Downlink joint transmission by a cluster of remote radio-heads (RRHs) is essential for enhancing throughput in future cellular networks. This method requires global channel state information (CSI) at the processing unit that designs the joint precoder. To this end, extensive CSI must be shared between the RRHs and that unit. This paper proposes two contributions. The first is a new upper bound on the rate loss, which implies a lower bound on the achievable rate for an RRHs-cluster employing joint zero-forcing (ZF) with incomplete CSI with single-user decoding (SUD) at the mobile stations (MSs). The second contribution, which follows insights from the bound, is a new CSI sharing scheme that drastically reduces the significant overhead associated with acquiring global CSI for joint transmission. In a nutshell, each RRH applies a local precoding matrix that creates low-dimensional effective channels that can be quantized more accurately with fewer bits, thereby reducing the overhead of sharing CSI. In addition to the CSI sharing-overhead, this scheme reduces the data rate delivered to each RRH in the cluster.

Joint transmission by a cluster of smart remote radio heads is an important technique for enhancing network throughout in future cellular networks. This transmission requires global channel state information at the processing unit which optimizes the joint precoder. To this end, a large amount of channel state information must be shared between the smart remote radio heads and that unit. In this paper, we present a new channel state information sharing scheme, which drastically reduces the large overhead in acquiring global channel state information for joint transmission. The idea is that each smart remote radio head applies a local precoding matrix, whose objective is to create equivalent channels which can be quantized more accurately with fewer bits, thereby to reduce the overhead of sharing channel state information. The proposed scheme facilitate joint transmission in a cluster in which the smart remote radio heads are interconnected by limited-rate link.

Interference mitigation is central in enhancing the total throughput of wireless systems. Efficient mitigation requires, however, accurate channel state information, which puts a heavy load on the wireless control channels. In this paper, we focus on the multiple-input single-output (MISO) interference channel, and propose a novel enhanced CSI feedback scheme that reduces the feedback overhead required for conveying cross-link CSI to the transmitters. For simplicity, we consider a two-user setting, where single user decoding is employed while treating interference as noise. The proposed scheme creates, by projecting the original channels into an appropriately chosen low-dimensional subspace, an effective cross-link channel which can be quantized more accurately for a given number of quantization bits. The idea is to choose the new subspace such that the effective cross-link channel, as well as the direct channel, maintain their array gain. We show via a comprehensive numerical study that the proposed scheme offers a significant reduction of CSI feedback overhead. By providing much more accurate CSI at the transmitters, while maintaining high array gains, the scheme allows for enhanced interference mitigation, and increases the total network throughput.

This paper studies the Rayleigh fading two-user multiple-input single-output interference channel with single user decoding and limited channel state information (CSI) feedback. Two contributions are presented. First, the achievable rate-region with partial CSI at the transmitters due to channel quantization errors is analyzed. We derive an analytically tractable inner bound on that region, which provides insights into the problem. It is shown that, similarly to the case of perfect transmitter CSI, beamforming is optimal for achieving every boundary point of the inner bound. The second contribution is a novel CSI feedback scheme that reduces the cross-link CSI feedback overhead, and enhances throughput for any rate-limited feedback scheme. Such feedback reduction is crucial when control channels for sharing information between transmitters and unintended receivers are rate limited. The proposed scheme takes feedback into account already at the channel estimation stage. While maintaining a high array gain in the direct channel towards the intended user, the scheme induces an effective low-dimensional cross-link channel, which can be quantized more accurately than the full-dimensional channel, leading to enhanced performance.

We present schemes for acquiring the null space of the interference channel between a User Equipment (UE) and an interfering Base Station Group (BSG) in Cooperative Multi-point cellular networks, whose only required network information is the interference levels at the UE. Specifically, the interfering BSG transmits a sequence of learning signals which inflicts interference on a UE served by a neighboring BSG. The UE treats interference as noise, measures its overall interference plus noise power, and feeds this value back to its serving BSG. Then, the latter distributes this information to the interfering BSG, from which it learns the null space of the interfering channel. We also present a null space tracking algorithm, whose performance includes an inherent tradeoff between the accuracy of the null space learning and the inflicted interference during learning, and characterize analytically and via simulations its performance under channel variations and noisy measurements. The proposed algorithms do not affect the transmission protocol between the UE and the serving BSG, do not add any signaling to the control channel between them, and do not require any protocol changes from the UE side.

