidate styles is shown in fig. 1C. At 105C, the first node may select a starting candidate location associated with the search space. In some examples, the first node may comprise a UE. In some examples, the starting candidate location may correspond to an aggregation level 8 candidate (e.g., corresponding to eight CCEs). Alternatively and/or additionally, the starting candidate location may correspond to an aggregation level 4 candidate (e.g., corresponding to four CCEs). Alternatively and/or additionally, the starting candidate location may correspond to an aggregation level 2 candidate (e.g., corresponding to two CCEs). Alternatively and/or additionally, the starting candidate position may correspond to an aggregation level 1 candidate (e.g., to one CCE). Alternatively and/or additionally, the starting candidate location may correspond to a control region that includes a number of CCEs greater than eight (e.g., 16,32,64, etc.).

alternatively and/or additionally, the hash function may be defined as:

i＝0,…,L-1

alternatively and/or additionally, m may be defined as:

m＝0,…,M^(L)-1

Y_k＝(A·Y_k-1)modD

in some examples, A, D and/or Y_k-1May be defined by the first node and/or the second node (e.g., network and/or BS) (e.g., a 39827, D65537). Alternatively and/or additionally, Y_k-1May be based on (e.g., 16 bit) Radio Network Temporary Identifier (RNTI) (e.g., n)_RNTI)。

In some examples, the first node may determine a candidate pattern based on the starting candidate position and a value at 110C. In some examples, the value may be a (e.g., non-zero) integer. In some examples, the first node may determine a first number of (e.g., randomly selected) candidate locations based on the value. For example, the first node may select one or more candidate locations based on one or more random step sizes (e.g., based on a first number of candidate locations).

In some examples, the first node may determine the second number of candidate locations based on the value. For example, the first node may select one or more candidate locations based on one or more defined step sizes (e.g., based on the second number of candidate locations).

In some examples, the value may be a maximum step size value (e.g., of the one or more random step sizes). Alternatively and/or additionally, the value may be the number of available candidate locations (e.g., at a particular aggregation level and/or control region). Alternatively and/or additionally, the value may be a candidate number configured for the first node. In some examples, the first node may determine the candidate pattern, the first number of candidate locations (e.g., randomly selected), and/or the second number of candidate locations based on: a starting candidate location and/or a set of values comprising a maximum step value, a number of available candidate locations and/or a number of candidates configured for the first node.

In some examples, the first node may determine the candidate style, the first number of candidate locations (e.g., randomly selected), and/or the second number of candidate locations according to a relationship (e.g., a formula) based on a set of values including a maximum step value, a number of available candidate locations, and/or a number of candidate locations configured for the first node. In some examples, the relationship may be defined as:

Q×step_max+(M-Q)≤Z

in some examples, M may represent the number of candidates configured for the first node. Alternatively and/or additionally, Z may represent the number of available candidate positions. Alternatively and/or additionally, step _ max may represent a maximum step value. Alternatively and/or additionally, (e.g. the largest integer value of) Q may represent the first number of (e.g. randomly selected) candidate locations. Alternatively and/or additionally, the second number of candidate locations may be based on the first number of candidate locations (e.g., randomly selected) and/or the number of candidates configured for the first node. For example, the second number of candidate locations may be based on a combination (e.g., a subtraction) of the first number of candidate locations (e.g., randomly selected) and the number of candidates configured for the first node.

In some examples, the first node may select one or more candidate locations (e.g., corresponding to a candidate pattern) using a random step size based on a first number of candidate locations (e.g., randomly selected). Alternatively and/or additionally, the first node may select one or more candidate locations (e.g., corresponding to a candidate pattern) based on the second number of candidate locations using a defined step size.

In some examples, the candidate pattern and/or the starting candidate location may be used to determine one or more candidates corresponding to a first control region comprising a first number of CCEs and/or one or more candidates corresponding to a first aggregation level.

At 115C, the first node may (e.g., then) perform blind detection on a candidate (e.g., control channel) based on the candidate pattern and/or the starting candidate location. The first node may perform blind detection on the one or more candidates.

An example method 100D of selecting a candidate style is shown in fig. 1D. In some examples, the first node may define a set of bitmaps. In some examples, the first node may comprise a BS and/or a network. The set of bitmaps may correspond to a plurality of candidate styles. For example, a first bitmap of the set of bitmaps may correspond to a first candidate pattern of the plurality of candidate patterns. In some examples, the set of bitmaps includes a first number of bitmaps corresponding to a second number of candidate patterns included in the plurality of candidate patterns. The first number (e.g., of bitmaps) may be equal to the second number (e.g., of candidate patterns). Alternatively and/or additionally, the second node may transmit a message to the second node (e.g., UE) including a set of bitmaps (e.g., corresponding to one or more control regions and/or one or more aggregation levels). In some examples, the message may include an RRC message and/or a different type of message. In some examples, the first node may determine the plurality of candidate styles based on the set of bitmaps, at 105D.

In some examples, the first node may select one candidate style from the plurality of candidate styles at 110D. In some examples, the first node may randomly select a candidate pattern (e.g., using a hash function). In some examples, the first node may transmit an indication of a selection corresponding to the candidate pattern to the second node.

At 115D, the first node may (e.g., then) generate an indication corresponding to blind detection of a candidate (e.g., control channel) based on the candidate pattern. At 120D, the first node may (e.g., then) transmit the indication to the second node.

An example method 100E of selecting a candidate style is shown in fig. 1E. At 105E, the first node may select a starting candidate location associated with the search space. In some examples, the first node may comprise a BS and/or a network. In some examples, the first node may randomly select the starting location. For example, the first node may select a starting candidate position based on a hash function. Alternatively and/or additionally, the first node may select the starting candidate position based on: an aggregation level corresponding to the starting candidate location, a number of CCEs included within a control region corresponding to the starting candidate location, a highest aggregation level corresponding to the candidate pattern, a largest (e.g., highest) number of CCEs corresponding to a control region determined (e.g., by the first node) to include a higher number of CCEs than one or more other control regions, an identified candidate index, a time unit index, and/or an RNTI.

