23-06-2014, 10:34 AM
Linear Precoder Designs for Cognitive Radio Multiuser MIMO Downlink Systems
[attachment=65183]
ABSTRACT
In this paper, we develop linear precoding methods for cognitive radio (CR) multi user multiple-input multiple output (MU-MIMO) broadcast systems where unlicensed secondary users (SUs) simultaneously use the same spectrum of the licensed primary user (PU). When the zero-forcing block diagonalization (ZF-BD) precoder is extended to the CR network, a transmit power boost problem occurs. Therefore, we propose a regularized BD precoder based on the minimum mean-square error (MMSE) criteria subject to zero interference constraint for the PU. As a result, the proposed MMSE-BD scheme improves the signal-to-interference-plus-noise ratio at each SU’s receiver compared to the ZF-BD based method. Simulation results demonstrate that the proposed algorithm outperforms the ZF based technique by more than 5 dB at the sum-rate 10 bps/Hz for CR MU-MIMO downlink systems.
INTRODUCTION
COGNITIVE RADIO
Recently, cognitive radio (CR) technology has been studied as a promising solution to tackle the spectrum deficiency problem due to a radio frequency limit. Among diverse wireless technology supporting Internet access and other stream traffic services, a different vision is to integrate different wireless systems/networks and to appropriately use one of them based on the communication environments and the application requirements, based on reconfigurable communication and networking. Cognitive radio pioneered by J. Mitola from software defined radio (SDR) was originally considered to improve spectrum utilization and FCC endorsed such an idea shortly. Upon to this scenario, cognitive radio is primarily a link-level technology for dynamic access of radio spectrum for physical layer radio transmission, as a sort of configurable wireless communication technology.
However, cognitive radio provides not only spectrum advantages but also networking “macro-scale diversity ” above link-layer to bridge our integrated re-configurable system/networking vision. We call such a scenario for future wireless networks as cognitive radio networks (CRN), which is pretty much consistent of Haykin’s definition of cognitive radio. Cognitive radio is an intelligent wireless communication system that is aware of its surrounding environment (i.e., outside world), and uses the methodology of understanding-by-building to learn from the environment and adapt its internal states to statistical variations in the incoming RF stimuli by making corresponding changes in certain operating parameters (e.g., transmit-power, carrier-frequency, and modulation strategy) in real-time, with two primary objectives in mind: highly reliable communication whenever and wherever needed; efficient utilization of the radio spectrum. In other words, once cognitive radios can find the opportunities using the “spectrum holes” for communications, cognitive radio networking to transport packets on top of cognitive radio links is a must to successfully facilitate useful applications and services.
TERMINAL CAPABILITY OF COGNITIVE RADIO NETWORKS
The capabilities of cognitive radios as nodes of CRN can be classified according to their functionalities. A cognitive radio shall sense the environment (cognitive capability ), analyze and learn sensed information (self-organized capability ) and adapt to the environment(reconfigurable capabilities).
Cognitive Capability
Spectrum Sensing
A cognitive radio can sense spectrum and detect “spectrum holes” which are those frequency bands not used by the licensed users or having limited interference with them.
Spectrum Sharing
A cognitive radio could incorporate a mechanism that would enable sharing of spectrum under the terms of an agreement between a licensee and a third party. Parties may
FIGURE 1 UBIOUITOUS COGNITIVE RADIO HETEROGENEOUS
eventually be able to negotiate for spectrum use on an ad hoc or real-time basis, without the need for prior agreements between all parties.
