Patent Description:
In wireless telecommunications, a wireless signal being transmitted between a transmitter and receiver generally degrades due to interference from other wireless signals and/or other physical phenomena (e.g. fading and blockage). This has generally been addressed by improving the transmission characteristics (e.g. higher power transmissions or repeaters) or transmission processing techniques (e.g. more robust modulation schemes). An emerging concept in wireless telecommunications is the concept of a reconfigurable propagation environment, or "smart radio environment", which may improve the transmission quality. This may be achieved by use of a surface of electromagnetic material, often known as a Reconfigurable Intelligent Surface (RIS), which may be operated to apply a change to an incident wireless signal, such as a change in phase, amplitude, frequency and polarisation, so as to improve the transmission quality between the transmitter and the receiver.

One use of the RIS is to assist in beamforming between the transmitter and receiver. A conventional method of beamforming in a wireless telecommunications network (that is, a wireless telecommunication network that does not include an RIS) is to use a phased array based hybrid precoder. However, the equipment costs and resource consumption (including energy resource and processing resource) are known to be high compared to wireless telecommunications networks that do not implement beamforming. It has been shown that an RIS may be configured to assist a transmitter to beamform, such as in the Institute of Electrical and Electronics Engineers (IEEE) article, "Reconfigurable Intelligent Surface Based Hybrid Precoding for THz Communications" by Yu Lu and Linglong Dai. As the RIS is nearly passive and easy to deploy, this system is a cost-effective and resource-efficient beamforming solution.

Alternative names for the RIS include large intelligent surface, large intelligent metasurface, programmable metasurface, reconfigurable metasurface, smart reflect-arrays, intelligent reflective surface, software-defined surface, and passive intelligent surface. The term "reconfigurable" is often used to indicate that the angle of reflection can be configured regardless of the angle of incidence. Although most RISs are implemented as reflective surfaces, an RIS may also be a transmissive surface in which the incident wireless signal is transmitted through the surface. However, these have generally not been considered as a feasible technology, especially for telecommunications, due to the propagation loss that is experienced as the wireless signal passes through the RIS.

<CIT> introduces an active intelligent reflection surface into the design of a wireless communication system, assists original signal transmission and further improves frequency spectrum efficiency and energy efficiency of the wireless communication system.

"<NPL>et al. , discloses an Intelligent Reflecting Surface (IRS) which is capable to adjust propagation conditions by controlling phase shifts of the reflected waves that impinge on the surface.

"<NPL> et al. , discloses Reconfigurable Intelligent Surfaces for downlink multi-user communication designed to improve energy collection performance while satisfying wireless information and Power Transfer (WIPT).

"<NPL> ET AL discloses a downlink multi-user Reconfigurable Intelligent Surfaces-assisted communication network.

According to a first aspect of the invention, there is provided a method as claimed in Claim <NUM>.

The beamforming vector, first phase shift matrix, second phase shift matrix and combining vector may be determined as a solution to a wireless communications performance optimisation problem. The wireless communications performance optimisation problem may be solved in a series of iterations until a convergence criterion is met. The convergence criterion may be based on a current wireless communication performance for a current iteration of the series of iterations, and the current wireless communications performance is determined based on: a beamforming vector for the current iteration of the series of iterations, based on the first phase shift matrix of a previous iteration of the series of iterations, the last phase shift matrix of the previous iteration of the series of iterations and the combining vector of the previous iteration of the series of iterations; a first phase shift matrix of the current iteration of the series of iterations, based on the beamforming vector of the previous iteration of the series of iterations, the last phase shift matrix of the previous iteration of the series of iterations and the combining vector of the previous iteration of the series of iterations; a last phase shift matrix of the current iteration of the series of iterations, based on the beamforming vector of the previous iteration of the series of iterations, the first phase shift matrix of the previous iteration of the series of iterations and the combining vector of the previous iteration of the series of iterations; and a combining vector of the current iteration of the series of iterations, based on the beamforming vector of the previous iteration of the series of iterations, the first phase shift matrix of the previous iteration of the series of iterations and the last phase shift matrix of the previous iteration of the series of iterations. The previous iteration may be the immediately preceding iteration.

The combining vector for the current iteration of the series of iterations may be determined as: <MAT> where:.

The first phase shift matrix of the first transmissive RIS layer of the current iteration of the series of iterations may be determined as: <MAT> where:.