In some examples, the first node may define a set of bitmaps. The set of bitmaps may correspond to a plurality of candidate styles. For example, a first bitmap of the set of bitmaps may correspond to a first candidate pattern of the plurality of candidate patterns. In some examples, the set of bitmaps includes a first number of bitmaps corresponding to a second number of candidate patterns included in the plurality of candidate patterns. The first number (e.g., of bitmaps) may be equal to the second number (e.g., of candidate patterns). Alternatively and/or additionally, the second node may transmit a message to the second node (e.g., UE) including a set of bitmaps (e.g., corresponding to one or more control regions and/or one or more aggregation levels). In some examples, the message may include an RRC message and/or a different type of message.

In some examples, the first node may select one candidate style from the plurality of candidate styles at 110E. In some examples, the first node may randomly select the candidate pattern (e.g., using a hash function). In some examples, the first node may transmit an indication of a selection corresponding to the candidate pattern to the second node.

At 115E, the first node may (e.g., then) generate an indication corresponding to blind detection of a candidate (e.g., control channel) based on the candidate pattern and/or the starting candidate location. At 120E, the first node may (e.g., next) transmit the indication to the second node.

An example method 100F of determining candidate styles is shown in fig. 1F. At 105F, the first node may select a starting candidate location associated with the search space. In some examples, the first node may comprise a BS and/or a network. In some examples, the first node may randomly select the starting location. For example, the first node may select a starting candidate location based on a hash function. Alternatively and/or additionally, the first node may select the starting candidate position based on: an aggregation level corresponding to the starting candidate location, a number of CCEs included within a control region corresponding to the starting candidate location, a highest aggregation level corresponding to the candidate pattern, a largest (e.g., highest) number of CCEs corresponding to a control region determined (e.g., by the first node) to include a higher number of CCEs than one or more other control regions, an identified candidate index, a time unit index, and/or an RNTI.

In some examples, the first node may determine a candidate pattern based on the starting candidate position and a value at 110F. In some examples, the first node may determine the value based on a number of available control channel elements and/or a number of available candidate locations (e.g., at a particular aggregation level and/or control region). In some examples, the first node may transmit the value (e.g., within an RRC message and/or a different type of message) to a first node (e.g., a UE). In some examples, the value may be an integer. In some examples, the first node may determine a first number of (e.g., randomly selected) candidate locations based on the value. For example, the first node may select one or more candidate locations based on one or more random step sizes (e.g., based on a first number of candidate locations).

In some examples, the value may be a maximum step size value (e.g., of the one or more random step sizes). Alternatively and/or additionally, the value may be the number of available candidate locations (e.g., at a particular aggregation level and/or control region). Alternatively and/or additionally, the value may be a candidate number configured for the first node. In some examples, the first node may determine the candidate pattern, the first number of candidate locations (e.g., randomly selected), and/or the second number of candidate locations based on a starting candidate location and/or a set of values including a maximum step value, a number of available candidate locations, and/or a number of candidates configured for the first node.

In some examples, the first node may determine the candidate style, the first number of candidate locations (e.g., randomly selected), and/or the second number of candidate locations according to a relationship (e.g., a formula) based on a set of values including a maximum step value, a number of available candidate locations, and/or a number of candidate locations configured for the first node. In some examples, the relationship is defined as:

Q×step_max+(M-Q)≤Z

in some examples, M may represent the number of candidates configured for the first node. Alternatively and/or additionally, Z may represent the number of available candidate positions. Alternatively and/or additionally, step _ max may represent the maximum step value in the set of values. Alternatively and/or additionally, (e.g. the largest integer value of) Q may represent the first number of (e.g. randomly selected) candidate locations. Alternatively and/or additionally, the second number of candidate locations may be based on the first number of candidate locations (e.g., randomly selected) and/or the number of candidates configured for the first node. For example, the second number of candidate locations may be based on a combination (e.g., a subtraction) of the first number of candidate locations (e.g., randomly selected) and the number of candidates configured for the first node.

At 115F, the first node may (e.g., then) generate an indication corresponding to blind detection of a candidate (e.g., control channel) based on the candidate pattern and/or the starting candidate location. At 120F, the first node may (e.g., next) transmit the indication to the second node.

Fig. 2 shows a diagram 200 of one or more candidate (e.g., control channel) patterns. The diagram 200 includes a plurality of (e.g., two) candidate patterns. The plurality of candidate styles may include a first candidate style Pattern0 (style 0) and/or a second candidate style Pattern1 (style 1). The plurality of candidate patterns may correspond to a control region comprising 32 CCEs, wherein a first candidate Pattern0 comprises four aggregation level 8 candidates and/or a second candidate Pattern1 comprises four aggregation level 8 candidates. Each candidate corresponds to eight CCEs.

The first Candidate style Pattern0 includes a first Candidate selection corresponding to a first aggregation level 8 Candidate (e.g., Candidate 0) and/or a second Candidate selection corresponding to a second aggregation level 8 Candidate (e.g., Candidate 1). Based on the first candidate Pattern0, the third aggregation level 8 candidate and/or the fourth aggregation level 8 candidate are not selected.

The second candidate Pattern1 includes a first candidate selection corresponding to the first aggregation level 8 candidate (e.g., candidate 0) and/or a second candidate selection corresponding to the third aggregation level 8 candidate (e.g., candidate 1). Based on the second candidate Pattern1, the second aggregation level 8 candidate and/or the fourth aggregation level 8 candidate are not selected.

Fig. 3 shows a diagram 300 of one or more candidate (e.g., control channel) patterns. The diagram 300 includes a plurality of (e.g., two) candidate patterns. The plurality of candidate styles may include a first candidate style Pattern0 and/or a second candidate style Pattern 1. The plurality of candidate patterns may correspond to a control region including 16 CCEs and/or two aggregation level 8 candidates. The first candidate Pattern0 may include four aggregation level 4 candidates and/or the second candidate Pattern1 may include four aggregation level 4 candidates. Each candidate corresponds to four CCEs. In some examples, one or more (e.g., four) aggregation level 4 candidates are located within one or more (e.g., two) aggregation level 8 candidates of the UE in order to reuse channel estimation results between different aggregation levels (e.g., aggregation level 4 and/or aggregation level 8).