Location Identification
The ability to determine its location and the location of other transmitters, and then select the appropriate operating parameters such as the power and frequency allowed at its location. In bands such as those used for satellite downlinks that receive-only and do not transmit a signal, location technology may be an appropriate method of avoiding interference because sensing technology would not be able to identify the locations of
nearby receivers
ARCHITECTURE OF COGNITIVE RADIO NETWORK
In addition to spectrum sensing to effective improve spectrum utilization, a cognitive radio in CRN can sense available networks and communication sy stems around it. A Cognitive Radio Network (CRN) is thus not just another network to interconnect cognitive radios. The CRNs are composed of various kinds of communication systems and networks, and can be viewed as s sort of heterogeneous networks. The heterogeneity exists in wireless access technologies, networks, user terminals, applications, and service providers . The design of cognitive radio network architecture is toward the objective of improving
the entire network utilization, rather than just link spectral efficiency. From the users perspective, the network utilization means that they can always fulfill their demands anytime and any where through accessing CRNs. From the operators perspective, they can provide better services to mobile users, and allocate radio and network resources to deliver more packets per unit bandwidth in a more efficient way.
Network Architecture
The CRNs can be deployed in network-centric, distributed, ad hoc, and mesh architectures, and serve the needs of both licensed and unlicensed applications. The basic components of CRNs are mobile station (MS), base station/access point (BSs/APs) and backbone/core networks. These three basic components compose three kinds of network architectures in the CRNs: Infrastructure, Ad-hoc and Mesh architectures, which are introduced as follows
MULTI-C HANNEL TRANSMISSION
So far, we have considered the single-channel transmission for both primary and secondary users, and shown that even when some primary users are active for transmission, the secondary user is still able to achieve opportunistic spectrum sharing with active primary users by utilizing multiple transmit antennas and properly designing its transmit spatial spectrum. In this section, a more general multi-channel transmission is studied where both primary and secondary users transmit over parallel single-channels. This scenario is applicable when, e.g., both primary and secondary users transmit over multi-tone or orthogonal-frequency-division-multiplexing
SYSTEM MODEL
We consider a CR system in Fig.1 where a MIMO link for PU coexists with an MU-MIMO downlink system consisting of KSUs. First, the PU’s access point (PU-AP) is licensed to directly communicate with the PU. Then, the unlicensed SU-AP accesses the spectrum of the licensed link assigned to the PUs to broadcast to the K SUs. Here we assume that the PU-AP and the PU have antennas, the SU-AP is equipped with transmit antennas, and the kth SU has receive antennas. The total number of the SU receive antennas denotes
[attachment=65183]
ABSTRACT
In this paper, we develop linear precoding methods for cognitive radio (CR) multi user multiple-input multiple output (MU-MIMO) broadcast systems where unlicensed secondary users (SUs) simultaneously use the same spectrum of the licensed primary user (PU). When the zero-forcing block diagonalization (ZF-BD) precoder is extended to the CR network, a transmit power boost problem occurs. Therefore, we propose a regularized BD precoder based on the minimum mean-square error (MMSE) criteria subject to zero interference constraint for the PU. As a result, the proposed MMSE-BD scheme improves the signal-to-interference-plus-noise ratio at each SU’s receiver compared to the ZF-BD based method. Simulation results demonstrate that the proposed algorithm outperforms the ZF based technique by more than 5 dB at the sum-rate 10 bps/Hz for CR MU-MIMO downlink systems.
INTRODUCTION
COGNITIVE RADIO
Recently, cognitive radio (CR) technology has been studied as a promising solution to tackle the spectrum deficiency problem due to a radio frequency limit. Among diverse wireless technology supporting Internet access and other stream traffic services, a different vision is to integrate different wireless systems/networks and to appropriately use one of them based on the communication environments and the application requirements, based on reconfigurable communication and networking. Cognitive radio pioneered by J. Mitola from software defined radio (SDR) was originally considered to improve spectrum utilization and FCC endorsed such an idea shortly. Upon to this scenario, cognitive radio is primarily a link-level technology for dynamic access of radio spectrum for physical layer radio transmission, as a sort of configurable wireless communication technology.
