This paper considers the optimum single cell power-control maximizing the aggregate (uplink) communication rate of the cell when there are peak power constraints at mobile users, and a low-complexity data decoder (without successive decoding) at the base station. It is shown, via the theory of majorization, that the optimum power allocation is binary, which means links are either "on" or "off". By exploiting further structure of the optimum binary power allocation, a simple polynomial-time algorithm for finding the optimum transmission power allocation is proposed, together with a reduced complexity near-optimal heuristic algorithm. Sufficient conditions under which channel-state aware time-division-multiple-access (TDMA) maximizes the aggregate communication rate are established. Finally, a numerical study is performed to compare and contrast the performance achieved by the optimum binary power-control policy with other sub-optimum policies and the throughput capacity achievable via successive decoding. It is observed that two dominant modes of communication arise, wideband or TDMA, and that successive decoding achieves better sum-rates only under near-perfect interference cancellation efficiency.
In this paper, we are motivated by recent work on interference networks that shows that binary power-control is often close to optimal when interference is treated as Gaussian noise, links have maximum (peak) power constraints, and the objective is to maximize the sum-rate, even if it is not necessarily optimal in general @cite_18 . Binary'' here just means that a link is either on'' or off'', either at zero power, or maximum power, without taking any value in the continuum of possible values between @math and the peak power level.
@cite_18
2068771370 2150586017 2060512747 2126158338
This paper considers the optimum single cell power control maximizing the aggregate (uplink) communication rate of the cell when there are peak power constraints at mobile users, and a low-complexity data decoder (without successive decoding) at the base station. It is shown that the optimum power allocation is binary, which means that links are either “on” or “off.” By exploiting further structure of the optimum binary power allocation, a simple polynomial-time algorithm for finding the optimum transmission power allocation is proposed, together with a reduced complexity near-optimal heuristic algorithm. Sufficient conditions under which channel-state aware time division multiple access (TDMA) maximizes the aggregate communication rate are established. In a numerical study, we compare and contrast the performance achieved by the optimum binary power-control policy with other suboptimum policies and the throughput capacity achievable via successive decoding. It is observed that two dominant modes of communication arise, wideband or TDMA, and that successive decoding achieves better sum-rates only under near perfect interference cancellation efficiency. In this paper, we exploit the theory of majorization to obtain the aforementioned results. In the final part of this paper, we do so to solve power-control problems in the areas of femtocells and cognitive radio and find that, again, optimal solutions have a binary (or almost binary) character. We consider allocating the transmit powers for a wireless multi-link (N-link) system, in order to maximize the total system throughput under interference and noise impairments, and short term power constraints. Employing dynamic spectral reuse, we allow for centralized control. In the two-link case, the optimal power allocation then has a remarkably simple nature termed binary power control: depending on the noise and channel gains, assign full power to one link and minimum to the other, or full power on both. Binary power control (BPC) has the advantage of leading towards simpler or even distributed power control algorithms. For N>2 we propose a strategy based on checking the corners of the domain resulting from the power constraints to perform BPC. We identify scenarios in which binary power allocation can be proven optimal also for arbitrary N. Furthermore, in the general setting for N>2, simulations demonstrate that a throughput performance with negligible loss, compared to the best non-binary scheme found by geometric programming, can be obtained by BPC. Finally, to reduce the complexity of optimal binary power allocation for large networks, we provide simple algorithms achieving 99 of the capacity promised by exhaustive binary search. We consider the full-duplex transmission over bidirectional channels with imperfect self-interference cancelation in wireless networks. In particular, together using propagation-domain interference suppression, analog-domain interference cancellation, and digital-domain interference cancellation, we develop the optimal dynamic power allocation schemes for the wireless full-duplex sum-rate optimization problem which aims at maximizing the sum-rate of wireless full-duplex bidirectional transmissions. In the high signal-to-interference-plus-noise ratio (SINR) region, the full-duplex sum-rate maximization problem is a convex optimization problem. For interference-dominated wireless full-duplex transmission in the high SINR region, we derive the closed-form expression for the optimal dynamic power allocation scheme. For non-interference-dominated wireless full-duplex transmission in the high SINR region, we obtain the optimal dynamic power allocation scheme by numerically solving the corresponding Karush-Kuhn-Tucker (KKT) conditions. While the full-duplex sum-rate maximization problem is usually not a convex optimization problem, by developing the tightest lower-bound function and using the logarithmic change of variables technique, we convert the full-duplex sum-rate maximization problem to a convex optimization problem. Then, using our proposed iteration algorithm, we can numerically derive the optimal dynamic power allocation scheme for the more generic scenario. Also presented are the numerical results which validate our developed optimal dynamic power allocation schemes. This paper addresses the following question, which is of interest in the design of a multiuser decentralized network. Given a total system bandwidth of W Hz and a fixed data rate constraint of R bps for each transmission, how many frequency slots N of size W N should the band be partitioned into in order to maximize the number of simultaneous links in the network? Dividing the available spectrum results in two competing effects. On the positive side, a larger N allows for more parallel, non- interfering communications to take place in the same area. On the negative side, a larger N increases the SINR requirement for each link because the same information rate must be achieved over less bandwidth. Exploring this tradeoff and determining the optimum value of N in terms of the system parameters is the focus of the paper. Using stochastic geometry, the optimal SINR threshold - which directly corresponds to the optimal spectral efficiency - is derived for both the low SNR (power-limited) and high SNR (interference-limited) regimes. This leads to the optimum choice of the number of frequency bands N in terms of the path loss exponent, power and noise spectral density, desired rate, and total bandwidth.