The first candidate Pattern0 includes a first candidate selection corresponding to a first aggregation level 4 candidate (e.g., 1) and/or a second candidate selection corresponding to a second aggregation level 4 candidate (e.g., 1). Based on the first candidate Pattern0, the third aggregation level 4 candidate (e.g., 0) and/or the fourth aggregation level 4 candidate (e.g., 0) are not selected.

The second candidate Pattern1 includes a first candidate selection corresponding to the first aggregation level 4 candidate (e.g., 1) and/or a second candidate selection corresponding to the third aggregation level 4 candidate (e.g., 1). Based on the second candidate Pattern1, the second aggregation level 4 candidate (e.g., 0) and/or the fourth aggregation level 4 candidate (e.g., 0) are not selected.

Fig. 4 shows a diagram 400 of one or more candidate (e.g., control channel) patterns. The graph 400 includes a plurality of (e.g., two) candidate styles. The plurality of candidate styles may include a first candidate style Pattern0 and/or a second candidate style Pattern 1. The plurality of candidate patterns may correspond to a control region including 16 CCEs and/or two aggregation level 8 candidates. The first candidate Pattern0 may include eight aggregation level 2 candidates and/or the second candidate Pattern1 may include eight aggregation level 2 candidates. Each candidate corresponds to two CCEs. In some examples, one or more (e.g., eight) aggregation level 2 candidates are located within one or more (e.g., two) aggregation level 8 candidates in order to reuse channel estimation results between different aggregation levels (e.g., aggregation level 2 and/or aggregation level 8).

The first candidate Pattern0 includes a first candidate selection corresponding to a first aggregation level 2 candidate (e.g., 1), a second candidate selection corresponding to a second aggregation level 2 candidate (e.g., 1), a third candidate selection corresponding to a third aggregation level 2 candidate (e.g., 1), a fourth candidate selection corresponding to a fourth aggregation level 2 candidate (e.g., 1), a fifth candidate selection corresponding to a fifth aggregation level 2 candidate (e.g., 1), and/or a sixth candidate selection corresponding to a sixth aggregation level 2 candidate. Based on the first candidate Pattern0, the seventh aggregation level 2 candidate (e.g., 0) and/or the eighth aggregation level 2 candidate (e.g., 0) are not selected.

The second candidate Pattern1 includes a first candidate selection corresponding to a first aggregation level 2 candidate (e.g., 1), a second candidate selection corresponding to a third aggregation level 2 candidate (e.g., 1), a third candidate selection corresponding to a fifth aggregation level 2 candidate (e.g., 1), a fourth candidate selection corresponding to a sixth aggregation level 2 candidate (e.g., 1), a fifth candidate selection corresponding to a seventh aggregation level 2 candidate (e.g., 1), and/or a sixth candidate selection corresponding to an eighth aggregation level 2 candidate. Based on the first candidate Pattern1, the second aggregation level 2 candidate (e.g., 0) and/or the fourth aggregation level 2 candidate (e.g., 0) are not selected.

Fig. 5 illustrates a diagram 500 of one or more candidate (e.g., control channel) patterns. The diagram 500 includes a plurality of (e.g., two) candidate styles. The plurality of candidate styles may include a first candidate style Pattern0 and/or a second candidate style Pattern 1. The plurality of candidate patterns may correspond to a control region including 16 CCEs and/or two aggregation level 8 candidates. The first candidate Pattern0 may include 16 aggregation level 1 candidates and/or the second candidate Pattern1 may include 16 aggregation level 1 candidates. Each candidate corresponds to a CCE. In some examples, one or more (e.g., 16) aggregation level 1 candidates are located within one or more (e.g., two) aggregation level 8 candidates in order to reuse channel estimation results between different aggregation levels (e.g., aggregation level 1 and/or aggregation level 8).

In some examples, the first candidate style Pattern0 includes six candidate selections corresponding to six aggregation level 1 candidates (e.g., 1). In some examples, ten aggregation level 1 candidates (e.g., 0) are not selected based on the first candidate Pattern 0. In some examples, the second candidate style Pattern1 includes six candidate selections corresponding to six aggregation level 1 candidates (e.g., 1). In some examples, ten aggregation level 1 candidates (e.g., 0) are not selected based on the first candidate Pattern 1.

Fig. 6 shows a diagram 600 of an example of one or more candidate styles and/or one or more bitmaps. In some examples, the one or more bitmaps correspond to the one or more candidate patterns. For example, the first bitmap [ 1100 ] corresponds to the first candidate Pattern0 in the diagram 200. In some examples, the MIB of the first bitmap [ 1100 ] corresponds to the first aggregation level 8 candidate of the first candidate style Pattern0 (e.g., in the diagram 200). Alternatively and/or additionally, the second bitmap [ 1010 ] corresponds to the second candidate Pattern1 in the diagram 200.

In some examples, the third bitmap [ 1100 ] corresponds to the first candidate Pattern0 in the diagram 300. In some examples, the MIB of the third bitmap [ 1100 ] corresponds to the first aggregation level 4 candidate of the first candidate style Pattern0 (e.g., in the diagram 300). Alternatively and/or additionally, the fourth bitmap [ 1010 ] corresponds to the second candidate Pattern1 in the diagram 300.

In some examples, the fifth bitmap [ 11111100 ] corresponds to the first candidate Pattern0 in the diagram 400. In some examples, the MIB of the fifth bitmap [ 11111100 ] corresponds to the first aggregation level 2 candidate of the first candidate Pattern0 (e.g., in diagram 400). Alternatively and/or additionally, the sixth bitmap [ 10101111 ] corresponds to the second candidate Pattern1 in the diagram 300.