The last phase shift matrix of the last transmissive RIS layer of the current iteration of the series of iterations may be determined as: <MAT> Where:.

The beamforming vector of the current iteration of the series of iterations may be determined as: <MAT> where:.

The multi-layer transmissive RIS may include one or more intermediate transmissive RIS layers and the transmission of the wireless signal from the transmitter to the receiver passes through the first transmissive RIS layer, each intermediate transmissive RIS layer and the last transmissive RIS layer, and the method further comprises the steps of: determining an intermediate phase shift matrix for each of the one or more intermediate transmissive RIS layers to be applied by each respective intermediate transmissive RIS layer; and causing the first intermediate transmissive RIS layer to use the determined first intermediate phase shift matrix in the transmission of the wireless signal from the transmitter to the receiver, wherein the current wireless communications performance is determined based on: the beamforming vector for the current iteration of the series of iterations, that is further based on each intermediate phase shift matrix of the previous iteration of the series of iterations, the first phase shift matrix of the current iteration of the series of iterations, that is further based on each intermediate phase shift matrix of the previous iteration of the series of iterations, each intermediate phase shift matrix for the current iteration of the series of iterations, each intermediate phase shift matrix being based on the beamforming vector of the previous iteration of the series of iterations, the first phase shift matrix of the previous iteration of the series of iterations, the last phase shift matrix of the previous iteration of the series of iterations, the combining vector of the previous iteration of the series of iterations and, if there is more than one intermediate transmissive RIS layer, each other intermediate phase shift matrix of the previous iteration of the series of iterations, the last phase shift matrix of the current iteration of the series of iterations, that is further based on each intermediate phase shift matrix of the previous iteration of the series of iterations, and the combining vector of the current iteration of the series of iterations, that is further based on each intermediate phase shift matrix of the previous iteration of the series of iterations.

Each intermediate phase shift matrix for the current iteration of the series of iterations may be determined as: <MAT> where:.

The wireless communications performance problem may be a Signal to Noise Ratio, SNR, optimisation problem.

According to a second aspect of the invention, there is provided a computer program comprising instructions which, when the program is executed by a computer, cause the computer to carry out the steps of the first aspect of the invention. The computer program may be stored on a computer readable carrier medium.

According to a third aspect of the invention, there is provided a device as claimed in Claim <NUM>.

The transmitter and multi-layer RIS may be enclosed in a housing having electromagnetic shielding.

The device may further comprise one or more processing modules configured to carry out the steps of the first aspect of the invention.

According to a fourth aspect of the invention, there is provided a system as claimed in Claim <NUM>.

The system may further comprise one or more processing modules configured to carry out the steps of the first aspect of the invention. The one or more processing modules may be disposed on one or more of a group comprising: the device, the receiver, and an external node.

Embodiments of the present invention provide a system and method for beamforming in a wireless telecommunications network having a Reconfigurable Intelligent Surface (RIS). An overview of the system and a model of its wireless communication performance will be described with reference to <FIG> illustrates a UE, a multi-layer transmissive RIS having R transmissive layers, and a base station. Each transmissive layer of the multi-layer transmissive RIS, indexed by r, has Nr elements, and we assume that each transmissive layer has the same number of elements for simplicity (such that Nr is equal to N for all values of r). The base station has M antennas and the UE has K antennas.

Let <MAT> denote the frequency-domain channel matrix from the UE to the first layer of the multi-layer RIS, <MAT> denote the frequency-domain channel matrix from the (r - <NUM>)-th layer of the multi-layer transmissive RIS to r-th layer of the multi-layer transmissive RIS for all r ∈ {<NUM>,<NUM>,. , R}, and <MAT> denote the frequency-domain channel matrix from the R-th layer of the multi-layer transmissive RIS to the base station. Denote Θr = diag(θr) = diag(θr,<NUM>,. , θr,N) for all r ∈ {<NUM>,<NUM>,. , R} as the phase shift matrix at the multi-layer transmissive RIS's r-th layer, where |θr,n| = <NUM> for all n ∈ {<NUM>,<NUM>,. The signal vector received at the BS can be represented as: <MAT> where w is the beamforming vector with power constraint <MAT> and s is the transmitted symbol. Additive White Gaussian Noise (AWGN) introduced in the base station's decoding satisfies n ~ <IMG>(<NUM>, σ<NUM>IM) where σ<NUM> is the noise power and IM is an identity matrix of size M. The communication performance of the device may be evaluated by the decoding Signal to Noise Ratio (SNR) as: <MAT> where v denotes the combining vector at the base station and <MAT> denotes the equivalent channel between the UE and the base station. In this context, the "equivalent" channel is the overall channel matrix applied to the transmitted signal as it arrives at the base station, based on all channel matrices experienced by the transmitted signal. Assuming that the Channel State Information (CSI) is perfectly known, the beamforming vector w at the UE, the combining vector v at the base station, and the phase shift matrix Θ<NUM>,. , ΘR of the multi-layer transmissive RIS should be jointly optimized to maximize SNR.