This paper studies the problem of competitive spectrum management in the presence of channel estimation errors. In particular, we study the effect of the channel estimation error on the Bayesian Interference Game (BIG), in which two selfsh wireless systems (players) share the same frequency band, where each player knows its own channel gains but does not know the other players channel gains. In the case where the channel is estimated perfectly, the BIG is known to have a spectrally effcient equilibrium point, which produces a higher payoff to both players than the trivial equilibrium, in which both players always interfere with each other. However, the assumption that each player knows its own channel gains impeccably is not practical due to estimation error. The latter leads to payoff perturbations, which can reduce spectral effciency by driving the spectrally effcient equilibrium point unstable. In this paper, we show that the spectral effciency is robust to small estimation errors; i.e., the BIGs spectrally effcient equilibrium point preserves its properties in the presence of estimation errors.

Cooperative Multi-Point (CoMP) has emerged as a new paradigm to improve both average cell and cell edge throughput in cellular networks. However, the performance is significantly degraded due to Out-of-Group Interference (OGI). One way to mitigate OGI is to restrict the interfering signal to lie inside the null space of the unintended receiver. Yet, accurately tracking this null space is a challenge in time-varying channels. In this work, we address the effect of null space variations on the OGI mitigation. A measure for accuracy of null space estimates is proposed and bounds are derived on the residual average worst-case interference. We define the Null Space Update Rate as the inverse of the Null Space Coherence Time; i.e., the time it takes a null space estimate to become outdated relative to an interference threshold on the unintended receiver. In the case of Rayleigh fading channels, we derive a bound on the Null Space Coherence Time in closed form, and compare the performance of a given null space tracking algorithm against this bound. Both Monte Carlo simulations and the analytical bounds show that the worst-case interference levels are sensitive to null space variations, independent of the null space learning algorithm employed.

We propose a learning technique for MIMO communication systems to perform spatial division multiple access with minimal cooperation between users. In the proposed technique, each user (in a two-user receiver-transmitter pair) learns the null space of the interference channel to the other user by transmitting a learning signal and observing an affine function of the other user's interference plus noise power. The only requirement is that each system broadcasts, through a low-rate control channel, a periodic beacon that is a function of its noise plus interference power, which in practice is typically known by each system's receiver and transmitter. Thus, the learning can be made by the two users' transmitters without affecting the communication protocol between each user's receiver and transmitter. The proposed learning scheme is particularly attractive for underlay cognitive radio, where only the secondary user (SU), which must not interfere with the primary user (PU), has to learn the null space. In this case, the PU can broadcast the scheme's beacon without being aware of the SU. Furthermore, if the PU uses a power control mechanism which maintains a constant signal to interference plus noise ratio, the SU can learn the null space even without a beacon, i.e., without any cooperation with the PU.

Cooperative Multi-Point is a technology for improved spectrum utilization where a group of neighboring base stations coordinate their transmissions. However, the performance gain is severely degraded due to out-of-group interference. We propose simple schemes for null space learning and tracking of the interference channel between a terminal and an interfering base station group, whose only required network information is the interference levels at the terminal. In the proposed schemes, the interfering base station group transmits a sequence of learning signals which inflict low interference on a terminal of a neighboring group. The schemes utilize the fact that each terminal normally measures its interference levels and feeds them back to its serving base stations. Then, the serving group distributes this information to the interfering group, from which it learns the null space of the interfering channel. The proposed algorithms do not affect the transmission protocol between the terminal and the serving base stations, and do not add any signaling to the control channel between them. Each terminal communicates only with its serving group and treats the learning signals of the interfering group as noise.

Gene expression analysis is generally performed on heterogeneous tissue samples consisting of multiple cell types. Current methods developed to separate heterogeneous gene expression rely on prior knowledge of the cell-type composition and/or signatures--these are not available in most public datasets. We present a novel method to identify the cell-type composition, signatures and proportions per sample without need for a-priori information. The method was successfully tested on controlled and semi-controlled datasets and performed as accurately as current methods that do require additional information. As such, this method enables the analysis of cell-type specific gene expression using existing large pools of publically available microarray datasets.

This paper proposes a blind technique that enables a MIMO cognitive radio Secondary User (SU) to transmit in the same band simultaneously with a Primary User (PU) by utilizing separate spatial dimensions than the PU. Specifically, the SU transmits in the null space of the interference channel to the PU. The SU learns this null space without burdening the PU with any knowledge or explicit cooperation with the SU. The only condition required is that during the learning period, the SU is allowed to inflict “non-harmful” interference to the PU. The SU measures a monotonic function of this interference in order to learn the null space. Specifically, during the learning interval, the SU learns the null space by iteratively modifying the spatial orientation of its transmitted signal and measures the effect of this modification on the monotonic function that it observes. We provide simulation results demonstrating that the algorithm converges rapidly and is robust to quantization noise and other sources of interference.

