The scaling of coherent and non-coherent channel capacity is studied in a single-input multiple-output (SIMO) block Rayleigh fading channel as both the bandwidth and the number of receiver antennas go to infinity jointly with the transmit power fixed. The transmitter has no channel state information (CSI), while the receiver may have genie-provided CSI (coherent receiver), or the channel statistics only (non-coherent receiver). Our results show that if the available bandwidth is smaller than a threshold bandwidth which is proportional (up to leading order terms) to the square root of the number of antennas, there is no gap between the coherent capacity and the non-coherent capacity in terms of capacity scaling behavior. On the other hand, when the bandwidth is larger than this threshold, there is a capacity scaling gap. Since achievable rates using pilot symbols for channel estimation are subject to the non-coherent capacity bound, this work reveals that pilot-assisted coherent receivers in systems with a large number of receive antennas are unable to exploit excess spectrum above a given threshold for capacity gain.

We consider a noncoherent uplink and downlink with a large antenna array at the base station. The modulation used is ON-OFF keying, with symbol-by-symbol single-user detection. A ray tracing propagation model is assumed with knowledge at the base station and transmitters of only the ray arrival angles and amplitudes. We identify the sources of performance degradation, quantify notions of diversity gain (related to the detection error performance) and multiplexing gain (related to the number of users simultaneously supported), and present numerical results to demonstrate these gains. Our results indicate that in this noncoherent system, increasing the number of antenna elements can support multiple users, with a vanishing probability of detection error, as long as the number of users is below a certain threshold which increases with the number of antennas. This contrasts with the fact that in a rich scattering propagation environment, uncoded noncoherent systems performing symbol-by-symbol detection cannot support more than one user with a vanishing probability of error.

We consider a one-shot communication setting in which several single antenna transmitters communicate with a receiver with a large number of antennas, i.e., the receiver decodes transmitted information at the end of every symbol time. Motivated by the optimal noncoherent detector in a Rayleigh fading channel, we consider a noncoherent energy-based communication scheme that does not require any knowledge of instantaneous channel state information at either the transmitter or the receiver; it uses only the statistics of the channel and noise. We show that, for general channel fading statistics, the performance of the considered one-shot multiuser noncoherent scheme is the same, in a scaling law sense, as that of the optimal coherent scheme exploiting perfect channel knowledge and coding across time. Furthermore, we present a numerical evaluation of the performance of this scheme in representative fading and noise statistics.

An uplink system with a single antenna transmitter and a single receiver with a large number of antennas is considered. We propose a single-shot noncoherent scheme which does not use the instantaneous channel state information (CSI), but rather only the knowledge of the channel statistics, a transmitter that modulates information only in the amplitude of the symbols, and a receiver which measures only the average received energy across the antennas. This system model is motivated by the simplicity of the circuit design and the energy efficiency it entails for both the transmitter and the receiver. We propose constellation designs which are asymptotically optimal with respect to symbol error rate (SER) with an increasing number of antennas, for any finite signal-to-noise power ratio (SNR), under different assumptions on the availability of CSI statistics. We describe in detail the case when there is a bounded uncertainty on the moments of the fading distribution. We present the numerical results on the SER performance achieved by these designs and find that they outperform the existing amplitude-modulation-based noncoherent scheme of amplitude shift keying (ASK). They also achieve a smaller peak-to-average power ratio (PAPR) for scenarios with a low SNR or a large line-of-sight (LOS) component.

We consider one single-antenna transmitter communicating over a block-fading channel with a receiver that has a large number of antennas. We analyze coherent schemes with training overhead and noncoherent schemes with no training overhead. The latter systems only know the large scale fading statistics and the additive Gaussian noise power. In contrast to prior work on capacity characterizations of such systems, our approach is based on comparing error exponents with an increasing number of receive antennas. We provide an analytical lower bound on the error exponent for both coherent and noncoherent systems, and describe it explicitly for the case of a communication system with PSK constellations. Based on these analytic expressions, we show that noncoherent schemes can significantly outperform coherent schemes in channels with a strong line of sight component, low SNR or a short coherence time. We present numerical comparisons of our analytical BER performance bounds with Monte Carlo simulations for typical PSK constellations, system sizes and fading/noise statistics.