Based on this model, an SNR optimisation problem may be formulated as: <MAT>.

A first embodiment of a method of the present invention, discussed below, describes an iterative algorithm for obtaining the optimal solution of all variables to this SNR optimisation problem. Before discussing this method, an embodiment of a cellular telecommunications network of the present invention will be described with reference to <FIG>.

The cellular telecommunications network includes a device <NUM> and a base station <NUM>. The device <NUM> includes a User Equipment (UE) <NUM> and a multi-layer RIS <NUM>, all enclosed within a housing <NUM>, and further includes a processor <NUM>. In this embodiment, the UE <NUM> and each layer of the multi-layer RIS <NUM> are connected to the processor <NUM> via a wired communications link. The multi-layer RIS <NUM> includes a first transmissive RIS <NUM> (being a first layer of the multi-layer RIS <NUM>) and a second transmissive RIS <NUM> (being a second layer of the multi-layer RIS <NUM>). In this embodiment, the UE <NUM> and first transmissive RIS <NUM> are separated by a distance of around <NUM>, the first transmissive RIS <NUM> and second transmissive RIS <NUM> are separated by a distance of around <NUM>, and the second transmissive RIS <NUM> and base station <NUM> are separated by a distance of around <NUM>.

The housing <NUM> includes a first, second, third and fourth elongate supporting member 19a, 19b, 19c, 19d and a first, second, third and fourth wall 19e, 19f, <NUM>, <NUM>, such that the housing <NUM> has an open-ended rectangular cuboidal shape (the open-ends being the faces of the rectangular cuboidal shape that are not defined by the walls 19e, 19f, <NUM>, <NUM>). The first, second, third and fourth walls 19e, 19f, <NUM>, <NUM> are constructed of a radio-frequency absorbing material. This radio-frequency absorbing material should be chosen to protect the stability of the internal channels (that is, f<NUM> to fR) such as by absorbing any radio-frequencies from external sources that would otherwise cause interference. Furthermore, this absorbing material should avoid undesired diffraction or reflection.

The UE <NUM> includes a first and second transceiver 11a, 11b arranged as a Uniform Linear Array (ULA). The base station <NUM> includes a first, second, third, fourth, fifth and sixth transceiver 21a, 21b, 21c, 21d, 21e, 21f, also arranged as a ULA. The UE <NUM> and base station <NUM> may communicate with each other using a cellular telecommunications protocol, such as the <NUM>th Generation (<NUM>) protocol defined by the <NUM>rd Generation Partnership Project (3GPP). An uplink wireless signal from the UE <NUM> to the base station <NUM> may be transmitted through the first transmissive RIS <NUM>, then through the second transmissive RIS <NUM>, and then to the base station <NUM>.

The first transmissive RIS <NUM> includes a plurality of elements <NUM>, each of which may be controlled by processor <NUM> to apply a particular phase shift to a wireless signal passing through that element <NUM>. Similarly, the second transmissive RIS <NUM> includes a plurality of elements <NUM>, each of which may be controlled by processor <NUM> to apply a particular phase shift to the wireless signal passing through that element <NUM>. In this embodiment, a count of elements in the plurality of elements <NUM> for the first transmissive RIS <NUM> is equal to a count of elements in the plurality of elements <NUM> for the second transmissive RIS <NUM>. <FIG> illustrates the first transmissive RIS <NUM> and second transmissive RIS <NUM> both having a square form, but the skilled person will understand that this is non-essential.

The first embodiment of the method of the present invention will now be described with reference to the flow diagram of <FIG>. As noted above, this embodiment provides an iterative algorithm to obtain an optimal solution of all variables of the SNR optimisation problem. This is achieved by decoupling the coupled variables - the beamforming vector w, the combining vector v, and the phase shift matrices Θr - and alternately optimising each variable with the other variables fixed. The algorithm will be described for a multi-layer RIS having R layers (including a first layer, a last layer, and optionally one or more intermediate layers between the first and last layers), which can be applied to the two-layer RIS of the first embodiment of the cellular network described above.