However, cognitive radio provides not only spectrum advantages but also networking “macro-scale diversity ” above link-layer to bridge our integrated re-configurable system/networking vision. We call such a scenario for future wireless networks as cognitive radio networks (CRN), which is pretty much consistent of Haykin’s definition of cognitive radio. Cognitive radio is an intelligent wireless communication system that is aware of its surrounding environment (i.e., outside world), and uses the methodology of understanding-by-building to learn from the environment and adapt its internal states to statistical variations in the incoming RF stimuli by making corresponding changes in certain operating parameters (e.g., transmit-power, carrier-frequency, and modulation strategy) in real-time, with two primary objectives in mind: highly reliable communication whenever and wherever needed; efficient utilization of the radio spectrum. In other words, once cognitive radios can find the opportunities using the “spectrum holes” for communications, cognitive radio networking to transport packets on top of cognitive radio links is a must to successfully facilitate useful applications and services.
TERMINAL CAPABILITY OF COGNITIVE RADIO NETWORKS
The capabilities of cognitive radios as nodes of CRN can be classified according to their functionalities. A cognitive radio shall sense the environment (cognitive capability ), analyze and learn sensed information (self-organized capability ) and adapt to the environment(reconfigurable capabilities).
Cognitive Capability
Spectrum Sensing
A cognitive radio can sense spectrum and detect “spectrum holes” which are those frequency bands not used by the licensed users or having limited interference with them.
Spectrum Sharing
A cognitive radio could incorporate a mechanism that would enable sharing of spectrum under the terms of an agreement between a licensee and a third party. Parties may
FIGURE 1 UBIOUITOUS COGNITIVE RADIO HETEROGENEOUS
eventually be able to negotiate for spectrum use on an ad hoc or real-time basis, without the need for prior agreements between all parties.
Location Identification
The ability to determine its location and the location of other transmitters, and then select the appropriate operating parameters such as the power and frequency allowed at its location. In bands such as those used for satellite downlinks that receive-only and do not transmit a signal, location technology may be an appropriate method of avoiding interference because sensing technology would not be able to identify the locations of
nearby receivers
ARCHITECTURE OF COGNITIVE RADIO NETWORK
In addition to spectrum sensing to effective improve spectrum utilization, a cognitive radio in CRN can sense available networks and communication sy stems around it. A Cognitive Radio Network (CRN) is thus not just another network to interconnect cognitive radios. The CRNs are composed of various kinds of communication systems and networks, and can be viewed as s sort of heterogeneous networks. The heterogeneity exists in wireless access technologies, networks, user terminals, applications, and service providers . The design of cognitive radio network architecture is toward the objective of improving
the entire network utilization, rather than just link spectral efficiency. From the users perspective, the network utilization means that they can always fulfill their demands anytime and any where through accessing CRNs. From the operators perspective, they can provide better services to mobile users, and allocate radio and network resources to deliver more packets per unit bandwidth in a more efficient way.
Network Architecture
The CRNs can be deployed in network-centric, distributed, ad hoc, and mesh architectures, and serve the needs of both licensed and unlicensed applications. The basic components of CRNs are mobile station (MS), base station/access point (BSs/APs) and backbone/core networks. These three basic components compose three kinds of network architectures in the CRNs: Infrastructure, Ad-hoc and Mesh architectures, which are introduced as follows
MULTI-C HANNEL TRANSMISSION
So far, we have considered the single-channel transmission for both primary and secondary users, and shown that even when some primary users are active for transmission, the secondary user is still able to achieve opportunistic spectrum sharing with active primary users by utilizing multiple transmit antennas and properly designing its transmit spatial spectrum. In this section, a more general multi-channel transmission is studied where both primary and secondary users transmit over parallel single-channels. This scenario is applicable when, e.g., both primary and secondary users transmit over multi-tone or orthogonal-frequency-division-multiplexing
SYSTEM MODEL
We consider a CR system in Fig.1 where a MIMO link for PU coexists with an MU-MIMO downlink system consisting of KSUs. First, the PU’s access point (PU-AP) is licensed to directly communicate with the PU. Then, the unlicensed SU-AP accesses the spectrum of the licensed link assigned to the PUs to broadcast to the K SUs. Here we assume that the PU-AP and the PU have antennas, the SU-AP is equipped with transmit antennas, and the kth SU has receive antennas. The total number of the SU receive antennas denotes