We consider one single antenna transmitter communicating with a receiver with a large number of antennas. Motivated by the optimal noncoherent detector in a Rayleigh fading channel, we propose a noncoherent energy-based communication scheme that does not require knowledge of instantaneous CSI (channel state information) at either the transmitter or the receiver; it uses only the statistics of the channel and noise. We explore the impact of coding to reduce the number of antennas needed for this system to achieve a given performance target. In particular, random coding error exponents for this system are used to determine tradeoff curves between the number of antennas and the blocklengths associated with a guaranteed performance target. However, since random codes have exponentially increasing decoding complexity with increasing blocklength, we also consider a simplified codebook design that has significantly lower encoding and decoding complexity. Simulations suggest that for small blocklengths, the performance of this simplified codebook is competitive with random coding constructions.

This paper studies noncoherent wideband systems with a single antenna transmitter and a multiple antenna receiver with many elements, under signaling with peak-to-average power ratio constraints. The analysis considers the scaling behavior of capacity and achievable rates by letting both the number of antennas and the bandwidth go to infinity jointly. In contrast to prior work on wideband single input single output (SISO) channels without a-priori channel state information, it is shown that a sufficiently large number of receive antennas can make up for the vanishingly small SNR at each antenna. In particular, it is shown that when bandwidth grows sufficiently slowly with the number of antennas, the capacity scaling with an increasing number of receive antennas is the same as the optimal coherent capacity scaling. If the bandwidth grows faster than a certain threshold, however, the additional bandwidth does not help because a finite transmit power is spread over an excessively large bandwidth.

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.

An uplink system with a single antenna transmitter and a single receiver with a large number of antennas is considered. For this system we propose an average energy-detection-based one-shot noncoherent communication scheme which does not use the instantaneous channel state information at either the transmitter or the receiver. We provide a constellation design that is asymptotically optimal in terms of achievable error exponent (in the number of receiver antennas) with an increasing constellation size. We also present numerical results on how this design performs in non-asymptotic regimes. Since the channel statistics may not be precisely known, we present a robust constellation design scheme which takes into account possible uncertainty in the large scale statistics and compare numerically its performance with the constellation design assuming perfect knowledge of channel and noise statistics. In terms of achievable symbol error rates, the robust constellation design is shown to perform almost as well as the scheme designed with perfectly known statistics despite mismatch in the channel statistics.

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.

An uplink system with a fixed number of single antenna transmitters and a single receiver with a large number of antennas is considered. For this system we propose an energy-based noncoherent communication scheme that does not use instantaneous channel state information at either the transmitter or the receiver: only the channel and noise statistics are used.We show that, in terms of the scaling law of achievable symmetric rates for two users, our scheme's performance is no different from that achievable with perfect CSI (channel state information) at the transmitters and the receiver. We also provide a simple constellation design using the design criterion of minimum distance and present numerical results on how these designs perform in non-asymptotic regimes with typical channel and noise statistics.

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.

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.

DNA sequencing technology has advanced to a point where storage is becoming the central bottleneck in the acquisition and mining of more data. Large amounts of data are vital for genomics research, and generic compression tools, while viable, cannot offer the same savings as approaches tuned to inherent biological properties. We propose an algorithm to compress a target genome given a known reference genome. The proposed algorithm first generates a mapping from the reference to the target genome, and then compresses this mapping with an entropy coder. As an illustration of the performance: applying our algorithm to James Watson's genome with hg18 as a reference, we are able to reduce the 2991 megabyte (MB) genome down to 6.99 MB, while Gzip compresses it to 834.8 MB.