In a first step, S101, the processor <NUM> determines values for a plurality of input parameters, including channel matrices f<NUM>. fR and g and the maximum transmit power Pmax, that may be considered as fixed input parameters throughout a single performance of this embodiment. As the space between the UE <NUM> and the first layer of the multi-layer RIS (that is, the first transmissive RIS <NUM> in the first embodiment above) and the space between each layer of the multi-layer RIS are fixed and substantially shielded from electromagnetic fields (other than those produced by the UE <NUM>) by the device's housing <NUM>, then channel matrices f<NUM>. fR are considered near-field static channels that are therefore unchanging following installation. Accordingly, f<NUM>. fR may be measured and recorded during installation of the device <NUM>. The channel matrix g is a far-field channel which may change with time and/or location. This may be measured by conventional channel estimation and channel feedback mechanisms. The value for the maximum transmit power Pmax is, in this embodiment, stored in the UE <NUM> and may be retrieved by the processor <NUM>.

In step S103, the processor <NUM> sets initial values for a plurality of variable input parameters. In this embodiment, the processor <NUM> sets w and v at <NUM> and generates initial values for Θ<NUM>. ΘR randomly. The processor <NUM> then enters an iterative loop. In step S105, the processor <NUM> updates its value for the combining vector v based on equation <NUM>-<NUM> below and its current values for all other variable parameters (being the initial values determined in step S103 on the first iteration, or the values determined in the previous iteration of the iterative loop on subsequent iterations). For the combining vector, v, the SNR optimisation problem can be reformulated as: <MAT> where <MAT> is a positive definite matrix. So the maximum of SNR is the largest eigenvalue of A, and the corresponding v is written as <MAT> Where:.

In step S107, the processor <NUM> updates its value for Θ<NUM>, the phase shift matrix for the first transmissive-RIS layer of the multi-layer RIS, based on equation <NUM>-<NUM> below and its current values for all other variable parameters (being the initial values determined in step S103 on the first iteration, or the values determined in the previous iteration of the iterative loop on subsequent iterations). For Θ<NUM>, the SNR optimisation problem can be reformulated as: <MAT>.

Because θ<NUM> is a vector whose components all have norm <NUM>, the optimal value for θ<NUM> is <MAT> Where:.

If the multi-layer RIS is comprised of two layers only, then the process skips to step S111. If the multi-layer RIS is comprised of three or more layers, then, in step S109, the processor <NUM> updates its value for θl, where l = <NUM>,. , R - <NUM>, being the phase shift matrix for each intermediate transmissive layer of the multi-layer RIS based on equation <NUM>-<NUM> below and its current values for all other variable parameters (being the initial values determined in step S103 on the first iteration, or the values determined in the previous iteration of the iterative loop on subsequent iterations). For θl, the SNR optimization problem can be reformulated as: <MAT>.

Because θl is a vector whose components all have norm <NUM>, the optimal value for θl is <MAT> Where:.

In step S111, the processor <NUM> updates its value for ΘR, the phase shift matrix for the last transmissive layer of the multi-layer RIS, based on equation <NUM>-<NUM> below and its current values for all other variable parameters (being the initial values determined in step S103 on the first iteration, or the values determined in the previous iteration of the iterative loop on subsequent iterations). For θR, the SNR optimization problem can be reformulated as: <MAT>.

Because θR is a vector whose components all have norm <NUM>, the optimal value for θR is <MAT> Where:.

In step S113, the processor <NUM> updates its value for the beamforming vector w based on equation <NUM>-<NUM> below and its current values for all other variable parameters (being the initial values determined in step S103 on the first iteration, or the values determined in the previous iteration of the iterative loop on subsequent iterations). The optimal solution of normalized w, that is, <MAT>, is: <MAT>.

Considering the constraint <MAT>, the optimal solution of w is <MAT> Where:.

