We study a continuous-time power policy to maximize the ergodic channel throughput of an energy harvesting transmitter over a Markov fading channel. In particular, we consider transmission power policies that are adapted to the fading process of the channel as well as the storage process of the battery. We obtain a set of equations that determine the probability density of the energy in the battery at each channel state. Specifically, for an ergodic battery storage process, these equations describe the relation between the probability density of stored energy and the transmission power at each channel state. From these equations, we derive an upper bound on the average transmission power and an upper bound on the average transmission rate. To compute a lower bound on the average transmission rate, we apply a calculus of variations technique to a non-linear throughput maximization problem. As a result, we obtain a system of coupled ordinary differential equations for locally optimal power policies. We then focus on the Gilbert-Elliot channel as a special case and derive some structural results for specific classes of fast and slow fading channels. Furthermore, we numerically find a locally optimal transmission power policy for the two channel state scenario.

We study the transmission of a set of correlated sources (U1, . . . , UK) over a Gaussian multiple access relay channel with time asynchronism between the encoders. We assume that the maximum possible offset dmax(n) between the transmitters grows without bound as the block length n → ∞, while the relative ratio dmax(n)/n of the maximum possible offset to the block length asymptotically vanishes. For such a joint source-channel coding problem and under specific gain conditions, we derive necessary and sufficient conditions for reliable communications and show that separate source and channel coding achieves optimal performance. In particular, we first derive a general outer bound on the source entropy content for all channel gains as our main result. Then, using Slepian-Wolf source coding combined with the channel coding scheme on top of block Markov coding, we show that the thus achieved inner bound matches the outer bound. As a corollary, we also address the problem of sending a pair of correlated sources over a two-user interference channel in the same context.

We study the transmission of two correlated and memoryless sources (U1, U2) over several multiuser phase asynchronous channels. Namely, we consider a multiple access relay channel (MARC) with causal, and a MARC with non-causal unidirectional cooperation between encoders, referred to as phase-incoherent causal (respectively, non-causal) cognitive MARCs. We also consider phase-incoherent interference channel models with and without relay, in the same context. In all cases, the input signals are assumed to undergo non-ergodic phase shifts, which are unknown to the transmitters and known to the receivers as a realistic assumption. Both necessary and sufficient conditions in order to reliably send the correlated sources to the destinations are derived. In particular, for all of the channel models, by using a key lemma, we first derive an outer bound for reliable communication. Then, using separate source and channel coding and under specific gain conditions, we establish the same region as the inner bound. We thus conclude that without the knowledge of the phase shifts at transmitters, and under specific gain conditions, separation is optimal.

We investigate fundamental limits of lossy communication in a bi-directional (two-way), half-duplex relay channel, where users wish to exchange correlated Gaussian sources and do so by sending their data according to a two-phase transmission protocol. We first establish a general distortion inner bound using the data processing inequality. Based on uncoded transmission schemes, separate source and channel coding schemes, and a lattice coding scheme, we then develop several cooperative joint source-channel coding (JSCC) approaches. Furthermore, we compare the corresponding achievable distortion performances in terms of the correlation coefficient between Gaussian sources. This comparison particularly shows that separate source and channel coding in combination with decode-and-forward relaying achieves the best distortion values of any joint source-channel coding scheme in this paper.

We investigate the problem of continuous-time energy harvesting in communication systems operating over fading wireless channels. We model the fading as a finite-state continuous-time Markov process, and the battery dynamics as a storage dam process with reflecting boundary conditions. We describe a set of necessary conditions for the ergodicity of the dam process. Followed by these conditions, we establish an upper bound on the ergodic channel throughput. We further determine some structure for good transmission power policies based on a throughput maximization problem. Specifically, using calculus of variations techniques, we derive Euler-Lagrange equations as a necessary condition for optimal power policies. In the case of a Markov channel with two channel states (i.e. Gilbert-Elliot channel), we characterize power policies by solving these equations numerically.

We study the transmission of a pair of correlated sources over a Gaussian multiple access channel with weak time asynchronism between the encoders. In particular, we assume that the maximum possible offset dmax(n) between the transmitters grows without bound as the block length n → ∞ while the ratio dmax(n)/n of the maximum possible offset to the block length asymptotically vanishes. For such a joint source-channel coding problem, we derive the capacity region and also show that separate source and channel coding achieves optimal performance. Specifically, we first derive an outer bound on the source entropy content as our main result. Then, using Slepian-Wolf source coding combined with the channel coding introduced in [1], we show that the thus achieved inner bound matches the outer bound.