In some examples, the seventh bitmap (e.g., aggregation level 1 candidate Pattern 0) corresponds to the first candidate Pattern0 in the diagram 500. In some examples, the seventh MIB corresponds to the first aggregation level 1 candidate of the first candidate style Pattern0 (e.g., in diagram 500). Alternatively and/or additionally, the eighth bitmap (e.g., aggregation level 1 candidate Pattern 1) corresponds to the second candidate Pattern1 in the diagram 500.

Fig. 7 shows a diagram 700 of one or more candidate (e.g., control channel) patterns applied to a control region corresponding to 32 CCEs. Graph 700 may include candidate configurations based on the first bitmap [ 1100 ] in graph 600.

In some examples, a first bitmap [ 1100 ] (e.g., in graph 600) may be applied to the control region. For example, the first bitmap [ 1100 ] may be applied using a randomly selected starting candidate location (e.g., determined using a hash function). For example, a second aggregation level 8 candidate may be selected as the starting candidate location. MIB (e.g., 1) of the first bitmap corresponds to the second aggregation level 8 candidate selected as the starting candidate location. The next bit (e.g., 1) may indicate that the third aggregation level 8 candidate may be selected. The next bit (e.g., 0) may indicate that the fourth aggregation level 8 candidate may not be selected. The next (e.g., last) bit (e.g., 0) may indicate that the first aggregation level 8 candidate may not be selected (e.g., as shown in diagram 700).

Fig. 8 shows a diagram 800 of one or more candidate (e.g., control channel) patterns applied to a control region corresponding to 16 CCEs. Graph 800 may include candidate configurations based on a fourth bitmap [ 1010 ] in graph 600.

In some examples, a fourth bitmap [ 1010 ] (e.g., in graph 600) may be applied to the control region. MIB (e.g., 1) of the first bitmap corresponds to a starting candidate location (e.g., determined using a hash function) for the UE. For example, the fourth bitmap [ 1010 ] may be applied based on a randomly selected starting candidate location (e.g., determined using a hash function). For example, the third aggregation level 4 candidate may be selected as the start candidate position. The MIB (e.g., 1) of the first bitmap may indicate that the third aggregation level 4 candidate may be selected. The next bit (e.g., 0) may indicate that the fourth aggregation level 4 candidate may not be selected. The next bit (e.g., 1) may indicate that the first aggregation level 4 candidate may be selected. The next (e.g., last) bit (e.g., 0) may indicate that the second aggregation level 4 candidate may not be selected (e.g., as shown in diagram 800).

Fig. 9 shows a diagram 900 of one or more candidate (e.g., control channel) patterns applied to a control region corresponding to 16 CCEs. Graph 900 may include candidate configurations based on the sixth bitmap [ 10101111 ] in graph 600.

In some examples, a sixth bitmap [ 10101111 ] (e.g., in graph 600) may be applied to the control region. MIB (e.g., 1) of the first bitmap corresponds to a starting candidate location (e.g., determined using a hash function) for the UE. For example, the sixth bitmap [ 10101111 ] may be applied based on a randomly selected starting candidate location (e.g., determined using a hash function). For example, the third aggregation level 2 candidate may be selected as the starting candidate position. The MIB (e.g., 1) of the first bitmap may indicate that the third aggregation level 2 candidate may be selected. The next bit (e.g., 0) may indicate that the fourth aggregation level 2 candidate may not be selected. The next bit (e.g., 1) may indicate that the fifth aggregation level 2 candidate may be selected. The next bit (e.g., 0) may indicate that the sixth aggregation level 2 candidate may not be selected. The next bit (e.g., 1) may indicate that the seventh aggregation level 2 candidate may be selected. The next bit (e.g., 1) may indicate that the eighth aggregation level 2 candidate may be selected. The next bit (e.g., 1) may indicate that the first aggregation level 2 candidate may be selected. The next bit (e.g., 1) may indicate that the second aggregation level 2 candidate may be selected (e.g., as shown in diagram 800).

Fig. 10 shows a diagram 1000 of one or more candidate (e.g., control channel) patterns applied to a control region corresponding to 16 CCEs. Diagram 1000 may include a candidate configuration based on a seventh bitmap (e.g., aggregation level 1 candidate style 0) in diagram 600.

In some examples, a seventh bitmap (e.g., aggregation level 1 candidate style 0) (e.g., in diagram 600) may be applied to the control region. MIB (e.g., 1) of the first bitmap corresponds to a starting candidate location (e.g., determined using a hash function) for the UE. For example, a seventh bitmap (e.g., aggregation level 1 candidate pattern 0) may be applied using a randomly selected starting candidate position (e.g., determined using a hash function). For example, an eighth aggregation level 1 candidate may be selected as the start candidate position.

Fig. 11 shows a diagram 1100 of a search space containing one or more candidates (e.g., control channels) indicated by one or more bit maps for each aggregation level corresponding to 32 CCEs. In some examples, graph 1100 may include one or more candidate styles in graph 700, graph 800, graph 900, and/or graph 1000. In some examples, the search space is configured to reuse channel estimation results at different aggregation levels (e.g., aggregation level 8, aggregation level 4, aggregation level 2, and/or aggregation level 1).

Fig. 12 shows a diagram 1200 of an example of one or more candidate styles and/or one or more bitmaps. The MIB (e.g., 1) of the bitmap corresponds to a starting candidate location for the UE (e.g., determined using a hash function) or a first candidate location of a corresponding candidate pattern or a first candidate location of a control region. In some examples, the one or more bitmaps correspond to the one or more candidate patterns. In some examples, the one or more bitmaps are based on a control region and/or an aggregation level. For example, the first bitmap [ 11 ] may have a first number of bits (e.g., 2) based on a ratio of the control region (e.g., 16 CCEs) to the aggregation level 8 (e.g., 8 CCEs). Alternatively and/or additionally, the fifth bitmap [ 11000000 ] may have a second number of bits (e.g., 8) based on a ratio of the control region (e.g., 64 CCEs) and the aggregation level 8 (e.g., 8 CCEs).