In step S115, the processor <NUM> estimates the SNR of a transmission from the device <NUM> to the base station <NUM> using the values for w, v, and Θr determined in steps S105 to S113 above for the current iteration. This SNR estimate is stored in memory, together with an iteration identifier. In step S117, the processor <NUM> evaluates the SNR estimates to determine whether a convergence condition has been satisfied. In this embodiment, the convergence condition is that the difference between the estimated SNR for the current iteration and the estimated SNR for the previous iteration is less than or equal to <NUM>%. If the convergence condition has not been satisfied (such as when there has only been one iteration of the algorithm), then the process loops back to step S105 for a further iteration. Once the convergence condition has been satisfied, then the processor <NUM> has determined final values of w, v, and Θr as those generated in the final iteration of the algorithm. The process then proceeds to step S119 in which the processor <NUM> causes the UE <NUM> to use beamforming vector w in transmissions of wireless signals from the device <NUM> to the base station <NUM>, causes each layer of the multi-layer RIS to use its respective phase shift matrix Θr (that is, causes the first transmissive RIS <NUM> to use Θ<NUM> and causes the second transmissive RIS <NUM> to use Θ<NUM>) to apply phase shifts to the wireless signals passing through that layer, and causes the base station <NUM> to use the combining vector v to receive the wireless signals transmitted from the device <NUM> to the base station <NUM>.

In this embodiment, the processor <NUM> implements a processor module for the UE and each layer of the multi-layer RIS, wherein the processing modules are configured to apply the beamforming vector to the UE's transmitted wireless signal or apply the respective phase shift matrix to the wireless signal as it passes through a layer of the multi-layer RIS. The base station <NUM> may also include a processing module for applying the combining vector to the received wireless signal. The processing modules for applying the phase shift matrices may be based on a Field Programmable Gate Array (FPGA) architecture in similar manner to that described in "Programmable artificial intelligence machine for wave sensing and communications", Che Lui et al.

Subsequently, the process may be triggered again (with new input values, particularly for g as the channel between the multi-layer RIS and the base station <NUM> changes over time) to determine new values for w, v, and Θr. The trigger may be present in a signal from the base station or UE indicating that g has been updated.

In summary, the iterative algorithm may be defined as:.

The performance of the above method, when applied to the cellular network of <FIG> having a two-layer RIS, can be evaluated by simulation, as shown in the <FIG> and <FIG>.

<FIG> illustrates a simulation of the cellular telecommunications network <NUM> of <FIG>, in which the UE includes two antennas in a ULA, the multi-layer transmissive RIS includes two layers each having <NUM> elements, and the base station includes <NUM> antennas in a ULA. The UE and first layer of the multi-layer RIS are separated by <NUM>, the first layer of the multi-layer RIS and the second layer of the multi-layer RIS are separated by <NUM>, and the second layer of the multi-layer RIS and the base station are separated by <NUM> (such that the UE and base station are separated by <NUM>). The UE transmits at a frequency of <NUM>. The loss factor when a wireless signal passes through each layer of the multi-layer RIS is <NUM>.

<FIG> illustrates a simulation of an alternative cellular telecommunications network, for the purposes of comparison, in which the UE includes two antennas in a ULA, the RIS is a single-layer RIS having <NUM> elements, and the base station includes <NUM> antennas in a ULA. The UE and single-layer RIS are separated by <NUM>, and the single-layer RIS and base station are separated by <NUM> (such that the UE and base station are again separated by <NUM>). The UE transmits at a frequency of <NUM>. This simulation may be used for both a single-layer transmissive RIS (in which the loss factor when the wireless signal passes through the single-layer transmissive RIS is <NUM>) or a single-layer reflective RIS, as the channel models are the same.

<FIG> illustrates a simulation of another alternative cellular telecommunications network, for the purposes of comparison, in which the UE includes two antennas in a ULA and the base station includes <NUM> antennas in a ULA. There is no RIS in this alternative network. The UE and base station are again separated by <NUM>.

<FIG> is a graph illustrating the decoding SNR against the power constraint, Pmax for the four scenarios covered by these simulations, <NUM>) the alternative network having no RIS, <NUM>) the alternative network having a single-layer transmissive RIS, <NUM>) the alternative network having a single-layer reflective RIS, and <NUM>) the first embodiment of the network of the present invention having a two-layer transmissive RIS. It can be seen that the cellular network of the embodiment of the present invention has the best wireless communication performance with a SNR improvement of around 4dB above the next best performing scenario (the alternative network having a single-layer reflective RIS). The alternative network having a single-layer transmissive RIS scenario had an SNR around 2dB lower than the alternative network having a single-layer reflective RIS, and the alternative network having no RIS was the worst performing scenario. These simulation results illustrate the improvement in wireless communication performance provided by the above embodiments of the invention relative to traditional RIS-based networks. Furthermore, these simulations are based on a two-layer RIS having half the number of elements compared to the single-layer reflective RIS and single-layer transmissive RIS, illustrating the cost-efficiency and power-efficiency benefits of the present invention (as the material costs and power requirements of a RIS are proportional to the number of elements).