We study simultaneous channel sharing of collocated primary and secondary networks at three different levels of coexistence: pure interference, asymmetric, and symmetric. At the pure interference level, both networks operate simultaneously in the same frequency band regardless of their interference to each other. At the asymmetric level, only the secondary network performs user scheduling based on various degrees of interference and channel gain knowledge while at the symmetric level both networks do so. Using a lemma on the asymptotic behavior of the largest order statistic and a proposition on the asymptotic sum of lower order statistics, we derive asymptotic primary and secondary sum-rates under simultaneous channel sharing at each coexistence level. As a baseline comparison, time-division (TD) channel sharing is considered. While maintaining the same asymptotic primary sum-rate, the asymptotic secondary sum-rate under TD is compared with that achievable by simultaneous channel sharing. The results indicate that simultaneous channel sharing at both asymmetric and symmetric co-existence levels can outperform TD. Furthermore, this enhancement is achievable asymptotically when user scheduling in uplink mode is based only on the interference gains to the opposite network and not on a network's own channel gains.

Sufficient and necessary conditions for reliable lossless communication of two correlated sources over classes of phase asynchronous cognitive interference channels are derived. Namely, we consider interference channels in which one of the encoders, i.e., the secondary or cognitive user, is causally or non-causally aware of the other's message. Moreover, as a practical constraint, we assume the transmitters are not aware of the phase shifts introduced by channels. We show that, for both classes of causal and noncausal cooperation, under strong interference conditions, separate source and channel coding is optimal for reliable communication of both users. Also, we derive necessary and sufficient conditions for reliable communication of the cognitive radio transmission while the primary is able to maintain the same information rate it could reliably send in the absence of the secondary. To the best of our knowledge, this is the first work to find fundamental limits of lossless reliable communication for cognitive interference channels.

We study the lossy communication of a bivariate Gaussian source over an interference channel with the presence of a helping relay. We assume that the wireless links introduce random phase shifts which are unknown to the transmitters (including the relay) and call the channel a phase incoherent interference relay channel (PI-IRC). We derive inner and outer bounds for the achievable distortion region, where the inner bound is derived under specific strong interference conditions as well as strong gain conditions between transmitters and the relay. When the sources are correlated, we find an approximate achievable distortion region in the high SNR regime. In case of independent sources, the bounds are tight and by explicitly providing the achievable distortion region, we show that a separation theorem results for the PI-IRC under strong interference conditions. By removing the relay, the result also specializes to an interference channel.

Cognitive radio is a promising approach for efficient utilization of radio spectrum. Due to its high spectral efficiency and flexibility, OFDM is considered as a good signaling scheme for cognitive radios. In this paper, we investigate the problem of cross-band interference minimization in OFDM-based cognitive systems. Cross-band interference is mainly caused by high OFDM sidelobes. In the first part of our work, we propose a framework to study the trade-off between two recently proposed techniques, adaptive symbol transition which is performed in the time domain, and active interference cancellation which is performed in the frequency domain. We use the trade-off study results to maximize the useful data rate for a desired level of interference. Simulation results show that the best trade-off depends on the configuration of spectral opportunities. In the second part, a new method for interference reduction in multiple-antenna cognitive systems is developed. We show that with knowledge of the channel, the secondary transmitted sequences can be jointly optimized over multiple antennas such that the interference at the primary receiver location is better minimized. Computer simulations demonstrate an improvement of almost 10 dB compared to separate-antenna optimization.

A half-duplex wireless network with 6 lateral nodes, 3 transmitters and 3 receivers, and a central relay is considered. The transmitters wish to send information to their corresponding receivers via a two phase communication protocol. The receivers decode their desired messages by using side information and the signals received from the relay. We derive an outer bound on the capacity region of any two phase protocol as well as 3 achievable regions by employing different relaying strategies. In particular, we combine physical and network layer coding to take advantage of the interference at the relay, using, for example, lattice-based codes. We then specialize our results to the exchange rate. It is shown that for any snr, we can achieve within 0.5 bit of the upper bound by lattice coding and within 0.15 bit, if we take the best of the 3 strategies. Also, lattice coding asymptotically achieves the upper bound as snr -> \infty.

We consider the coexistence of collocated primary and secondary point-to-multipoint systems such as cellular systems that simultaneously access the same spectrum. In particular, we analyze the number of users and sum-rate of the secondary system as a function of the maximum impact it is allowed to have on the primary system when simultaneous transmissions are treated as interference. As a base reference, we compare to channel sharing via time division (TD).We find that provided the secondary system is given sufficient channel state information, the impact of the interference to the primary system on its sum-rate can be mitigated by judicious choice of the active secondary nodes and quantify the improvement that is realized. This quantification is achieved by a key lemma on the sum of low order statistics of an exponential random variable. We also analyze the more symmetric case when both primary and secondary networks are given sufficient channel state information to selectively activate nodes.