For example, the first bitmap [ 1100 ] corresponds to the first candidate Pattern0 in the diagram 200. In some examples, the MIB of the first bitmap [ 1100 ] corresponds to the first aggregation level 8 candidate of the first candidate style Pattern0 (e.g., in diagram 200). Alternatively and/or additionally, the second bitmap [ 1010 ] corresponds to the second candidate Pattern1 in the diagram 200.

Fig. 13 shows a diagram 1300 of an example of one or more candidate styles and/or one or more bitmaps. The MIB (e.g., 1) of the bitmap corresponds to a starting candidate location for the UE (e.g., determined using a hash function) or a first candidate location of a corresponding candidate pattern or a first candidate location of a control region. In some examples, the one or more bitmaps correspond to the one or more candidate patterns. For example, the first bitmap [ 110 ] may correspond to a first candidate pattern. Alternatively and/or additionally, the second bitmap [ 101 ] may correspond to a second candidate pattern.

Fig. 14 shows a diagram 1400 of a candidate (e.g., control channel) pattern applied to a first control region corresponding to 24 CCEs, a second control region corresponding to 32 CCEs, and/or a third control region corresponding to 48 CCEs. Diagram 1400 may include candidate configurations based on the second bitmap [ 101 ] in diagram 1300.

In some examples, the second bitmap [ 101 ] corresponds to a particular aggregation level. For example, the second bitmap [ 101 ] may correspond to aggregation level 8. As shown in diagram 1400, when the second bitmap [ 101 ] is applied to one or more control regions (e.g., 24 CCEs, 32 CCEs, 48 CCEs, etc.), only the first aggregation level candidate is selected and/or the third aggregation level candidate is selected (e.g., only). In some examples, the relationship is defined as:

X_k＝((B·X_k-1+L)modR)modN_L

in some examples, k is a current time unit index (e.g., an index value of a candidate pattern determined in the current time unit). In some examples, B is a predefined parameter and/or R is a predefined parameter. In some examples, N is the number of candidate styles defined for a particular aggregation level. In some examples, L is an aggregation level.

Fig. 15 shows a diagram 1500 of candidate (e.g., control channel) patterns. In some examples, a node (e.g., a UE and/or a BS) may determine a candidate pattern based on a maximum step value step _ max, a number of available candidate locations Z, and/or a number of candidates configured for the node M based on the following relationship:

Q×step_max+(M-Q)≤Z

in some examples, the available candidate position Z may be equal to 8. Alternatively and/or additionally, the number of candidates M configured for the node may be equal to 6. Alternatively and/or additionally, the maximum step value step _ max may be (e.g., randomly configured to) equal 2. The first number Q of (e.g., randomly selected) candidate locations may be determined to be equal to 2 based on the relationship.

In some examples, the starting candidate position (e.g., corresponding to candidate index 0) is randomly determined (e.g., using a hash function) as candidate position index 2.

Based on the starting candidate position and/or the (e.g., random) step size, the second candidate position (e.g., corresponding to candidate index 1) may be equal to candidate position index 4:

candidate index 0 position + step _ size 2+ 24 mod 8 4

Based on the second candidate position and/or the second (e.g., random) step size, step _ size, the third candidate position (e.g., corresponding to candidate index 2) may be equal to candidate position index 6:

candidate index 1 position + step _ size 4+ 26 mod 8 6

Based on the defined step size, a fourth candidate location (e.g., corresponding to candidate index 3) may be determined (e.g., depleting the first number Q of candidate locations using random step sizes). Based on the third candidate position and/or the defined step size, the fourth candidate position may be equal to candidate position index 7:

candidate index 2 position + step _ size 6+ 17 mod 8 7

Based on the defined step size, a fifth candidate position (e.g., corresponding to candidate index 4) may be determined. Based on the fourth candidate position and/or the defined step size, the fifth candidate position may be equal to the candidate position index 0:

candidate index 3 position + step _ size 7+ 18 mod 80

Based on the defined step size, a sixth candidate position (e.g., corresponding to candidate index 5) may be determined. Based on the fifth candidate position and/or the defined step size, the sixth candidate position may be equal to the candidate position index 1:

candidate index 4 position + step _ size 0+ 1mod 81

Fig. 16 presents an exemplary architecture diagram 1600 for a base station 1650 that may utilize at least some of the techniques provided herein. Such a base station 1650 may vary widely in configuration and/or capability, alone or in combination with other base stations, nodes, terminal units, servers, and/or the like, to provide services such as at least some of one or more other disclosed techniques, scenarios, and/or the like. For example, base station 1650 can connect one or more User Equipments (UEs) to a (e.g., wireless and/or wired) network (e.g., which can be connected to and/or include one or more other base stations), such as a Code Division Multiple Access (CDMA) network, a Time Division Multiple Access (TDMA) network, a Frequency Division Multiple Access (FDMA) network, an orthogonal FDMA (ofdma) network, a single-carrier FDMA (SC-FDMA) network, and so forth. The network may implement radio technologies such as Universal Terrestrial Radio Access (UTRA), CDMA2000, global system for mobile communications (GSM), evolved UTRA (E-UTRA), IEEE 802.11, IEEE 802.16, IEEE 802.20, flash-OFDM, and so on. The base station 1650 and/or the network may communicate using a standard such as Long Term Evolution (LTE).

Base station 1650 can include one or more (e.g., hardware) processors 1610 that process instructions. The one or more processors 1610 may optionally include a plurality of cores; one or more coprocessors, such as math coprocessors or integrated Graphics Processing Units (GPUs); and/or one or more local cache memory layers. Base station 1650 can include memory 1602 that stores various forms of applications (such as operating system 1604); one or more base station applications 1606; and/or various forms of data such as database 1608 and/or a file system, among others. Base station 1650 can include various peripheral components, such as a wired and/or wireless network adapter 1614 that can connect to a local area network and/or a wide area network; one or more storage components 1616, such as hard disk drives, solid State Storage Devices (SSDs), flash memory devices, and/or magnetic and/or optical disk readers; and/or other peripheral components.