The benefits of the two-layer transmissive RIS relative to a single-layer RIS architecture can also be understood on review of the power distribution of each architecture. <FIG> includes a power distribution chart of a single-layer transmissive RIS and <FIG> includes a first power distribution chart of a first layer of the two-layer transmissive RIS and a second power distribution chart of a second layer of the two-layer transmissive RIS. It can be seen by comparison of <FIG> that the element utilisation of the RIS is comparatively low for the single-layer transmissive RIS compared to the two-layer transmissive RIS. In other words, only a small proportion of the elements of the single-layer transmissive RIS are utilised compared to the proportion of elements of both layers of the two-layer transmissive RIS. It can also be seen from <FIG> that the power distribution on the second layer of the two-layer transmissive RIS is relatively smooth compared to the first layer.

In the above embodiments, the processor communicates with the UE and each layer of the multi-layer RIS by a wired connection. However, this is non-essential and the processor may communicate with the UE and each layer of the multi-layer RIS via a wireless connection. It is also non-essential that the steps of the method of the present invention are carried out by a single processor. That is, the processing may be performed by the UE, by the base station, by an external node, or in a distributed manner, with suitable wired or wireless communication links between the various elements.

The skilled person will also understand that the present invention may be applied to a downlink communication (that is, from the base station to the UE) where the UE receives the downlink communication through each layer of the multi-layer RIS. Furthermore, the present invention is not limited to a cellular telecommunications example, such that it may be applied to a wireless signal between a transmitter and a receiver in other forms of wireless telecommunications network, such as between a transmitter and receiver of a wireless local area network or a wireless wide area network. Nonetheless, the present invention has particular benefits when applied to a wireless signal being transmitted from a low-powered and/or low-cost device, such as a UE, where conventional beamforming techniques are prohibitively costly and/or resource intensive. Furthermore, the skilled person will understand that this invention may be applied to transmissions from a single transmitter to multiple receivers.

In the above embodiments, the UE had two antennas in a ULA and the base station had six antennas in a ULA. The skilled person will understand that this is non-essential and different antenna configurations are possible. That is, the channel matrices between the UE and first transmissive RIS layer of the multi-layer RIS is sufficiently static such that the channel matrix may be measured and recorded at installation and thereafter used in the algorithm. Furthermore, the channel matrix between the last transmissive RIS layer and the base station is sufficiently far field that it is not affected by a change in antenna configuration and, in any event, are measured by a channel estimation process.

The skilled person will also understand that the present invention is not limited to optimising SNR performance, and other wireless communications performance metrics may be used, such as latency or throughput.

In the above embodiment, each layer of the multi-layer transmissive RIS had the same number of elements. The skilled person will understand that the present invention is not limited in this way and can be applied to multi-layer RISs where one or more layers have a differing number of elements. This would merely result in a change in the dimensions of the matrices and potentially a change in the form of the device housing (e.g. from a cuboidal shape to a flared structure).

Claim 1:
A method of controlling a transmission of a wireless signal in a wireless telecommunications network, the wireless telecommunications network including a transmitter (<NUM>), a multi-layer transmissive Reconfigurable Intelligent Surface, RIS, including a first transmissive RIS layer (<NUM>) and a last transmissive RIS layer (<NUM>), and a receiver (<NUM>), the method comprising the steps of:
determining a beamforming vector to be applied by the transmitter (<NUM>), a first phase shift matrix to be applied by the first transmissive RIS layer (<NUM>), a last phase shift matrix to be applied by the last transmissive RIS layer (<NUM>), and a combining vector to be applied by the receiver (<NUM>); and, in a transmission of a wireless signal from the transmitter (<NUM>) to the receiver (<NUM>), the wireless signal passing through the first transmissive RIS layer (<NUM>) and the last transmissive RIS layer (<NUM>),
causing the transmitter (<NUM>) to use the determined beamforming vector,
causing the first transmissive RIS layer (<NUM>) to use the determined first phase shift matrix,
causing the last transmissive RIS layer (<NUM>) to use the determined last phase shift matrix, and
causing the receiver (<NUM>) to use the determined combining vector.