Blind Null Space Learning [1] has recently been proposed for fast and accurate learning of the null-space associated with the channel matrix between a secondary transmitter and a primary receiver. In this paper we propose a channel tracking enhancement of the algorithm, namely the Blind Null Space Tracking algorithm, that allows transmission of information to the Secondary Receiver while simultaneously learning the null-space of the time-varying target channel. Specifically, the enhanced algorithm initially performs a sweep in order to acquire the null space. Then, it performs modified Jacobi rotations such that the induced interference is kept lower than a given threshold PThwith probability p while information is transmitted to the secondary receiver simultaneously. The learning process is performed based on sensing whether the transmit power of the primary user has increased or decreased between adaptations. We present simulation results indicating that the proposed approach has strictly better performance over the Blind Null Space Learning algorithm for channels with independent Rayleigh fading at a low Doppler frequency.

This paper proposes a blind technique for MIMO cognitive radio Secondary Users (SU) to transmit in the same band simultaneously with a Primary User (PU) under a maximum interference constraint. In the proposed technique, the SU is able to meet the interference constraint of the PU without explicitly estimating the interference channel matrix to the PU and without burdening the PU with any interaction with the SU. The only condition required of the PU is that for a short time interval it uses a power control scheme such that its transmitted power is a monotonic function of the interference inflicted by the SU. During this time interval, the SU iteratively modifies the spatial orientation of its transmitted signal and measures the effect of this modification on the PU's total transmit power. The entire process is based on energy measurements which is very desirable from an implementation point of view. The scheme can also be used as a multiple access technique in networks where users have equal priority, however, active users are protected from interference by new users.

We propose a learning technique for MIMO secondary users (SU) to spatially coexist with Primary Users (PU). By learning the null space of the interference channel to the PU, the SU can utilize idle degrees of freedom that otherwise would be unused by the PU. This learning process does not require any handshake or explicit information exchange between the PU and the SU. The only requirement is that the PU broadcasts a periodic beacon that is a function of its noise plus interference power, through a low rate control channel. The learning process is based on energy measurements, independent of the transmission schemes of both the PU and SU, i.e. independent of their modulation, coding etc. The proposed learning technique also provides a novel spatial division multiple access mechanism for equal-priority MIMO users sharing a common channel that highly increases the spectrum utilization compared to time-or frequency-base multiple access.

This paper considerers a symmetric Gaussian interference game with incomplete information where players choose between frequency division multiplexing (FDM) and full spread (FS) of their transmit power. Previously, the only known Nash equilibrium point for this game was the point where players mutually choose FS and interfere with each other. This point may lead to undesirable outcome from global network point of view and even for each user individually. It happens when mutual FDM is better to both users than mutual FS. In this paper, we show that if users agree to use different sub-bands in the case of FDM, then there exist a non pure-FS Nash equilibrium point, i.e. an equilibrium point where players choose FDM for some channel realizations and FS for the others. This Nash equilibrium point increases each user's throughput and therefore improves the spectrum utilization. Furthermore, to reach this point, the only instantaneous channel state information (CSI) required by each user is its interference-to-signal ratio.

In this letter we propose a novel distributed technique for dynamic spectrum management of DSL lines. The proposed method generalizes several known techniques, by imposing pricing for use of spectrum. We propose a simple mechanism that allows each line to choose an appropriate pricing function independently of the other lines. Finally, by incorporating a total power constraint, the algorithm is capable of self-correcting an overly ambitious pricing function. We also provide simulated examples based on measured DSL lines.

The problem of higher-order cumulants estimation is addressed in this paper. Higher-order cumulants are necessary in many applications, such as blind source separation (BSS) and blind deconvolution. In these applications, the cumulants are usually estimated using sample estimation. In this paper, a parametric method for cumulants estimation using the Gaussian mixture model (GMM) is derived. The cumulants are expressed in terms of the GMM parameters, and estimated using the maximum-likelihood estimator. The performance of the proposed model-based method was evaluated and compared to sample estimation using computer simulations. The results show that the model-based estimation outperforms the sample estimation in terms of root-mean-square error.