Base station 1650 can include a motherboard featuring one or more communication buses 1612 to interconnect processor 1610, memory 1602, and/or various peripherals using various bus technologies, such as variations of serial or parallel AT attachment (ATA) bus protocols, Universal Serial Bus (USB) protocols, and/or small computer system interface (SCI) bus protocols. In a multi-bus scenario, the communication bus 1612 may interconnect the base station 1650 with at least one other server. Other components that may optionally be included in base station 1650 (although not shown in schematic diagram 1600 of fig. 16) include: a display; a display adapter, such as a Graphics Processing Unit (GPU); input peripherals such as a keyboard and/or mouse; and/or a flash memory device that can store basic input/output system (BIOS) routines that help to start the base station 1650 to a ready state, etc.

Base station 1650 may operate in various physical enclosures such as a desktop or tower and/or may be integrated with a display into an "all-in-one" device. The base station 1650 may be horizontally mounted and/or mounted in a cabinet or rack, and/or may simply comprise a set of interconnected components. Base station 1650 may include a dedicated and/or shared power supply 1618 that supplies and/or regulates power for other components. Base station 1650 may provide power to and/or receive power from another base station and/or a server and/or other device. Base station 1650 may include a shared and/or dedicated climate control unit 1620 that adjusts climate attributes such as temperature, humidity, and/or airflow. A plurality of such base stations 1650 may be configured and/or adapted to utilize at least some of the techniques provided herein.

Fig. 17 presents an exemplary architecture diagram 1700 of a User Equipment (UE)1750 (e.g., a node) upon which at least some of the techniques provided herein may be implemented. Such UEs 1750 vary widely in configuration and/or capabilities in order to provide various functionalities to users. The UE 1750 may be provided in a variety of form factors, such as a mobile phone (e.g., a smartphone); a desktop or tower workstation; an "all-in-one" device integrated with the display 1708; a laptop, a tablet, a convertible tablet, or a palm device; wearable devices, such as may be mounted on headphones, glasses, earphones, and/or a wristwatch, and/or integrated with an article of apparel; and/or a component of a piece of furniture, such as a table top, and/or a component of another piece of equipment, such as a vehicle or residence. The UE 1750 may serve users in various roles, such as a phone, a workstation, a kiosk, a media player, a gaming device, and/or an appliance.

The UE 1750 may include one or more (e.g., hardware) processors 1710 that process the instructions. The one or more processors 1710 optionally may include: a plurality of cores; one or more coprocessors, such as math coprocessors or integrated Graphics Processing Units (GPUs); and/or one or more local cache memory layers. The UE 1750 may include memory 1701 to store various forms of applications (such as an operating system 1703); one or more user applications 1702, such as a document application, a media application, a file and/or data access application, a communication application, such as a web browser and/or email client, a utility, and/or a game; and/or drivers for various peripherals. The UE 1750 may include various peripheral components, such as a wired and/or wireless network adapter 1706 that may connect to a local area network and/or a wide area network; one or more output components, such as a display 1708 coupled to a display adapter (optionally including a Graphics Processing Unit (GPU)), a sound adapter coupled to speakers, and/or a printer; an input device for receiving input from a user, a touch-sensitive component such as a keyboard 1711, a mouse, a microphone, a camera, and/or a display 1708; and/or environmental sensors, such as a GPS receiver that detects the position, velocity, and/or acceleration of the UE 1750, a compass, an accelerometer, and/or a gyroscope that detects the physical orientation of the UE 1750. Other components that may optionally be included in the UE 1750 (although not shown in the schematic architecture diagram 1700 of fig. 17) include: one or more storage components, such as a hard disk drive, a solid State Storage Device (SSD), a flash memory device, and/or a magnetic and/or optical disk reader; a flash memory device that can store basic input/output system (BIOS) routines that assist in starting the UE 1750 to a ready state; and/or a climate control unit that adjusts climate properties such as temperature, humidity and/or airflow, etc.

The UE 1750 may include a motherboard featuring one or more communication buses 1712 that interconnect the processor 1710, the memory 1701, and/or various peripherals using various bus technologies, such as variants of serial or parallel AT attachment (ATA) bus protocols, Universal Serial Bus (USB) protocols, and/or small computer system interface (SCI) bus protocols. The UE 1750 may include a dedicated and/or shared power supply 1718 that supplies and/or regulates power for other components, and/or a battery 1704 that stores power for use when the UE is not connected to a power source via the power supply 1718. The UE 1750 may provide power to and/or receive power from other client devices.

Fig. 18 is a diagram of a scenario 1800 involving an example non-transitory computer-readable medium 1802. The non-transitory computer-readable medium 1802 may include processor-executable instructions 1812 that, when executed by the processor 1816, cause performance (e.g., by the processor 1816) of at least some of the provisions herein (e.g., embodiment 1820). The non-transitory computer-readable medium 1802 may include: memory semiconductors (e.g., semiconductors utilizing Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), and/or Synchronous Dynamic Random Access Memory (SDRAM) technology), hard disk drive disks, flash memory devices, or magnetic or optical disks such as Compact Disks (CDs), Digital Versatile Disks (DVDs), and/or floppy disks. The example non-transitory computer-readable medium 1802 stores computer-readable data 1804, which when read 1806 by a reader 1810 (e.g., a read head of a hard disk drive, or a read operation invoked on a solid state storage device) of the device 1808, represents processor-executable instructions 1812. In some embodiments, the processor-executable instructions 1812, when executed, cause performance of operations, such as, for example, at least some of the example method 100A of fig. 1A, the example method 100B of fig. 1B, the example method 100C of fig. 1C, the example method 100D of fig. 1D, the example method 100E of fig. 1E, and/or the example method 100F of fig. 1F. In some embodiments, the processor-executable instructions 1812 are configured to cause implementation of at least some of a system and/or scenario, such as the example system corresponding to diagram 200 of fig. 2, the example system corresponding to diagram 300 of fig. 3, the example system corresponding to diagram 400 of fig. 4, the example system corresponding to diagram 500 of fig. 5, the example system corresponding to diagram 600 of fig. 6, the example system corresponding to diagram 700 of fig. 7, the example system corresponding to diagram 800 of fig. 8, the example system corresponding to diagram 900 of fig. 9, the example system corresponding to diagram 1000 of fig. 10, the example system corresponding to diagram 1100 of fig. 11, the example system corresponding to diagram 1200 of fig. 12, the example system corresponding to diagram 1300 of fig. 13, the example system corresponding to diagram 1400 of fig. 14, and/or the example system corresponding to diagram 1500 of fig. 15.

As used in this application, the terms "component," "module," "system," "interface," and/or the like are generally intended to refer to a computer-related entity, either hardware, a combination of hardware and software, or software in execution. For example, a component may be, but is not limited to being, a process running on a processor, an object, an executable, a thread of execution, a program, and/or a computer. By way of illustration, both an application running on a controller and the controller can be a component. One or more components can reside within a process and/or thread of execution and a component can be localized on one computer and/or distributed between two or more computers (e.g., nodes).

Unless otherwise stated, "first," "second," and/or the like are not intended to imply temporal, spatial, order, or the like. Rather, such terms are merely used as labels, names, etc. for features, elements, items, etc. For example, the first object and the second object typically correspond to object a and object B or two different or two identical objects or the same object.

Also, "examples," as used herein, is intended to serve as an example, illustration, or the like, and not necessarily to be advantageous. As used herein, "or" is intended to mean an inclusive "or" rather than an exclusive "or". In addition, the use of "a" and "an" in this application is generally understood to mean "one or more" unless specified otherwise or clear from context to be directed to a singular form. Moreover, at least one of A and B and/or similar terms typically refer to either A or B or both A and B. Furthermore, to the extent that "includes," having, "" has, "" with, "and/or variants thereof are used in either the detailed description or the claims, such terms are intended to be inclusive in a manner similar to the term" comprising.

Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example forms of implementing at least some claims.

Furthermore, the claimed subject matter may be implemented as a method, apparatus, or article of manufacture using standard programming and/or engineering techniques to produce software, firmware, hardware, or any combination thereof to control a computer (e.g., a node) to implement the disclosed subject matter. The term "article of manufacture" as used herein is intended to encompass a computer program accessible from any computer-readable device, carrier, or media. Of course, various modifications may be made to this configuration without departing from the scope or spirit of the claimed subject matter.

Various operations of embodiments and/or examples are provided herein. The order of some or all of the operations described herein should not be construed as to imply that these operations are necessarily order dependent. One skilled in the art will understand alternative sequences with the benefit of this description. Further, it will be understood that not all operations are necessarily present in each embodiment and/or example provided herein. Moreover, it will be understood that not all operations are necessarily in some embodiments and/or examples.

Moreover, although the disclosure has been shown and described with respect to one or more implementations, equivalent alterations and modifications will occur to others skilled in the art upon the reading and understanding of this specification and the annexed drawings. The present disclosure includes all such modifications and alterations, limited only by the scope of the following claims. In particular regard to the various functions performed by the above described components (e.g., elements, resources, etc.), the terms used to describe such components are intended to correspond, unless otherwise indicated, to any component which performs the specified function of the described component (e.g., that is functionally equivalent), even though not structurally equivalent to the disclosed structure. In addition, while a particular feature of the disclosure may have been disclosed with respect to only one of several implementations, such feature may be combined with one or more other features of the other implementations as may be desired and advantageous for any given or particular application.

Claims

1. A method, comprising:

determining a plurality of candidate styles based on a set of bitmaps;

selecting a candidate style from the plurality of candidate styles; and

performing blind detection of a candidate control channel based on the candidate pattern.

2. The method of claim 1, wherein the first and second light sources are selected from the group consisting of,

the candidate pattern is randomly selected.

3. The method of claim 1, wherein the first and second light sources are selected from the group consisting of,

the candidate pattern corresponds to a first control region corresponding to a first number of control channel elements.

4. The method of claim 3, wherein the first and second light sources are selected from the group consisting of,

the plurality of candidate patterns includes a second candidate pattern; and is

The second candidate pattern corresponds to a second control region corresponding to a second number of control channel elements.

5. The method of claim 1, wherein the first and second light sources are selected from the group consisting of,

the candidate style corresponds to an aggregation level; and is

The candidate control channels correspond to the aggregation level; and is

The aggregation level corresponds to a first number of control channel elements.

6. The method of claim 5, wherein the first and second light sources are selected from the group consisting of,

the plurality of candidate patterns includes a second candidate pattern; and is

The second candidate pattern corresponds to a second aggregation level.

7. The method of claim 6, wherein the first and second light sources are selected from the group consisting of,

the second aggregation level corresponds to a first number of the control channel elements.

8. The method of claim 6, wherein the first and second light sources are selected from the group consisting of,

the second aggregation level corresponds to a second number of control channel elements.

9. The method of claim 6, comprising:

selecting the second candidate pattern corresponding to the second aggregation level; and

performing blind detection of a second candidate control channel based on the second candidate pattern.

10. The method of claim 1, wherein the first and second light sources are selected from the group consisting of,

the candidate pattern is applied to a first number of control regions corresponding to control channel elements.

11. The method of claim 10, comprising:

applying the candidate pattern to a second control region corresponding to a second number of control channel elements.

12. The method of claim 11, wherein the first and second light sources are selected from the group consisting of,

the first number of control channel elements is equal to the second number of control channel elements.

13. The method of claim 11, wherein the first and second light sources are selected from the group consisting of,

the first number of control channel elements is not equal to the second number of control channel elements.

14. A method, comprising:

selecting a starting candidate location associated with a search space;

selecting a candidate style from a plurality of candidate styles; and

performing blind detection of a candidate control channel based on the candidate pattern and the starting candidate position.

15. The method of claim 14, wherein the first and second light sources are selected from the group consisting of,

the starting candidate position is randomly selected.

16. The method of claim 14, comprising:

determining the plurality of candidate styles based on a set of bitmaps.

17. The method of claim 14, wherein the first and second light sources are selected from the group consisting of,

the candidate pattern is randomly selected.

18. The method of claim 14, wherein the first and second light sources are selected from the group consisting of,

the starting candidate position corresponds to a control channel element.

19. The method of claim 14, wherein the first and second light sources are selected from the group consisting of,

the starting candidate position corresponds to a bit in the bitmap that is determined to be associated with a higher importance level than one or more other bits in the bitmap.

20. The method of claim 14, wherein the first and second light sources are selected from the group consisting of,

21. The method of claim 20, wherein the first and second portions are selected from the group consisting of,

the plurality of candidate patterns includes a second candidate pattern; and is

22. The method of claim 14, wherein the first and second light sources are selected from the group consisting of,

the candidate style corresponds to an aggregation level; and is

The candidate control channels correspond to the aggregation level; and is

23. The method of claim 22, wherein the first and second portions are selected from the group consisting of,

the plurality of candidate patterns includes a second candidate pattern; and is

The second candidate pattern corresponds to a second aggregation level.

24. The method of claim 23, wherein the first and second light sources are selected from the group consisting of,

25. The method of claim 23, wherein the first and second light sources are selected from the group consisting of,

26. The method of claim 14, comprising:

27. The method of claim 26, comprising:

28. The method of claim 27, wherein the first and second light sources are selected from the group consisting of,

29. The method of claim 27, wherein the first and second light sources are selected from the group consisting of,

30. A method, comprising:

selecting a starting candidate location associated with a search space;

determining a candidate pattern based on the starting candidate position and a value; and

31. The method of claim 30, wherein said step of selecting said target,

the starting candidate position is randomly selected.

32. The method of claim 30, wherein said step of selecting said target,

the starting candidate position corresponds to a control channel element.

33. The method of claim 30, comprising:

a first number of candidate locations is determined based on the value.

34. The method of claim 33, comprising:

randomly selecting one or more candidate locations based on the first number of candidate locations.

35. The method of claim 34, wherein said step of selecting said target,

the one or more candidate locations are randomly selected based on a random step size.

36. The method of claim 35, comprising:

a second number of candidate locations is determined based on the value.

37. The method of claim 36, comprising:

selecting one or more candidate locations using a defined step size based on the second number of candidate locations.

38. A method, comprising:

determining a plurality of candidate styles based on a set of bitmaps;

selecting a candidate style from the plurality of candidate styles;

generating an indication corresponding to blind detection of a candidate control channel based on the candidate pattern; and

transmitting the indication to a node.

39. The method of claim 38, comprising:

transmitting the set of bitmaps to the node.

40. The method of claim 38, wherein the first and second portions are selected from the group consisting of,

the candidate pattern is randomly selected.

41. The method of claim 38, wherein the first and second portions are selected from the group consisting of,

42. The method of claim 41, wherein said step of selecting said target,

the plurality of candidate patterns includes a second candidate pattern; and is

43. The method of claim 38, wherein the first and second portions are selected from the group consisting of,

the candidate style corresponds to an aggregation level;

the candidate control channels correspond to the aggregation level; and is

44. The method of claim 43, in which the first and second regions are separated,

the plurality of candidate patterns includes a second candidate pattern; and is

The second candidate pattern corresponds to a second aggregation level.

45. The method of claim 44, wherein said step of selecting said target,

46. The method of claim 44, wherein said step of selecting said target,

47. The method of claim 44, comprising:

48. The method of claim 38, wherein the first and second portions are selected from the group consisting of,

49. The method of claim 48, comprising:

50. The method as set forth in claim 49, wherein,

51. The method as set forth in claim 49, wherein,

52. A method, comprising:

selecting a starting candidate location associated with a search space;

selecting a candidate style from a plurality of candidate styles;

generating an indication corresponding to blind detection of a candidate control channel based on the candidate pattern and the starting candidate position; and

transmitting the indication to a node.

53. In accordance with the method set forth in claim 52,

the starting candidate position is randomly selected.

54. The method of claim 52, comprising:

determining the plurality of candidate styles based on a set of bitmaps.

55. In accordance with the method set forth in claim 52,

the candidate pattern is randomly selected.

56. In accordance with the method set forth in claim 52,

the starting candidate position corresponds to a control channel element.

57. In accordance with the method set forth in claim 52,

58. In accordance with the method set forth in claim 52,

59. In accordance with the method set forth in claim 58,

the plurality of candidate patterns includes a second candidate pattern; and is

60. In accordance with the method set forth in claim 52,

the candidate style corresponds to an aggregation level;

the candidate control channels correspond to the aggregation level; and is

61. In accordance with the method set forth in claim 60,

the plurality of candidate patterns includes a second candidate pattern; and is

The second candidate pattern corresponds to a second aggregation level.

62. In accordance with the method set forth in claim 61,

63. In accordance with the method set forth in claim 61,

64. The method of claim 52, comprising:

65. The method of claim 64, comprising:

66. The method in accordance with claim 65, wherein,

67. The method in accordance with claim 65, wherein,

68. A method, comprising:

determining a starting candidate position associated with the search space;

determining a candidate pattern based on the starting candidate position and a value;

transmitting the indication to a node.

69. In accordance with the method set forth in claim 68,

the value is determined based on the number of available control channel elements.

70. The method in accordance with claim 69,

transmitting the value to the node.

71. In accordance with the method set forth in claim 68,

the starting candidate position is randomly selected.

72. In accordance with the method set forth in claim 68,

the starting candidate position corresponds to a control channel element.

73. The method of claim 68, comprising:

based on the value, a first number of candidate locations is determined.

74. The method of claim 73, comprising:

75. In accordance with the method set forth in claim 74,

76. The method of claim 75, comprising:

based on the value, a second number of candidate locations is determined.

77. The method of claim 76, comprising:

78. A communication device, comprising:

a processor; and

memory including processor-executable instructions that when executed by the processor cause performance of the method of any one of claims 1 to 77.

79. A non-transitory computer readable medium having stored thereon processor-executable instructions that, when executed, cause performance of the method of any one of claims 1-77.